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Grief Tech Uses AI to Give You (and Your Loved Ones) Digital Immortality

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Grief Tech Uses AI to Give You (and Your Loved Ones) Digital Immortality

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Generative AI has found a host of uses since it exploded into the public consciousness with the release of ChatGPT last year. From drafting essays for students to helping customer service agents field calls to writing code for software engineers, it seems there’s no end to the ways these tools can make life a little easier. But some entrepreneurs are trying to put AI to use in a very different way: making the afterlife a little easier. Not for us—that’s a whole other level of metaphysics and technology we have yet to reach—but for those we leave behind.

Losing a loved one sets off a process of grieving that’s different for everyone. But one component of grief is universal: we miss the person who’s gone and wish we could talk to them again, spend time with them, hear their voice and their laughter. A company called MindBank Ai is building a product that would let us do just that.

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Generative AI has found a host of uses since it exploded into the public consciousness with the release of ChatGPT last year. From drafting essays for students to helping customer service agents field calls to writing code for software engineers, it seems there's no end to the ways these tools can make life a little easier. But some entrepreneurs are trying to put AI to use in a very different way: making the afterlife a little easier. Not for us-that's a whole other level of metaphysics and technology we have yet to reach-but for those we leave behind.

Losing a loved one sets off a process of grieving that's different for everyone. But one component of grief is universal: we miss the person who's gone and wish we could talk to them again, spend time with them, hear their voice and their laughter. A company called MindBank Ai is building a product that would let us do just that.

"When you lose someone you have moments where you want to look through photos or videos of them in a bittersweet way," Emil Jimenez, the company's founder, told Singularity Hub in an interview. "Now you can have those moments, but have a conversation with the person too."

MindBank plans to create digital twins of its users, replicating their personalities, ways of thinking and speaking, and other characteristics as closely as possible. These twins will interact with our loved ones after we're gone-and teach us about ourselves while we're still here.

"What we've created is a personalized layer of AI that sits on top of a generalized model," Jimenez said. "I like to call it AI-enhanced humanity." That's actually the title of his book, which comes out in October. It starts two million years ago with homo habilus, the first hominid that was making tools, and one of its themes is how tools change our culture. "AI is just another tool that's going to change our culture," he said.

The Voice of a Database

One day when Jimenez's daughter was four years old, she was playing games on an ipad when Siri, Apple's AI assistant, popped up. The child started asking the AI questions, and within minutes had formed a bond with it. "I started thinking," Jimenez said. "Siri is just an interface to a database. How can I become that database, so that my little girl can always ask me a question and get a response?"

He wondered what the world will look like 40 years from now, when his daughter is his age. What might the future hold for her, and for all the other kids out there who are interacting with technology, screens, and AI in a way no previous generation ever has?

While we don't know quite what that future will look like, we can bet personal data will play a significant role. That data-and who can access it, and how-will likely be regulated and controlled differently than it is today. We'll be able to opt in or out of sharing our data in various ways, and will get products and services (and ads, of course) that are correspondingly personalized.

"The digital twin is a data lake for you to connect to other services," Jimenez said. "That same database of your stories and your life can also be art, or music, or medical records. It's digitizing humanity, and being able to use all the tools available to offer you better services."

The concept of a personal digital twin essentially boils down to representing you using your data. That would include data you give only to the MindBank app. To get a sense of your personality and how you think, the app asks you a series of questions; the more questions you answer, the better it gets to know you.

"You answer questions and it records your voice," Jimenez said. "All it takes is one minute a day. The more data the better, but it's more important that you do a consistent frequency at low doses."

A Personalized Turing Test

So what are the technologies that will make personal digital twins possible? A key one is natural language processing, which underlies ChatGPT and other large language models.

Natural language processing (NLP) is, in short, the AI field of understanding and mimicking human speech. NLP algorithms are trained on massive datasets of text-in ChatGPT's case, billions of pages from the internet-and they use that data to figure out the relationships between words. Text or speech generators with a transformer architecture (that's what the "T" in GPT stands for) model the relationships between all the words in a sentence at once, weighing how likely it is that a given word will be preceded or followed by another word, and how much that likelihood changes based on the other words in the sentence.

Through finding the relationships and patterns between words in its training dataset, NLP algorithms learn from their own inferences in what's called unsupervised machine learning.

To create convincing digital twins, MindBank will have to take things a step further. Rather than a fixed set of rules its algorithms would use to respond to questions and interact with others, the model would ideally evolve over time. This would mean having some level of understanding of users' experiences and how they've impacted them, and being able to integrate that into the way they interact. This is obviously more complicated than reflecting back the patterns in how someone talks or writes.

How easy is it to "learn" a person, anyway? While we all have our own unique speech patterns, go-to phrases, and ingrained habits and ways of thinking, we're also dynamic beings who aren't always predictable.

Think about the last time you asked friends or relatives for advice. You probably had an idea what kind of response you'd get from different people: friend X tells it like it is, so you only go to her if you're up for some tough love. Friend Y tends to be gentle and say what he knows you want to hear. Every once in a while, though, those friends surprise you. Maybe the tough-love friend is going through a life challenge of her own that's making her see things differently. Or the gentle friend is having a bad day and feeling less sympathetic than usual.

Humans are complex creatures who are constantly being influenced by our environment and interactions. To the extent that our thoughts and behavior fall into patterns, Mindbank is aiming to capture it all. "We want the longitudinal data of emotions and personality," Jimenez said. "Today you might have a good day, tomorrow a bad day. We want the ups and downs."

Know Thyself

Understanding those ups and downs won't just help your descendants know you better in the future-it can help you know yourself better in the present. Alongside being a trove of data on your personality and stories for others, Jimenez wants MindBank to be a tool for deeper self-awareness. "We can help people move from healthcare to self-care by empowering them with their health data, specifically their mental health data," he said.

He likened MindBank's digital twins to a highly advanced form of journaling. If you write down your thoughts and feelings while navigating a difficult experience, going back to read them could give you insights into why you made a certain decision, give you more awareness of how you process things, or remind you to behave differently in the future. "The product we have today is literally a dashboard of the mind," he said. "Imagine Google Analytics of your mind, where you can see data of how you are emotionally and personality-wise, how you evolve, and you can track your progression over time."

Personal digital twins will be like living versions of a journal you can talk to and learn from. Through techniques like identifying and labeling words-ie as positive or negative, intrinsic or extrinsic-MindBank will produce a model of your personality, traits, and emotions, and mirror it back to you.

"There's a feedback loop to it," Jimenez said. "We have the ability to scale that, make it data-driven, and give people interesting insights."

Next-Gen Grieving?

Whether you're using them to understand yourself better or to keep your memory alive after you're gone, MindBank Ai and other platforms like it pose some intriguing possibilities. But there's a definite creepiness factor, and some big questions worth considering.

Would it be healthy to continue talking to a loved one after they're deceased? Would the grieving process become easier-or harder? Could there be nefarious uses for digital twins that bad actors would take advantage of? And, social media has in many ways made people more self-absorbed and less authentic; would digital twins make this effect even worse?

In terms of grieving a loved one, Jimenez thinks MindBank will help people get through hard times. "Reliving is part of the therapeutic cycle," he said. "It's part of coming to grips with what happened and moving on. This will be another tool that can help us get past that stage."

We already use texts, emails, letters, or voicemails to 'engage' with and remember the deceased during grieving. How different would it be to talk them in the form of a digital twin? For some people, the opportunity could provide relief, preventing them from getting bogged down in sorrow or being unable to let go. It could even make it easier to prepare for a loved one's passing knowing that you'll be able to "talk" to them after they're gone.

The opposite risk may exist, too. What if the algorithm gets your loved one's personality wrong or says something totally out of place at a particularly difficult moment? It would be a visceral reminder that the real person is gone and nothing can replace them, and would likely make the user feel even more grief and stress.

There's also the question of how far we may let digital twins go. If they become commonplace, how much power would we want to give them? Say you need to make an important decision about your own children or your career-would you ask your deceased parent's digital twin for advice? Could a digital twin remain on a board of directors after the real person has passed away?

Not So Sci-Fi

Creating and interacting with digital twins is already part of some people's lives today, and multiple platforms are using AI to piece together likenesses of those who've departed.

You, Only Virtual recreates the "unique dynamics of a relationship" and generates what it calls an authentic essence of a loved one using real-time and archived communication. The company's (somewhat eerie) tagline? "Never have to say goodbye."

HereAfter AI calls itself a virtual biographer: the app interviews users by giving them story prompts, then uses their replies to design a "legacy avatar" of them. Their loved ones can then ask the app questions and get responses in the user's voice. HereAfter's founder started the company after using AI to create a chatbot version of his father, which he called the Dadbot.

A key difference between HereAfter and Mindbank is that while the former sticks to a set of questions and stories, MindBank is aiming for something closer to open-ended conversation-a far more complex feat for AI to master.

Outside of these dedicated "grief tech" services, people are using ChatGPT to recreate their loved ones' voices in written form, feeding the algorithm texts and emails to train it then having it respond to them as if it were the relative or friend themselves. People have reported finding comfort in these interactions.

Creepiness aside, perhaps our assessment of services like Mindbank AI should come down to one question: does it help people? But as with many technologies, interacting with digital twins in a way that's enriching rather than destructive will ultimately be up to each user. While the platforms should try to build in guardrails to keep users from, say, getting addicted or using avatars for nefarious purposes, it seems they'll primarily be self-policed; only you know if having a two-hour "conversation" with your deceased parent makes your grief more bearable, or less.

Jimenez, for his part, is nothing but optimistic about AI's potential to help us remember our loved ones and stay connected to them after they're gone. "I think it's a beautiful thing," he said.
Image Credit: Ahmet Sali on Unsplash

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Grief Tech Uses AI to Give You (and Your Loved Ones) Digital Immortality

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MindBank AI plans to create digital twins of users, replicating their personalities, voices, and ways of thinking as closely as possible.

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Angst vor Blackout: Kraftwerke sind zu wenig geschützt - so könnte die Lösung aussehen

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Angst vor Blackout: Kraftwerke sind zu wenig geschützt - so könnte die Lösung aussehen

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Die Schweizer Kraftwerke sind konfrontiert mit einem tiefen Sicherheitsniveau, zunehmenden Hackerangriffen und steigendem politischem Druck. Die Stadtwerk-Allianz Swisspower offeriert nun eine Lösung.

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Angst vor Blackout: Kraftwerke sind zu wenig geschützt - jetzt soll eine Lösung her

Die Schweizer Kraftwerke sind konfrontiert mit einem tiefen Sicherheitsniveau, zunehmenden Hackerangriffen und steigendem politischem Druck. Die Stadtwerk-Allianz Swisspower offeriert nun eine Lösung.

Florence Vuichard / ch media

Turbinen, Generatoren, Pumpen: Das Herzstück der Strom produzierenden Kraftwerke besteht noch immer aus schweren Maschinen. Betrieben hingegen werden diese heute oft nicht mehr analog und vor Ort, sondern vermehrt digital und von aussen. Es findet eine Verquickung statt zwischen IT und OT, wie es im Fachjargon heisst, also zwischen der Informatik und der operativen Technologie.

Und genau das mache die Kraftwerke anfällig für Hackerangriffe, sagt Adrian Märklin, Experte für Cybersicherheit bei der Stadtwerke-Allianz Swisspower. Mit potenziell gravierenden Folgen: So könnten etwa von Hackern manipulierte Sollwerte für Turbinenumdrehungen ganze Kraftwerke stilllegen - etwa so wie im Bestseller "Blackout" von Marc Elsberg, in welchem die verheerenden Auswirkungen eines grossflächigen, von Cyberangriffen ausgelösten Stromausfalls in Europa und dann auch in den USA aufgezeigt werden.

"Letztlich ist alles, was an Computernetzwerke angeschlossen ist, gefährdet", sagt Märklin. Eine Einsicht, die in den meisten IT-Abteilungen über die vergangenen Jahre gereift ist. Im Kraftwerk-Maschinenraum jedoch fehlt dieses Risikobewusstsein oft - ebenso wie in vielen Chefetagen der Elektrizitätswerke.

Das jedenfalls lässt sich aus einer 2020 durchgeführten Umfrage des Bundesamtes für wirtschaftliche Landesversorgung und des Branchenverbands der Schweizer Elektrizitätswirtschaft (VSE) herauslesen. Beim Thema Cybersicherheit erreichen die meisten Schweizer Kraftwerkbetreiber auf einer Skala von 0 bis 4 eine durchschnittliche Maturitätsstufe von gerade mal 1.

Das bedeutet: Die einzelnen Kraftwerke haben zur Erreichung eines Minimalstandards bei der IT-Sicherheit Massnahmen zwar "partiell umgesetzt", aber "nicht vollständig definiert und abgenommen". Oder in den klaren Worten von Märklin: "Eine 1 ist schlicht und einfach ungenügend."

Und gut möglich, dass der wahre Wert noch tiefer ausfallen würde. Der Verdacht jedenfalls liegt nahe, dass nur jene Elektrizitätswerke an der freiwilligen Umfrage teilgenommen haben, die sich schon einmal mit diesen Sicherheitsfragen auseinandergesetzt hatten.

Empfohlen von den Experten wird ein Mindestwert von 2,6. Sie erwarten, dass dieser 2025 vom Bund für obligatorisch erklärt wird. Lange werden sich die Energieversorgungsunternehmen also nicht mehr um Cybersicherheitsfragen foutieren können. Denn der politische Druck nimmt zu. Das Parlament will mit der Revision des Informationssicherheitsgesetzes eine Meldepflicht für Cyberangriffe auf kritische Infrastrukturen einführen. Das heisst: In Zukunft werden auch Kraftwerke allfällige Hackerattacken beim Nationalen Zentrum für Cybersicherheit (NCSC) melden müssen.

Was könnte helfen?

Die Kraftwerke sind konfrontiert mit einem tiefen Sicherheitsniveau, einem zunehmenden Risiko von Hackerangriffen sowie steigenden politischen Anforderungen.

Die Herausforderungen würden also nicht kleiner, sondern grösser, sagt Märklin. Deshalb hat seine Swisspower-Gruppe ein Lösungspaket für das OT-Sicherheitsproblem der Schweizer Energieversorgungsunternehmen ausgearbeitet und ist hierfür eine Kooperation eingegangen mit zwei internationalen Konzernen:

Erstens mit Hitachi Energy, welche von ABB die Unternehmenseinheit übernommen hat, von der die Steuerungsgeräte in den meisten Schweizer Kraftwerken stammen.

Sowie zweitens mit dem hiesigen IT-Unternehmen BW Digitronik aus der europaweit agierenden Cybertech-Gruppe, die mehrere Sicherheitszentren betreibt, sogenannte Security Operation Center.

Die drei Unternehmen auditieren und überprüfen zuerst einmal die Sicherheitsarchitektur des Kraftwerks und bauen dann Sensoren ein, die den Alltag registrieren, wie Swisspower-Geschäftsleitungsmitglied Märklin erklärt.

Die Meldung werde dann automatisch an ein Security Operation Center weitergeleitet, das täglich 24 Stunden besetzt sei. Die permanente Erreichbarkeit sei eine Schweizer Premiere. Und sie sei wichtig. "Denn Zeit ist bei einem Hackerangriff ein entscheidender Faktor", betont Märklin. "Je früher der Angriff bemerkt wird, desto schneller kann er abgewehrt werden."

Im März haben die drei Unternehmen die Kooperationsverträge unterschrieben, seit Mai wird das Angebot den Energieversorgungsunternehmen vorgestellt. Ansprechpartner ist die Swisspower-Allianz, zu welcher 22 Stadtwerke gehören von Genf über Bern, Basel und Aarau bis nach Chur.

"Wir rechnen damit, dass wir noch im laufenden Jahr die ersten Aufträge erhalten und mit der Umsetzung anfangen können", sagt Märklin und verweist darauf, dass das Konzept als Test bereits beim Elektrizitätswerk Zermatt implementiert worden sei.

Die Kosten für eine nachweisliche Erhöhung der Sicherheit gegen Cyberangriffe können die Kraftwerkbetreiber über die Netzkosten an die Kundschaft weiterreichen. Dennoch: Bis alle rund 600 Schweizer Energieversorgungsunternehmen aufgerüstet haben, dürften noch ein paar Jahre vergehen. Bis dahin ist zu hoffen, dass aus der Fiktion "Blackout" nie eine Realität wird.

(aargauerzeitung.ch)

Energiekrise und drohender Blackout

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Angst vor Blackout: Kraftwerke sind zu wenig geschützt - so könnte die Lösung aussehen

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Die Stadtwerk-Allianz Swisspower offeriert nun eine Lösung für Schweizer Kraftwerke, um diese besser vor Cyberattacken zu schützen.

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Wie sich AlpineAI als Schweizer Alternative zu ChatGPT positionieren will

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Wie sich AlpineAI als Schweizer Alternative zu ChatGPT positionieren will

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Das kürzlich gegründete Unternehmen verspricht Datenhaltung in der Schweiz, hohe Privacystandards und stellt erste Produkte vor. Mit an Bord sind einige hiesige KI-Forschende.

"Die Welt spricht über generative KI", betont Pascal Kaufmann, Mitgründer und CEO von AlpineAI, an einer Medienkonferenz am Flughafen Zürich. Wobei, wenn es um KI-Produkte gehe, die Welt dann schnell auf die USA und China schrumpfen würde. Diese würden bei Large Language Models (LLMs) einen Marktanteil von über 80% halten. Das will das Ende Juli gegründete Startup AlpineAI ändern und dabei nicht zuletzt auf "Swissness" setzen.

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SPONSORED BY:

Cloud Security ConsultantBaar

Meta lanciert KI für Software-Developer

"Code Llama" soll Entwicklerinnen und Entwicklern dabei helfen, Code zu schreiben, zu vervollständigen oder Fehler zu finden.

Von Reto Vogt, publiziert am 25.8.2023

Big Tech investieren Millionen in KI-Firma Hugging Face

Salesforce, Amazon, IBM, Intel - sie alle setzen auf das Startup, das Tools für KI-Developer bereitstellt. Hugging Face wird jetzt mit 4,5 Milliarden Dollar bewertet.

Von Philipp Anz, publiziert am 25.8.2023

Ransomware-Attacke auf Hochschule Luzern

Eine IT-Laboreinrichtung der HSLU wurde angegriffen. Der Schulbetrieb sei nicht beeinträchtigt, erklärt die Hochschule.

Von Philipp Anz, publiziert am 23.8.2023 4

Meta präsentiert KI-Sprachmodell für Übersetzungen

SeamlessM4T soll fast 100 Sprachen übersetzen können - gesprochen und geschrieben.

Von Nadja Baumgartner, publiziert am 23.8.2023

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Wie sich AlpineAI als Schweizer Alternative zu ChatGPT positionieren will

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Das kürzlich gegründete Unternehmen verspricht Datenhaltung in der Schweiz, hohe Privacystandards und stellt erste Produkte vor. Mit an Bord sind einige hiesige KI-Forschende.

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Faille de sécurité: Des pirates s'emparent de données de la police bernoise

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Faille de sécurité: Des pirates s'emparent de données de la police bernoise

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Une importante faille de sécurité dans une application utilisée par la police cantonale bernoise a entraîné une fuite de données. Elle a entre-temps été colmatée, mais des pirates ont eu le temps de s'emparer d'informations sensibles.

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Fuite de données chez Xplain : fragments du Système de journal et de rapport de la Police militaire concernés - plainte pénale déposée

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Fuite de données chez Xplain : fragments du Système de journal et de rapport de la Police militaire concernés - plainte pénale déposée

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Les analyses effectuées après l’attaque contre le fournisseur de logiciels Xplain ont montré que les auteurs ont également dérobé des fragments de JORASYS, le Système de journal et de rapport de la Police militaire, ainsi que des profils d’utilisateurs incomplets et en partie obsolètes de membres de la Police militaire. L’armée a déposé une plainte pénale et informe les personnes concernées.

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Cyberattack on Civilian Critical Infrastructures in a Taiwan Scenario

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Cyberattack on Civilian Critical Infrastructures in a Taiwan Scenario

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Expressions of surprise that the Chinese military targeted critical infrastructure in Guam for cyber reconnaissance are misleading. Of course the People’s Liberation Army (PLA) is conducting cyber reconnaissance; China has been probing U.S. critical infrastructure networks for vulnerabilities since the Obama administration, if not before. From a military perspective, this is the kind of reconnaissance any capable nation would engage in against a potential opponent to identify targets and possibly prepare them for cyberattack. What was misleading in these reports is that critical infrastructure in Guam was not the primary target. The primary targets, particularly those that would support U.S. forces in any engagement over Taiwan, are located in the United States. China is engaged in a major cyber reconnaissance effort against them. If China is willing to accept the risk of broadening a conflict over Taiwan, it may decide that cyber actions against civilian infrastructure in the United States could usefully disrupt communications and the flow of material needed for military operations.

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Center for Strategic and International Studies

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Cyberattack on Civilian Critical Infrastructures in a Taiwan Scenario

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CSIS’s James Lewis writes about the targets that China is likely to attack as part of a larger cyber operation against Taiwan.

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Is China a Leader in Quantum Technologies?

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Is China a Leader in Quantum Technologies?

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Under Xi Jinping, China is redoubling its efforts to compete and lead in cutting-edge strategic technologies. China is investing heavily in the emerging field of quantum technologies, which exploit properties of quantum mechanics to enable breakthroughs in sensing, communication, and computing. Many of these technologies are still developmental, but Chinese researchers are making rapid progress and have become major players in quantum technologies. In some areas—especially quantum communication—China has positioned itself as the global leader.

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Under Xi Jinping, China is redoubling its efforts to compete and lead in cutting-edge strategic technologies. China is investing heavily in the emerging field of quantum technologies, which exploit properties of quantum mechanics to enable breakthroughs in sensing, communication, and computing. Many of these technologies are still developmental, but Chinese researchers are making rapid progress and have become major players in quantum technologies. In some areas-especially quantum communication-China has positioned itself as the global leader.

Quantum Technologies Explained

Quantum technologies are a loose set of nascent technologies that harness the principles of quantum mechanics to enable revolutionary breakthroughs across various fields. Quantum technologies can be grouped into three main areas-sensing, communication, and computing.

Quantum Sensing: In the field of quantum sensing, researchers are pushing to develop ultra-sensitive devices capable of measuring minute changes in motion and electromagnetic fields. As these technologies advance, they could lend unprecedented levels of precision to medical imaging and diagnosis, navigation, radar, geophysics, and more.

Quantum Communication: Quantum communication promises to enable ultra-fast and highly secure data transmissions. With conventional electronics, data is typically encrypted and then sent as bits representing 1s and 0s. However, quantum bits-or qubits-can be transmitted in a state of superposition in which they can represent combinations of 1 and 0 simultaneously. This allows for virtually un-hackable communications-a highly sought after capability for governments, militaries, financial firms, and others.

Quantum Computing: The third area of quantum technologies-quantum computing-has garnered the most attention globally and promises to be the most transformative of the three. Like quantum communication devices, quantum computers exploit the properties of superposition, which enables many calculations simultaneously. Theoretically, quantum computers equipped with enough qubits can perform complex calculations at speeds exponentially faster than even the most advanced supercomputers in use today.

While quantum technologies are immensely promising, many of the more advanced capabilities still remain largely confined to research and development work. Most quantum devices require complex and precise engineering in order to work. Some quantum computers, for example, must be chilled to extremely low temperatures to avoid disturbances and information loss. As a result of these limitations, certain quantum technologies face major obstacles to commercialization and are unlikely to see widescale adoption for years or even decades.

China's Quantum Ambitions

Despite the nascent status of quantum technologies, their potentially revolutionary qualities have caught the attention of Chinese policymakers. In 2021, the Chinese government enshrined quantum technology into its latest national economic blueprint, the 14th Five Year Plan, listing it along with six other cutting-edge fields that should be prioritized in the coming years.

China's focus on quantum technologies is part of a broader push for global technological leadership that has intensified under Chinese leader Xi Jinping. Xi has repeatedly called for transforming China into a global "science and technology power" (科技强国). Beijing's technology ambitions have taken on new urgency amid growing U.S.-China tension over strategic technologies. In March 2023, during a key annual political gathering in Beijing, Xi stressed that China needs to focus on scientific and technological "self-reliance and self-strengthening" in the face of "fierce international competition."

Toward this end, China is investing heavily in scaling up research and development (R&D). Chinese spending on R&D has more than doubled since Xi came to power in 2012. This is not merely a byproduct of economic growth, but also higher prioritization. In 2012, Chinese R&D spending amounted to 1.91 percent of its GDP, and by 2022 that figure had climbed to 2.55 percent of GDP.

However, China's interests in quantum go well beyond abstract notions of global leadership. Policymakers hope that quantum technologies will have tangible economic and security benefits.

Quantum computing, for example, could have force-multiplying impacts on Beijing's other key focus areas, like artificial intelligence. In a seminal 2017 report, the " New Generation Artificial Intelligence Development Plan," the Chinese government laid out hopes that quantum computing could advance AI capabilities by enhancing the performance of machine learning algorithms.

There are also hopes that advanced quantum computers could revolutionize scientists' ability to model complex systems to achieve breakthroughs in areas like materials sciences and biomedicine. If these capabilities materialize, they could have transformative effects and reap enormous economic dividends.

Chinese scientists and strategists are also well aware that quantum technologies have major security and military applications. The 13th Five Year Special Plan for Science and Technology Military-Civil Fusion Development, issued in 2017, includes quantum communication and computing among a list of priority strategic technologies. More pointedly, a researcher at China's Academy of Military Sciences described quantum technologies as a "bolting dark horse" that is "poised to change the mechanisms of future military victories."

A key area of concern is encryption. Armed with unparalleled computing power, an advanced quantum computer could break through existing conventional encryption methods that are used by individuals, banks, internet companies, governments, and militaries to secure data. Some experts worry that such a "Q-Day" scenario is not far away and could bring unprecedented challenges.

Chinese researchers claim to have already developed an algorithm that can be run on a small quantum computer that can decipher an advanced encryption system commonly used by governments and financial institutions. However, scientists outside of China have viewed these claims with deep skepticism.

"Quantum, as a 'bolting dark horse,' has made a significant impact on the information age... and is poised to change the mechanisms of future military victories."
Col. Gao Dongguang, Researcher, PLA Academy of Military Sciences

In the United States, the Biden administration has taken moves to prepare for quantum's potential threats to data security and to shore up the country's own quantum capabilities. The U.S. Department of Commerce's National Institute of Standards and Technology identified four new encryption algorithms to be integrated into the department's security standards that should be resilient to potential hacking from quantum computers.

Other quantum technologies could have additional applications in the military domain. Quantum sensing can potentially improve lidar and radar for intelligence, surveillance, and reconnaissance and also provide positioning and navigational capabilities in the absence of satellite-based systems. Such capabilities remain in development, but China is making progress. Chinese researchers are reportedly developing a quantum radar device that could improve the Chinese military's ability to detect stealth aircraft.

China is not alone in pursuing quantum technologies for military and security ends. Both the U.S. Navy and Air Force have created quantum research centers, with the goal of building quantum-equipped forces capable of faster threat detection and response. Although mature quantum technology is not yet ready for major military integration, NATO has already identified quantum as one of its key emerging and disruptive technologies.

China's State-Led Approach

China has taken a characteristic state-led approach to funding and conducting R&D of quantum technologies. This stands in stark contrast to the approach taken by the United States and other leading quantum technology players, where the private sector has typically led.

The Chinese government has invested impressive amounts into developing quantum technologies. Exact figures are not available, but existing studies suggest China leads by a wide margin in government spending. McKinsey estimates that as of 2022, the Chinese government has announced a total of $15.3 billion in funding, which is almost double that of the European Union ($8.4 billion) and more than triple that of the United States ($3.7 billion). However, these figures are not uniformly accepted. Studies by Quantum Insider have put the range of Chinese government investment between $4 billion and $17 billion.

Conversely, China lags in private sector investment. According to McKinsey's estimates, between 2001 and 2022, Chinese quantum startups received only $482 million from the private sector. That is a fraction of private sector investment in the United States, which led the world with an estimated $3.3 billion from non-government sources. Even Canada and the United Kingdom were well ahead of China, with both at roughly $1.1 billion of investment.

Unsurprisingly, state-linked institutions drive much of China's quantum R&D. The National University of Defense Technology has been an important player, conducting research on quantum communication technology since the 1990s. In 2001, China established the Key Laboratory of Quantum Information at the University of Science and Technology of China (USTC), which serves as the country's most important institution dedicated to the quantum field.

However, these trends are not set in stone. As the United States has geared up for competition with China in areas of key technology, government-led initiatives and funding to promote R&D for quantum have grown. In 2018, Congress passed the National Quantum Initiative Act to "accelerate quantum research and development for the economic and national security of the United States." Since 2019, the U.S. federal budget on quantum for various national laboratories and universities has more than doubled, from over $400 million in 2019 to an estimated $900 million in 2022.

Other economies are stepping up investments as well. In 2018, the European Union initiated the Quantum Flagship, which will pump some €1 billion into quantum research over the next decade. It also aims to build up a network of 5,000 quantum-related researchers across the EU. Similarly, in 2023, Canada launched the National Quantum Strategy for government investment in quantum research ($141 million), talent ($45 million), and commercialization ($169 million). The Japanese government developed its Quantum Technology and Innovation Strategy in 2020, which created eight new R&D centers. Japan followed this up in 2022 with its Vision of Quantum Future Society strategy, which aims to leverage quantum technologies to strengthen the country's economic and societal resilience.

Evaluating China's Quantum Progress

China has made impressive advances in quantum technologies in recent years. While China has not particularly distinguished itself in quantum sensing, it is widely viewed as the global leader in quantum communication. When it comes to quantum computing, China is behind the United States-the global leader-in some aspects, but its scientists have made eye-catching achievements and are progressing rapidly.

In recent years, China has not publicly demonstrated world-leading breakthroughs in quantum sensing. There has been some reporting suggesting China is working on quantum sensors for submarine detection, and in 2021, researchers at the prestigious Tsinghua University claimed they made advances in a quantum radar that could detect stealth aircraft by generating a small "electromagnetic storm." In 2022, a research group at USTC published findings showing that they used diamonds to improve the capabilities of a developmental quantum radar.

Although Chinese researchers have published notable research on quantum sensing, especially on quantum radar, these findings (which have been questioned) appear largely constrained to laboratories. Pan Jianwei-sometimes hailed as China's "father of quantum"- wrote in 2020 that China "started late" in the field and was working to close the gaps.

"[China] started late in the field of quantum precision measurement, and there is a certain gap compared with developed countries as a whole, but in recent years, the gap has been rapidly narrowed, and in some directions it is equivalent to the highest international level publicly reported."
Pan Jianwei, Executive Vice President, University of Science and Technology of China

In comparison, Chinese quantum researchers have achieved several high-profile successes in the field of quantum communication. In 2016, China made a historic breakthrough with the successful launch of Micius, the world's first quantum-enabled satellite. Developed by Pan Jianwei and a team of scientists at USTC, Micius made groundbreaking strides toward an un-hackable satellite-based communication system. Chinese researchers are reportedly developing a constellation of quantum-enabled satellites.

In another major quantum communication breakthrough, a team of researchers also led by Pan Jianwei developed a 2,000 kilometer quantum-secured communication link connecting Beijing and Shanghai. This was later expanded to become an integrated quantum network of four quantum metropolitan-area networks and two ground-satellite links.

China has also pushed to become a leader in quantum computing. In 2021, China became the third country-after Canada and the United States-to develop a complete domestically-built quantum computer. The device was created by Origin Computing, a powerful driver of China's quantum successes.

Since then, China has developed new quantum computers and processors. In May 2023, China's 176-qubit processor, Zuchongzhi, went online, making it China's quantum processor with the largest number of qubits. Developed by the Chinese Academy of Sciences (CAS), Zuchongzhi is already capable of processing tasks millions of times faster than a traditional supercomputer can. However, this still lags behind the world's fastest processor, IBM's 433-qubit processor which was revealed in 2022.

Despite the already high number of qubits in these processors, quantum computers will likely require thousands of qubits to perform tasks at the complexity and caliber that scientists are looking to reach. IBM aims to debut a quantum computer with 1,121 qubits in 2023 and a quantum computer with more than 4,000 qubits by 2025.

While these feats are impressive, they do not allow for a comprehensive comparison across countries. Patents, however, serve as one useful indicator of which countries or firms are leading in certain areas.

According to one study, the China National Intellectual Property Administration (CNIPA) received 1,554 applications for quantum communication patents between 2010 and 2022. This is nearly twice the number of applications to the U.S. Patent and Trademark Office (USPTO) and four times the number of applications to Japan's patent office.

A different picture emerges when looking at applications for quantum computing. CNIPA received just over 900 quantum computing patent applications from 2010 to 2022-less than half as many applications to the USPTO. Still, China's office received twice as many quantum computing applications as Japan's and far more than other patent offices.

Yet fully understanding the state of play requires looking at certain kinds of patents. International patents-those filed in patent offices across multiple countries-are harder to acquire, and they are typically reserved for more high-quality and high-value patents. When it comes to these international patent families, Chinese companies lag.

According to a 2023 study by the European Patent Office, only one Chinese company, Alibaba, ranked among the top-20 entities in terms of international patents in quantum computing. By comparison, U.S. companies and universities took up half of the top-20 list, with IBM taking the top spot with 401 international patent families. Japan had an impressive five companies in the top-20, with Toshiba/Nuflare Technologies taking the number-two spot.

Even patents do not tell the whole story. Some analysts have criticized China's large number of patent filings as an inaccurate representation of the country's true output, arguing that Chinese patent figures are inflated by the filing of patents with marginal technological differences from existing patents.

An alternative metric for comparing countries is the publication of research papers. China has published the largest volume of academic papers across all three areas of quantum technology. Between 2011 and 2020, China published more than 14,000 publications on quantum technology. While quantity is important, it is also crucial to assess the quality of publications. The Hirsch Index (H-index) is often used to help with this. It measures both the total number of research papers published by an entity and the number of citations the research receives.

Between 2018 and 2022, China ranked first globally in the field of quantum communication, with an H-index score of 48. The United States followed closely behind with an H-index of 43. However, China falls slightly behind in quantum sensing research and further behind in quantum computing research. The United States was ranked the highest in quantum computing with an H-index of 91, nearly double China's H-index of 52.

Overall, China's quantum capabilities have been steadily developing, yet China's future progress will not be without challenges. The Biden administration has taken several moves to limit China's development of certain technologies that are deemed a threat to U.S. national security. In October 2022, the United States imposed on China a suite of landmark export controls on advanced semiconductors and chip-making equipment, and Washington has successfully pushed for key allies-namely Japan and the Netherlands-to join in restricting China's access to key chip technologies.

Quantum technologies have emerged as another key front in intensifying U.S.-China technological competition. In 2021, the U.S. Department of Commerce placed three Chinese companies specializing in quantum technology on the Department's Entity List, restricting exports to these firms. More recently, in August 2023, the Biden administration announced new restrictions on U.S. investments into China related to quantum technologies (as well as semiconductors and artificial intelligence). Once in effect, the rules are poised to prohibit U.S. persons from investing in all three areas of quantum technologies in China. The most painful impact of these restrictions may not be financial, but may instead result from limiting the transfer of knowledge and expertise into China.

Authors:
Brian Hart, Bonny Lin, Samantha Lu, Hannah Price, Yu-jie (Grace) Liao, Matthew Slade

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Is China a Leader in Quantum Technologies? | ChinaPower Project

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This ChinaPower feature breaks down the complexities of quantum technologies and analyzes China's progress relative to other countries.

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U.S. and Chinese Military AI Purchases - Center for Security and Emerging Technology

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U.S. and Chinese Military AI Purchases - Center for Security and Emerging Technology

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This report uses procurement records published by the Department of Defense (DOD) and the People’s Liberation Army (PLA) between April and November of 2020 to assess, and, where appropriate, compare what each military is buying when it comes to artificial intelligence (AI). Specifically, it analyzes the U.S. and Chinese militaries’ respective purchases of equipment, services, and research and development (R&D) activities in seven categories commonly identified as priority areas for military AI: intelligent and autonomous vehicles; intelligence, surveillance, and reconnaissance (ISR); predictive maintenance and logistics; information and electronic warfare; simulation and training; command and control (C2); and automatic target recognition (ATR). Our key findings include:

1. Procurement data between April and November of 2020 indicates that U.S. and Chinese military forces are devoting comparable levels of attention to a similar suite of AI applications, particularly in regard to AI for intelligent and autonomous vehicles, as well as ISR tools. Neither military appears to have made significant investments in procuring AI for command and control, though this may be a result of our data and research approach.

2. Despite the fact that both the United States and China have relatively concentrated defense-industrial bases, each country’s ecosystem of military AI suppliers (as reflected in the procurement records we reviewed), appears to be more distributed and composed of smaller vendors. The 300 U.S. military AI contracts in our dataset are distributed among 249 unique vendors. Notably, only 36 vendors were awarded multiple contracts and just eight won three or more contracts. Most of the remaining vendors were small, bespoke defense companies. The 119 Chinese military AI contracts in our dataset were distributed across 102 unique vendors. Furthermore, universities accounted for a much larger share of AI contracts in China (14 percent) than the United States (3 percent).

Our research methodology, data, and overall analysis all have limitations. The aforementioned list of seven application areas for military AI development is far from exhaustive, and in fact, a significant number of procurement records could not be neatly categorized into these buckets. The procurement records themselves are also limited, providing a snapshot of military purchasing patterns during an extraordinary period of time when the COVID-19 pandemic disrupted supply chains and business operations. The findings from this study may therefore not be generalizable to broader military AI procurement patterns. Also, any comparison made between U.S. and Chinese military AI purchases must account for their vastly different military bureaucracies, defense-industrial bases, and procurement systems, all of which inherently shape how each organization develops, obtains, and potentially uses emerging technologies such as AI going forward.

Even with these limitations, our study provides a unique perspective on military AI procurement as well as a rich set of concrete examples of U.S. and Chinese AI-enabled technologies and capabilities that are in relatively advanced stages of research and development or are already ready for deployment. The findings of this report, when combined with other types of data and analysis, could provide useful insights for U.S. defense planners as they think about technological competition with China and examine how AI is being integrated into core U.S. and Chinese military functions, doctrine, and operational concepts for future warfare.

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Center for Security and Emerging Technology

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The Chinese military's progress in artificial intelligence largely depends on continued access to high-end semiconductors. By analyzing thousands of purchasing records, this policy brief offers a detailed look at how China's military comes to access these devices. The authors find that most computer chips ordered by Chinese military units are designed by American companies, and outline steps that the U.S. government could take to curtail their access.

This report examines nearly 350 artificial intelligence-related equipment contracts awarded by the People's Liberation Army and state-owned defense enterprises in 2020 to assess how the Chinese military is adopting AI. The report identifies China's key AI defense industry suppliers, highlights gaps in U.S. export control policies, and contextualizes the PLA's AI investments within China's broader strategy to compete militarily with the United States.

National security leaders view AI as a priority technology for defending the United States. This two-part analysis is intended to help policymakers better understand the scope and implications of U.S. military investment in autonomy and AI. It focuses on the range of autonomous and AI-enabled technologies the Pentagon is developing, the military capabilities these applications promise to deliver, and the impact that such advances could have on key strategic issues.

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To learn more, please review this policy. By continuing to browse the site, you agree to these terms.

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U.S. and Chinese Military AI Purchases - Center for Security and Emerging Technology

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This data brief uses procurement records published by the U.S. Department of Defense and China's People's Liberation Army between April and November of 2020 to assess, and, where appropriate, compare what each military is buying when it comes to artificial intelligence.

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China's Censors Back Down on Generative AI

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China's Censors Back Down on Generative AI

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China’s finalized rules for generative Artificial Intelligence (AI), effective from August 15, are considerably less restrictive than a draft version that had circulated previously. This shows that authorities, particularly China’s internet regulator, the Cyberspace Administration of China (CAC), have been receptive to public comments and industry concerns as countries race to develop the most powerful large language models (LLMs) – algorithms that can generate texts close to human language.

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China's finalized rules for generative Artificial Intelligence (AI), effective from August 15, are considerably less restrictive than a draft version that had circulated previously. This shows that authorities, particularly China's internet regulator, the Cyberspace Administration of China (CAC), have been receptive to public comments and industry concerns as countries race to develop the most powerful large language models (LLMs) - algorithms that can generate texts close to human language.

To understand the present and future of AI governance in China, it is important to consider that filtering undesirable information is a major - but not the only - lens through which Beijing thinks about algorithms. In keeping with an emerging trend in China's digital governance, its new regulations aim to nudge the design and deployment of Chinese LLMs toward alignment with national interests.

The CAC has had a tough awakening that it needs to balance control and censorship with freedom for technological development. Compared to its previous regulatory action in the field of AI, this is one of the first times it has exercised its authority in a field that is subject to intense geopolitical competition. This put the CAC's security-first approach in open contradiction with China's innovation and geopolitical ambitions.

China's AI industry, which the government considers of immense economic and strategic significance, has entered a period of intense uncertainty. U.S. export control measures from October 2022 have complicated Chinese firms' access to advanced chips needed to train AI models to create text, specifically some GPU models sold by American chip design giant Nvidia.

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As a result, the new rules have been among the most significantly amended regulations in the history of the CAC. Most crucially, these regulations now exempt any non-public-facing applications - such as LLMs for medical diagnosis or industrial process automation - from scrutiny, including all research and development activities.

Specific requirements have also been softened. Domestic AI experts had called for major revisions to clauses requiring companies to guarantee that their training data was sufficiently accurate and true. Such guarantees would be an enormous task, given the billions of training data used for large language models. Now, these clauses have been watered down to require only "sufficient measures" as a guarantee.

Ever since the publication of the New Generation Artificial Intelligence Development Plan in 2017, the party-state, with Xi Jinping at its helm, has signaled a firm intention to both tame and harness those AI algorithms that have the power to shape public opinion online. One way it has done so is through regulatory actions, most notably landmark rules on recommender systems that represent one of the most pervasive forms of AI today. In the Chinese Communist Party's vision, algorithms that push content to users should not be used to enrich powerful corporations, but instead to promote officially endorsed values. For example, the CAC wants social media platforms to use algorithmic recommendations to refute rumors and promote " positive energy."
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Yet, it would be a mistake to see censorship as the only driver. China's tech rectification campaign has illustrated that authorities are not interested in sustaining platforms' business models if addiction, financial risk, and socioeconomic scandals are the imminent costs to society. Regulators are tackling societal concerns and possible harms from risks like AI-generated drug prescriptions or scams. Some regulations contain specific clauses to prevent digital addiction of minors while others require special accessibility modes to support the elderly, among other things.

In 2000, U.S. President Bill Clinton famously remarked that China's attempt at developing a censored internet was "sort of like trying to nail Jell-O to the wall"; Chinese internet giants proceeded to create the world's most vibrant digital economy, their platforms complying and even assisting with Beijing's censorship requirements. The generative AI rules similarly leave room for flexibility to support China's industry in taking on OpenAI's ChatGPT, even as they expect companies to overcome the "political alignment problem" - figure out technical ways to ensure that as little generated content as possible violates the Chinese Communist Party's strict information controls. They seek to square the circle between security and development objectives around new technology.

While the CAC will remain wary of netizen-facing chatbots, China's central and local governments are supporting model training that embraces generative AI applications in areas that matter for Xi Jinping's much touted "real economy," from accelerating the discovery of new drugs and streamlining medical communications to upgrading manufacturing. In July, Huawei launched Pangu 3.0, a set of pre-trained models that tackle industrial pain points, like faults in rail freight carriages. Generative AI is welcomed even in law, to help courts automate document summaries and filing. This is why the new regulations purposely exempt them from their purview.

Overall, Beijing's regulatory actions on generative AI do not appear to pose a risk of killing innovation and chilling private tech firms. Even as the CAC is, by its very mandate, primarily interested in limiting the influence of ChatGPT-like products on public opinion, it had to back down and soften the requirements for LLM developers. China's AI governance may continue to surprise observers as the government tries to strike a balance between control and development.

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China's Censors Back Down on Generative AI

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The Cyberspace Administration of China (CAC) has had a tough awakening that it needs to balance control and censorship with freedom for technological development.

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Last month, the Biden administration marshalled executives from Google, Amazon, Microsoft, Meta, OpenAI, Anthropic, and Inflection to the White House to sign a “voluntary” agreement that, among other things, would subject their AI systems to audits before public release and require data sharing with the government and academics.

To attempt to remedy this, the administration negotiated the now-signed “voluntary” agreement with America’s leading AI firms.  It’s likely fair to assume that the White House may have made concessions to individual companies that signed the agreement, and in turn may have threatened actions if a company refused to sign.  Indeed, just as unlucky military members “discover” reasons to volunteer for dangerous missions, there were undoubtedly reasons these companies signed the White House’s “voluntary” agreement.

It gets worse.  The signatories’ not-so-voluntary volunteerism will continue far beyond the agreement’s signing, if the federal government has its way.  Astonishingly, the Washington Post reports that deviations from the agreements by its signatories might be deemed deceptive practices that run afoul of Section 5 of the FTC Act, according to a Commission official “who spoke on the condition of anonymity to discuss the agency’s thinking on enforcement.”

Nevertheless, the Post notes that “the pledge does not include specific deadlines or reporting requirements—and the mandates are outlined in broad language—which could complicate regulators’ efforts to hold the companies to their promises.”  Yet, during a speech about the pledge, President Biden claimed that “these commitments are real, and they are concrete.”  So which is it?  How would enforcement work?  Are only the seven businesses that signed the so-called “voluntary” agreement liable for breaking it?  Are firms that did not sign it liable too?

None of this is how our government is supposed to work.  Usually, government officials are required publicly to explain how they will enforce regulations either in court or through agency administrative processes.  Alternatively, they would garner public support for legislation and get a bill signed into law the increasingly old-fashioned way.

But there are no regulations for AI; nor are there clear and accepted guidelines to distinguish fair and unfair trade practices for AI.  An anonymous government official might lob threats as to how an agency might punish deviations from a “voluntary” agreement, but any FTC efforts to do so would have to go to court in public, where a skeptical judge might wonder how a “voluntary” agreement to which the FTC is not a party could be enforced by it.

Indeed, asking a federal agency to enforce a voluntary agreement to which it is not a party makes little sense, particularly when the agency has no statutory authority to regulate the content of the agreement.   To the extent AI requires regulation, the better approach would be for Congress to pass a law carefully instructing an agency such as the FTC on how to regulate AI.  That hasn’t happened.

Inviting top business leaders to the White House to sign a “voluntary” agreement that a federal agency secretly seeks to enforce—despite no statutory authority to regulate the substance of the agreement—is no way to run a government.

Ultimately, the White House’s AI moves should be considered “artificial regulation.”  While the threats are real, the regulation is anything but.

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There’s an old military adage: “never volunteer for anything.” At first blush, it might seem downright unpatriotic. But perhaps this saying was coined by an officer who spent too much time in Washington.

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The Pentagon just launched a generative AI task force

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The Pentagon just launched a generative AI task force

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Generative AI programs like ChatGPT and Google’s Bard have captivated public attention and lawmaker scrutiny, though so far the Pentagon has been reluctant to adopt them. But Thursday, the Defense Department announced a task force to understand how it might use such tools safely, reveal situations where it’s unsafe for the department to use them, and explore how countries like China might use generative AI to harm the United States.
The task force, dubbed Task Force Lima, “will assess, synchronize, and employ generative artificial intelligence (AI) across the Department,” according to a draft press statement viewed by Defense One.

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Generative AI programs like ChatGPT and Google's Bard have captivated public attention and lawmaker scrutiny, though so far the Pentagon has been reluctant to adopt them. But Thursday, the Defense Department announced a task force to understand how it might use such tools safely, reveal situations where it's unsafe for the department to use them, and explore how countries like China might use generative AI to harm the United States.

The task force, dubbed Task Force Lima, "will assess, synchronize, and employ generative artificial intelligence (AI) across the Department," according to a draft press statement viewed by Defense One.

Generative AI refers to "a category of AI algorithms that generate new outputs based on the data they have been trained on," according to the World Economic Forum. That's very different from far more simple machine-learning algorithms that just take structured data like numerical values and output the next likely statistical outcome. Public-facing generative AI tools include so-called large language models like ChatGPT that can write new text that is largely indistinguishable from human speech. These tools have already been used to write essays, business plans, and even scientific papers. But because large language models are trained on data corpora as large as the searchable Web, they sometimes lie-or, in the parlance of AI researchers, "hallucinate." For this reason, Pentagon officials have expressed reluctance to embrace generative AI.

The new effort will be led by Craig Martell, the Defense Department's chief digital officer. Martell said much is still up in the air, but a main objective will be to find a "set of use cases, internally to the department, where we believe that generative AI can help us do our job and where the dangers, the difficulty of generative AI can be mitigated. For example...if I need to do first draft generation of some document, that's fine, because I'm going to take full responsibility to edit that document, make sure it's actually correct before I pass it up the line, because my career is on the line."

However, there are a number of use cases where the risks of hallucination will be too high to employ a large language model, such as "anything kinetic," or having to deal with lethal weapons, he said.

"Let's just get clear on what the acceptability conditions are." Martell said. For instance, if someone in the department very quickly needs to summarize a lot of text into a legal document, an LLM might be a good tool for that purpose, but only if the user can satisfy certain concerns. "How do we then mitigate against the potential hallucinations that are in that text? If there are procedures by which that text can easily be checked, then we're probably going to be on board with that."

Martell came to the Defense Department from Lyft, and he's publicly warned of the dangers large language models can pose. But, he says, such models "aren't a Pandora's Box, either." The Defense Department has to understand where they can be used safely-and where adversaries might deploy them, he said. "There's going to be a set of use cases that are going to be new attack vectors, not just to the DOD, but to corporations as well. And we're going to have to... to figure out diligently how to solve those."

The task force will also help the Pentagon better understand what it needs to buy to achieve new AI ambitions. That could mean more cloud services, data or synthetic data, models, or none of those. The effort is too young to know exactly what it will mean for industry, Martell said.

"If we decide that we're gonna build our own foundational model, we're gonna be scaling up compute, there's no question about it. In order to build your own foundation model, you need lots of compute power. If we're going to buy it from someone else, or if we're going to take an open source one and fine tune it," they may need different contracts, he said.

Martell said that a broader question is whether the Defense Department even has enough use cases where generative AI could be helpful, given a better understanding of the risks. While the department has a lot of carefully curated internal data, terabytes of jet engine diagnostics or years of drone surveillance footage over the Middle East do not a large language model make. Because they have to pull from large text corpora, a certain amount of unreliability is inherent in the way they work. The question the academic field has yet to answer is whether that inherent unreliability can be accurately quantified to reveal the risks of use.

"It's an open question whether we have enough data that covers broad enough coverage that the value of the model could maintain without that pre-trained data. On the other hand...my hypothesis is, the more pre-trained data, the more likely that hallucinations. So that's a trade off we're really going to have to explore. I don't, I actually don't think the scientific community knows the answer to that yet," he said.

One of the potential benefits of the task force is to illuminate the best path forward for industry-at least if the goal is to produce products or services that meet Defense Department standards.That will likely mean giving companies the courage and incentive to not only reduce the inherent risks in their models, but also to increase ease of use for more people. For instance, Martell said, one reason why programs like ChatGPT aren't suitable for the Defense Department now is the amount of question engineering required to produce suitable results. Lengthy prompt trees are fine for hobbyists, but an operator who has to do several other complex tasks needs an interface that is intuitive and functions better from the beginning.

"There's lots of research that has to be done, not just for the DOD but in the community as a whole, about those two pieces: what [does] automated, prompt engineering and context mean, and what...automatic hallucination mitigation look like? These things are unknown," he said.

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The Pentagon just launched a generative AI task force

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A conversation with the leader of Task Force Lima reveals its objectives-and the main questions it's trying to answer.

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White House launches AI-based contest to secure government systems from hacks

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White House launches AI-based contest to secure government systems from hacks

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The White House on Wednesday said it had launched a multimillion-dollar cyber contest to spur use of artificial intelligence (AI) to find and fix security flaws in U.S. government infrastructure, in the face of growing use of the technology by hackers for malicious purposes.

"Cybersecurity is a race between offense and defense," said Anne Neuberger, the U.S. government's deputy national security advisor for cyber and emerging technology.
"We know malicious actors are already using AI to accelerate identifying vulnerabilities or build malicious software," she added in a statement to Reuters.

Numerous U.S. organizations, from healthcare groups to manufacturing firms and government institutions, have been hacking targets in recent years, and officials have warned of future threats, especially from foreign adversaries.

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SAN FRANCISCO, Aug 9 (Reuters) - The White House on Wednesday said it had launched a multimillion-dollar cyber contest to spur use of artificial intelligence (AI) to find and fix security flaws in U.S. government infrastructure, in the face of growing use of the technology by hackers for malicious purposes.

"Cybersecurity is a race between offense and defense," said Anne Neuberger, the U.S. government's deputy national security advisor for cyber and emerging technology.

"We know malicious actors are already using AI to accelerate identifying vulnerabilities or build malicious software," she added in a statement to Reuters.

Numerous U.S. organizations, from healthcare groups to manufacturing firms and government institutions, have been hacking targets in recent years, and officials have warned of future threats, especially from foreign adversaries.

Neuberger's comments about AI echo those Canada's cybersecurity chief Samy Khoury made last month. He said his agency had seen AI being used for everything from creating phishing emails and writing malicious computer code to spreading disinformation.

The two-year contest includes around $20 million in rewards and will be led by the Defense Advanced Research Projects Agency (DARPA) - the U.S. government body in charge of creating technologies for national security - the White House said.

Alphabet's (GOOGL.O) Google, Anthropic, Microsoft (MSFT.O), and OpenAI - the U.S. technology firms at the forefront of the AI revolution - will make their systems available for the challenge, the government said.

The contest signals official attempts to tackle an emerging threat that experts are still trying to fully grasp. In the past year, U.S. firms have launched a range of generative AI tools such as ChatGPT that allow users to create convincing videos, images, texts, and computer code. Chinese companies have launched similar models to catch up.

Experts say such tools could make it far easier to, for instance, conduct mass hacking campaigns or create fake profiles on social media to spread false information and propaganda.

"Our goal with the DARPA AI challenge is to catalyze a larger community of cyber defenders who use the participating AI models to race faster - using generative AI to bolster our cyber defenses," Neuberger said.

The Open Source Security Foundation (OpenSSF), a U.S. group of experts trying to improve open source software security, will be in charge of ensuring the "winning software code is put to use right away," the U.S. government said.

Reporting by Zeba Siddiqui in San Francisco; additional reporting by Raphael Satter in Washington; editing by Jonathan Oatis

Our Standards: The Thomson Reuters Trust Principles.

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White House launches AI-based contest to secure government systems from hacks

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The White House on Wednesday said it had launched a multimillion-dollar cyber contest to spur use of artificial intelligence (AI) to find and fix security flaws in U.S. government infrastructure, in the face of growing use of the technology by hackers for malicious purposes.

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CISA, NSA and NIST Publish New Resource for Migrating to Post-Quantum Cryptography 

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CISA, NSA and NIST Publish New Resource for Migrating to Post-Quantum Cryptography 

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The Cybersecurity and Infrastructure Security Agency (CISA), National Security Agency (NSA), and National Institute of Standards and Technology (NIST) published a factsheet today about the impacts of quantum capabilities. The agencies urge all organizations, especially those that support critical infrastructure, to begin early planning for migration to post-quantum cryptographic (PQC) standards by developing their own quantum-readiness roadmap.

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Cybersecurity and Infrastructure Security Agency CISA

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Official websites use .gov
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CISA, NSA and NIST Publish New Resource for Migrating to Post-Quantum Cryptography    | CISA

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Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock (Lock A locked padlock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

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The Next Frontier in AI Regulation Is Procedure

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The Next Frontier in AI Regulation Is Procedure

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As the promise and dangers of artificial intelligence (AI) grow more evident by the day, observers across the private sector, the media, academia, and government seem convinced that it’s more or less unregulated. Existing laws, they claim, have little to say about this new and radically unfamiliar technology; new ones must be built to control it.

Others beg to differ, especially when it’s the private sector making the argument. These skeptics think we already have plenty of AI laws. What’s really missing, according to them, is the willpower and resources to enforce them. On this view, private-sector calls for new laws sometime in the future seem disingenuous—a PR strategy to come across as responsible while diminishing harms already being caused and resisting any oversight in the present.

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As the promise and dangers of artificial intelligence (AI) grow more evident by the day, observers across the private sector, the media, academia, and government seem convinced that it's more or less unregulated. Existing laws, they claim, have little to say about this new and radically unfamiliar technology; new ones must be built to control it.

Others beg to differ, especially when it's the private sector making the argument. These skeptics think we already have plenty of AI laws. What's really missing, according to them, is the willpower and resources to enforce them. On this view, private-sector calls for new laws sometime in the future seem disingenuous-a PR strategy to come across as responsible while diminishing harms already being caused and resisting any oversight in the present.

The skeptics have a point. Many existing laws do bear on AI, and they can and should play a major role in how society responds to the technology. But on the other hand, the real novelty and far-reaching implications of many aspects of modern machine learning, and the lack of major regulatory action on AI to date, both suggest that today's laws, on their own, won't be enough to tackle the massive challenges AI poses-no matter how many resources are committed to the task. Reforms and innovations are needed, and in any event, they're likely coming, one way or another.

To make these new laws effective, we need to understand why existing laws aren't likely to cut it alone. Many seem to assume it's because these laws aren't "about" AI; that is, they don't address the specific ins and outs of current AI technology, like training data, large-scale computing, and problems like model bias and hallucination. But these AI-specific issues are relevant to governance only insofar as they relate to potential harms, like consumer deception, discrimination, or threats to safety. Those harms fall well within the purview of many existing laws-from the Federal Trade Commission Act, the Civil Rights Act of 1964, and other federal statutes to long-standing tort laws-and existing regulators and law enforcers, such as the federal and state regulatory agencies, public prosecutors, and private litigants.

If that's the case, why haven't these laws made a bigger impact on the AI sector? In part, the devil may be in the procedural details. Historically, outmoded enforcement and adjudication processes have often turned legal protections against the dangers of technology into dead letters. Accordingly, the history of technology regulation in the United States is rich with procedural innovations, from the development of an administrative system for workplace injury claims (as opposed to complex, costly lawsuits) in the 19th century to laws allowing private citizens to enforce environmental and telecommunication laws in the 20th.

Procedure could prove equally pivotal in regulating AI today. Tackling questions like "who enforces the law," "which evidence counts," and "who hears disputes" may be drier work than banning facial recognition or launching new global agencies, but history suggests they could make the difference between regulatory impact and irrelevance. Legislatures, regulatory agencies, and the courts alike should start working now to build new procedures and institutions ready for the AI era.
What the Law Already Covers
When many commentators talk about the need to "regulate AI," they seem to imagine something that is specifically scoped to the technical components of an AI model, like its underlying data or training algorithm. This may help explain why a primary theme in AI regulatory proposals to date involves mandating "algorithmic audits" or data transparency. AI-specific rules and regulations adopted or proposed in New York City, the European Union, and even the U.S. Congress all emphasize these types of interventions. Others have suggested AI risks can best be curtailed by restricting access to or requiring the reporting of large quantities of computing power, another critical AI input.

All plausible enough. But it's a mistake to assume that "AI regulation" has to have been written with AI's specific inputs and elements in mind, like controls on algorithms or training runs. Artificial intelligence isn't the first powerful new technology legislators have confronted. Long before the advent of machine learning, governments were developing rules and doctrines to tackle potentially harmful technologies. Examples include workers' compensation laws enacted during America's industrialization, new liability doctrines introduced as railways and automobiles spread, and the slew of federal statutes created in the environmental revolution of the 1960s and 1970s. These generally applicable laws, often radical in their time, have become utterly familiar today-so familiar, it seems, as to be easily forgotten as the sparks of the AI revolution begin to fly.

To see how much these laws still have to say, consider a stylized account of how AI typically unfolds in the real world. First, a company uses data and statistical methods to create an AI algorithm. It builds the algorithm into a product and sells the product to users. The users deploy the product to automate a task, perhaps related to the user's business, research, or other important activities. In the process, the algorithm in the product may receive and process more data, which may be provided by or related to third parties.

The story of AI in the real world, in other words, is in many ways perfectly familiar. It's a story about corporate activity, and marketing, and the development of commercial products; about the use and exchange of information; about the ways different kinds of organizations make decisions and take action. And viewed as such, it's a story with plenty of openings for regulators today, using familiar laws and authorities long predating the rise of AI.

The Federal Trade Commission (FTC), for instance, has long-standing authority to regulate "unfair or deceptive" business practices-practices just as tempting to AI producers today as to the FTC's targets a century ago. Indeed, the FTC recently announced an inquiry into industry leader OpenAI and, in a series of blog posts, emphasized that this authority covers AI products, especially with respect to models marketed with performance claims that cannot be substantiated, models that are discriminatory, and models that are themselves " effectively designed to deceive" by enabling the production of deepfakes or other deceptive content.

Similarly, the FTC and the U.S. Department of Justice have signaled an openness to using antitrust law against major AI companies. Modern generative AI systems rely on large troves of data and computational power, both of which may be accessible only to the largest tech companies. Acquisitions and cloud computing arrangements between big tech firms like Microsoft and AI startups like OpenAI have already sparked antitrust concerns, as have attempts by some search companies to restrict access to internet search data from rival companies trying to train their own AI models.

But existing legal tools may be thickest of all when it comes to AI's specific, real-world "end uses." Take something like discrimination in automated employment decisions. This topic has sparked a large body of AI-specific research and guidance dedicated to operationalizing " fairness metrics," " debiasing" training data or finalized models, or otherwise intervening to reduce the bias of AI systems. But for purposes of enforcing anti-discrimination laws, it is generally irrelevant whether a human or an automated system is doing the discrimination. The Equal Employment Opportunity Commission uses a simple heuristic called the " four-fifths rule" in cases of employment discrimination: If any protected class passes through a selection process with less than 80 percent the success rate of any other group, the agency will view this as presumptive evidence of discrimination. AI-specific best practices and technical interventions can help keep AI systems from violating the constraint, but it applies to AI either way.

Medical device regulations are another case in point. The Food and Drug Administration (FDA) is charged to ensure these devices are safe and effective, no matter the particular technologies embedded in them. Earlier this year, the FDA used this existing authority to impose detailed testing and labeling conditions on algorithmic lesion detectors used in endoscopy. Additional examples might be drawn from copyright law, internet platform regulation, or other areas. Regardless, it's clear that there's already plenty of "AI law" on the books.
If the Law Already Covers AI, Where Is the Action?
With so many opportunities for regulation, why have we seen relatively few AI-specific enforcement actions to date? As a case study of some of the dynamics that may be in play, consider the FTC. With its broad authorities and history of regulating digital technologies, the FTC might seem in pole position to act on AI risks. But so far, despite its many blog posts, the agency has yet to actually claim that any specific AI company has violated the law, though its recent letter to OpenAI hints at possible action in the indefinite future. Gaps like this may help explain why so many observers believe that AI regulation is next to nonexistent.

There are several possible explanations. The FTC may simply be getting up to speed with the latest developments in AI, which in many cases are pushing the technology into meaningful real-world applications for the first time. As a sign of the FTC's growing interest in AI, for example, the agency announced a new Office of Technology in February. It will be a while before the impact of this new office on the FTC's actual enforcement behavior can be observed, and likely longer still before other agencies are similarly able to begin prioritizing AI-related issues without dedicated staff.

In addition, agencies tend to be cautious about applying older, often broadly phrased authorities to novel technology. For example, in 2002, the FTC for the first time claimed that egregious failures to secure consumers' personal information could constitute an "unfair or deceptive" business practice under Section 5 of the FTC Act (the relevant text of which dates to 1938). But although the agency brought enforcement actions against dozens of companies over the following decade, it hesitated to issue formal data security regulations.

It wasn't until 2015 that a court explicitly held that data security fell within the FTC's jurisdiction. The FTC v. Wyndham court leaned on the FTC's history of informal statements and past adjudications as providing "fair notice" to companies regarding their legal obligation to protect consumer data, even in the absence of a formalized regulation.

The FTC may currently be applying this lesson to AI: Before applying an existing law to an emerging technology, first produce a long record of blog posts and interpretive guidance, so that you can demonstrate companies were provided with fair notice. From the perspective of an executive agency like the FTC, this is a reasonable strategy. But it also delays any actual enforcement actions substantially, and may limit how far agencies are willing to apply laws on the books.
Pay Attention to Procedure
It's understandable that regulators may be cautious to apply broad, existing statutes to new technologies, or need time to turn general authorities into AI-specific dos and don'ts. But we doubt these factors fully explain the dearth of regulatory action so far. Perhaps more important-and easier to overlook-are seemingly mundane procedural constraints that keep existing law from playing a meaningful role.

Here again, history is instructive. Just like the advent of real-world AI isn't the first time regulators have confronted a new technology, it's also not the first time the procedural design of existing laws and institutions (as opposed to their substantive scope)prevented them from effectively applying them to that technology and its effects. The procedural innovations that developed in response were often at least as consequential as "substantive" changes, such as explicitly bringing new technologies within the scope of the law.

Consider, for example, the apparently dry topic of standing to sue. Investigating legal violations and enforcing the law in court are costly, lengthy processes. In many cases, regulatory agencies must shoulder the burden alone, because their organic statutes give them alone the authority to sue violators. Some commentators argue that these agencies need more enforcement resources to tackle AI. They do. But as long as they're the only enforcers in town, it will never be enough. There's too much happening, too fast, for even the most lavishly resourced agencies to tackle alone.

Lawmakers in the 1970s faced a similar enforcement dilemma with the widespread pollution that America's industrial development had created. They responded by creating the citizen suit, empowering private individuals to sue polluters who broke the Clean Air Act, Clean Water Act, and other key laws. In the 1980s, advances in telecommunications unleashed pollution of a different sort: endless robocalls and " junk faxes" threatening to swamp American consumers and the federal agencies meant to protect them. In 1991, the architects of the Telephone Consumer Protection Act (TCPA) imposed rules and technical standards on the telemarketing industry and-crucially-empowered individual consumers to enforce them in court. Today, both the TCPA and the environmental laws of the 1970s have significantly reduced the harms they were built to address (though new challenges have inevitably arisen over the years, from " non-criteria" climate emissions to VoIP robocalls). Citizen enforcement played an important role in this success, giving teeth to the law and driving change in how technology was developed and deployed.

The history of workplace safety regulation provides another case in point. Although employers had long been potentially liable for workers' injuries, actually suing employers was such a burdensome process that the doctrines had little practical impact as late as the 19th century. But as industrialization drove a wave of grisly workplace accidents, state governments eventually swept away the old courtroom procedures in favor of (comparatively) streamlined administrative regimes that paid injured workers without litigation. This procedural shift, coupled with the move to no-fault compensation, helped make redress a reality for those injured in the industrial workplace. Could similar streamlining help vindicate the rights of AI victims without deep pockets-for example, people denied welfare benefits by faulty models, or artists claiming copyright infringement by generative AI?

There were other procedural barriers that kept existing laws from making much of a difference during this era. For example, injured victims in stagecoach crashes could theoretically sue the driver for damages under the common law of trespass. But until the mid-19th century, the laws of evidence blocked any witness with an interest in the outcome from testifying- including the victim, whose testimony would obviously be critical in many cases (especially when it came to harms like pain and suffering). Crash victims also faced byzantine filing requirements similar to those that discouraged worker injury lawsuits, and contingent-fee arrangements with lawyers were illegal at the time, meaning only relatively affluent victims could afford to sue in the first place. Small wonder that personal injury lawsuits were rare. It took reforming these procedural roadblocks, among other changes, to make meaningful recovery possible under the common law.
Considerations for the Way Forward
History shows how seemingly obscure procedural issues-such as who exactly is allowed to sue when the law is broken, whether claims are heard by a judge or an agency, and which evidence is heard-can shape how technology regulation plays out in the real world. In some cases, getting these questions right could matter more than creating new rules and limits on technology, or empowering new regulators to enforce them.

For AI policymakers, the lesson is clear: Sweat the structural and procedural details, both for new laws and for the many existing ones that speak to AI's challenges. The unfolding deployment of AI will affect the whole of society, from workers whose jobs are changed or even eliminated by machine learning technologies, to already marginalized populations subjected to biased algorithms, to the public as a whole, placed at risk from future AI systems of potentially existential danger. Diverse interests are at stake, embodied in people and organizations with widely varying resources and constraints. It's critical to think now about how our legal procedures and institutions can efficiently mobilize, protect, and adjudicate among these diverse interests and stakeholders. To that end, before drafting new substantive rules for AI-or at least in parallel to those efforts-policymakers need to think hard about process questions like:

Who enforces limits on AI? Is the task left to federal agencies, or can state and local governments, private companies, nonprofits, or individual citizens join the fray? Which ones?

What kind of relief can the enforcers get, and at what point in the process? Can enforcers obtain injunctive relief-for example, forcing AI companies to stop disputed practices-or are fines and monetary damages the main remedy? Do they have to wait until harm has occurred, or can they enforce prospectively?

Is collective action possible? Can individual victims of AI-related harm "speak" for others similarly situated-and obtain and enforce penalties on their behalf? Or does each victim need to step up individually, potentially diminishing the force of regulation?

Who has the burden of proof? How much evidence do AI regulators or litigants need to produce, before getting relief-or before the burden shifts to the AI companies or users being challenged? What tools should enforcers have (perhaps comparable to civil discovery or administrative subpoenas) to ensure that material, non-public evidence, such as disputed training data, comes to light?

Who hears disputes? Are challenges to AI-related applications or outcomes heard in generalist federal or state courts, in administrative fora, or by private arbitrators? How much do these processes cost, how long do they take, and who pays? How knowledgeable are the judges about AI?

The right answers to these questions will vary depending on how AI is being deployed, by whom, and with what potential consequences. But it's critical to ask these questions in the first place, rather than fixating on AI's technical particulars. And it's just as important to recognize that these questions apply equally to the many existing laws that speak to AI, not just hypothetical new ones.

These are questions for Congress and state legislatures first and foremost, as the institutions that create regulatory agencies and have the power to define their enforcement and adjudication procedures. That said, many laws leave agencies discretion to set their own procedures, creating openings for innovation that can be very significant: For example, some federal agencies have created their own class action mechanisms. The same is often true for the courts, particularly at the state level.

Who exactly has the power to act will vary from case to case, meaning that policymakers of all sorts have roles to play in making sure legal procedures are up to AI's challenge. But none will be the first to confront the profound consequences of new technology. America's history of technology regulation suggests that the rise of powerful AI is an opportunity for procedural experimentation and reform. Policymakers should seize that opportunity to make the most of the tools they already have and ensure the success of the new ones to come.

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The Next Frontier in AI Regulation Is Procedure

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From "Who enforces?" to "How are disputes adjudicated?" prosaic procedural questions may make the difference between regulatory impact and irrelevance.

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Software Must Be Secure by Design, and Artificial Intelligence Is No Exception

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Software Must Be Secure by Design, and Artificial Intelligence Is No Exception

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Discussions of artificial intelligence (AI) often swirl with mysticism regarding how an AI system functions. The reality is far more simple: AI is a type of software system.
And like any software system, AI must be Secure by Design. This means that manufacturers of AI systems must consider the security of the customers as a core business requirement, not just a technical feature, and prioritize security throughout the whole lifecycle of the product, from inception of the idea to planning for the system’s end-of-life. It also means that AI systems must be secure to use out of the box, with little to no configuration changes or additional cost.

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Unpacking Cyber Capacity-Building Needs: Part I. Mapping the Foundational Cyber Capabilities 

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Unpacking Cyber Capacity-Building Needs: Part I. Mapping the Foundational Cyber Capabilities 

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There is a growing emphasis among Member States on the need to support the implementation of the Framework for Responsible State Behaviour in the ICT environment (the Framework), including through dedicated guidance, assistance, and dedicated capacity-building efforts.

In response to this demand and to increase the cybersecurity and resilience of Member States, the UNIDIR Security and Technology programme conducted a research study with three main objectives, which are to identify foundational cyber capabilities, strengthen States’ ability to effectively prevent or mitigate the impact of selected malicious ICT activities, and design a tool to better identify requirements and prioritize capacity-building interventions.

The outcomes of the research project can be found in these two publications. The first report, Part I. Mapping the Foundational Cyber Capabilities, is centered on the concept of Foundational Cyber Capabilities (FCCs), which are defined as the combination of policies and regulations, processes and structures, partnerships and networks, people and skills, and technology necessary to implement the Framework.

The second report, Part II. Introducing a Threat-Based Approach, proposes an approach that would allow governments to better assess their readiness to leverage the Framework to prevent or respond to specific malicious ICT activities and threats.

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Unpacking Cyber Capacity-Building Needs: Part I. Mapping the Foundational Cyber Capabilities | UNIDIR

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Part 1 of 2: A report on how the combination of policies and regulations, processes and structures, partnerships and networks, people and skills, and technology can be used to implement the ICT Framework.

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Unpacking Cyber Capacity-Building Needs: Part II. Introducing a Threat-Based Approach

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Unpacking Cyber Capacity-Building Needs: Part II. Introducing a Threat-Based Approach

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There is a growing emphasis among Member States on the need to support the implementation of the Framework for Responsible State Behaviour in the ICT environment (the Framework), including through dedicated guidance, assistance, and dedicated capacity-building efforts.

In response to this demand and to increase the cybersecurity and resilience of Member States, the UNIDIR Security and Technology programme conducted a research study with three main objectives, which are to identify foundational cyber capabilities, strengthen States’ ability to effectively prevent or mitigate the impact of selected malicious ICT activities, and design a tool to better identify requirements and prioritize capacity-building interventions.

The outcomes of the research project can be found in these two publications. The first report, Part I. Mapping the Foundational Cyber Capabilities, is centered on the concept of Foundational Cyber Capabilities (FCCs), which are defined as the combination of policies and regulations, processes and structures, partnerships and networks, people and skills, and technology necessary to implement the Framework.

The second report, Part II. Introducing a Threat-Based Approach, proposes an approach that would allow governments to better assess their readiness to leverage the Framework to prevent or respond to specific malicious ICT activities and threats.

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There is a growing emphasis among Member States on the need to support the implementation of the Framework for Responsible State Behaviour in the ICT environment (the Framework), including through dedicated guidance, assistance, and dedicated capacity-building efforts.

In response to this demand and to increase the cybersecurity and resilience of Member States, the UNIDIR Security and Technology programme conducted a research study with three main objectives, which are to identify foundational cyber capabilities, strengthen States' ability to effectively prevent or mitigate the impact of selected malicious ICT activities, and design a tool to better identify requirements and prioritize capacity-building interventions.

The outcomes of the research project can be found in these two publications. The first report, Part I. Mapping the Foundational Cyber Capabilities, is centered on the concept of Foundational Cyber Capabilities (FCCs), which are defined as the combination of policies and regulations, processes and structures, partnerships and networks, people and skills, and technology necessary to implement the Framework.

The second report, Part II. Introducing a Threat-Based Approach, proposes an approach that would allow governments to better assess their readiness to leverage the Framework to prevent or respond to specific malicious ICT activities and threats.

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Unpacking Cyber Capacity-Building Needs: Part II. Introducing a Threat-Based Approach | UNIDIR

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There is a growing emphasis among Member States on the need to support the implementation of the Framework for Responsible State Behaviour in the ICT environment (the Framework), including through dedicated guidance, assistance, and dedicated capacity-building efforts.

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In 10 years, CEOs who don't understand data and AI won't be eligible for the top job in any industry.

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In 10 years, CEOs who don't understand data and AI won't be eligible for the top job in any industry.

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Ali Ghodsi, CEO of Databricks, a cloud-based data and AI company, says AI is "eating software." As generative AI becomes more ubiquitous, Ghodsi says companies that prioritize data will have an advantage. Ghodsi says it will be so disruptive, in five to 10 years most CEOs will need some background in AI. This story is part of "What's Next?," a series where we ask CEOs of prominent companies across industries about how rapidly evolving trends influence their approach to leadership.

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Ali Ghodsi, CEO of Databricks, a cloud-based data and AI company, says AI is "eating software."

As generative AI becomes more ubiquitous, Ghodsi says companies that prioritize data will have an advantage.

Ghodsi says it will be so disruptive, in five to 10 years most CEOs will need some background in AI.

This story is part of "What's Next?," a series where we ask CEOs of prominent companies across industries about how rapidly evolving trends influence their approach to leadership.

This as-told-to essay is based on a conversation with Ali Ghodsi, CEO of cloud-based data and AI company Databricks. It has been edited and condensed for clarity.
Marc Andreessen wrote the article " Why Software Is Eating the World" in 2011. I think it's obvious now that software has eaten the world.

What he meant by that is that you'll find software everywhere. Electric cars today are just fancy computers, your Nest thermostat is a piece of software, your watch is a piece of software, and your phone is a piece of software. Everything we do has become digitized, running on software. Every company is now a software company.

In every industry, software is going to become extremely important. Industries that you traditionally might not have thought were software industries - healthcare, retail, finance - all of it is software. Andreessen was very prescient in 2011. He articulated well that software companies are - soon - going to run everything.

In 2013, we at Databricks started saying that AI will eat all software. Our point is that all software that was built to revolutionize the planet is unintelligent because it doesn't use AI, and it's all going to be eaten by AIs that are much, much smarter.

The reason AI works is the data. If we can process data and feed it into AI, we can make all the software on the planet intelligent. That's why we've always called Databricks a software and AI company. Generative AI is going to disrupt any software company that exists today. These models are going to change how humans interact with machines and how machines interact with humans. Every company needs to figure out how to introduce generative AI into their software so that it can become intelligent as it interacts with human beings. Our job at Databricks is to be the platform for that introduction, so these companies can easily build their AI protocols out and process all their company data.

Databricks' focus on AI is unique.

Only a few companies leveraged AI effectively a long time ago, but it was a handful of companies and in specific use cases, such as with Facebook and ad sales. But we believe that in every industry, the winners going forward are going to be data and AI companies.

Uber disrupted the cab industry using data and AI: Uber tells you the estimated time of arrival and estimated price of the trip in advance using AI. People may take this for granted now, but 15 years ago you probably couldn't know the price or the exact time of arrival of your ride before it got to you. You just sat there and waited - the car might not show up at all. The leading companies in the future are going to be data and AI companies - healthcare, retail, you name it.

I'll go so far as to say, in five or 10 years, to be the CEO in any of these industries, you'll need to have a data-and-AI background. If you're completely illiterate when it comes to data and AI, you probably won't have the credentials for that job.
Are you a business leader exploring the professional uses of AI and do you want to share your story? Email Ashley Davis at adavis@insider.com.

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I run a $38 billion software company. In 10 years, CEOs who don't understand data and AI won't be eligible for the top job in any industry.

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Ali Ghodsi, CEO of Databricks, says aspiring CEOs in any industry must be prepared with some background or working knowledge of AI.

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Machines are becoming ubiquitous across the twenty-first-century battlespace, and modern militaries must embrace human-machine teaming (HMT) or risk conceding military edge to competitors effectively leveraging artificial intelligence (AI) and autonomy. This report investigates the implications of the increasing incorporation of AI into military operations with a particular focus on understanding the parameters, advantages, and challenges to the US Department of Defense’s (DOD’s) adoption of the concept of HMT.

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How modern militaries are leveraging AI

Table of contents

The importance of human-machine teaming and key insights

Modern militaries must embrace HMT or risk conceding military edge to competitors effectively leveraging AI and autonomy.

Machines are becoming ubiquitous across the twenty-first-century battlespace, and modern militaries must embrace human-machine teaming (HMT) or risk conceding military edge to competitors effectively leveraging artificial intelligence (AI) and autonomy.This paper uses the term human-machine teaming (HMT) to signify both the teaming of human operators with semiautonomous machines (sometimes referred to as manned-unmanned teaming) and interactions between humans and AI agents that do not take physical form. This report investigates the implications of the increasing incorporation of AI into military operations with a particular focus on understanding the parameters, advantages, and challenges to the US Department of Defense's (DOD's) adoption of the concept of HMT.From November 2022 to June 2023, the authors held workshops and interviews with defense and national security experts and practitioners to help inform their research.

Key takeaways

Looking through the lens of three military applications for HMT, the authors arrive at the following conclusions.

HMT has the potential to change warfare and solve key operational challenges: AI and HMT could potentially transform conflict and noncombat operations by increasing situational awareness, improving decision-making, extending the range and lethality of human operators, and gaining and maintaining advantage across the multi-domain fight. HMT can also drive efficiencies in many supporting functions such as logistics, sustainment, and back-office administration, reducing the costs and timelines of these processes and freeing up humans to carry out higher-value tasks within these mission areas.

DOD must expand its definitions for HMT: Definitions of HMT should be expanded to include the breadth of human interactions with autonomous uncrewed systems and AI agents, including those that have no physical form (e.g., decision support software). Extending the definition beyond interactions between humans and robots allows DOD to realize the wide-ranging use cases for HMT-ranging from the use of lethal weapon systems and drone swarms in high-intensity warfare to leveraging algorithms to fuse data and realize virtual connections in the information domain.

HMT development and employment must prioritize human-centric teaming: AI development is moving at an impressive pace, driving potential leaps ahead in machine capability and placing a premium on ensuring the safety, reliability, and trustworthiness of AI agents. As much attention must be dedicated to developing the competencies, comfort levels, and trust of human operators to effectively exploit the value of HMT and ensure humans stay at the center of human-machine teams.

DOD must move from the conceptual to the practical: HMT as a concept is gaining momentum within some elements of the DOD enterprise. Still, advocates for increased AI and HMT adoption stress the need to move the conversation from the conceptual to the practical-transitioning capability development into the real-time testing and employment of HMT capabilities, as has been demonstrated by the Navy's experimentation with AI through Task Force 59-to better articulate and demonstrate the operational advantages HMT can deliver.

Experimentation is crucial to building trust: Iterative, real-world experimentation in which humans develop new operational concepts, test the limits of their machine teammates, and better understand their breaking points, strengths, and weaknesses of machines in a range of environments will play a key role in speeding HMT adoption. This awareness is also essential to human operators as they develop the trust in their AI teammates required to effectively capitalize on the potential of HMT.

DOD must address bureaucratic challenges to AI adoption: DOD's risk-averse culture and siloed bureaucracy is slowing acquisition, experimentation, and adoption of HMT concepts and capabilities. Increasing the agility and flexibility of the acquisition process for HMT capabilities, iterative experimentation, incentives to take on risks, and digital literacy across the force are necessary to overcome these adoption challenges.

Applications of human-machine teaming

HMT is most frequently envisioned narrowly as the process of humans interacting with anywhere from one to several hundred or more autonomous uncrewed systems. In its most basic form, this vision of HMT is not new: Humans have collaborated with intelligent machines for decades-with early machine talents epitomized in 1997 by supercomputer Deep Blue defeating world champion Gary Kasparov in a game of chess -and militaries have long tested concepts to move the needle in this critical capability. The recent impressive pace of development in AI as well as in robotics, however, is driving increased consideration of the new capabilities, efficiencies, and advantages these technologies can enable.

The "loyal wingman" concept is a frequently cited example of this manifestation of HMT in which a human pilot controls the tasking and operations of a handful of relatively inexpensive, modular, attritable autonomous uncrewed aerial systems (UAS). These wingman aircraft can fly forward of the crewed aircraft to carry out a range of missions, including electronic attack or defense, intelligence, surveillance, and reconnaissance (ISR), or strike, or as decoys to attract fire away from other assets and " light up " enemy air defenses.

Interest in this manifestation of HMT has increased not just in the United States but also in most modern militaries. In addition to the United States, Australia, China, Russia, the United Kingdom, Turkey, and India all have at least one active loyal wingman development program, while the sixth-generation fighter efforts Global Combat Air Programme (United Kingdom, Italy, Japan), Next Generation Air Dominance programs (US Air Force and Navy), and Future Combat Air System (Germany, France, Spain) involve system of systems concepts of airpower that stress both HMT and machine-machine teaming.

As important as this category of HMT is and will continue to be to emerging military capabilities, discussion of HMT should include the full breadth of human interaction with AI agents (which learn from and make determinations based on their environments, experiences, and inputs), including the overwhelming majority of interactions that occur with algorithms that possess no physical form. Project Maven is one example of how DOD and now the National Geospatial-Intelligence Agency use this category of HMT to autonomously detect, tag, and trace objects or humans of interest from various forms of media and collected intelligence, enabling human analysts and operators to prioritize their areas of focus.

HMT can provide multiple layers of overlapping advantages to the United States and its allies and partners.

Beyond imagery analysis and target identification, nonphysical manifestations of HMT can support a range of important tasks such as threat detection and data processing and analysis. It is essential to military efficacy in operational environments marked by significant increases in speed, complexity, and available data. They also can generate efficiencies in logistics and sustainment, training, and back-office administrative tasks that reduce costs and timelines for execution.

By combining the processing power and decision support capabilities of AI with the social intelligence and judgment of humans and, in some cases, the force-multiplying effects of uncrewed systems with different degrees of autonomy, HMT can provide multiple layers of overlapping advantages to the United States and its allies and partners, including those high-level advantages listed in Figure 1.
Figure 1: High-level description of three layers of HMT value.

DOD recognition of the current and future multilayered value of HMT as part of broader efforts to "accelerate the adoption of AI and the creation of a force fit for our time " has increased. Still, several persistent challenges to adoption of AI and HMT endure throughout the Pentagon. To accelerate and deepen HMT adoption, the DOD must commit to an approach that aligns development efforts and private sector engagement, creating flexibility for acquisition officials to scale HMT solutions across the defense enterprise. This approach must be complemented by:

Continued and increased focus on building trust between humans and machine partners;

Leading in establishing best practices and norms around ethics and safety;

Aggressive and iterative experimentation; and

Clear and consistent messaging

These elements are crucial to realizing the value and advantages HMT can deliver across the multi-domain future fight.

Demonstrating advantage through use cases

Use cases serve as a means of better understanding how HMT can provide value and lead to advantage in and across several missions and environments. Certainly, practitioners have seen or experienced use cases in various settings-including through war games and analysis of the ongoing war in Ukraine-and the slow nature of HMT adoption may call into question the utility of use cases. Still, highlighting the varied and, in certain instances, underappreciated applications of HMT can help demonstrate the different contexts in which HMT acts as a force multiplier and how this capability can support the US military in meeting the demands of the modern battlefield.

However, changing the perceptions of HMT across a large organization such as DOD is an iterative task and requires the frequent reinforcement of its value, especially as the technologies and concepts supporting HMT create new or enhanced opportunities. The three use cases discussed below are far from inclusive-our workshops and research explored several other compelling use cases-but the authors chose them because they reflect the layered value of HMT in serving missions and meeting operational threats and challenges being urgently considered by defense planners, as described in Table 1.HMT's relevance to the future of cognitive and information warfare, anticipatory logistics and resilient sustainment, training, and military medicine were all raised through project interviews and workshops.

Table 1: A high-level review of the advantages of each of the case studies discussed in this briefing.

Use cases of HMT

Key advantages

A2/AD conflict

The use of masses of expendable and attritable systems to penetrate A2/AD environments, sustain forces, and extend the range of effects from crewed platforms operating outside A2/AD cordons.

Finding, fixing, and tracking key nodes and threats in a data-rich environment.

Providing situational awareness at the edge.

Sense-making and targeting

Enhancing situational awareness and threat detection through data processing and fusion improves situational awareness and threat detection and ensures decisions are made at the speed of relevance.

Finding connections that human analysts and operators did not know were present.

Supporting humans in target identification and prioritization across domains.

Improving precision of effects by recommending to human decision-makers the most applicable kinetic or non-kinetic weapon to engage targets.

Presence, prioritization, and deterrence

Extending the range of high-value crewed assets through the use of ISR meshes that incorporate several uncrewed systems.

Identifying anomalies and disruptions in activity patterns across geographically dispersed theaters.

Prioritizing threats and ensuring efficient resource allocation based on AI-driven assessments.

Aggressive and iterative experimentation in real-world environments builds trust between operators and their machine partners.

Anti-access and area-denial (A2/AD): Coping with mass, range, and attrition

Determining how to conduct operations in an A2/AD environment is a clear priority for defense planners. This is especially the case in the Indo-Pacific, where the military modernization effort of the People's Republic of China has emphasized utilizing pervasive multi-domain sensors and a surfeit of kinetic and non-kinetic strike assets to establish cordons in which US and allied forces are highly vulnerable to enemy fires and, in the worst-case scenario, unable to operate effectively.

HMT will not mitigate all risks of operating against robust A2/AD systems, though it can help US and allied forces better manage these risks in several ways, including in processing and analyzing large and complex datasets to support better and faster human decision-making.

Teaming uncrewed systems, crewed assets, and human operators

Reducing risk to human operators does not mean eliminating this risk, and the use of attritable uncrewed aerial systems (UAS) and uncrewed surface vehicles (USVs) will still incur costs.

The use of attritable and expendable uncrewed systems, in conjunction with crewed assets and human controllers and decision-makers, can achieve several important objectives in the A2/AD context. Most notably, these smaller, less expensive, generally modular systems can be used to saturate A2/AD systems, identify enemy defenses, and force adversaries to expend their deep stores of munitions-all while extending the operating range of higher-value crewed and uncrewed assets and reducing the risks to crewed assets and their human operators.

The use of attritable and expendable uncrewed systems, in conjunction with crewed assets and human controllers and decision-makers, can achieve several important objectives in the A2/AD context.Attritable or reusable systems are those that are considerably lower cost than higher-end uncrewed systems such as the MQ-9 Reaper.The generally acknowledged cost range for attritable or reusable aircraft is $2 million to $20 million, though there is no consensus as to whether this cost range includes only the aircraft or also includes mission systems. While designed to be reused, the significantly lower cost of these systems in comparison to high-value uncrewed systems and advanced crewed systems means that they can be placed into contested environments in advance of more-expensive platforms with a considerably reduced risk to force structure cost, especially if paired with in-theater means of reconstituting these systems. Expendable systems are even lower cost than attritable systems and are most often designed for a single use. Loitering munitions are one category of expendable uncrewed systems. Most notably, these smaller, less expensive, generally modular systems can be used to saturate A2/AD systems, identify enemy defenses, and force adversaries to expend their deep stores of munitions-all while extending the operating range of higher-value crewed and uncrewed assets and reducing the risks to crewed assets and their human operators.

To that end, reducing risk to human operators does not mean eliminating this risk, and the use of attritable uncrewed aerial systems (UAS) and uncrewed surface vehicles (USVs) will still incur costs. Moreover, even attritable systems and their payloads can be worth several million dollars. New calculations of value as well as increased capacity to reconstitute systems must keep pace with expected levels of attrition to ensure the right sizing of HMT-enabling force structures.

Sense-making and decision support: Enhanced situational awareness and improved decision-making at the pace of relevance

The A2/AD environment also serves as an example of how machines can support humans in the crucial and increasingly demanding task of sense-making-the art of interpreting and fusing data for enhanced decision-making.

Sense-making in A2/AD environments

A2/AD environments will be characterized by an abundance of complex datasets and both signal and noise across domains including in the electromagnetic spectrum. The amount of data available to operators will be overwhelming. Multi-domain sensors and surveillance and strike assets will be actively operating and engaging with both friendly and adversary forces, creating a need for AI agents to help process and filter data and feed relevant information back to the warfighter. The result will be to improve the quality and speed at which human operators filter data and then fix and track critical nodes in adversary A2/AD systems. This high-level A2/AD example reveals one context in which AI-enabled data fusion and processing of complex datasets can increase situational awareness and speed up decision-making. But the applications of this sense-making are impressively broad, including in increasing the speed and precision of identifying targets, determining appropriate kinetic or non-kinetic weapons to use to strike the target, and ensuring precision of effects.

Targeting: Joint all-domain command and control (JADC2)

DOD's "connect everything" JADC2 effort offers another example of how HMT can be applied to support improved targeting and speeding up sensor-to-shooter processing. As a January 2022 Congressional Research Service report observed, "JADC2 intends to enable commanders to make better decisions by collecting data from numerous sensors, processing the data using AI algorithms to identify targets, then recommending the optimal weapon-both kinetic and non-kinetic-to engage the target." While JADC2 is still largely a concept rather than an architecture for future military operations, the US military is already using AI to help find and track possible targets or entities of interest on the battlefield. In September 2021, Secretary of the US Air Force Frank Kendall acknowledged that the Air Force had "deployed AI algorithms for the first time to a live operational kill chain" to provide "automated target recognition." Kendall noted that by doing so, the Air Force hoped to "significantly reduce the manpower-intensive tasks of manually identifying targets-shortening the kill chain and accelerating the speed of decision-making."

AI in the intelligence field

Sense-making through human-machine teams is also shaping the future of disciplines such as intelligence analysis and mission planning (see sidebar below), in which AI-enabled data fusion, pattern and anomaly detection, and research and analysis support are helping analysts manage and exploit the explosion in available sources and data. Tasks that would take days for humans to perform can now be performed in hours, allowing humans to concentrate on the most relevant pieces of information derived from large datasets. For example, through the war in Ukraine, the Ukrainian Armed Forces are already using natural language processing tools that leverage AI to translate and analyze intercepted Russian communications, saving analysts time and allowing them to focus on key messages and intelligence. The use of AI is not only speeding up analysis but also demonstrating value in bringing "unknown knowns"-observed but ignored or forgotten connections, insights, and information-to the attention of an analyst and articulating the value or quality of information to the human decision-maker.Phone interview with Harry Kemsley, president of Government and National Security, Janes, January 31, 2023.

Tasks that would take days for humans to perform can now be performed in hours, allowing humans to concentrate on the most relevant pieces of information derived from large datasets.

In January 2023, the Intelligence Advanced Research Projects Activity (IARPA), situated within the Office of the Director of National Intelligence, announced a program known as REASON (Rapid Explanation, Analysis, and Sourcing Online). The program will use AI-enabled software to improve human-authored intelligence assessment products. The software reviews human-authored reporting and automatically generates recommendations for additional sources the human may not be aware of or did not use and also offers suggestions of how to improve the analytical quality of the report, performing the role of an autonomous red-team reviewer. This program offers a step in the right direction, demonstrating one of many ways in which AI can help humans make connections between data and sources and improve analysis and decision-making.Phone interview with Harry Kemsley, president of Government and National Security, Janes, January 31, 2023.

Presence, prioritization, and deterrence: Coping with distance and complexity

The US military faces significant, varied, and frequently dispersed challenges across its geographic combatant commands. Take, for example, US Indo-Pacific Command (INDOPACOM). INDOPACOM covers more of the globe than any other combatant command and is home to the DOD's "pacing challenge" of China, a range of potential military and geopolitical hot spots, several US allies and partners, and a growing list of security challenges. While differing in its prioritization, US Central Command (CENTCOM) is similarly complex in nature. CENTCOM covers nearly 4 million square miles, an area that includes key waterways and shipping lanes, active conflicts, ethnic and sectarian violence, and a diverse set of threats to regional security and US national interests.

HMT offers a solution to meet an increasingly sophisticated threat landscape while operating with insufficient resources.

While each command faces unique problem sets, the size and complexity of today's security challenges are straining the limited number of forces available to deter, identify, assess, or respond to a fast-moving threat or crisis in a timely manner. HMT offers a solution to meet an increasingly sophisticated threat landscape while operating with insufficient resources.

Exporting Task Force 59: A sandbox for AI experimentation and integration

In September 2021, CENTCOM's 5th Fleet established Task Force 59 -the Navy's testing ground for unmanned systems and AI-to experiment with teaming human operators and both smart robots and AI agents to increase presence across the region, provide persistent and expanded maritime domain awareness (MDA), and prioritize threats for in-demand crewed and high-value assets.

Task Force 59's experimentation efforts combine AI and uncrewed systems-particularly USVs but also vertical take-off and landing UAS, which are valuable in expanding the coverage of the Navy's ISR networks in the region-and are further amplified by cooperation with partners and allies. In comments at the February 2023 International Defence Conference in Abu Dhabi, 5th Fleet Commander Vice Admiral Brad Cooper explained that each of the small USVs with which Task Force 59 is experimenting can extend the range of ISR networks by thirty kilometers, meaning that even a modest investment in smart uncrewed systems can deliver a significant increase in MDA.One of the authors, Tate Nurkin, attended the conference on February 19, 2023, and moderated the panel in which these comments were made

AI agents then process the millions of data points collected by the uncrewed systems to create an understanding of normal patterns of life, which, in turn, serves as a baseline for AI agents to identify anomalies that require further investigation by human operators and watchkeepers. In doing so, human-machine teams can cultivate a deeper understanding of the operational environment, moving toward a predictive model in which the DOD can possibly anticipate and prevent future threats and prioritize allocation of limited resources to areas that AI agents determined are most vulnerable to disruption.

Building on the success of the Navy's Task Force 59, both the Army and Air Force components of CENTCOM have stood up task forces to experiment with emerging technology and HMT concepts. The Army component, Task Force 39, was established in November 2022 to advance experimentation in counter-small UAS solutions that can be scaled not just within CENTCOM environments but across the DOD. The Air Force's Task Force 99 was stood up in October 2022 as an operational task force to pair unmanned and digital technologies to improve air domain awareness in much the same way that Task Force 59 seeks to improve MDA by expanding presence and anticipating emergent threats. Lieutenant General Alexus Grynkewich, commander of CENTCOM's Air Force component, stated in February 2023 that the objective is "not just tracking objects in the air, but maybe finding things that could be on the ground about to be launched into the air and how those could be a threat to us."

The applicability of this HMT approach is not limited to CENTCOM; indeed, it is now being adopted in other commands. In April 2023, the US Navy announced the expansion of its experimentation with unmanned and AI tools into US Southern Command's 4th Fleet to increase MDA awareness in a region with its own particular dynamics, threats, and MDA requirements. Notably, 4th Fleet is taking a different approach to adoption of HMT lessons and new HMT capabilities, deciding to integrate them into its command and staff structure rather than through the standing up of a discrete task force. While these task forces provide valuable models for quickly bringing off-the-shelf technologies to the warfighter, the challenge rests in scaling these solutions across services and domains (and the defense enterprise at large).

Pathways to HMT adoption at scale and pace

The pace of AI and HMT technology development has exceeded the capacity of the DOD to adopt these technologies at scale. To more fully realize the advantages of HMT, DOD must:

Address several cultural, bureaucratic, and organizational challenges;

Ensure continued focus on HMT ethics, safety, and agency;

Embrace rapid and realistic experimentation of HMT; and

Develop the human component and enhance trust in their machine teammates.

Culture, acquisition, and melting the "frozen middle"

Organizational and cultural constraints radiate across the DOD, affecting the way the enterprise acquires and adopts the technologies, concepts, and capabilities necessary to enable HMT. Innovation and adoption are hindered by DOD's "frozen middle"-layers of relatively senior military and civilian personnel within DOD bound by an underlying set of inherited assumptions, incentives, and instincts that are resisting the adaptive, collaborative practices necessary for adoption of HMT concepts and capabilities at pace and scale. These organizational layers and bureaucratic proclivities have a cascading effect as they embed in a DOD acquisition system. According to former Secretary of Defense Mark Esper and former Secretary of the Air Force Deborah Lee James, co-chairs of the Atlantic Council's Commission on Defense Innovation Adoption, the effect is slowing adoption by requiring multiple levels of review "because incentives do not exist to be bold and to move fast."

Organizational "tribalism" also has an enervating effect on the DOD's acquisition of key technologies and adoption of HMT-relevant capabilities. Examples of cross-service and cross-command collaboration do exist. Workshop participants highlighted joint projects on AI transparency between service laboratories and the ability of the US Special Operations Command, as a functional command, to work across regions and services. Far more frequently, though, disjointed development and vendor engagement and procurement efforts have been the norm. To achieve the necessary momentum for adoption across DOD-and, most importantly, the salutary outcomes of this adoption-requires an increased willingness to share and subsequently align research, information, development efforts, acquisition models, and best practices across the department and, particularly, the services.

In June 2022, David Tremper, director for electronic warfare in the Office of the Under Secretary of Defense for Acquisition and Sustainment, provided an example of how DOD siloes slow adoption of HMT-enabling capabilities. Tremper highlighted an incident in which the Navy developed an electronic warfare algorithm that was of interest to the Army, though transfer of the algorithm could not take place without a memorandum of understanding (MoU) between the two service's labs. It took nine months for the lab that developed the algorithm to approve the transfer, and, after eighteen months, the MoU was determined to be inadequate to support the algorithm migration. According to Tremper, "one service just lawyered up against the other . . . and prevented a critical capability . . . from going from one service to another service." This example highlights the hurdles blocking the services from learning and gaining from one another's strides in HMT; the cross-service applicability of HMT solutions is critical to avoid a service unnecessarily depleting its resources in a field where its sister service has already paved the way.

To melt this frozen middle and reduce cultural and organizational obstacles to HMT adoption, the DOD must center its efforts around three high-level aims. First, acquisition reform is a necessary and foundational step. The ability to develop, build, and acquire advanced platforms and systems with longer development times remains important to achieving DOD missions. There is also growing and urgent demand for the technologies and capabilities enabling HMT, such as software and lower-cost uncrewed systems, across the Pentagon. The program-centric system used to acquire and develop many advanced platforms-with the development of requirements, budgets, and capabilities completed individually for hundreds of programs-is not optimized for the rapid acquisition of the innovative technologies and capabilities developed by traditional defense suppliers and the commercial sector.

While the objective of this report is not to provide a comprehensive set of recommendations for acquisition reform, two principles are useful in guiding this reform: 1) the need for increased flexibility and agility within the acquisition process, enabling response to changing technologies and requirements; and 2) new incentives that stimulate an enhanced degree of risk tolerance.In April 2023, the Atlantic Council's Commission on Defense Innovation Adoption released its interim report, which offers actionable recommendations to engender these changes. Lofgren et al., Atlantic Council Commission on Defense Innovation Adoption Interim Report.

Second, among the workforce setting requirements for and working alongside machine teammates, a lack of understanding surrounding HMT and its applications is slowing adoption. The human element of this equation must be adequately prioritized through recruitment, retention, and training programs. Specifically, DOD must provide its personnel with opportunities to enhance digital literacy and understanding of the technologies, concepts, and applications of HMT capabilities within not just the acquisition force and those that set requirements but also among many senior decision-makers.

The human element of this equation must be adequately prioritized through recruitment, retention, and training programs.

Third, DOD should work to press the limits of existing authorities for "quick wins" in support of rapid acquisition and experimentation, similar to the Task Force 59 model. According to Schuyler Moore, CENTCOM's chief technology officer, one of the key lessons of command innovation efforts is "that [CENTCOM] can actually work with the existing [authorities] fairly flexibly" and officers must consider "whether or not the authority exists or whether it's simply never been tried before."US Central Command Chief Technology Officer Schuyler Moore and Innovation Oasis Winner Sgt. Mickey Reeve Press Briefing," US Central Command". Officers must be incentivized to use their own ingenuity as a tool for testing new HMT capabilities and demonstrating operational successes.

The human component: Trust and training

Building and calibrating appropriate levels of human trust in AI teammates is essential to HMT adoption across the DOD. This discussion typically begins with several actions designed to ensure the reliability-that is, the trustworthiness-of AI agents, such as establishment and implementation of effective best practices for the design, rigorous testing, iterative experimentation, and deployment of AI-enabled machines. Enhancing data security is another frequently cited component of building trust: Humans cannot trust algorithms if they lack faith in the integrity of the data being processed or used to train the algorithm.

Limitations in AI transparency (i.e., how machines perceive the environments they are in) and explainability (i.e., how machines come to decisions) pose another challenge. While not prohibitive to the employment of HMT in many missions and contexts, reducing the "black box" nature of AI outputs will have a positive impact on the capacity for humans to trust their machine teammates and, in turn, on the pace of adoption for the range of HMT applications.

Increased experimentation-in terms of frequency, rigor, and realism-can enhance the human operator's understanding of how an AI agent reached a certain conclusion. The DOD must embrace accelerated experimentation across a diverse set of scenarios and use cases. By asking humans to "try to break the technology," operators and analysts can develop a better understanding of the limits, strengths, and weaknesses of their AI-enabled teammates as well as any possible unexpected behaviors. Rapid and aggressive experimentation in conditions that replicate real-world operational conditions has been a regularly highlighted feature of Task Force 59's success to date, though there is concern that this practice has not been adopted broadly across DOD.

Human trust in machines, however, is only one part of the discussion of the human component of HMT. At least as much attention should be focused on building up the competencies and confidence of humans to effectively engage with and employ their machine partners. Humans will require just as much preparation and training as their machine teammates to become effective team leaders and to ensure that the value of human-machine teams is realized. In a worst-case scenario, adequate human training will help avoid a circumstance in which ineffectual human engagement with machines (the inability to recognize a hallucinating machine, for example) or unnecessary interference with machine operations could lead to mistakes that carry deleterious tactical, operational, or strategic outcomes. One salient example is seen in the Marine Corps experimentation with LLMs. Experiment organizers noted that the tendency of these models to hallucinate means "falsification is still a human responsibility" and that "absent a trained user, relying on model-produced outputs risks confirmation bias," risking the potential of individuals "prone to [acting] off the hallucinations of machines."

This is especially the case in situations in which a general-purpose machine or set of machines is interacting with multiple humans rather than just one human controller. Protocols for determining who is in control, whose priorities are executed, and how to manage contradictory instructions become essential.

Ethics, agency, judgment, and reliability: Maintaining leadership and retaining relevance

DOD efforts to maintain leadership at home and internationally in establishing AI and HMT ethics and safety guidelines should be viewed as a process to follow rather than an end state to be achieved.

Pathways to adoption of HMT at speed and scale do not solely rely on the DOD doing things radically differently or applying new incentives and structures. Rather, they can build on the momentum of ongoing initiatives.

The DOD has already expended commendable energy in understanding and addressing questions of ethics, safety, alignment, and agency-all of which emerged as acute areas of interest and concern during the project workshops. The DOD, in collaboration with other parts of the US government, has been proactive in articulating its positions on key questions of ethics and safety around AI development and use for military purposes since first adopting principles for AI ethics in February 2020. In February 2023, at the conclusion of the Responsible AI in the Military Domain conference, the US Department of State released a "Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy" as the foundation for the establishment of international norms and standards for the development and use of military AI. The document includes twelve best practices that emphasize concepts such as safe and secure development, extensive testing, and human control and oversight, among others, that endorsing states should implement.

These principles and best practices as well as current laws, policies, and regulations provide a useful foundation for resolving critical questions about agency, such as:

Which component of the human-machine teams makes decisions under what circumstances?

When might the machine's judgment supersede that of the commander?

What implications will these answers have on recruitment, reskilling, and retention of talent?

How does the United States avoid or mitigate tactical events and AI/HMT mistakes that have significant strategic consequences?

How do we judge the possible risk of AI-enabled decision-making in kill chains relative to the current and demonstrated risk of human error in these decisions?

Still, as technologies, concepts, applications, and competitions evolve, new questions are likely to be raised. Old practices may need to be iteratively revisited to ensure that adoption efforts remain relevant, current, and appropriate. DOD efforts to maintain leadership at home and internationally in establishing AI and HMT ethics and safety guidelines should be viewed as a process to follow rather than an end state to be achieved.

Understanding knock-on effects of HMT incorporation

DOD adoption of HMT into military operations will elicit responses from competitors and lead to changing risks, competitions, opportunities, and doctrine. For example, the use of HMT-enabled UAS swarms could shift targeting priorities from forward-deployed machines to the human controllers and decision-makers situated well behind contested environments. Another example can be found in the use of an AI-enabled machine to patrol a contested border, which could reduce immediate risk to human life and, as a result, reduce risks of rapid escalation. However, immediate loss of life may not be the only escalation pathway perceived by an adversary. If destroying autonomous systems is lower stakes than firing on humans, how many downed UASs would the United States tolerate? Alternatively, what if the AI-enabled machine recommends or makes a bad decision that leads to an unprovoked loss of life in the adversary state?

The United States and its allies should intensify examination of these types of what-if scenarios associated with HMT adoption. First, to preempt arguments that DOD has not thought through the long-term implications of HMT employment, and then to determine the most efficacious concepts of use and prepare for possible future risks. A combination of iterative tabletop, seminar-style, and live war games and model-based simulations would be especially useful in identifying and preparing for longer-term implications of HMT. These exercises could then inform live testing of HMT concepts and capabilities.

Shaping the narrative around military applications of HMT

Mixed feelings and misperceptions about the DOD's use of AI and uncrewed systems persist within the defense enterprise and the US polity and society more broadly. Despite an increase in the number of young personnel who are considered to be more intuitively inclined to technological adoption, pockets of stubbornness and contrarianism, and efforts to protect institutional equities, remain throughout the force. In addition, some social and political perspectives on HMT may be skewed by fears of fully autonomous weapons systems and singularities in which AI overcomes human control with devastating consequences. Identifying and managing the variety of strategic, operational, social, and philosophical concerns about accelerating HMT adoption will necessitate a coherent and consistent campaign to shape the narrative around military AI use and HMT. This narrative should stress:

The demonstrated and prospective advantages HMT will provide to warfighters and decision-makers, and the budgetary efficiencies that could be gained through deeper adoption of HMT;

The measures DOD is taking to ensure the safe and ethical development and use of AI and to build humans' trust in machines; and

The centrality of human agency to human-machine teams.

Summary of recommendations and conclusion

The United States' AI strides are not occurring in a vacuum; if the Pentagon is slow to adopt HMT at scale, it risks conceding military edge to strategic competitors like China that view AI as a security imperative.

HMT offers several advantages to twenty-first-century militaries. As such, the DOD must invest sufficient time and resources to address the challenges to adoption discussed above. Catalyzing HMT adoption will necessitate a combination of new ideas, procedures, and incentives, as well as intensification of promising ongoing adoption acceleration efforts, especially related to the following areas:

Developing an enterprise-wide approach to HMT adoption that builds on and provides sufficient money and authority to the establishment of centralized structures such as the Chief Digital and Artificial Intelligence Office to ensure that requirements, capability, infrastructure, and strategy development as well as procurement and vendor engagement are mutually reinforcing across the DOD.

Rapid, iterative, and aggressive experimentation in settings that replicate the challenges of a real-world operational environment will facilitate adoption by helping humans test and understand the breaking points of HMT technologies. Different levels of experimentation can also build human trust in their AI teammates necessary to optimize HMT value.

Melting the "frozen middle" through reforms that increase incentives for moving quickly, align DOD and congressional reform priorities, and reinforce efforts to ensure an enterprise-wide-rather than service-by-service or command-by-command-approach to HMT adoption. The Atlantic Council Commission on Defense Innovation Adoption has developed several specific recommendation sthat apply to the acquisition and adoption of HMT capabilities.Lofgren et al., Atlantic Council Commission on Defense Innovation Adoption Interim Report

Articulating and demonstrating the multilayered value of HMT in gaining advantage over competitors and potential adversaries in a future operational environment characterized by significantly increased pace of operations, amount of available data, and complexity of threats.

Continuing to lead on issues of agency and ethics by prioritizing the ethical and responsible development and use of trustworthy AI and keeping humans-and human judgment-at the center of human-machine teams. The US government and private sector should revisit and update guidelines around agency and ethics to reflect contemporaneous technology development trends and capabilities.

Development of strategic messages that highlight the value and safety of HMT for consumption by DOD and congressional stakeholders as well as American society more broadly.

The United States' AI strides are not occurring in a vacuum; if the Pentagon is slow to adopt HMT at scale, it risks conceding military edge to strategic competitors like China that view AI as a security imperative. Machines and AI agents are becoming ubiquitous across the twenty-first-century battlespace, and the onus is on DOD to demonstrate, communicate, and realize HMT's value to achieving future missions and national objectives.

Acknowledgements

To produce this report, the authors conducted a number of interviews and workshops. Listed below are some of the individuals consulted and whose insights informed this report. The analysis and recommendations presented here are those of the authors alone and do not necessarily represent the views of the individuals consulted. Moreover, the named individuals participated in a personal, not institutional, capacity.

Maj Ezra Akin, USMC, technical PhD analyst, Commandant's Office of Net Assessment

Samuel Bendett, adjunct senior fellow, Technology and National Security Program, Center for a New American Security

August Cole, nonresident senior fellow, Forward Defense, Scowcroft Center for Strategy and Security, Atlantic Council

Owen J. Daniels, Andrew W. Marshall fellow, Center for Security and Emerging Technology, Georgetown University

Phil Freidhoff, vice president for human centered design projects, 2Mi

Dr. Gerald F. Goodwin, senior research scientist, personnel sciences, US Army Research Institute for the Behavioral and Social Sciences

Dr. Neera Jain, associate professor, School of Mechanical Engineering, Purdue University

LCDR Marek Jestrab, USN, senior US Navy fellow, Forward Defense, Scowcroft Center for Strategy and Security, Atlantic Council

Zak Kallenborn, policy fellow, Schar School of Policy and Government, George Mason University

Harry Kemsley OBE, president, government and national security, Janes Group

Dr. Margarita Konaev, nonresident senior fellow, Forward Defense, Scowcroft Center for Strategy and Security, Atlantic Council

Justin Lynch, nonresident senior fellow, Forward Defense, Scowcroft Center for Strategy and Security, Atlantic Council

Dr. Joseph Lyons, principal research psychologist, Air Force Research Laboratory

Brig Gen Patrick Malackowski, USAF (ret.), director, F-16, conventional weapons, and total force, Lockheed Martin Corporation

Col Michelle Melendez, USMC, senior US Marine Corps fellow, Forward Defense, Scowcroft Center for Strategy and Security, Atlantic Council

Rob Murray, nonresident senior fellow, Forward Defense, Scowcroft Center for Strategy and Security, Atlantic Council

Dr. Julie Obenauer-Motley, senior national security analyst, Johns Hopkins University Applied Physics Lab

John T. Quinn II, head, futures branch, concepts and plans division, Marine Corps Warfighting Lab

Dr. Laura Steckman, program officer, trust and influence, Air Force Office of Scientific Research

This report was generously sponsored by Lockheed Martin Corporation. The report is written and published in accordance with the Atlantic Council Policy on Intellectual Independence. The authors are solely responsible for its analysis and recommendations. The Atlantic Council and its donors do not determine, nor do they necessarily endorse or advocate for, any of this report's conclusions.
About the authors

Forward Defense, housed within the Scowcroft Center for Strategy and Security, generates ideas and connects stakeholders in the defense ecosystem to promote an enduring military advantage for the United States, its allies, and partners. Our work identifiesthe defense strategies, capabilities, and resources the United States needs to deter and, if necessary, prevail in future conflict.

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Tate Nurkin and Julia Siegel explore the implications of incorporating artificial intelligence into military operations and the challenges of the US Department of Defense's adoption of human-machine teaming.

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Many Members of Congress have raised concerns over the frequency, types, and impacts of cyber incidents during hearings, speeches, and in legislation. Cyber incidents affect nearly every national entity, from federal and state government agencies to private companies and individuals.
One course of action to stymie attacks has been to investigate the adversaries that conduct cyberattacks, what types of activities they conduct online, and how the U.S. government can identify them.
To assist with Congress’s understanding of cyberattacks, this report describes attribution in cyberspace, confidence of
attribution, and common types of cyberattack. Listed in this report are two categories of cyberattacks by foreign adversaries against entities in the United States: 30 cyberattack campaigns that the federal government has attributed to actors operating on behalf of other nation-states, and 30 cyberattacks the government has attributed to criminal actors seeking personal gain.
In investigating cyber incidents, the U.S. government attempts to unmask those behind the incident and attribute it as an
attack. Attributing cyberattacks is difficult, but not impossible. Officials seek to develop a comprehensive understanding of
the cyber incident not just from the victim, but also by corroborating that information with other government and private
sector evidence to make a claim of attribution. While a process exists to repeatedly and consistently develop a claim of attribution and a confidence level in it, adversaries take steps to complicate these efforts by obfuscating and removing any trace of their activity, and using new infrastructure to make it difficult to track attack campaigns.
Nation-states are some of the most sophisticated actors that conduct cyberattacks. The Director of National Intelligence is required annually to deliver to Congress an assessment from the intelligence community on worldwide threats. Recent assessments have highlighted cyberspace as an area of strategic concern, with China, Russia, North Korea and Iran as the leading threat actors. Attacks from these countries include spying on government agencies by accessing agency computers,
stealing sensitive information from public and private sector entities in the United States, stealing intellectual property, and
destroying or potentially destroying computer equipment.
Cyber criminals are less resourced than nation-state actors and are less likely to employ novel and cutting-edge techniques in campaigns, yet their attacks are often highly effective. Most criminals are financially motivated and use cyberspace as a medium for conducting profit-bearing schemes. However, gaining money is not a requirement for illicit activity.
Cyberattacks against victims in the United States from actors located abroad include compromising computers to create and
maintain botnets, business email compromise schemes, hack and release campaigns, and ransomware attacks.

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Microsoft soll eine kritische Schwachstelle in Azure Active Directory nach mehreren Monaten noch immer nicht vollständig behoben haben. Amit Yoran, der CEO des Sicherheitsunternehmens Tenable, bezeichnete den Konzern einem Bericht von Ars Technica zufolge als "grob unverantwortlich". Über die Sicherheitslücke sei es nicht authentifizierten Angreifern möglich, einen unbefugten Zugriff auf via Azure AD verwaltete Anwendungen und Daten zu erhalten.

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Create a Perfect Digital Copy of Your Voice and Speak the Languages You Don't!

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Create a Perfect Digital Copy of Your Voice and Speak the Languages You Don't!

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Professional Voice Cloning presents an opportunity for convenience and consistency across a variety of audio use-cases

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Threat Actors are Interested in Generative AI, but Use Remains Limited

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Threat Actors are Interested in Generative AI, but Use Remains Limited

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Since at least 2019, Mandiant has tracked threat actor interest in, and use of, AI capabilities to facilitate a variety of malicious activity. Based on our own observations and open source accounts, adoption of AI in intrusion operations remains limited and primarily related to social engineering.

In contrast, information operations actors of diverse motivations and capabilities have increasingly leveraged AI-generated content, particularly imagery and video, in their campaigns, likely due at least in part to the readily apparent applications of such fabrications in disinformation. Additionally, the release of multiple generative AI tools in the last year has led to a renewed interest in the impact of these capabilities.

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Figure 1: AI-enabled IO toolkit leveraging various media forms

Figure 2: An AI-generated image used as a profile photo by a persona in a pro-Cuban government network displayed a text box showing that the image was generated using the website thispersondoesnotexist.com

As with typical DRAGONBRIDGE activity, we did not observe these messages to garner significant engagement.

Figure 4: Screenshots from video containing AI-generated "news presenter" promoted by DRAGONBRIDGE, likely created using a platform offered by D-ID

Figure 6: AI-generated tweet in 2016 that created lure content based on target's own previous tweets (source)

Media reports indicate financially-motivated actors have developed a generative AI tool, WormGPT, which allows malicious operators to create more persuasive BEC messages. Additionally, this tool can allow threats to write customizable malware code.

Figure 5: A screenshot from a deepfake video showing Ukrainian President Zelensky announcing the country's capitulation to Russia in March 2022

Figure 3: Suspected DRAGONBRIDGE tweet (left) containing AI-generated image created by Eliot Higgins (right) using Midjourney

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Threat Actors are Interested in Generative AI, but Use Remains Limited | Mandiant

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As with typical DRAGONBRIDGE activity, we did not observe these messages to garner significant engagement. Media reports indicate financially-motivated actors have developed a generative AI tool, WormGPT, which allows malicious operators to create more persuasive BEC messages. Additionally, this tool can allow threats to write customizable malware code.

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Report: Cyber threat actors embracing use of generative AI, especially for info ops

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Report: Cyber threat actors embracing use of generative AI, especially for info ops

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Security firm Mandiant sees growing use of artificial intelligence tools by cyber threat actors in areas like disinformation and slower adoption in "intrusion campaigns," but cautions that generative AI is poised to accelerate uses in both areas.

“Based on our own observations and open-source accounts, adoption of AI in intrusion operations remains limited and primarily related to social engineering,” according to the report, “Threat Actors Are Interested in Generative AI, but Use Remains Limited,” released Thursday.

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Security firm Mandiant sees growing use of artificial intelligence tools by cyber threat actors in areas like disinformation and slower adoption in "intrusion campaigns," but cautions that generative AI is poised to accelerate uses in both areas.

"Based on our own observations and open-source accounts, adoption of AI in intrusion operations remains limited and primarily related to social engineering," according to the report, " Threat Actors Are Interested in Generative AI, but Use Remains Limited," released Thursday.

"In contrast," Mandiant says, "information operations actors of diverse motivations and capabilities have increasingly leveraged AI-generated content, particularly imagery and video, in their campaigns, likely due at least in part to the readily apparent applications of such fabrications in disinformation. Additionally, the release of multiple generative AI tools in the last year has led to a renewed interest in the impact of these capabilities."

The report says, "We anticipate that generative AI tools will accelerate threat actor incorporation of AI into information operations and intrusion activity."

John Hultquist, chief analyst at Mandiant Intelligence and Google Cloud, commented, "While we expect the adversary to make use of generative AI, and there are already adversaries doing so, adoption is still limited and primarily focused on social engineering. There's no doubt that criminals and state actors will find value in this technology, but many estimates of how this tool will be used are speculative and not grounded in observation."

Mandiant is a Google subsidiary.

According to Mandiant, "Generative AI will enable information operations actors with limited resources and capabilities to produce higher quality content at scale. ... Hyper-realistic AI-generated content may have a stronger persuasive effect on target audiences than content previously fabricated without the benefit of AI technology."

The report provides a detailed look at AI-generated images, video and text, and explains the evolving uses of AI in social engineering and areas like voice impersonation.

Mandiant also "anticipates that threat actors will increase their use of [large language models] to support malware development. LLMs can help threat actors write new malware and improve existing malware, regardless of an attacker's technical proficiency or language fluency."

But it notes, "LLMs possess shortcomings in their malware generation that may require human intervention for correction, [although] the ability of these tools to significantly assist in malware creation can still augment proficient malware developers, and enable those who might lack technical sophistication."

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Report: Cyber threat actors embracing use of generative AI, especially for info ops

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Security firm Mandiant sees growing use of artificial intelligence tools by cyber threat actors in areas like disinformation and slower adoption in "intrusion campaigns," but cautions that generative AI is poised to accelerate uses in both areas.

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Greatest cyber threats to aircraft come from the ground

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Greatest cyber threats to aircraft come from the ground

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How vulnerable are commercial airliners to cybersecurity breaches? It depends on what part of their IT systems you're talking about. The avionics equipment that runs the aircraft is quite resistant to hacking, although not bulletproof. However, the inflight internet access systems that connect passengers to the web are as vulnerable as any ground-based network to hackers.

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"Avionics systems have a limited surface area to attack remotely purely by the nature of the architecture." Kiley tells CSO. "Avionics systems do go through extensive review by both the manufacturer, industry and the FAA, but these reviews do not exclusively focus on security but are heavily focused on safety."

Enhancing safety is why modern aircraft avionics systems are so heavily networked. But this trend has not kept pace with the need for enhanced cybersecurity, warns the Thales Group in a blog post. "The aviation industry has reaped the benefits of digitization over the past ten years, but this has also triggered new risks, including social and technical vulnerabilities that had never previously been addressed," it said.

However, Sean Reilly, VP of air transport management and digital solutions at the ground-to-aircraft broadband service provider SmartSky Networks, disagrees with this negative assessment. "Security protocol on avionics is actually very, very stringent," says Reilly. To bypass it, a hacker would need to understand the fundamentals of an ARINC 429 bus, which is basically an aircraft's main data bus, plus insider knowledge of what's actually inside "the software layer on top of that piece of avionics and be able to tie into" it, he explains. "It's not just something you can go in and grab at the end of the day."

Why inflight internet access could be a problem

Ask cybersecurity experts about known hacks of commercial aircraft, and chances are they'll cite white hat hacker Chris Roberts. According to a 2015 article on Wired.com, "Chris Roberts, a security researcher with One World Labs, told the FBI agent during an interview in February that he had hacked the in-flight entertainment system, or IFE, on an airplane and overwrote code on the plane's Thrust Management Computer while aboard the flight."

An FBI affidavit filed by Special Agent Mark S. Hurley in support of the Bureau's seizure of Roberts' iPad, MacBook Pro, and various storage media stated that Roberts had hacked into various commercial aircraft's IFE systems by opening up the seat electronic boxes under the seat and connecting his laptop to them using a CAT6 cable.

"He stated that he successfully commanded the system he had accessed to issue the 'CLB' or climb command," said the FBI affidavit. "He stated that he thereby caused one of the airplane engines to climb resulting in a lateral or sideways moment of the plane." In fairness to Roberts, the 15-20 IFE hacks he performed while flying on selected Airbus and Boeing aircraft between 2011 and 2014 were done "because he would like the vulnerabilities to be fixed," the FBI affidavit says.

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Greatest cyber threats to aircraft come from the ground

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Passengers' Wi-Fi devices offer hackers more access to commercial airliners than onboard avionics do

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OpenAI deploys web crawler in preparation for GPT-5

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OpenAI deploys web crawler in preparation for GPT-5

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OpenAI has introduced a web crawling tool named “GPTBot,” aimed at bolstering the capabilities of future GPT models. The company says the data amassed through GPTBot could potentially enhance model accuracy and expand its capabilities, marking a significant step in the evolution of AI-powered language models.

Web crawlers – also referred to as web spiders – play a pivotal role in indexing content across the vast expanse of the internet. Renowned search engines such as Google and Bing rely on these bots to populate their search results with relevant web pages.

OpenAI’s GPTBot will have a distinct purpose: to gather publicly available data while carefully sidestepping sources that involve paywalls, personal data collection, or content that contravenes OpenAI’s policies. Website owners have the ability to prevent GPTBot from crawling their sites simply by implementing a “disallow” command within a standard server file. This grants them control over which portions of their content are accessible to the web crawler.

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Ryan is a senior editor at TechForge Media with over a decade of experience covering the latest technology and interviewing leading industry figures. He can often be sighted at tech conferences with a strong coffee in one hand and a laptop in the other. If it's geeky, he's probably into it. Find him on Twitter (@Gadget_Ry) or Mastodon (@gadgetry@techhub.social)

OpenAI has introduced a web crawling tool named "GPTBot," aimed at bolstering the capabilities of future GPT models.

The company says the data amassed through GPTBot could potentially enhance model accuracy and expand its capabilities, marking a significant step in the evolution of AI-powered language models.

Web crawlers - also referred to as web spiders - play a pivotal role in indexing content across the vast expanse of the internet. Renowned search engines such as Google and Bing rely on these bots to populate their search results with relevant web pages.

OpenAI's GPTBot will have a distinct purpose: to gather publicly available data while carefully sidestepping sources that involve paywalls, personal data collection, or content that contravenes OpenAI's policies.

Website owners have the ability to prevent GPTBot from crawling their sites simply by implementing a "disallow" command within a standard server file. This grants them control over which portions of their content are accessible to the web crawler.

OpenAI's announcement follows closely on the heels of the company's submission of a trademark application for "GPT-5," which is anticipated to succeed the current GPT-4 model.

The filing, made with the United States Patent and Trademark Office on July 18, encompasses the usage of "GPT-5" in AI-based human speech and text, audio-to-text conversion, voice recognition, and speech synthesis.

However, while the GPT-5 trademark application has generated excitement among AI enthusiasts, OpenAI's CEO Sam Altman cautioned against premature expectations. Altman revealed that the company is still far from initiating GPT-5 training, as extensive safety audits need to be conducted prior to embarking on the process.

OpenAI's recent endeavours have not been without their share of controversy. Concerns have arisen over the company's data collection practices, particularly surrounding copyright and consent issues.

In June, Japan's privacy regulator issued a warning to OpenAI concerning unauthorised data collection. Earlier this year, Italy temporarily prohibited the use of ChatGPT due to alleged violations of European Union privacy laws.

OpenAI and Microsoft also currently face a class-action lawsuit filed by 16 plaintiffs who claim that private information from ChatGPT user interactions was accessed without proper consent. The companies have also been hit with a lawsuit over GitHub Copilot, with the claimants alleging the code-generation tool infringed on the rights of developers by scraping their code without providing due attribution.

Should these allegations prove true, both OpenAI and Microsoft could potentially be found in violation of the Computer Fraud and Abuse Act, a legal precedent with relevance to web-scraping cases.

As OpenAI continues to push the boundaries of AI technology, it must navigate these challenges to ensure responsible and ethical development in the AI landscape.
(Image Credit: Gerd Altmann from Pixabay) See also: Meta launches Llama 2 open-source LLM

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The event is co-located with Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Ryan is a senior editor at TechForge Media with over a decade of experience covering the latest technology and interviewing leading industry figures. He can often be sighted at tech conferences with a strong coffee in one hand and a laptop in the other. If it's geeky, he's probably into it. Find him on Twitter (@Gadget_Ry) or Mastodon (@gadgetry@techhub.social)

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OpenAI deploys web crawler in preparation for GPT-5

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OpenAI has introduced a web crawling tool named "GPTBot," aimed at bolstering the capabilities of future GPT models.

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Chips gegen die AfD: Wie Ostdeutschland zwei Halbleiter-Giganten zu sich holte

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Chips gegen die AfD: Wie Ostdeutschland zwei Halbleiter-Giganten zu sich holte

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Im Osten Deutschlands fällt nicht nur die rechtsextreme AfD auf fruchtbaren Boden, sondern auch die international geprägte Mikrochip-Industrie – und für einmal profitiert die Region von ihrer DDR-Vergangenheit.

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Darum baut der weltweit grösste Chip-Hersteller seine neuste Fabrik in Ostdeutschland

Im Osten Deutschlands fällt nicht nur die rechtsextreme AfD auf fruchtbaren Boden, sondern auch die international geprägte Mikrochip-Industrie - und für einmal profitiert die Region von ihrer DDR-Vergangenheit.

Die Chip-Krise hat Europas Abhängigkeit von Asien aufgedeckt.

Nun holt Deutschland die grossen Chip-Giganten TSMC und Intel nach Ostdeutschland.

Die Ansiedlung kostet zig Milliarden an Subventionen. Der Nutzen ist umstritten.

Halbleiter sind "das Erdöl des 21. Jahrhunderts", sagte Bundeskanzler Olaf Scholz im Mai beim symbolischen Spatenstich einer neuen Chip-Fabrik in Dresden. Und wie beim Erdöl ist Europa abhängig von anderen Ländern. Weit mehr als die Hälfte aller produzierten Mikrochips stammt von Marktführer TSMC aus Taiwan. Bei den modernsten Varianten hat TSMC einen Weltmarktanteil von mehr als 90 Prozent. Weniger als zehn Prozent der produzierten Chips kommen aus europäischer Fertigung.

Viel zu wenig, meint die EU. Die Winzlinge stecken bekanntlich vom Kinderspielzeug bis zum Marschflugkörper in allen technischen Geräten - ohne Chips funktioniert gar nichts mehr.

Die Chipknappheit während der Coronapandemie, als Fabriken stillstanden oder auf Sparflamme produzierten, ist Politik und Industrie in die Knochen gefahren. Ein winziges Virus führte uns die fragilen Lieferketten und die Dominanz einiger weniger Länder schonungslos vor Augen.

Das Geschäft mit den Chips: Es ist kompliziert

Das Ausgangsmaterial für Halbleiter, sogenannte Wafer, stammt oft aus Japan. Über 60 Prozent der daraus hergestellten Chips werden in Taiwan gefertigt, der zweitwichtigste Fabrikationsstandort ist Südkorea. In Europa und den USA gibt es bislang keine Auftragsfertiger, die in ganz grossen Volumen Chips fabrizieren.

Umgekehrt ist Asien vom patentierten Chip-Design aus den USA abhängig sowie von Hochpräzisionsmaschinen aus Europa, die für die Chip-Produktion unabdingbar sind. Das niederländische Unternehmen ASML liefert geschätzt 80 bis 90 Prozent aller modernen Chip-Maschinen, ohne die bei Intel, Samsung oder Taiwan Semiconductor (TSMC) fast nichts geht.

Auch wenn die USA und Taiwan in zentralen Bereichen eine Vormachtstellung innehaben, es gibt kein Land, das vom Rohstoffabbau über das Chip-Design bis zur Produktion, Montage und dem Testen sämtliche Produktionsschritte allein ausführen kann. Gegenseitige technische Abhängigkeiten bleiben bis auf Weiteres bestehen.

Spätestens seit dem Ukraine-Krieg und dem Wirtschaftskrieg zwischen den USA und China ist die EU aufgewacht. Europas Abhängigkeit von TSMC wäre fatal, sollte China den Inselstaat Taiwan dereinst angreifen oder Lieferketten und Handelswege blockieren.

Die Antwort der EU: Der "Chips Act"

Der Chipmangel in den vergangenen Jahren hat Europa besonders hart getroffen. Die EU reagierte auf diese Bedrohung mit dem "European Chips Act", einem milliardenschweren Förderprogramm für die Halbleiter-Industrie.

Die EU will bis zu 43 Milliarden Euro an öffentlichen und privaten Investitionen mobilisieren, um den europäischen Anteil an der globalen Chipproduktion bis 2030 auf 20 Prozent zu verdoppeln. Ein Ziel, das von Branchenkennern als unrealistisch eingestuft wird, da die USA, China, Japan und Südkorea ebenfalls milliardenschwere Subventionstöpfe anzapfen, um vom Rest der Welt unabhängiger zu werden. Für Europa geht es darum, den Status quo zu bewahren.

Erste Früchte trägt der "Chips Act" dennoch: Mit zig Milliarden an Subventionen wurden dieses Jahr Chip-Gigant Intel aus den USA und der weltweit grösste Halbleiterhersteller TSMC nach Ostdeutschland gelockt. Laut deutschen Medien erhält Intel bei einem Gesamtinvestitionsvolumen von gegen 30 Milliarden Franken bis zu zehn Milliarden Franken vom Staat, bei TSMC sollen es bis zu fünf Milliarden sein.

Aber warum ist eine Chip-Fabrik so unfassbar teuer? Um Chips zu produzieren, braucht es die wohl aufwändigsten Fabriken - genannt Fabs -, die es überhaupt gibt. Solche Chip-Fabs sind teils teurer als ein Flugzeugträger und trotzdem bloss ein paar Jahre technologisch an der Spitze.

Für einmal profitiert Ostdeutschland von seinem DDR-Erbe:

Sachsen konnte dank technischen Universitäten, gut ausgebildeten Fachkräften aus DDR-Zeiten und einem stetigen Fluss an Subventionen bereits ab den 1990er-Jahren ein Mikrochip-Cluster aufbauen, das heute unter dem Namen Silicon Saxony firmiert.

Im Mai hat der deutsche Halbleiterhersteller Infineon mit dem Projektstart einer fünf Milliarden Franken teuren Chip-Fabrik in Dresden begonnen. Auch Bosch und das US-Unternehmen Globalfoundries unterhalten seit Jahren Chip-Werke in Dresden. Dass nun die Schwergewichte TSMC und Intel ebenfalls in Ostdeutschland Milliarden in neue Chip-Fabriken investieren und mehrere Tausend Arbeitsplätze schaffen, ist beileibe kein Zufall. Sie treffen dort auf vor- und nachgelagerte Betriebe aus der Halbleiter-Branche.

Die mit 52'000 Quadratmetern grösste Chip-Fabrik Europas steht in Dresden

Das strukturschwache Ostdeutschland wird nebst den Niederlanden zu einem führenden Standort der europäischen Chip-Industrie. Die Bundesregierung frohlockt. Autarkie bleibt für Deutschland und Europa vorerst dennoch eine Illusion. Denn unabhängig davon, wie viele weitere Milliarden in Chip-Fabriken gebuttert werden, man wird trotzdem von importierten Rohstoffen wie Silizium abhängig bleiben.

Steuergelder für reiche Chip-Konzerne

Dass Deutschland mit Steuergeldern ausländische Chip-Hersteller anlockt, passt längst nicht allen: "Wir werfen das Geld zum Fenster raus", sagte Reint Gropp vom Leibniz-Institut für Wirtschaftsforschung Halle der "Süddeutschen Zeitung" im Juni und stiess damit in ein Wespennest. Es würden Milliarden an Subventionen in veraltete Technologie gesteckt, da TSMC in Ostdeutschland zunächst nicht seine modernsten Chips produziere. Der Ökonom hält nichts vom "internationalen Subventionswettlauf". Warum, so Gropp, "sollte man so profitablen Unternehmen noch Geld geben"?

Intel baue zwar ein grosses Werk, aber es würden keine bedeutenden Aktivitäten für Forschung und Entwicklung (F&E) angesiedelt, kritisierte der Professor für Volkswirtschaftslehre. Statt veraltete Chip-Fabriken zu subventionieren, wäre es "besser, Zukunftstechnologien zu fördern" und das Geld für Forschung an Universitäten und in Unternehmen auszugeben.

Was die Bundesregierung nicht an die grosse Glocke hängt: Sein europäisches Zentrum für Forschung und Entwicklung baut Intel nicht in Deutschland, sondern in Frankreich. Dort sollen neue Chipgenerationen designt werden. Frankreich versüsst Chip-Herstellern die Ansiedlung ebenfalls mit Milliardensubventionen.

Von Plänen für den Ausbau einer Chip-Industrie in Europa hält auch der Geschäftsführer der deutschen Halbleiter-Vertriebsfirma Rutronik, Thomas Rudel, wenig. Von den Subventionen profitierten ausländische Konzerne. Eigene Chip-Hersteller aufzubauen, lohne sich nicht, sagte Rudel. Dafür müsste ein Unternehmen "acht bis zehn Milliarden investieren".

Die notwendigen hochspezialisierten Maschinen erhalte man aber derzeit in Deutschland ebenso wenig wie genügend Elektroingenieure. Der Fachkräftemangel sei das gravierendste Problem. Ausserdem fehlten neuen Herstellern die Patente für die Chip-Designs. "Und ohne Patente kannst du nichts verkaufen."

Letztlich sind die nun geplanten Chip-Fabriken eine ungewisse Wette auf die Zukunft. Sie machen Deutschland und Europa zwar nicht unabhängig, aber bei einer weiteren Krise weniger verwundbar. Zudem geht es nicht nur um die Versorgungssicherheit, sondern auch um die technologische Souveränität. Die EU möchte eine moderne Halbleiter-Industrie fördern, die bei neuen Technologien wie künstlicher Intelligenz vorn mitspielt.

Chips gegen die AfD

Die teuer erkaufte Ansiedelung von TSMC und Intel wird von der Bundesregierung als geopolitische Absicherung für den Standort Deutschland verkauft. Für lokale Politiker zählen aber in erster Linie Arbeitsplätze: möglichst rasch und möglichst viele. Der mit den Chip-Fabriken erhoffte wirtschaftliche Aufschwung soll der AfD in ihrer Hochburg Sachsen den Wind aus den Segeln nehmen. Das kann nur aufgehen, wenn auch bei Zulieferern und in verwandten Branchen im grossen Stil neue Arbeitsplätze entstehen.

Ein Jobwunder über Nacht sind die Chip-Fabriken aber nicht. Das TSMC-Werk soll frühestens Ende 2027 die Produktion aufnehmen und der Fachkräftemangel könnte das Hochlaufen der Fabrik verzögern. Im Juni äusserte sich der taiwanische Chipfertiger besorgt und schlug vor: Deutsche Studenten könnten nach Taiwan entsendet werden, um sie dort in den betreffenden Fächern auszubilden.

Die Intel-Fabrik in Magdeburg kämpft noch mit anderen Sorgen: Die Chip-Fabrik dürfte viele Millionen Liter Wasser für Kühlungs- und Reinigungsprozesse verbrauchen - pro Tag. Sachsen-Anhalt leidet aber schon seit Jahren unter Dürren und die Lage wird in den kommenden Jahrzehnten nicht besser. Intel muss daher den Verbrauch mithilfe einer Aufbereitungsanlage drastisch senken.

Was Wassermangel für Chip-Hersteller bedeutet, bekam Rivale TSMC bereits zu spüren. Nach einer der immer häufigeren Dürreperioden in Taiwan fiel die auf Wasserkraft angewiesene Stromversorgung 2021 zweimal aus. TSMC musste seinen Wasserverbrauch auf Anordnung der Regierung drosseln. Das Problem: TSMC verbraucht pro Tag 212 Millionen Liter Wasser, "was dem täglichen Verbrauch von rund 1,5 Millionen Europäerinnen und Europäern entspricht", rechnete das Center for Security Studies der ETH vor.

Der wichtigste Chip-Fertiger hat also nicht nur mit chinesischen Hackern und Chinas Hegemonialanspruch zu kämpfen, sondern auch mit den realen Folgen des Klimawandels.

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Chips gegen die AfD: Wie Ostdeutschland zwei Halbleiter-Giganten zu sich holte

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Europa will Halbleiter-Werke zu sich holen, um sich von der Abhängigkeit von Asien und den USA zu lösen. Dafür holt es die Computer-Chip-Firmen TSMC und Intel.

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Gallium, Germanium, and China — The Minerals Inflaming the Global Chip War

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Gallium, Germanium, and China — The Minerals Inflaming the Global Chip War

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China has fired a salvo in the tech tussle, limiting exports of two rare metals used in semiconductor manufacturing. Short-term damage is small, but the West must wake up.

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Gallium, Germanium, and China — The Minerals Inflaming the Global Chip War

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China has limited exports of gallium and germanium used in chip manufacturing. The immediate damage is small, but the West must wake up.

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The Superconductor Sensation Has Fizzled, and That's Fine

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The Superconductor Sensation Has Fizzled, and That's Fine

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It’s rare that condensed matter physics is the talk of the town. But recently a remarkable amount of Internet discussion and news coverage has revolved around a purported advance in the field: bombshell claims from a team of South Korean scientists professing to have discovered an ambient-pressure, room-temperature superconductor. Independent confirmation of the researchers’ claims would’ve meant that their buzzy new material—a compound of copper, lead, phosphorus and oxygen dubbed “LK-99”—could conduct electricity frictionlessly, without any loss of current, in a wider array of environmental conditions than any other known substance. Such a capability could, in principle, lead to revolutionary advances in power plants, energy grids, computers and transportation.

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It's rare that condensed matter physics is the talk of the town. But recently a remarkable amount of Internet discussion and news coverage has revolved around a purported advance in the field: bombshell claims from a team of South Korean scientists professing to have discovered an ambient-pressure, room-temperature superconductor. Independent confirmation of the researchers' claims would've meant that their buzzy new material-a compound of copper, lead, phosphorus and oxygen dubbed "LK-99"-could conduct electricity frictionlessly, without any loss of current, in a wider array of environmental conditions than any other known substance. Such a capability could, in principle, lead to revolutionary advances in power plants, energy grids, computers and transportation.

Yet weeks removed from the release of the initial research findings, those claims seem all but debunked. After a fortnight of unfettered positivity and interest on social media (including some amateur experimental attempts that were streamed on Twitch), authoritative efforts from physicists around the world to double-check the South Korean team's claims have mostly deflated the hype. All of LK-99's bizarre behavior that hinted at superconductivity-such as its partial levitation over a magnet-can apparently be explained away by odd but distinctly nonsuperconductive properties, such as ferromagnetism, the same structural quirk that allows magnetic fields to permeate iron and reconfigure the metal's electrons.

Considering the new work, "[LK-99's] room-temperature superconductivity seems less and less likely," says Nadya Mason, a materials physicist at the University of Illinois at Urbana-Champaign. "The experimental papers showing ferromagnetism were pretty convincing, and the new theories are also more carefully done." Richard Greene, a physicist studying superconductors at the University of Maryland, generally agrees. " It is still a bit too early to put the nail in the coffin," he says. "But we are getting close. The coffin is there, the nails are ready, and a hammer is ready, too."

Of course, the boom-and-bust cycle of LK-99 is a classic demonstration of science in action. The scientific method worked as it should. But in the fading afterglow of LK-99's social-media-fueled rise and fall, it's probably worth examining one dimension of what might've driven all that excitement in the first place. Many Internet posters, along with some researchers and journalists, have positioned a room-temperature superconductor as a technological panacea for climate change that could accelerate the world's transition away from reliance on fossil fuels.

On the heels of Earth's hottest month in recorded history, it's easy to understand why people are eager for positive news and signs of progress. After all, with the perfect material, electricity could flow along transmission lines with flawless efficiency-but that's just the start of potentially revolutionary applications. In principle, a room-temperature superconductor could lead to more compact wind turbines that are easier to build and less resource-intensive, says Susie Speller, a materials scientist specializing in superconductors at the University of Oxford. Electronic devices from computers to electric vehicles would need far less power if they all contained some ideal superconducting substance. Deeper into the land of hypotheticals, the right superconductor could enable scalable nuclear fusion reactors to offer an abundant source of carbon-free energy, Speller says.

Even if LK-99 had proved to be a room-temperature superconductor, its feasibility for addressing energy and climate concerns would rest on an exceedingly flimsy foundation of faraway "ifs." If LK-99 were a superconductor, if it could withstand high currents, if it weren't too brittle to form into wire, if it were easy and cheap to synthesize, if the materials for its manufacture could be readily acquired, and if policy and funding followed suit, then maybe it could provide a small boost for energy efficiency a decade or more down the line. In short, it would be far from the quick climate fix that the U.S. seems particularly hungry for.

Superconductivity alone isn't enough. For a material to be useful in power generation and electronics, it would need to have many other exceptional qualities. Some superconductors lose their capacity to freely transport electricity at high currents or in the presence of magnetic fields, yet both abilities are necessary for an electrical component to be practical. Ductility and flexibility are also crucial, points out Michael Norman, director of the Quantum Institute at Argonne National Laboratory: if you can't easily stretch LK-99 into a wire, then using it in turbines, transmission lines or fusion reactors becomes much more challenging. Processing a material into thin films could offer a wireless workaround, Norman notes, but then the problem becomes one of difficulty and cost-which has disqualified past proved superconducting products, such as cuprate tape, from being widely manufactured.

Beyond those questions of material properties, even the most ideal "all-weather" superconductor still wouldn't solve climate change, Mason adds. That's partially because it would bring only marginal improvements to the transmission lines of most state-of-the-art power grids, which are already quite efficient. "They only lose about 5 percent of their energy as heat," Mason notes, and we're not going to "solve climate change at the 5 percent scale."

Pablo Duenas Martinez, an engineer studying energy decarbonization at the Massachusetts Institute of Technology, agrees. In his field, Duenas Martinez says, no one is really talking about superconductors. "We are more worried about, for example, materials for batteries," he explains. For power grids, transmission losses are trivial compared to the harder problem of having sufficient energy storage to fully benefit from solar and wind power, which can only intermittently provide electricity. And beyond the need for better batteries, there are even harder problems still, such as the societal challenge of changing attitudes and behaviors that have held the world in thrall to fossil fuels for so long.

Boosting transmission efficiency, Duenas Martinez notes, doesn't require wondrous superconductors at all and can instead be achieved with existing technology by simply updating old infrastructure. It's a lack of investment, not a lack of possibility, that's kept the grid as-is. Then, he adds, there are policy decisions, such as continuing to provide subsidies for fossil fuels, that maintain oil, gas and coal as artificially inexpensive options despite the fact that wind and solar power have rapidly become comparatively cheaper. Climate change is a problem that requires immediate and sustained action-on multiple fronts-if we're to avoid ever worsening consequences. Our governments and global economies are so enmeshed with fossil fuels that the emissions crisis can't be fixed by scientific or technological advances alone.

All that doesn't mean the possibility of a practical room-temperature superconductor is worthless-such a thing would be an enormous boon for technological development and scientific research, perhaps in ways we can't yet fully comprehend. But in retrospect, the enthusiasm surrounding LK-99 may well say more about our collective desire for easy answers and propensity for wishful thinking than it ever did about the potential of the material itself.

"We cannot wait for a disruptive technology to happen to start decarbonizing," Duenas Martinez says. The wind turbines we have now aren't perfect, but we need more of them. Power grids must be expanded and upgraded, even if doing so relies on transmission lines made from ordinary copper. Weaning ourselves from fossil fuels requires somehow moderating our enormous energy demands and rethinking economic myths of infinite growth. There is no silver (or LK-99) bullet that can snap the planet's perilously warming climate back to some preindustrial idyll, Duenas Martinez emphasizes. At first, a floating rock might look like magic, but it isn't-it never is.

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The Superconductor Sensation Has Fizzled, and That’s Fine

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The rise and fall of LK-99 offers a lesson on how to consider technology’s role in urgently needed energy transitions

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Quantum Can't Be Business as Usual: Issues for the Reauthorization of the National Quantum Initiative Act

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Quantum Can't Be Business as Usual: Issues for the Reauthorization of the National Quantum Initiative Act

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Quantum technologies are at an inflection point—from research and development to practical commercial and military applications. As Freeke Heijman, cofounder of the Dutch quantum umbrella organization Quantum Delta NL, expresses it: “Given that there is a solid scientific foundation underpinning the potential of quantum technology, the question is not if it will happen but when.”

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Photo: VCG/VCG/Getty Images

Quantum technologies are at an inflection point-from research and development to practical commercial and military applications. As Freeke Heijman, cofounder of the Dutch quantum umbrella organization Quantum Delta NL, expresses it: "Given that there is a solid scientific foundation underpinning the potential of quantum technology, the question is not if it will happen but when."

U.S. policy may be at an inflection point as well. Given that this transition will happen, is the United States taking the measures necessary to ensure it takes the lead? The question could not be timelier, particularly given the upcoming reauthorization of the National Quantum Initiative Act (NQIA). To address this potential turning point, this paper identifies policies and actions that government, academia, and industry should address right now.

From Science to Application

While it offers significant commercial opportunities, quantum computing poses new challenges for policy. To begin with, the technology is not widely understood. Quantum computing and quantum communications exemplify a field of computing, based on the principles of quantum theory, that seeks to explain the nature of matter and energy at the atomic and subatomic level. However, reflecting the lack of broad understanding of its scientific underpinnings, some promoters hail quantum computing as a font of spectacular technological breakthroughs, while others disparage it as an overhyped phenomenon that is fostering yet another investment bubble.

The initial skepticism with respect to quantum computing may reflect the fact that it is based on a number of esoteric, difficult-to-grasp concepts. For the purposes of clarity, some key notions are defined below.

"Superposition" refers to the ability of quantum systems to exist in multiple states simultaneously.

"Qubits" are computing units that employ superposition to encode information in two states-0 or 1-but, unlike a classical bit or switch, qubits can also be in a superposition of 0 and Physical qubits can be made from different types of media, including superconductors, trapped ions, and photonic light-based processors. The computational power of a quantum computer increases exponentially (relative to a binary-based computer) with the addition of more qubits.

"Entanglement" refers to a state in which "two or more quantum objects in a system can be intrinsically linked such that measurement of one dictates the possible measurement outcomes for another," regardless of how far apart the two objects are in physical space. This phenomenon holds the promise of completely secure communications, for example between satellites and ground stations.

"Decoherence" refers to the degradation of superposition and entanglement, which can occur over time as a result of qubits interacting with the environment, including temperature changes and other factors. Decoherence is perhaps the principal technical hurdle to the practical application of quantum technology.

While quantum concepts remain counterintuitive to many, they have been validated throughout the twentieth century. In October 2022, three scientists were award ed the Nobel Prize in Physics for their experimental work in superposition and quantum physics, which has "laid the foundation for a 'new era of quantum technology'," including quantum computing and communications.

Bracing for New Applications

The startling practical applications of quantum information science (QIS), including their potential military and intelligence advantages, are only beginning to become apparent, but some have already attracted the attention-and in some cases alarm-of policymakers and businesses around the world.

Notably, experts are concerned that sophisticated quantum computers could decrypt information stored on encrypted systems. A May 2022 Biden administration National Security Memorandum warns that "a quantum computer of sufficient size and sophistication . . . could jeopardize civilian and military communications, undermine supervisory and control systems for critical infrastructure, and defeat security protocols for most Internet-based financial transactions."

Application of quantum concepts to sensors-which is the basis of atomic clocks and is close to implementation in other applications-could enhance the effectiveness of military intelligence, surveillance, and reconnaissance (ISR), enabling improved submarine detection and the detection of concealed underground structures, troop concentrations, and nuclear weapons. Ultrasensitive quantum accelerometers may be used for navigation where GPS is not available-such as underwater, underground, or in scenarios in which GPS systems fail.

Some observers believe that quantum technologies will enable dramatic advances in machine learning-a subdiscipline of AI-leading to more accurate and effective autonomous weapons systems. Quantum-based communications systems could enable secure communications impervious to covert interception by adversaries, as interception would destroy the message and could reveal the interceptor's existence.

These applications may only be the start. In October 2022, a Dutch research team disclosed that they had placed a record number (six) of silicon spin qubits on a chip, a feat that suggests it may be possible to mass-produce qubits like semiconductor chips.

The Emerging Competitive Landscape

Reflecting the multifaceted potential of quantum, the private sector in the United States and abroad is making major commitments to develop commercially relevant products and processes, frequently with substantial governmental support.

AWS, Google, Honeywell (through its controlling stake in Quantinuum), IBM, Cisco Systems, Microsoft, and other large U.S.-based information technology firms are building quantum computers and components. IBM unveiled a 433-qubit chip in November 2022 and plans to release a 1,121-qubit device in 2023, and over 200,000 IBM clients are already using the company's quantum devices.

The United States is not alone. China's Baidu has reportedly launched a 10-qubit quantum computer based on superconducting technology, and the country's Origin Quantum sold its 24-qubit Wuyuan computer to an undisclosed buyer.

Reflecting the multifaceted potential of quantum, the private sector in the United States and abroad is making major commitments to develop commercially relevant products and processes, frequently with substantial governmental support.

Numerous start-ups and small- to medium-sized companies around the world are also investing in developing the equipment, components, software, and materials that will be needed to support the manufacturing of a broad range of QIS-based products.

A number of quantum computing companies are now publicly listed, including IonQ, Quantum Computing, Inc., D-Wave, and Rigetti. U.S.-based Zyvex Labs recently announced the development of an extremely precise lithography system for fabricating quantum chips, producing line-widths smaller than the deep ultraviolet and extreme ultraviolet systems currently used in semiconductor fabs. Meanwhile, an Australian firm, Silex Systems, launched a pilot facility in 2022 to demonstrate the production of ultra-purified silicon wafers supporting the fabrication of qubits. And the French start-up Quandela, with support from the government's CEA-Leti research institute, will begin manufacturing photonic chips for application in QIS beginning in 2023.

With respect to space applications, the European Space Agency (ESA) has announced a plan for a consortium of 20 companies to launch a quantum satellite in 2024, with ground-to-space communications based on quantum key distribution (QKD) technology, exchanging encryption keys known only between shared parties. Singapore's aerospace company SpeQtral-1 plans to launch a QKD-based satellite in 2024, in partnership with the European company Thales and with support from Singapore's Office for Space Technology and Industry. Richard Branson's Virgin Orbit plans five QKD satellite launches beginning in 2023, in partnership with the UK firm Arqit Quantum.

National and Regional Programs to Support the Development and Deployment of Quantum Technology

While quantum computing originated in the United States (with the work of physicist Richard Feynman and mathematician Peter Shor, among others), there is no guarantee that the United States will lead the world in this potentially revolutionary technology. U.S. government programs in this field still focus primarily on research, while governments in other countries place more emphasis on creating and supporting quantum-based industries, processes, and commercial products.

Europe in particular, which enjoys deep scientific competency, appears to be making rapid strides. In 2018, the European Union launched the Quantum Technologies Flagship initiative, committing 1 billion euros to support hundreds of quantum researchers across Europe. In addition, pursuant to the European High Performance Computing Joint Undertaking, the European Commission (EC) plans to build state-of-the-art pilot quantum computers based purely on European technology in 2023, to be located at six centers across Europe for use by industry and academia. The European Quantum Communication Infrastructure Initiative, launched in 2019, further commits all EU member states to the creation of a quantum communications infrastructure covering the entire European Union, and a number of industry consortia are pursuing various aspects of this effort.

U.S. government programs in this field still focus primarily on research, while governments in other countries place more emphasis on creating and supporting quantum-based industries, processes, and commercial products.

At the national level, the German government pledged 2 billion euros to support the development of quantum technologies in 2021. Of this total, the Ministry for Education and Research (BMBF) will administer about 1.1 billion euros through 2025 to build a competitive quantum computer in Germany and create an "associated ecosystem" for potential users. Germany's Ministry of Economic Affairs and Climate Action (BMWK) is reportedly providing 740 million euros in support of various quantum computing projects, including the construction of a quantum cloud computing service. At the same time, a number of major German companies (including Volkswagen, Infineon, BASF, Merck, BMW, and others) have formed a consortium, the Quantum Technology and Application Consortium (QUTAC), to bring quantum computing to the level of large-scale industrial application.

The government of the United Kingdom has pledged to spend 2.5 billion pounds over the next 10 years pursuant to a new "Plan for Quantum," more than doubling the 1 billion pounds already allocated to quantum computing in the National Quantum Technologies Programme. The nearly complete National Quantum Computing Centre in Oxfordshire will play a key role in the new plan, which will also feature distributed research hubs around the country.

In March 2023, Japan's Riken research institute, which is largely supported by public funding, made the country's first homegrown quantum computer available for research. A 100-qubit machine will reportedly be ready by 2025, and Fujitsu, working with Riken, hopes to debut a 1,000-qubit quantum computer in 2026. In 2021, a number of major Japanese companies, including Hitachi, Toyota, and NTT, formed the Quantum Strategic Industry Alliance for Revolution (Q-STAR) to explore new applications for quantum technology. Among other themes, Q-STAR is working with Japanese logistics companies to create quantum software that can determine the best transportation options based on cost, type of vehicle, and traveling time.

Israel's Innovation Authority is providing $33 million to five Israeli companies to develop two quantum processors-one based on superconducting technology and one based on ion trap technology-with sizes in dozens of qubits. The five-company consortium represents the largest single project in the Innovation Authority's history.

In sum, other nations of the world see the mastery of this technology as critical to their future economic prospects and to their national security in a challenging geopolitical context.

The U.S. Response

The U.S. government has not been inactive. It has been interested in QIS since the mid-1990s, when the National Institute of Standards and Technology (NIST) and the Department of Defense (DOD) convened workshops on the subject and over time laid out a substantial federal policy architecture for developing and exploiting U.S. capabilities in quantum computing.

Building on this, in December 2018, Congress enacted the National Quantum Initiative Act (Pub L. No. 115-368), which authorized federal agencies to establish consortia and research centers to enable QIS R&D and mandated the coordination of QIS programs across the government and in conjunction with industry and academia. Federal agencies have received progressively higher levels of budget authority for QIS activities in each fiscal year, with $877 million requested for FY 2022. Following this push, four federal QIS coordinating or advisory bodies have been established, including the Subcommittee on Quantum Information Science.

Significant quantum developmental efforts are also under way in over a dozen federal agencies, most notably at NASA, NIST, the DOD, and the Department of Energy (DOE), as well as within the intelligence community. The DOD's QIS programs, which have been underway for many years, are implemented pursuant to the National Defense Authorization Act. In early 2023, DARPA initiated "Underexplored Systems for Utility-Scale Quantum Computing" (US2QC) to determine whether any revolutionary or underexplored approaches to quantum computing are capable of reaching utility-scale operation faster than conventional predictions.

Challenges for the Federal Response

Achieving Scale and Commitment: Even with the increasing attention of White House officials, substantial outlays from Congress, and the development of agency programs, there remain concerns as to whether the federal effort is adequate in its focus and scale. While some observers draw on the historic analogies of the Manhattan and Apollo projects to capture the significance and focus of the quantum undertaking, it is worth noting that those earlier efforts had clearly defined endpoint targets. Developing quantum technologies is more akin to a race without a finish line, requiring a significant commitment of resources and national will over an undefined long term. It will also require new thinking about procurement and the education and training systems needed to buttress this extended effort.

Focusing beyond Cryptography: Another concern centers on whether current U.S. government thinking on QIS places too much attention on cryptography to the detriment of other major potential applications. It is important that no one application, however critical, dominate the U.S. research agenda. Still, reflecting the centrality of these concerns, in December 2022, Congress passed the Quantum Computing Cybersecurity Preparedness Act to provide a framework for the migration of U.S. government information technology systems to post-quantum cryptography.

Revising Curricula and Training: U.S. university physics curricula frequently do not address quantum physics and its potential applications. As one critic puts it, "rather than expose students to quantum phenomena, most physics curricula today are designed to start with the physics ABCs-riveting topics such as strings on pulleys and inclined planes." This is starting to change as coursework on quantum topics is being introduced at institutions such as UCLA, the University of Wisconsin, and the University of Pittsburgh. Ohio recently became the first state to introduce quantum training in its K-12 science curriculum.

Building the Quantum Workforce: While there is no central repository of information about the landscape of the U.S. QIS workforce, an NSTC report released in February 2022 concluded from a review of available and anecdotal evidence that "there appears to be a talent shortage at all levels."

Attracting and Retaining Foreign Talent: Fewer than 5 percent of U.S. doctorate degree holders in relevant fields focus on quantum science. In addition to doctoral level researchers, workers with skills in quantum hardware manufacturing and software development are in great demand. Many U.S. graduates with relevant degrees in physics and engineering are foreign nationals who are educated at leading U.S. universities and then required by the U.S. immigration system to leave the country upon graduation. To address the trained workforce challenges described here, in addition to greatly expanded programs to grow the domestic workforce, a "quantum carve-out" is needed in U.S. immigration policy-one that would enable QIS graduates to remain in the United States with a path to permanent resident status. Quantum-trained engineers are simply too rare and too valuable to be pushed out of the United States' nascent quantum ecosystem. Similarly, it is essential that any new export controls provide a "deemed export exclusion" so that qualified foreign nationals are free to work within the U.S. industrial sector.

Building the Domestic Skilled Talent Pipeline: Reflecting the recent emergence of the discipline, there is a general lack of awareness within the U.S. education system of the employment opportunities available in quantum research and industry. There is also no efficient pipeline for transitioning graduates into jobs (though recently adopted best practices in semiconductor training, such as those from the National Institute for Innovation and Technology, may prove applicable). In the semiconductor industry, this challenge was successfully addressed in the 1980s through the establishment of the Semiconductor Research Corporation (SRC), an industry-funded consortium enabling commercially relevant microelectronics research at U.S. universities, which later received government matching funds as well. Individual companies in the quantum field have already begun some programs, but the scale and scope of these efforts is currently too small. Cooperative efforts similar to the SRC, backed by government cost share for quantum-related fields, should be initiated.

Building the Skilled Technical Workforce: Beyond this, there are numerous positions in quantum-related fields-perhaps as much as half of the workforce-that will not need deep QIS competencies but must possess relevant skills in areas such as coding, materials science, data analysis, circuit design, optical materials, and mechanical engineering. Research centers, universities-including second- and third-tier institutions-and the best community colleges need access to quantum hardware (single-photon detectors, dilution refrigerators, cryostats, measurement equipment, etc.) to enable hands-on applied research and training. Because this equipment is expensive, there is a clear federal role in creating sharing arrangements and in serving as a first adopter for new equipment. In a promising step, the NSF now has a new organization to address the challenges in QIS and other emerging technologies, the Directorate for Technology, Innovation and Partnerships (TIP); still, the potential of this new directorate needs to be rapidly realized and its resources sustained.

Building Partnerships: Given that most existing federal agencies simply lack the autonomy, the culture, and the mandate necessary to pursue high-risk and high-potential research strategies, the United States may need to add a specialized and well-funded complement to DARPA's portfolio-or perhaps even an ARPA-E type effort-as well as funding mechanisms and trials to de-risk the adoption of innovative quantum technologies by federal organizations.

Of note here is the Quantum Economic Development Consortium (QED-C), a public-private partnership that is designed and available for collaboration with any government agency that wishes to partner with the quantum business community. Established with support from the National Institute of Standards and Technology (NIST), it is a part of the federal strategy for advancing QIS, as called for by the National Quantum Initiative Act enacted in 2018.

According to recent testimony by Bob Sutor, an executive at the U.S. quantum computing products maker ColdQuanta, there is currently a shortage of federal support to help small companies transition promising QIS technologies to the stages of prototyping and scale-up production capability. The Small Business Innovation Research program (SBIR), while widely used by quantum firms, is regarded by some as too rigid in terms of timeframes and maximum grant sizes for it to accommodate the requirements of QIS; both those issues can and should be addressed administratively or within a similar quantum-focused system.

Facilitating International Partnerships: Transatlantic cooperation in the development of quantum technologies is essential for the advancement of this rapidly evolving field. With the United Kingdom, Europe, and the United States at the forefront of quantum research, cooperative efforts to pool resources, expertise, and knowledge in order to address major challenges may enable faster progress and breakthroughs in pre-competitive technologies. Collaboration between UK, EU, and U.S. companies, universities, and research institutions can not only help accelerate the development of quantum technologies but also help create a common framework for quantum standards and regulation that could well prove crucial for transatlantic competitiveness. Concretely, there is an opportunity to enhance transatlantic collaboration by emulating initiatives like the Defense Innovation Accelerator for the North Atlantic ( DIANA) to facilitate the successful industrialization of defense related quantum technologies.

Expanding Capital: A June 2022 study by McKinsey reported that funding for quantum-focused start-ups doubled from $700 million in 2020 to $1.4 billion in 2021. However, the same study found that more recently "the rate of new quantum start-ups coming to market globally has started to slow," reflecting factors such as the shortage of talent and the relative immaturity of the quantum hardware sector needed to support commercial quantum applications.

Indeed, the quantum hardware makers that are necessary to support this nascent industry are frequently small, artisanal businesses that require long lead times to make small batches of equipment. Many lack the steady drumbeat of orders that would enable them to scale up to assembly-line operations, hence the importance of early public sector adopters and available early-stage finance to help firms cross the "valley of death." Fundamentally, successful companies don't thrive on venture capital; they thrive on customers.

Perhaps reflecting these factors, access to patient capital is cited as a bottleneck to the commercialization of quantum technologies. That challenge is linked to the long lead times that may be required to generate revenue from the sale of quantum products, lead times that do not normally mesh with the business models of venture funds.

Finding Early Adopters-the Role of Federal Procurement: One source of customers should be the federal government. Federal procurement of integrated circuits by NASA and the DOD in the early years of the semiconductor industry drove the industry up the learning curve in reliability and performance and drove down costs rapidly. Focused federal procurement-especially coupled with the use of Other Transaction Authority (OTA) mechanisms to speed acquisition and development-might well play a similar, powerful role in enabling the commercial introduction of quantum tools, software, materials, and systems to address national missions and help meet the growing challenge of deployment by friends and adversaries.

Developing quantum technologies is more akin to a race without a finish line, requiring a significant commitment of resources and national will over an undefined long term.

Looking Ahead

What is clear is that the United States needs to launch a much larger, multifaceted effort to support the development of quantum-based technologies-one with substantially increased funding, backed by new or enhanced policy tools and expanded domestic and international partnerships to support early applications and accelerate research progress. With the NQIA up for reauthorization in 2023, now is the time to put in place the federal policies necessary to ensure U.S. leadership in this critical emerging technology. The competition in the quantum domain is fierce from both friends and adversaries. Delays and half measures are genuine threats to the United States' future growth and security.
Sujai Shivakumar is senior fellow and director of the Renewing American Innovation (RAI) Project at the Center for Strategic and International Studies (CSIS). Charles Wessner is a non-resident senior adviser to the CSIS RAI Project and teaches global innovation policy at Georgetown University. Thomas Howell is an international trade attorney specializing in the semiconductor industry and a consultant with the CSIS RAI Project. This report is made possible through the generous support of Cisco Systems.

This report is produced by the Center for Strategic and International Studies (CSIS), a private, tax-exempt institution focusing on international public policy issues. Its research is nonpartisan and nonproprietary. CSIS does not take specific policy positions. Accordingly, all views, positions, and conclusions expressed in this publication should be understood to be solely those of the author(s).

© 2023 by the Center for Strategic and International Studies. All rights reserved.

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American Innovation , Technology , and Technology and Innovation

Director and Senior Fellow, Renewing American Innovation Project

Senior Adviser (Non-Resident), Renewing American Innovation Project

Thomas Howell

Consultant, Renewing American Innovation Project

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Quantum Can't Be Business as Usual: Issues for the Reauthorization of the National Quantum Initiative Act

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To make the most of the quantum opportunity, the United States cannot resort to half-measures. It must work to support the applications, partnerships, and workforce development efforts that will serve as the foundation for an emerging quantum industry.

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Comment ChatGPT a déjà bousculé les plus grands groupes de la planète

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Comment ChatGPT a déjà bousculé les plus grands groupes de la planète

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Effondrements en bourse, licenciements, revirements stratégiques… En à peine six mois, ChatGPT et l'intelligence artificielle générative ont renversé l'ordre établi.
Six mois et déjà des dégâts. En un semestre seulement, ChatGPT, et plus généralement l'IA générative, ont bouleversé la sphère économique, y compris les plus grands groupes de la planète. Certains d'entre eux ont vu leur business model menacés presque du jour au lendemain, comme Chegg, un géant américain de l'aide aux devoirs. Son cours de bourse s'est effondré de 50% le 2 mai, 24 heures après avoir révélé que ChatGPT pesait sur sa croissance. Un mouvement de panique qui s'est étendu chez son concurrent britannique Pearson. Valorisé à plus de 5 milliards de livres début 2023, ce mastodonte a vu son action perdre 12% le même jour. En cumulé, avec ces seuls deux groupes, c'est 1,5 milliard de dollars de valorisation boursière qui se sont évaporés en une seule journée…

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Effondrements en bourse, licenciements, revirements stratégiques... En à peine six mois, ChatGPT et l'intelligence artificielle générative ont renversé l'ordre établi.

Six mois et déjà des dégâts. En un semestre seulement, ChatGPT, et plus généralement l'IA générative, ont bouleversé la sphère économique, y compris les plus grands groupes de la planète. Certains d'entre eux ont vu leur business model menacés presque du jour au lendemain, comme Chegg, un géant américain de l'aide aux devoirs. Son cours de bourse s'est effondré de 50% le 2 mai, 24 heures après avoir révélé que ChatGPT pesait sur sa croissance. Un mouvement de panique qui s'est étendu chez son concurrent britannique Pearson. Valorisé à plus de 5 milliards de livres début 2023, ce mastodonte a vu son action perdre 12% le même jour. En cumulé, avec ces seuls deux groupes, c'est 1,5 milliard de dollars de valorisation boursière qui se sont évaporés en une seule journée...

La roue tourne chez les Gafam

Même des groupes que l'on croyait intouchables sont ébranlés. Fin 2021, Facebook faisait du métaverse sa priorité, au point de se renommer Meta. Un an et demi plus tard, le projet est presque enterré. Dans un communiqué publié le 14 mars, l'entreprise a annoncé que l'IA devenait sa nouvelle priorité. Chez Google, une directive interne a demandé aux salariés d'incorporer l'intelligence artificielle dans tous ses produits. Le chatbot d' OpenAI l'a poussé à dévoiler dans la précipitation son outil Bard, avec des erreurs à la clef qui lui ont valu les railleries des observateurs. D'un coup, la firme de Mountain View est devenue ringarde.

Un qualificatif qui collait à la peau de Microsoft depuis des années. Là aussi, ChatGPT a changé le cours de l'histoire. Avec ses 10 milliards investis dans OpenAI, couplés à l' intégration de l'IA dans ses produits, la groupe de Richmond s'est offert un coup de jeune doublé d'un sacré coup de fouet en bourse : +76% depuis le début de l'année, contre +33% pour Google.

Dernier épisode en date chez les champions du digital, Adobe a dévoilé en cette fin mai une nouvelle version de son célèbre logiciel Photoshop, au fonctions d'IA générative impressionnantes. Le groupe lui-même l'admet : après plus de trois décennies d'existence, c'est un "nouveau chapitre" qui s'ouvre pour l'outil de retouche photo.

L'IA générative de... licenciements

Chez IBM, le DG ne s'en cache pas, il prévoit de remplacer une partie des effectifs de l'IA : "30% du personnel administratif pourrait être facilement remplacé d'ici cinq ans". Dropbox n'a pas attendu. A la recherche d'économies, la société a dû licencier une partie de son personnel. Celles-ci seront investies pour que l'entreprise "soit à l'avant-garde de l'ère de l'IA" selon son CEO.

A mi-chemin entre les secteurs menacés par ChatGPT et ceux qui profitent de ses fonctionnalités, on trouve les médias. Le groupe allemand Axel Springer, propriétaire des publications phares Bild et Die Welt, a annoncé le 1er mars la transformation de ses deux publications papier en purs médias web, avec des licenciements à la clef. Pas grave : "l'intelligence artificielle est capable de remplacer certains journalistes", s'est félicité le patron du groupe. Des propos sans nul doute approuvés par son homologue de chez CNET. D'après Futursim, la branche nord-américaine du média aurait licencié la moitié de son staff chargé de la rédaction des news et du montage des vidéos. Le lien entre ces licenciements et l'IA n'a pas été évoqué par la direction du média. Mais une telle hypothèse ne semble pas si farfelue quand on sait que CNET a confirmé début 2023 utiliser ChatGPT pour écrire des articles.

Car du côté des créateurs de contenus, l'objectif n'est pas de dominer la course à l'intelligence artificielle, mais bien de profiter de ses prouesses. Netflix a diffusé un court-métrage généré par l'IA tandis que Spotify a lancé une fonction "DJ" où l'intelligence artificielle crée des playlists. L'AMPTP, qui représente plus de 350 sociétés américaines de production télévisuelle et cinématographique, a refusé de signer une proposition interdisant l'usage professionnel de l'IA et souhaite au contraire profiter de "ses progrès technologiques". Mais a-t-elle le choix ? C'est cela, ou être balayé.

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Comment ChatGPT a déjà bousculé les plus grands groupes de la planète

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Effondrements en bourse, licenciements, revirements stratégiques... En à peine six mois, ChatGPT et l'intelligence artificielle générative ont renversé l'ordre établi.

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Cyberwelt - Die Zukunft ist jetzt - Die ganze Doku | ARTE

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Cyberwelt - Die Zukunft ist jetzt - Die ganze Doku | ARTE

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Cyberwelt - Die Zukunft ist jetzt - Die ganze Doku | ARTE

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Cyberwelt und Cyberkrieg: Die Vorsilbe “Cyber” stammt aus dem Altgriechischen und bedeutet so viel wie “kontrollieren”. Doch hat der Mensch wirklich noch alles unter Kontrolle? Seit der Einmischung fremder Mächte in die US-Wahlen von 2016 und die Manipulationen zur Beschleunigung des Brexit steht fe…

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A Front Row View of the NSA: Reflections from General Paul M. Nakasone

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A Front Row View of the NSA: Reflections from General Paul M. Nakasone

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Center for Strategic & International Studies

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A Front Row View of the NSA: Reflections from General Paul M. Nakasone

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The Chinese Communist Party’s Cognitive Warfare Campaign on American College Campuses

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The Chinese Communist Party’s Cognitive Warfare Campaign on American College Campuses

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The Chinese Communist Party’s Cognitive Warfare Campaign on American College Campuses

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Digital Empires: The Global Battle to Regulate Technology

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Digital Empires: The Global Battle to Regulate Technology

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Digital Empires: The Global Battle to Regulate Technology

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Podcast: Artificial Intelligence and the Future of Warfighting

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Podcast: Artificial Intelligence and the Future of Warfighting

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Image credit: U.S. Air Force / Kyle Brasier

August 08, 2023

In this week's episode, Jack Murphy talks about how AI will shape warfighting in the future with former U.S. Army Ranger Paul Scharre, executive vice president and director of studies at the Center for a New American Security. Scharre's most recent book, "Four Battlegrounds: Power in the Age of Artificial Intelligence," examines this topic and defines the battlegrounds for AI: data, computing power, talent, and institutions.

Listen to the full episode from Military Matters: A Stars and Stripes Podcast.

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Artificial Intelligence and the Future of Warfighting

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Developing strong, pragmatic and principled national security and defense policies.