# data[].attributes.headline
The 2023 A.I. Index: A Presentation by the Stanford Institute for Human-Centered A.I. | LIVE STREAM

# data[].attributes.subject
The 2023 A.I. Index: A Presentation by the Stanford Institute for Human-Centered A.I. | LIVE STREAM

# data[].attributes.attachment_link.title
The 2023 A.I. Index: A Presentation by the Stanford Institute for Human-Centered A.I. | LIVE STREAM

# data[].attributes.headline
China's economic outlook and the future of digital currency

# data[].attributes.subject
China's economic outlook and the future of digital currency

# data[].attributes.attachment_link.authors[].name
Peterson Institute for International Economics

# data[].attributes.attachment_link.title
China's economic outlook and the future of digital currency

# data[].attributes.headline
The future of AI: A discussion with AI experts

# data[].attributes.subject
The future of AI: A discussion with AI experts

# data[].attributes.attachment_link.title
The future of AI: A discussion with AI experts

# data[].attributes.headline
Congress Simulates Attack on Taiwan by Chinese Communist Party

# data[].attributes.subject
Congress Simulates Attack on Taiwan by Chinese Communist Party

# data[].attributes.attachment_link.title
Congress Simulates Attack on Taiwan by Chinese Communist Party

# data[].attributes.headline
LIVE | ChatGPT chief Sam Altman testifies before Congress on AI

# data[].attributes.subject
LIVE | ChatGPT chief Sam Altman testifies before Congress on AI

# data[].attributes.attachment_link.title
LIVE | ChatGPT chief Sam Altman testifies before Congress on AI

# data[].attributes.headline
Ray Kurzweil: Future of Intelligence | MIT 6.S099: Artificial General Intelligence (AGI)

# data[].attributes.subject
Ray Kurzweil: Future of Intelligence | MIT 6.S099: Artificial General Intelligence (AGI)

# data[].attributes.attachment_link.title
Ray Kurzweil: Future of Intelligence | MIT 6.S099: Artificial General Intelligence (AGI)

# data[].attributes.headline
ChatGPT-powered Wall Street: The benefits and perils of using artificial intelligence to trade stocks and other financial instruments

# data[].attributes.subject
ChatGPT-powered Wall Street: The benefits and perils of using artificial intelligence to trade stocks and other financial instruments

# data[].attributes.body
Artificial Intelligence-powered tools, such as ChatGPT, have the potential to revolutionize the efficiency, effectiveness and speed of the work humans do. And this is true in financial markets as much as in sectors like health care, manufacturing and pretty much every other aspect of our lives. I’ve been researching financial markets and algorithmic trading for 14 years. While AI offers lots of benefits, the growing use of these technologies in financial markets also points to potential perils. A look at Wall Street’s past efforts to speed up trading by embracing computers and AI offers important lessons on the implications of using them for decision-making.

# data[].attributes.attachment_link.title
ChatGPT-powered Wall Street: The benefits and perils of using artificial intelligence to trade stocks and other financial instruments

# data[].attributes.attachment_link.description
Wall Street’s history of embracing high-speed algorithmic trading suggests ChatGPT will pose similar – if bigger – risks to financial markets.

# data[].attributes.headline
SoekiaGPT - Das didaktische Sprachmodell

# data[].attributes.subject
SoekiaGPT - Das didaktische Sprachmodell

# data[].attributes.body
SoekiaGPT ist ein Textgenerator speziell für den Unterricht. Mit SoekiaGPT kannst Du hinter die Kulissen schauen und damit einige Grundprinzipien von Textgeneratoren wie ChatGPT kennenlernen. Textbasierte Sprachmodelle nutzen typischerweise eine riesige Dokumentenkollektion, z.B. Texte aus der Wikipedia, aus Büchern und Zeitschriften, aus News-Artikeln, aus Social Media Plattformen und vieles mehr. Die Dokumentenkollektion dient als Trainingsdaten und das Sprachmodell berechnet basierend aufgrund der mathematisch-statistischen Analyse zum Beispiel, welches Zeichen oder welches Wort in einem Text mit grosser Wahrscheinlichkeit als nächstes kommt und ergänzt so einen Text fortlaufend.

# data[].attributes.attachment_link.readable_text
SoekiaGPT ist ein Textgenerator speziell für den Unterricht. Mit SoekiaGPT kannst Du hinter die Kulissen schauen und damit einige Grundprinzipien von Textgeneratoren wie ChatGPT kennenlernen.

Textbasierte Sprachmodelle nutzen typischerweise eine riesige Dokumentenkollektion, z.B. Texte aus der Wikipedia, aus Büchern und Zeitschriften, aus News-Artikeln, aus Social Media Plattformen und vieles mehr. Die Dokumentenkollektion dient als Trainingsdaten und das Sprachmodell berechnet basierend aufgrund der mathematisch-statistischen Analyse zum Beispiel, welches Zeichen oder welches Wort in einem Text mit grosser Wahrscheinlichkeit als nächstes kommt und ergänzt so einen Text fortlaufend.

Vereinfachte Funktionsweise eines statistischen Sprachmodells

Hinweis: Textgeneratoren wie ChatGPT sind komplexe Informatiksysteme und Werkzeuge für den Einsatz im Alltag. SoekiaGPT ist eine Lernumgebung und nimmt deshalb eine ganze Reihe von didaktischen Vereinfachungen vor. Aus diesen Gründen eignet sich SoekiaGPT nicht als Werkzeug zur Textgenerierung im Alltag. Mit SoekiaGPT kannst du aber einzelne Schritte eines Sprachmodells selbst durchspielen und verschiedene Parameter einstellen.

SoekiaGPT basiert auf Ideen von Michael Hielscher und Werner Hartmann.
Entwicklung: Michael Hielscher (Pädagogische Hochschule Schwyz)
SoekiaGPT ist für moderne Webbrowser wie Chrome, FireFox, Safari und Edge konzipiert.

# data[].attributes.attachment_link.title
SoekiaGPT - Das didaktische Sprachmodell

# data[].attributes.attachment_link.description
SoekiaGPT ist ein Textgenerator speziell für den Unterricht. Mit SoekiaGPT kannst Du hinter die Kulissen schauen und damit einige Grundprinzipien von Textgeneratoren wie ChatGPT kennenlernen.

# data[].attributes.body
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4.

# data[].attributes.attachment_link.description
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers.

# data[].attributes.headline
Artificial Intelligence (AI): In-depth Guide for 2023

# data[].attributes.subject
Artificial Intelligence (AI): In-depth Guide for 2023

# data[].attributes.body
Artificial Intelligence (AI) allows computers to learn from experience, adapt to new inputs, and perform human-like tasks. Most of the AI examples you’ve heard about –from chess-playing machines to self-driving cars–rely heavily on deep learning, a subfield of AI. Thanks to deep learning, computers can perform complex tasks by processing vast quantities of data and by identifying data patterns.

As we observed numerous applications of AI under different business functions and industries, we believe that AI has still undiscovered potential and will enable new emerging technologies in the next few years. The increasing popularity of AI and the market growth, which we will mention in this article, support our thoughts.

# data[].attributes.body
Researchers are still struggling to understand how AI models trained to parrot Internet text can perform advanced tasks such as running code, playing games and trying to break up a marriage.

# data[].attributes.attachment_link.readable_text
No one yet knows how ChatGPT and its artificial intelligence cousins will transform the world, and one reason is that no one really knows what goes on inside them. Some of these systems' abilities go far beyond what they were trained to do-and even their inventors are baffled as to why. A growing number of tests suggest these AI systems develop internal models of the real world, much as our own brain does, though the machines' technique is different.

"Everything we want to do with them in order to make them better or safer or anything like that seems to me like a ridiculous thing to ask ourselves to do if we don't understand how they work," says Ellie Pavlick of Brown University, one of the researchers working to fill that explanatory void.

At one level, she and her colleagues understand GPT (short for generative pretrained transformer) and other large language models, or LLMs, perfectly well. The models rely on a machine-learning system called a neural network. Such networks have a structure modeled loosely after the connected neurons of the human brain. The code for these programs is relatively simple and fills just a few screens. It sets up an autocorrection algorithm, which chooses the most likely word to complete a passage based on laborious statistical analysis of hundreds of gigabytes of Internet text. Additional training ensures the system will present its results in the form of dialogue. In this sense, all it does is regurgitate what it learned-it is a " stochastic parrot," in the words of Emily Bender, a linguist at the University of Washington. But LLMs have also managed to ace the bar exam, explain the Higgs boson in iambic pentameter, and make an attempt to break up their users' marriage. Few had expected a fairly straightforward autocorrection algorithm to acquire such broad abilities.

That GPT and other AI systems perform tasks they were not trained to do, giving them "emergent abilities," has surprised even researchers who have been generally skeptical about the hype over LLMs. "I don't know how they're doing it or if they could do it more generally the way humans do-but they've challenged my views," says Melanie Mitchell, an AI researcher at the Santa Fe Institute.

"It is certainly much more than a stochastic parrot, and it certainly builds some representation of the world-although I do not think that it is quite like how humans build an internal world model," says Yoshua Bengio, an AI researcher at the University of Montreal.

At a conference at New York University in March, philosopher Raphaël Millière of Columbia University offered yet another jaw-dropping example of what LLMs can do. The models had already demonstrated the ability to write computer code, which is impressive but not too surprising because there is so much code out there on the Internet to mimic. Millière went a step further and showed that GPT can execute code, too, however. The philosopher typed in a program to calculate the 83rd number in the Fibonacci sequence. "It's multistep reasoning of a very high degree," he says. And the bot nailed it. When Millière asked directly for the 83rd Fibonacci number, however, GPT got it wrong: this suggests the system wasn't just parroting the Internet. Rather it was performing its own calculations to reach the correct answer.

Although an LLM runs on a computer, it is not itself a computer. It lacks essential computational elements, such as working memory. In a tacit acknowledgement that GPT on its own should not be able to run code, its inventor, the tech company OpenAI, has since introduced a specialized plug-in-a tool ChatGPT can use when answering a query-that allows it to do so. But that plug-in was not used in Millière's demonstration. Instead he hypothesizes that the machine improvised a memory by harnessing its mechanisms for interpreting words according to their context-a situation similar to how nature repurposes existing capacities for new functions.

This impromptu ability demonstrates that LLMs develop an internal complexity that goes well beyond a shallow statistical analysis. Researchers are finding that these systems seem to achieve genuine understanding of what they have learned. In one study presented last week at the International Conference on Learning Representations (ICLR), doctoral student Kenneth Li of Harvard University and his AI researcher colleagues-Aspen K. Hopkins of the Massachusetts Institute of Technology, David Bau of Northeastern University, and Fernanda Viégas, Hanspeter Pfister and Martin Wattenberg, all at Harvard-spun up their own smaller copy of the GPT neural network so they could study its inner workings. They trained it on millions of matches of the board game Othello by feeding in long sequences of moves in text form. Their model became a nearly perfect player.

To study how the neural network encoded information, they adopted a technique that Bengio and Guillaume Alain, also at the University of Montreal, devised in 2016. They created a miniature "probe" network to analyze the main network layer by layer. Li compares this approach to neuroscience methods. "This is similar to when we put an electrical probe into the human brain," he says. In the case of the AI, the probe showed that its "neural activity" matched the representation of an Othello game board, albeit in a convoluted form. To confirm this, the researchers ran the probe in reverse to implant information into the network-for instance, flipping one of the game's black marker pieces to a white one. "Basically, we hack into the brain of these language models," Li says. The network adjusted its moves accordingly. The researchers concluded that it was playing Othello roughly like a human: by keeping a game board in its "mind's eye" and using this model to evaluate moves. Li says he thinks the system learns this skill because it is the most parsimonious description of its training data. "If you are given a whole lot of game scripts, trying to figure out the rule behind it is the best way to compress," he adds.

This ability to infer the structure of the outside world is not limited to simple game-playing moves; it also shows up in dialogue. Belinda Li (no relation to Kenneth Li), Maxwell Nye and Jacob Andreas, all at M.I.T., studied networks that played a text-based adventure game. They fed in sentences such as "The key is in the treasure chest," followed by "You take the key." Using a probe, they found that the networks encoded within themselves variables corresponding to "chest" and "you," each with the property of possessing a key or not, and updated these variables sentence by sentence. The system had no independent way of knowing what a box or key is, yet it picked up the concepts it needed for this task. "There is some representation of the state hidden inside of the model," Belinda Li says.

Researchers marvel at how much LLMs are able to learn from text. For example, Pavlick and her then Ph.D. student Roma Patel found that these networks absorb color descriptions from Internet text and construct internal representations of color. When they see the word "red," they process it not just as an abstract symbol but as a concept that has certain relationship to maroon, crimson, fuchsia, rust, and so on. Demonstrating this was somewhat tricky. Instead of inserting a probe into a network, the researchers studied its response to a series of text prompts. To check whether it was merely echoing color relationships from online references, they tried misdirecting the system by telling it that red is in fact green-like the old philosophical thought experiment in which one person's red is another person's green. Rather than parroting back an incorrect answer, the system's color evaluations changed appropriately in order to maintain the correct relations.

Picking up on the idea that in order to perform its autocorrection function, the system seeks the underlying logic of its training data, machine learning researcher Sébastien Bubeck of Microsoft Research suggests that the wider the range of the data, the more general the rules the system will discover. "Maybe we're seeing such a huge jump because we have reached a diversity of data, which is large enough that the only underlying principle to all of it is that intelligent beings produced them," he says. "And so the only way to explain all of this data is [for the model] to become intelligent."

In addition to extracting the underlying meaning of language, LLMs are able to learn on the fly. In the AI field, the term "learning" is usually reserved for the computationally intensive process in which developers expose the neural network to gigabytes of data and tweak its internal connections. By the time you type a query into ChatGPT, the network should be fixed; unlike humans, it should not continue to learn. So it came as a surprise that LLMs do, in fact, learn from their users' prompts-an ability known as "in-context learning." "It's a different sort of learning that wasn't really understood to exist before," says Ben Goertzel, founder of the AI company SingularityNET.

One example of how an LLM learns comes from the way humans interact with chatbots such as ChatGPT. You can give the system examples of how you want it to respond, and it will obey. Its outputs are determined by the last several thousand words it has seen. What it does, given those words, is prescribed by its fixed internal connections-but the word sequence nonetheless offers some adaptability. Entire websites are devoted to "jailbreak" prompts that overcome the system's "guardrails"-restrictions that stop the system from telling users how to make a pipe bomb, for example-typically by directing the model to pretend to be a system without guardrails. Some people use jailbreaking for sketchy purposes, yet others deploy it to elicit more creative answers. "It will answer scientific questions, I would say, better" than if you just ask it directly, without the special jailbreak prompt, says William Hahn, co-director of the Machine Perception and Cognitive Robotics Laboratory at Florida Atlantic University. "It's better at scholarship."

Another type of in-context learning happens via "chain of thought" prompting, which means asking the network to spell out each step of its reasoning-a tactic that makes it do better at logic or arithmetic problems requiring multiple steps. (But one thing that made Millière's example so surprising is that the network found the Fibonacci number without any such coaching.)

In 2022 a team at Google Research and the Swiss Federal Institute of Technology in Zurich-Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov and Max Vladymyrov-showed that in-context learning follows the same basic computational procedure as standard learning, known as gradient descent. This procedure was not programmed; the system discovered it without help. "It would need to be a learned skill," says Blaise Agüera y Arcas, a vice president at Google Research. In fact, he thinks LLMs may have other latent abilities that no one has discovered yet. "Every time we test for a new ability that we can quantify, we find it," he says.

Although LLMs have enough blind spots not to qualify as artificial general intelligence, or AGI-the term for a machine that attains the resourcefulness of animal brains-these emergent abilities suggest to some researchers that tech companies are closer to AGI than even optimists had guessed. "They're indirect evidence that we are probably not that far off from AGI," Goertzel said in March at a conference on deep learning at Florida Atlantic University. OpenAI's plug-ins have given ChatGPT a modular architecture a little like that of the human brain. "Combining GPT-4 [the latest version of the LLM that powers ChatGPT] with various plug-ins might be a route toward a humanlike specialization of function," says M.I.T. researcher Anna Ivanova.

At the same time, though, researchers worry the window may be closing on their ability to study these systems. OpenAI has not divulged the details of how it designed and trained GPT-4, in part because it is locked in competition with Google and other companies-not to mention other countries. "Probably there's going to be less open research from industry, and things are going to be more siloed and organized around building products," says Dan Roberts, a theoretical physicist at M.I.T., who applies the techniques of his profession to understanding AI.

And this lack of transparency does not just harm researchers; it also hinders efforts to understand the social impacts of the rush to adopt AI technology. "Transparency about these models is the most important thing to ensure safety," Mitchell says.

# data[].attributes.attachment_link.description
No one yet knows how ChatGPT and its artificial intelligence cousins will transform the world, and one reason is that no one really knows what goes on inside them. Some of these systems' abilities go far beyond what they were trained to do-and even their inventors are baffled as to why.

# data[].attributes.headline
OpenAI CEO Sam Altman Agrees AI Must Be Regulated

# data[].attributes.subject
OpenAI CEO Sam Altman Agrees AI Must Be Regulated

# data[].attributes.body
Altman, whose company is on the extreme forefront of generative A.I. technology with its ChatGPT tool, testified in front of the Senate Judiciary Committee for the first time in a Tuesday hearing. And while he said he is ultimately optimistic that innovation will benefit people on a grand scale, Altman echoed his previous assertion that lawmakers should create parameters for AI creators to avoid causing “significant harm to the world.”

# data[].attributes.attachment_link.title
OpenAI CEO Sam Altman Agrees AI Must Be Regulated

# data[].attributes.attachment_link.description
In a Congressional hearing, Altman, whose company created ChatGPT, and senators expressed their fears about how AI could "go quite wrong."

# data[].attributes.headline
Cyberbedrohungen werden immer schneller: Eine historische Betrachtung

# data[].attributes.subject
Cyberbedrohungen werden immer schneller: Eine historische Betrachtung

# data[].attributes.body
Cyberbedrohungen werden immer schneller – und Sicherheitsteams haben Mühe, mit dem Tempo der Entwicklung Schritt zu halten. Das ist nicht überraschend, denn die gesamte Geschichte der Menschheit lehrt uns, dass dies so gut wie vorprogrammiert ist. Cyberangriffe sind zwar Angriffe auf Technologien, aber die Angriffswerkzeuge sind auch selbst Teil der Technologie. Es gibt keinen Grund, warum diese Entwicklung in nächster Zeit aufhören sollte, und es gibt keinen Grund, warum Angriffstools nicht demselben Trend folgen sollten.

# data[].attributes.attachment_link.readable_text
Cyberbedrohungen werden immer schneller - und Sicherheitsteams haben Mühe, mit dem Tempo der Entwicklung Schritt zu halten. Das ist nicht überraschend, denn die gesamte Geschichte der Menschheit lehrt uns, dass dies so gut wie vorprogrammiert ist. Cyberangriffe sind zwar Angriffe auf Technologien, aber die Angriffswerkzeuge sind auch selbst Teil der Technologie.

Es gibt keinen Grund, warum diese Entwicklung in nächster Zeit aufhören sollte, und es gibt keinen Grund, warum Angriffstools nicht demselben Trend folgen sollten.

Wie es soweit kommen konnte

Um zu verstehen, wie sich die technologische Entwicklung beschleunigt hat, ist es lehrreich, die Zeitlinien zu betrachten, die Absolute Software aufbereitet hat:

Für die Entwicklung der Schrift brauchte die Menschheit weitere 4.000 Jahre, und bald darauf folgte das erste Rad auf einer Achse. Steinwerkzeuge wurden etwa 3.000 Jahre nach dem Rad durch Bronzewerkzeuge abgelöst, die wiederum weniger als 2.000 Jahre später von Eisenwerkzeugen abgelöst wurden. Innerhalb von 1.000 Jahren kam das Papier auf, und wieder änderten sich die Dinge dauerhaft.

In den nächsten tausend Jahren kamen die Windmühle, das Schießpulver und einige innovative Werkzeuge für Schmiede und Steinmetze auf. Die nächsten 500 Jahre brachten Linsen, dann Brillen und schließlich den Buchdruck. Bald darauf kam das Mikroskop auf. Weniger als 300 Jahre nach dem Buchdruck, in der Mitte des 18. Jahrhunderts, kam die Dampfmaschine auf den Markt, und die Industrialisierung richtig in Fahrt.

Die nächsten 100 Jahre brachten Impfstoffe, die Dampflokomotive, die Fotografie und den Telegraphen. Um 1870 lag jedoch die Lebenserwartung für den größten Teil der Welt immer noch bei unter 30 Jahren. Die nächsten 50 Jahre brachten das Telefon, elektrisches Licht, das Automobil, das Flugzeug und Kunstdünger. Weitere 25 Jahre brachten das Fernsehen, Antibiotika zur Rettung der Menschen und die Atombombe, um sie zu töten, hervor. Es folgten auch die ersten Computer, und dann ging es erst richtig los.

Nur etwa 20 Jahre, nachdem die ersten Universalcomputer verkauft wurden, brachte die Raumfahrt Menschen auf den Mond. Die Pocken waren 1796 die erste Krankheit, gegen die es einen Impfstoff gab, und 1980 wurde sie als erste ausgerottet. Ungefähr zu dieser Zeit wurde das TCP/IP-Protokoll standardisiert und damit der Grundstein für das Internet gelegt, und dann ging es erneut erst richtig los.

Modernes Computing - und Hacking

Torsten George, VP von Absolute Software für weiter aus: Etwa ein Jahrzehnt nach der Ausarbeitung der Internetprotokolle wurde der erste sich selbst reproduzierende Internetwurm veröffentlicht. Das war noch ein paar Jahre, bevor die meisten Menschen überhaupt Zugang zum Internet hatten, geschweige denn es nutzten, geschweige denn sich für fast alle lebenswichtigen Dinge darauf verließen. Die Zahl der Innovationen, die sich seit der Verbreitung des Internets ergeben haben, ist kaum zu zählen. Wie jeder, der sich mit dem Thema Sicherheit beschäftigt, weiß, waren jedoch nicht alle diese Innovationen gut.

Die Menschen haben Raumstationen gebaut, gelernt, Gene zu verändern, Supercomputer in die Taschen der halben Weltbevölkerung gesteckt und mRNA-Impfstoffe entwickelt. Leider sind wir aber auch dazu übergegangen, uns vollständig auf Werkzeuge zu verlassen, die es erst seit 0,001 Prozent der Zeit gibt, in der es Werkzeuge gibt. Die gleiche Beschleunigung der Entwicklung, die es der Menschheit ermöglicht hat, so schnell voranzukommen, hat es auch ermöglicht, dass die Angriffe auf diese Systeme ebenfalls schnell voranschreiten.

Was kommt als Nächstes?

Heute, im Jahr 2023, etwa 3,4 Millionen Jahre nachdem unsere Vorfahren mit der Herstellung von Werkzeugen begonnen haben, wird viel über eine neue Klasse von „Werkzeugen" gesprochen, und zwar über ein Tool im Besonderen: Künstliche Intelligenz, und zwar ChatGPT.

ChatGPT ist weder lebendig noch magisch, obwohl es fast ein Beispiel für das ist, was der Science-Fiction-Autor Arthur C. Clark meinte, als er schrieb, dass „jede ausreichend fortgeschrittene Technologie nicht von Magie zu unterscheiden ist". Tatsächlich handelt es sich nur um ein weiteres, noch mächtigeres Werkzeug. Seitdem die erste steinerne Axt benutzt wurde, um einen anderen Hominiden zu erschlagen, wurden Werkzeuge sowohl zum Guten als auch zum Schlechten eingesetzt. Mit ChatGPT wird es nicht anders sein. Es wird sowohl Gutes als auch Böses bewirken können, und zwar besser und schneller als bisher. Das ist einfach der Lauf der Technik.

Die Cyberangreifer entwickeln sich schneller als je zuvor und das wird nicht nachlassen. Innovation kann aber auch etwas Gutes bewirken. Maschinelles Lernen und künstliche Intelligenz können sowohl zum Schutz als auch für Angriffe eingesetzt werden und werden es auch. Kriminelle werden versuchen, riesige maschinelle Lernmodelle wie ChatGPT zu nutzen, um neue Angriffe zu entwickeln, aber Verteidiger werden sie nutzen, um neue Abwehrmechanismen zu entwickeln. Diese Tools mögen zwar alle sehr neu sein, aber man darf nicht vergessen, dass sie in gewisser Weise genau so sind wie seit Millionen von Jahren.

www.absolute.com

# data[].attributes.attachment_link.title
Cyberbedrohungen werden immer schneller: Eine historische Betrachtung - Onlineportal von IT Management

# data[].attributes.attachment_link.description
Cyberbedrohungen werden immer schneller - und Sicherheitsteams haben Mühe, mit dem Tempo der Entwicklung Schritt zu halten. Das ist nicht überraschend, denn die gesamte Geschichte der Menschheit lehrt uns, dass dies so gut wie vorprogrammiert ist. Cyberangriffe sind zwar Angriffe auf Technologien, aber die Angriffswerkzeuge sind auch selbst Teil der Technologie.

# data[].attributes.headline
Fake ChatGPT desktop client steals Chrome login data

# data[].attributes.subject
Fake ChatGPT desktop client steals Chrome login data

# data[].attributes.body
Researchers are warning about an infostealer mimicking a ChatGPT Windows desktop client that’s capable of copying saved credentials from the Google Chrome login data folder.
ChatGPT has not released an official desktop client, but this bogus version looks remarkably similar to what one would expect.

# data[].attributes.attachment_link.readable_text
Researchers are warning about an infostealer mimicking a ChatGPT Windows desktop client that's capable of copying saved credentials from the Google Chrome login data folder.

ChatGPT has not released an official desktop client, but this bogus version looks remarkably similar to what one would expect.

Infostealer disguised as ChatGPT

The infostealer is being distributed via a zip archive carrying a file named ChatGPT For Windows Setup 1.0.0.exe.

During the installation process, the malware runs in the background and begins extracting Chrome login data using Havelock, a tool that extracts and decrypts accounts, cookies, and history from Chromium-based web browsers.

"The client connects to various domains such as http://api.telegram.org, http://facebook.com, http://lumtest.com (for querying geoIP location), http://graph.facebook.com (for getting data into and out of the Facebook platform), and http://api.aiforopen.com," Trend Micro revealed.

The grabbed data is exfiltrated via Telegram.

The fake ChatGPT client creates an AutoStart entry in the registry to ensure that the infostealer runs every time the infected machine starts up. It also has the ability to hide its console window and to extract web session cookies via sqlite3. Its many dependencies point to additional capabilities.

Chrome users in the crosshairs

Attackers have lately been exploiting users' desire for a ChatGPT desktop and mobile app to deliver different types of malware.

Trend Micro also found similarities between this malicious payload and a piece of malware dubbed DUCKTAIL, an infostealer designed to hijack Facebook Business accounts.

Users are advised to avoid downloading applications from untrusted or unauthorized sources. ChatGPT does not have an official desktop client or mobile app, which means that such claims or offers should be treated with caution.

# data[].attributes.attachment_link.title
Fake ChatGPT desktop client steals Chrome login data - Help Net Security

# data[].attributes.attachment_link.description
Researchers are warning about an infostealer mimicking a ChatGPT Windows desktop client that's capable of copying saved credentials from the Google Chrome login data folder. ChatGPT has not released an official desktop client, but this bogus version looks remarkably similar to what one would expect.

# data[].attributes.headline
The Untold Story of the Boldest Supply-Chain Hack Ever

# data[].attributes.subject
The Untold Story of the Boldest Supply-Chain Hack Ever

# data[].attributes.body
It was late 2019, and Adair, the president of the security firm Volexity, was investigating a digital security breach at an American think tank. The intrusion was nothing special. Adair figured he and his team would rout the attackers quickly and be done with the case—until they noticed something strange. A second group of hackers was active in the think tank’s network. They were going after email, making copies and sending them to an outside server. These intruders were much more skilled, and they were returning to the network several times a week to siphon correspondence from specific executives, policy wonks, and IT staff.

# data[].attributes.attachment_link.title
The Untold Story of the Boldest Supply-Chain Hack Ever

# data[].attributes.attachment_link.description
The attackers were in thousands of corporate and government networks. They might still be there now. Behind the scenes of the SolarWinds investigation.

# data[].attributes.headline
Behind AI, a massive infrastructure is changing geopolitics

# data[].attributes.subject
Behind AI, a massive infrastructure is changing geopolitics

# data[].attributes.body
AI technologies have many important applications ranging from industry and civil service to military and intelligence, and as such they can strengthen a country’s economy and improve its state capacity.

But developing and operating frontier AI technologies requires considerable material infrastructures, which can have more complicated geopolitical implications. The amount of computing power or compute required to train ChatGPT is such that it would take a single graphics processing unit (GPU) of the type you find in an ordinary high-end computer about 288 years to finish a training run.

To make any sort of progress, frontier AI developers use many GPUs in parallel. OpenAI is said to have used 10,000 GPUs to develop ChatGPT, reducing the training time from centuries to days. So where do you find thousands of GPUs set up for AI research? Possibly just two or three places in the UK: in hyperscale data centres owned by Amazon, Google, and Microsoft.

To do any sort of serious research on large AI models, you have to send your data to one of these so-called hyperscalers, and either pay them or partner up with them. And once you’ve trained your model, to actually deploy it into use so that it can be used by many people simultaneously, you may need even more compute.

As a result, pretty much all independent AI research labs, even ones explicitly established as an alternative to the hyperscalers, have eventually fallen right into the hyperscalers’ lap: OpenAI to Microsoft, DeepMind and Anthropic to Google, Hugging Face to Amazon, and so on (I am grateful to my student Divya Siddarth for this observation).

# data[].attributes.attachment_link.readable_text
AI technologies have many important applications ranging from industry and civil service to military and intelligence, and as such they can strengthen a country's economy and improve its state capacity.

But developing and operating frontier AI technologies requires considerable material infrastructures, which can have more complicated geopolitical implications. The amount of computing power or compute required to train ChatGPT is such that it would take a single graphics processing unit (GPU) of the type you find in an ordinary high-end computer about 288 years to finish a training run.

To make any sort of progress, frontier AI developers use many GPUs in parallel. OpenAI is said to have used 10,000 GPUs to develop ChatGPT, reducing the training time from centuries to days. So where do you find thousands of GPUs set up for AI research? Possibly just two or three places in the UK: in hyperscale data centres owned by Amazon, Google, and Microsoft.

To do any sort of serious research on large AI models, you have to send your data to one of these so-called hyperscalers, and either pay them or partner up with them. And once you've trained your model, to actually deploy it into use so that it can be used by many people simultaneously, you may need even more compute.

As a result, pretty much all independent AI research labs, even ones explicitly established as an alternative to the hyperscalers, have eventually fallen right into the hyperscalers' lap: OpenAI to Microsoft, DeepMind and Anthropic to Google, Hugging Face to Amazon, and so on (I am grateful to my student Divya Siddarth for this observation).

So there is a concern that the more we lean into AI as a society, the more dependent we become for both research and development (R&D) and for daily operations on infrastructures owned by a handful of hyperscale corporations domiciled abroad. It also means that on a geopolitical level, the UK may lack the autonomy to steer the development of frontier AI in a manner aligned with its values and objectives.

Many academic AI researchers deem this situation unacceptable and are calling for intervention from the government. Professor Michael Woolridge of the Alan Turing Institute and Oxford University is calling for a sovereign high-performance cloud infrastructure. Earlier this month the UK government's Independent Review of The Future of Compute published an excellent final report, which calls for the government to immediately establish a UK AI Research Resource incorporating at least 3,000 top-spec GPUs.

However, other governments have tried something like this before, and the results have not been entirely encouraging. France launched its first sovereign cloud project in 2009. But it never won widespread adoption and in 2020 it was shut down as a failure. In 2021 the French government announced a new "trusted cloud" initiative, delivered by Thales and Orange Telecom - but in partnership with Google and Microsoft. Germany's new "sovereign cloud" is likewise delivered by local firms in partnership with Google and Microsoft. Nominally these national clouds are controlled by the local firms, but in practice they remain part of the technological supply chains of the U.S. hyperscalers.

I think it's very important to ask why efforts to build and sustain sovereign cloud infrastructure have proven so difficult. My working hypothesis is the following. Like any infrastructure, cloud infrastructure requires sufficient scale to be viable. But for cloud computing, this efficient scale is extraordinarily high.

One Amazon data centre contains tens of thousands of servers, and the total number of servers that make up Amazon's global computing platform are measured in the millions. This means that costs such as cybersecurity, administration, and R&D are divided between millions of clients, enabling Amazon to develop and sustain a great variety of advanced services on top of the basic computing hardware, all of which benefit AI research.

If we build a sovereign cloud in the UK, can we sustain the scale of investment required to keep it up to date? Or even to reach this level in the first place? Google alone is able to invest about twice as much money into R&D each year as the entire UK public sector. So it is in fact conceivable that hyperscale cloud computing is a type of infrastructure in which the efficient scale is so extraordinarily high that it exceeds what a medium-sized state alone can sustain.

If this is the case, then two different geopolitical paths may be possible:

- One is that the UK collaborates with other countries, such as France and Germany, to reach the critical mass required to sustain cutting-edge cloud in public ownership.

- Another is that the UK simply doubles down on using the U.S. hyperscalers and attempts to manage that dependence on a political level. After all, the UK is already dependent on the U.S. in various other ways. Even GCHQ and MI5 reportedly store their secrets on Amazon's platform.

But to ascertain whether these are really the paths available, or whether a sovereign UK-only infrastructure is after all possible, we need to urgently pursue more research on the geography and economics of AI and the cloud computing infrastructures that power it.

--

Professor Lehdonvirta's new book " Cloud Empires: How Digital Platforms Are Overtaking the State and How We Can Regain Control " examines how large technology companies are complementing but also challenging and in some cases overtaking state power and sovereignty.

# data[].attributes.attachment_link.title
OII | Behind AI, a massive infrastructure is changing geopolitics

# data[].attributes.attachment_link.description
AI technologies have many important applications ranging from industry and civil service to military and intelligence, and as such they can strengthen a country's economy and improve its state capacity. But developing and operating frontier AI technologies requires considerable material infrastructures, which can have more complicated geopolitical implications.

# data[].attributes.body
It is widely believed that the world is on the brink of another military revolution. AI is about to transform the character of warfare, as gunpowder, tanks, aircraft, and the atomic bomb have in previous eras. Today, states are actively seeking to harness the power of AI for military advantage. China, for instance, has announced its intention to become the world leader in AI by 2030. Its “New General AI Plan” proclaimed that: “AI is a strategic technology that will lead the future.” Similarly, Russian President Vladimir Putin declared: “Whoever becomes the leader in this sphere will become ruler of the world.” In response to the challenge posed by China and Russia, the United States has committed to a “third offset” strategy. It will invest heavily in AI, autonomy, and robotics to sustain its advantage in defense.

# data[].attributes.attachment_link.description
Paul Scharre, Four Battlegrounds: Power in the Age of Artificial Intelligence (New York: W. W. Norton & Company, 2023). It is widely believed that the

# data[].attributes.headline
To make AI work for national security, invest in humans too

# data[].attributes.subject
To make AI work for national security, invest in humans too

# data[].attributes.body
ChatGPT is here, and its user base is growing faster than TikTok. In a 2017 survey by the AI software company Pega, only 34 percent of respondents thought they had used technology that incorporates artificial intelligence (AI). However, the actual number is much higher: 84 percent of the respondents—which included people in the United States, Britain, France, Germany, the Netherlands, and Australia—had used an AI-powered service such as a virtual home assistant, chatbot, or software that makes predictive suggestions.

# data[].attributes.attachment_link.readable_text
ChatGPT is here, and its user base is growing faster than TikTok.

In a 2017 survey by the AI software company Pega, only 34 percent of respondents thought they had used technology that incorporates artificial intelligence (AI). However, the actual number is much higher: 84 percent of the respondents-which included people in the United States, Britain, France, Germany, the Netherlands, and Australia-had used an AI-powered service such as a virtual home assistant, chatbot, or software that makes predictive suggestions.

The United States needs better AI literacy and understanding of this emerging technology, as AI intersects many aspects of American lives, including defense. For example, in October 2022 the Defense Department issued a formal request for information-often the first step in the process of calling for bids and issuing a government contract-to identify sources for its AI, machine learning, and data science workforce. In its words: "As the [Defense Department] expands its workforce in the AI workspace, it is crucial that it maintains a qualified and experienced workforce that can match industry innovations both in speed and execution."

This illustrates how AI has become a national security issue-and how an AI-educated workforce (and an AI-savvy Defense Department) is the way forward. As AI expands, it will be imperative to bolster the United States' thinking about how humans and AI can learn from one another, and to understand the opportunities, risks, and challenges of AI innovation. Although AI can be a complex technology to grasp, the Defense Department and other federal agencies can "decode" AI through training, initiatives, and investments that will help the country prepare for a future in which humans inevitably use AI on a greater scale.

To do so, the United States needs "human-centered AI," which involves humans throughout the research, design, training, testing, and decision-making processes of AI systems. This approach leverages both machine and human intelligence.

But the country also needs AI-centered humans.

The national security link to AI. The 2018 Department of Defense Artificial Intelligence Strategy defined AI as the ability of machines to perform tasks that typically require human intelligence. The Pentagon expects AI to strengthen the military, increase the effectiveness and efficiency of operations, and enhance homeland security.

The US government invested $2.5 billion in AI research and development in fiscal year 2022, but the United States is not the only country ramping up federal spending on artificial intelligence. With respect to military funding of AI, an October 2021 report by the Center for Security and Emerging Technology estimated that annual Chinese military spending on AI was "in the low billions of US dollars." According to the National Defense Industrial Association's magazine National Defense, this level of funding of AI is on a par with the Pentagon's investments. Other countries that are leading in AI investment activity include Israel, the United Kingdom, Canada, India, Japan, Germany, Singapore, and France. From both a national and global vantage point, it is clear that interest in artificial intelligence is expanding rapidly.

Many people already use AI regularly without realizing it-for example, individuals encounter AI through popular virtual assistants like Apple's Siri and Google's Assistant, quick translations of language, major online platforms such as Amazon and YouTube with recommendation algorithms, and tagging objects or people in images. AI does all this without becoming the dystopian superintelligence that critics have been warning about for decades. Yet AI has its pitfalls. For example, research has shown that training datasets can amplify biases; algorithmic decisions lack transparency and accountability; and biased criminal-justice algorithms make questionable predictions about sentencing.

Regardless of whether one is a technophile, technophobe, or in-between, we all need to recognize more nuance in the relationship between AI and humans. AI needs humans, and humans need AI.

AI needs humans. In recent months we've seen Elon Musk make drastic changes to Twitter. He disbanded the Human Rights Team led by Shannon Raj Singh and laid off thousands of Twitter employees-including much of the content moderation force. Behind computer screens, these teams worked to combat misinformation and disinformation, increase accessibility for people with disabilities, and protect users facing human rights violations worldwide. One team worked on ethical AI and algorithmic transparency.

Yesterday was my last day at Twitter: the entire Human Rights team has been cut from the company.

I am enormously proud of the work we did to implement the UN Guiding Principles on Business & Human Rights, to protect those at-risk in global conflicts & crises including Ethiopia,

- Shannon Raj Singh (@ShannonRSingh) November 4, 2022

Humans are crucial when it comes to these social and dynamic settings, both in society and the military. Ultimately, AI and its algorithms are constrained. For example, algorithms cannot understand parody, sarcasm, satire, or context the way a human can. Indeed, humans are fundamental to coding processes, AI systems, and platforms.

In 2020, the Defense Department published its recommendations for AI ethical principles, which would apply to combat and non-combat efforts. Then-Secretary of Defense Mark T. Esper said, "The United States, together with our allies and partners, must accelerate the adoption of AI and lead in its national security applications to maintain our strategic position, prevail on future battlefields, and safeguard the rules-based international order." The principles focus on five critical areas: responsibility, equitability, traceability, reliability, and governance. At the core of each of these principles is the critical role of humans who will exercise judgment and work to minimize unintended consequences and bias.

Humans need AI. AI can complete tasks and perform better than humans in several notable areas. For example, AI could potentially provide more accurate medical diagnoses, especially in the field of radiology and pathology, because of AI's ability to train on large sets of images, extract patterns from data mining, and identify specific, relevant features for diagnoses. Some research has shown that an AI program was able to detect breast cancer in mammograms, notably in the early stages of cancer. AI can also translate one's speech while retaining his or her voice through Google's AI, transcribe audio quickly, and proofread work.

Moreover, AI can learn from AI. For example, Google's AutoML and Microsoft DeepCoder can build the next generation of AI. These two machine-learning systems can not only retain the code given to them by the researchers, but can also investigate how the codes fit together, how they work, and how to learn other codes. In simple terms, AI can absorb large amounts of data, pick up on patterns, and provide relevant outputs at an incredible pace.

AI is not just widely used in everyday life. Society cannot ignore the expanding use of artificial intelligence in warfare and future conflicts. Semi-autonomous drones, which are guided by human operators, are already being used in the Russia-Ukraine war for surveillance and target identification. One can imagine that AI and human operators will increasingly work together in these conflict settings, especially with advanced drones. For example, the US Switchblade 600 requires a human operator to choose targets while viewing a live video feed.

One of the reasons people have distrusted AI is because the enabling algorithms are perceived as a "black box." The lack of explanation for coding decisions, as well as for the datasets used to train the algorithms, creates the potential for bias in AI. The necessary skills, limits of data quality, and fear of the unknown are additional issues in bridging the gap between humans and machines.

While the barriers to adopting AI are challenging, they are not insurmountable. With increased AI literacy, present and future adopters of AI can work to develop, deploy, and use the technology in responsible ways.

Putting humans in the driver's seat. "AI-centered humans" flips the concept of "human-centered AI" on its head. Rather than having humans interact at different stages of the decision-making process, AI-centered humans would, rightly, take the driver's seat. For example, when the US Defense Department adopted its five ethical principles for the use of AI, it brought together AI experts in industry, government, academia, and the general public. Additionally, Stanford University's Institute for Human-Centered Artificial Intelligence hosted an inaugural Congressional Boot Camp on AI last year, at which 25 bipartisan congressional staff members discussed recent developments in AI. Dialogues like these are not siloed in the technical community. With diverse perspectives and expertise, and an increased understanding and awareness of AI and its applications, humans can better assess the risks, opportunities, and limitations of AI.

There have already been some significant initiatives on this front. For example, in June the Association for Computing Machinery will hold its sixth annual cross-disciplinary Conference on Fairness, Accountability, and Transparency, bringing together computer scientists, social scientists, law scholars, statisticians, ethicists, and others who are interested in fairness, accountability, and transparency in socio-technical systems. The association is the world's largest computing society, and its conferences are widely considered the most prestigious in the field of human-computer interaction.

Germany has taken an AI-centered human approach that is inclusive, evidence-based, and focused on capacity-building. Specifically, the German Minister for Economic Affairs and Energy funded a free online course, The Elements of AI, to increase AI literacy. Users of the course can take the course at their own pace without prior experience in coding or specialized math skills. This is a step in the right direction.

Moving forward, the United States should devote more national attention, financial resources, and programming to strengthening AI education across federal agencies and civil society. Perhaps more important, the US federal government should formalize an AI education strategy with timeline-specific goals, highlighting both short-term and long-term aims. Specifically, US policy makers need to prioritize an AI-informed society, ensure transparency, and best assist the military.

There has been some progress along these lines; for example, the Pentagon's 2020 AI Education Strategy highlights priority areas and skills required to accelerate AI adoption from software and coding to data management and infrastructure. The strategy focuses on how to build up AI capabilities, raise AI awareness for senior leaders, and provide training on the responsible use of AI. While this is a good initial step, the strategy lacks the specifics of a timeline.

In the past year, the Joint Artificial Intelligence Center rolled out AI education pilot courses for thousands of Defense Department employees, spanning education for general officers to coding bootcamps. It would be beneficial to extend these initiatives beyond the Defense Department and to organize them into annual, five-year, and ten-year plans. The United States would greatly benefit from robust educational initiatives and AI investments across its departments-especially in Defense, Education, Homeland Security, and State-to strengthen the country's national security.

In March 2021, former Google CEO Eric Schmidt and former US Deputy Secretary of Defense Bob Work, who led the National Security Commission on AI, wrote in the commission's final report: "America is not prepared to defend or compete in the AI era." However, this does not have to be the United States' future when it comes to AI. Decoding AI through AI literacy is a critical national security issue. AI infiltrates almost all aspects of our daily lives in the United States. Governments, Big Tech, and the general public all have a vested interest in AI and its societal implications.

This entire op-ed was written by ChatGPT. Just kidding! Julie George (a human) did.

# data[].attributes.attachment_link.title
To make AI work for national security, invest in humans too

# data[].attributes.attachment_link.description
ChatGPT is here, and its user base is growing faster than TikTok. In a 2017 survey by the AI software company Pega, only 34 percent of respondents thought they had used technology that incorporates artificial intelligence (AI).

# data[].attributes.headline
To Restrict, or Not to Restrict, That Is the Quantum Question

# data[].attributes.subject
To Restrict, or Not to Restrict, That Is the Quantum Question

# data[].attributes.body
Innovation power—the ability to invent, scale, and adapt emerging technologies—will determine which country prevails in the great power competition of the 21st century. Export controls accordingly assume a central position in the U.S. foreign policy toolkit, carrying the ability to significantly impact an adversary’s innovation potential. In October 2022, the Biden administration introduced semiconductor, artificial intelligence, and supercomputing-related export controls on China and has since hinted that similar restrictions on other technologies, including quantum information science, may soon follow.

# data[].attributes.attachment_link.title
To Restrict, or Not to Restrict, That Is the Quantum Question

# data[].attributes.attachment_link.description
Export controls on quantum technology are not an immediately viable option. But the U.S. can take several steps to prepare for a future in which trade restrictions become more pertinent.

# data[].attributes.headline
L'éthicien Bertrand Kiefer: "L'intelligence artificielle va rendre l'humain de plus en plus bête"

# data[].attributes.subject
L'éthicien Bertrand Kiefer: "L'intelligence artificielle va rendre l'humain de plus en plus bête"

# data[].attributes.body
La démocratisation de l’intelligence artificielle (IA) amène l’humanité à un moment charnière de son histoire. Bertrand Kiefer, médecin et éthicien suisse en est convaincu. Pour lui, nous allons coévoluer avec elle. Ce, non sans risque. Interview.

# data[].attributes.attachment_link.readable_text
La démocratisation de l'intelligence artificielle (IA) amène l'humanité à un moment charnière de son histoire. Bertrand Kiefer, médecin et éthicien suisse en est convaincu. Pour lui, nous allons coévoluer avec elle. Ce, non sans risque. Interview.

Dans sa maison en campagne genevoise, Bertrand Kiefer semble bien loin de l'effervescence urbaine et technologique. Là où il reçoit, il n'y a qu'à observer les hautes herbes de son jardin à travers la fenêtre, pour voir que ce médecin, théologien et éthicien aime laisser la nature reprendre le dessus.

Pourtant, Betrand Kiefer s'intéresse aussi grandement à l'intelligence artificielle (IA) et à la manière dont elle pourrait impacter notre monde. Cette dernière constitue d'ailleurs l'un de ses sujets de prédilection, lorsqu'il écrit des articles en tant que rédacteur en chef de la "Revue médicale suisse".

Le vendredi 12 mai, Blick est allé à la rencontre de ce penseur suisse. Spoiler alert: selon lui, l'IA pourrait bien nous rendre tous et toutes un peu plus bêtes. Interview.

Bertrand Kiefer, on le voit dans votre demeure: l'intelligence artificielle est présente un peu partout. Même si vous vous trouvez dans un petit havre de paix. Bien sûr. On voit à quel point en 30 ans, nous, les humains, nous sommes totalement hybridés à l'intelligence artificielle. On sait de moins en moins qui l'on est sans elle. Elle est présente partout, dans quantité d'objets. Par exemple, le smartphone est une IA qui influence continuellement notre mode de pensée. Internet fonctionne aussi avec des IA. Tout comme les réseaux sociaux. Nous sommes dans une sorte de coévolution avec cette technologie.

Vous utilisez la notion d'hybridation. L'intelligence artificielle va-t-elle finir par prendre le dessus sur l'humain? Je ne crois pas que l'IA va prendre le pouvoir sur l'humain. En fait, le principal problème, c'est plutôt que nous nous soumettions à la machine. Vis-à-vis d'elle, on peut se sentir misérable, dévalorisé. Elle nous imite, nous en sommes fascinés et nous avons tendance à abandonner ce qui nous distingue d'elle.

L'intelligence artificielle peut donc paraitre parfois humaine. Qu'est-ce qui la différencie intrinsèquement de l'humain, au juste? L'IA est une intelligence qui excelle dans le domaine de la performance, la capacité de calculer. Son efficience est stupéfiante. L'humain peut de ce fait se sentir menacé. Mais ce qui fait de nous des êtres humains, ce sont toutes sortes de valeurs et notions qui ne sont pas de l'ordre de l'intelligence. Par exemple, l'amour, l'empathie, le désir, le plaisir, mais aussi la violence ou la peur de la finitude et de la mort. Les IA ne peuvent pas réellement comprendre ce qui se joue, là. Et c'est précisément pour cela qu'elles ne pourront jamais totalement nous remplacer.

Mais l'intelligence artificielle peut tout de même nous affecter. Oui. La vraie question - bien avant celle de se demander si l'IA prendra le pouvoir sur les humains - c'est de prendre conscience du pouvoir qu'elle a déjà pris sur nous. Comment? En transformant nos façons de penser, de créer, d'écrire. C'est le cas, par exemple, avec ChatGPT. Si je devais résumer tout cela, je dirais que, certes, l'IA va devenir de plus en plus intelligente. Mais aussi que l'humain risque de devenir de plus en plus bête et de moins en moins humain.

De moins en moins humain? Dans quel sens? L'humain a tendance à laisser tomber ce qui lui est propre, sa capacité de décider, ses valeurs, ses manières d'être ensemble. Un des grands dangers de l'IA, c'est qu'elle organise la vie humaine selon ses propres standards. Car l'IA uniformise, atténue les différences et a tendance à nous placer dans un système normatif. Pourtant, ce qui fait de nous des êtres humains est aussi la différence, la diversité et la singularité. Et nous devrions aimer cela.

Si l'humain devient "plus bête et moins humain", que pourrait-il se produire? Notre capacité de raisonner ne va pas disparaître, mais elle risque de s'appauvrir. Nous la garderons toutefois dans le cadre de notre vie de tous les jours, car l'IA n'est pas capable de nous remplacer dans l'intime des situations humaines et relationnelles. Certes, cet abêtissement pourrait aussi affecter nos choix de vie ou nos valeurs, mais nous n'y sommes pas encore. Je pense néanmoins que nous sommes à un moment charnière de l'histoire de l'humanité. Avec deux questions majeures : la question du climat - et de la durabilité - et la question de l'IA.

Justement, comment envisager le futur de l'humanité? En cohabitation avec les intelligences artificielles? Comme c'est parti, et je le mentionnais au début de notre échange, nous sommes dans une coévolution avec l'intelligence artificielle. Il faut qu'elle se fasse vers quelque chose qui a du sens, qui respecte les différences. L'enjeu n'est pas de s'en détacher complètement, puisque nous y sommes hybridés. Mais il faut que nous gardions les rênes. Et que la créature ne l'emporte pas sur le créateur.

# data[].attributes.attachment_link.title
L'éthicien Bertrand Kiefer: "L'intelligence artificielle va rendre l'humain de plus en plus bête"

# data[].attributes.attachment_link.description
La démocratisation de l'intelligence artificielle (IA) amène l'humanité à un moment charnière de son histoire. Bertrand Kiefer, médecin et éthicien suisse en est convaincu. Pour lui, nous allons coévoluer avec elle. Ce, non sans risque. Interview. Dans sa maison en campagne genevoise, Bertrand Kiefer semble bien loin de l'effervescence urbaine et technologique.

# data[].attributes.headline
EU risk managers warn cyber insurance could become “unviable product”

# data[].attributes.subject
EU risk managers warn cyber insurance could become “unviable product”

# data[].attributes.body
The Federation of European Risk Management Associations has warned cyber insurance could become an “unviable product” for companies as concerns grow over insurers failing to cover state-backed attacks and the impact of new exemption clauses.

# data[].attributes.attachment_link.title
EU risk managers warn cyber insurance could become “unviable product” | The Insurer

# data[].attributes.attachment_link.description
The Federation of European Risk Management Associations has warned cyber insurance could become an “unviable product” for companies as concerns grow over insurers fa…

# data[].attributes.headline
European Cybersecurity Competence Centre and Network

# data[].attributes.subject
European Cybersecurity Competence Centre and Network

# data[].attributes.body
The ECCC aims to increase Europe’s cybersecurity capacities and competitiveness, working together with a Network of National Coordination Centres (NCCs) to build a strong cybersecurity Community

# data[].attributes.headline
NCSI: Upgrading National Cyber Resilience - e-Governance Academy

# data[].attributes.subject
NCSI: Upgrading National Cyber Resilience - e-Governance Academy

# data[].attributes.body
Over the past 20 years, cybersecurity has grown to occupy a major part of our work at the eGA. We have helped more than 40 countries assess their national readiness to prevent and respond to cyber threats, establish cybersecurity governance and risk management mechanisms, draft the relevant legislation, strengthen incident prevention and response capacities, or take stock of best practices in cybersecurity awareness raising. The National Cybersecurity Index (NCSI) developed and managed by the e-Governance Academy has since 2016 served as a key tool to support these activities and to help countries develop their cybersecurity capacities.

# data[].attributes.attachment_link.readable_text
Over the past 20 years, cybersecurity has grown to occupy a major part of our work at the eGA. We have helped more than 40 countries assess their national readiness to prevent and respond to cyber threats, establish cybersecurity governance and risk management mechanisms, draft the relevant legislation, strengthen incident prevention and response capacities, or take stock of best practices in cybersecurity awareness raising. The National Cybersecurity Index (NCSI) developed and managed by the e-Governance Academy has since 2016 served as a key tool to support these activities and to help countries develop their cybersecurity capacities.

This publication presents the outcome of the NCSI methodology update conducted during 2021-2022 and describes how the NCSI has developed through the years. Furthermore, following the effort of our 2020 National Cyber Security in Practice handbook to give national policymakers, legislative experts, and others responsible for securing the digital environment an overview of the key elements that underpin the national cybersecurity architecture, this publication takes a closer look at additional areas of national cybersecurity: strategic leadership, global engagements, a stronger legal framework for cyber resilience, and crisis preparedness.

The e-Governance Academy wishes to thank Henrik Beckvard, Kadri Kaska, Hannes Krause, Merle Maigre, Anna-Maria Osula, Radu Serrano, and Martin Švéda for contributing their insights, and the number of experts who have been involved in developing the NCSI, including Markko Künnapu, Helar Laasik, Epp Maaten, Piret Pernik, Raul Rikk, Radu Serrano, and Hauke Schulz.

We are grateful to the Estonian Ministry of Foreign Affairs for their long-time support for the development of the NCSI and the expansion of the National Cyber Security in Practice handbook.

We wish you insightful and enjoyable reading!
READ THE PUBLICATION HERE

# data[].attributes.attachment_link.title
NCSI: Upgrading National Cyber Resilience - e-Governance Academy

# data[].attributes.attachment_link.description
Over the past 20 years, cybersecurity has grown to occupy a major part of our work at the eGA. We have helped more than 40 countries assess their national readiness to prevent and respond to cyber threats, establish cybersecurity governance and risk management mechanisms, draft the relevant legislation, strengthen incident prevention and response capacities, or take stock of best practices in cybersecurity awareness raising.

# data[].attributes.headline
Fachkräftemangel ist eine Be­dro­hung für die Cybersicherheit

# data[].attributes.subject
Fachkräftemangel ist eine Be­dro­hung für die Cybersicherheit

# data[].attributes.body
Beim Schutz vor Cyberbedrohungen sind Fachkräfte gefragt, um bestehende Strategien an die Bedrohungslandschaft anzupassen und neue zu entwickeln. Es lässt sich allerdings beobachten, dass sich potenzielle Sicherheitsexperten auf andere IT-Bereiche spezialisieren, anstatt im Cybersicherheitsbereich tätig zu werden. Dadurch entsteht im Security-Sektor ein Mangel an Fachpersonal, der eines der größten Probleme für Anbieter von Sicherheitslösungen darstellt.

# data[].attributes.attachment_link.readable_text
Talente binden, statt neu rekrutieren Fachkräftemangel ist eine Bedrohung für die Cybersicherheit

Anbieter zum Thema

Beim Schutz vor Cyberbedrohungen sind Fachkräfte gefragt, um bestehende Strategien an die Bedrohungslandschaft anzupassen und neue zu entwickeln. Es lässt sich allerdings beobachten, dass sich potenzielle Sicherheitsexperten auf andere IT-Bereiche spezialisieren, anstatt im Cybersicherheitsbereich tätig zu werden. Dadurch entsteht im Security-Sektor ein Mangel an Fachpersonal, der eines der größten Probleme für Anbieter von Sicherheitslösungen darstellt.

Das Interesse für die Informationstechnologie ist nicht gesunken, dennoch gibt es zu wenige potenzielle Kandidaten, um die Nachfrage nach Spitzenkräften in der Cybersicherheitsbranche zu decken. Viele Talente wandern zu attraktiveren Möglichkeiten ab, die mehr Flexibilität, Innovation und Bezahlung bieten. Die Abkehr von der traditionellen Bürokultur und die ständig wachsende Anzahl an Angriffen aufgrund geopolitischer Umstände tragen zusätzlich dazu bei, dass der Talentpool für traditionelle Cybersecurity-Rollen schrumpft.

Branchenführer prognostizieren, dass 2023 der Höhepunkt des Personalmangels erreicht werden wird. Der Wettkampf um die Talente ermöglicht lukrative Arbeitsbedingungen, somit wird die Branche für Einsteiger eine attraktive Option. Trotzdem gehen die Experten davon aus, dass die Unternehmen weiterhin zu kämpfen haben. Letztendlich wird der Wettbewerb um Talente im Jahr 2023 zwar abnehmen, der komplette Bedarf wird aber nicht gedeckt werden können.

Die Hochschulen haben die einmalige Chance, junge Interessenten früh an die Security-Branche heranzuführen. Sie müssen sich besser mit der Industrie abstimmen und Fachleute mit Praxiserfahrung einbeziehen, um die Studenten für den Security-Bereich zu begeistern und auf ihrem Weg in die Führungspositionen zu begleiten.

Unternehmen, die in ihre Mitarbeiter investieren, haben immer die Nase vorn, besonders in schwierigen Zeiten. Während die Rekrutierung von Spitzenkräften viele Branchen vor Herausforderungen stellt, ist es oftmals einfacher, sie zu halten. Die Unternehmensführungen sollten sich auf die Bereitstellung von Lernmöglichkeiten auf allen Ebenen des Unternehmens und die Bindung von Talenten konzentrieren. Schließlich macht der Verlust von ein oder zwei prestigeträchtigen Produzenten in wirtschaftlich schwierigen Zeiten einen großen Unterschied! Natürlich sollte man auch weiterhin extern nach Top-Talenten suchen. Die Förderung und Schulung eigener Top-Talente in anderen Bereichen der IT (IT OPS, SRE, IT Risk...) sollte aber keinesfalls vernachlässigt werden. Als Entscheidungsträger ist es an der Zeit, eine neue Kultur von Cyber-Führungskräften aufzubauen, die hochkompetent und gleichzeitig flexibel in ihren Fähigkeiten sind, um Prozesse zu rationalisieren und Effizienz zu schaffen.

Die Konvergenz der Bereiche IT-Betrieb, Risiko und Sicherheit setzt sich fort. Unternehmen müssen dies als Chance für die Transformation sehen. Daher ist es an der Zeit, auf Ihre Top-Talente zu hören und sich nicht länger an die Normen der Branche anzupassen. Die Führungskräfte müssen kundenorientiert arbeiten, Probleme von außen betrachten und die Außendarstellung des Unternehmens fördern.

Über den Autor: Erik Gaston ist Vice President Global Executive Engagement bei Tanium.

(ID:49252180)

# data[].attributes.attachment_link.title
Fachkräftemangel ist eine Be­dro­hung für die Cybersicherheit

# data[].attributes.attachment_link.description
(Bild: WrightStudio - stock.adobe.com) Beim Schutz vor Cyberbedrohungen sind Fachkräfte gefragt, um bestehende Strategien an die Bedrohungslandschaft anzupassen und neue zu entwickeln. Es lässt sich allerdings beobachten, dass sich potenzielle Sicherheitsexperten auf andere IT-Bereiche spezialisieren, anstatt im Cybersicherheitsbereich tätig zu werden. Dadurch entsteht im Security-Sektor ein Mangel an Fachpersonal, der eines der größten Probleme für Anbieter von Sicherheitslösungen darstellt.

# data[].attributes.headline
Course d'obstacles pour le cloud souverain européen

# data[].attributes.subject
Course d'obstacles pour le cloud souverain européen

# data[].attributes.body
En dépit de l’optimisme et du volontarisme affichés par les acteurs publics et privés du cloud souverain européen, il y a encore loin de la coupe aux lèvres pour le ce projet hautement stratégique. C’est ce qui ressort en filigrane de la table ronde « Vers un contre-modèle européen aux normes cloud US ? » organisée au sein du FIC 2023 à Lille.

Le constat qu’y dressait Solange Viegas Dos Reis, directrice juridique groupe chez OVHcloud, était sans appel : « En 2017, la part des acteurs européens dans le marché européen du cloud était de 27%. Cinq ans plus tard, le marché a explosé et les mêmes acteurs européens ne pesaient plus que 13 % ». Les Américains dominent le secteur de la tête et des épaules et l’Europe accuse son retard en termes de part de marché et de technologies.

# data[].attributes.attachment_link.readable_text
En dépit de l'optimisme et du volontarisme affichés par les acteurs publics et privés du cloud souverain européen, il y a encore loin de la coupe aux lèvres pour le ce projet hautement stratégique. C'est ce qui ressort en filigrane de la table ronde "  Vers un contre-modèle européen aux normes cloud US ?  " organisée au sein du FIC 2023 à Lille.

Le constat qu'y dressait Solange Viegas Dos Reis, directrice juridique groupe chez OVHcloud, était sans appel : "  En 2017, la part des acteurs européens dans le marché européen du cloud était de 27%. Cinq ans plus tard, le marché a explosé et les mêmes acteurs européens ne pesaient plus que 13 %  ". Les Américains dominent le secteur de la tête et des épaules et l'Europe accuse son retard en termes de part de marché et de technologies.

Et les défis sont loin d'être uniquement économiques, comme l'expliquait à la tribune Hugues Foulon, CEO d'Orange Cyberdefense. "  L'un des principaux problèmes pour certains de nos clients, ce sont les enjeux d'extraterritorialité, liés notamment au Cloud Act et au Patriot Act. Nous avons vocation chez Orange à ne pas être naïfs, à faire comprendre les conséquences des choix, quels qu'ils soient d'ailleurs.  "

L'extraterritorialité du droit américain ? Vous devenez justiciable des États-Unis dès lors que vous employez un bien ou un service américain, qu'il s'agisse d'un simple dollar ou d'une adresse Gmail. Conséquence directe : tout utilisateur d'un service de cloud d'une société américaine tombe sous le coup de la loi américaine et la confidentialité de ses données n'est dès lors plus garantie.

Face aux enjeux du cloud, " ne pas être naïf "

"  Nous parlons de protection des données et de son importance, mais nous oublions le lien avec les droits fondamentaux, c'est-à-dire le droit à la vie privée. Et la vie privée ne s'étend pas seulement aux individus qui vivent dans l'Union européenne, elle s'étend aussi aux personnes morales, comme les entreprises  ", détaillait Peter Sund, CEO du FISC (Finnish Information Security Cluster, l'association des entreprises finnoises de cybersécurité).

Le cloud en Europe ? Un marché totalement dominé par des acteurs étrangers parfois intrusifs, dont le gouvernement peut s'arroger le droit d'accéder aux données de tout citoyen ou entreprise, tout le contraire d'un " cloud de confiance ". Une expression très française, soulignait Rayna Stamboliyska, Uncertainty management specialist chez RS Strategy et animatrice de la table ronde, avant de demander aux intervenants ce que "  veut dire la confiance dans le cloud et comment ils la mettent en œuvre dans leur quotidien, technique, technologique et opérationnel  ".

"  Il se résume au fait que l'utilisateur a une liberté de choix grâce à l'interopérabilité des technologies, la réversibilité. De plus, il est sûr que ses données seront protégées et ne seront pas utilisées à d'autres fins que celles qu'il a choisies ", répondit Solange Viegas Dos Reis. Des principes opérationnels auxquels a souscrit Hugues Foulon, qui a plaidé pour "  des solutions pragmatiques qui permettent d'avancer et de gagner en autonomie stratégique. Et c'est ce qu'on a fait avec "Bleu"  ". Opérationnelle en 2024, cette co-entreprise d'Orange et de Capgemini, en partenariat avec Microsoft, visera précisément à fournir un " cloud de confiance " ou " souverain " aux acteurs publics et privés à la recherche du plus haut niveau de sécurité et de confidentialité.

" Schrems II ", les acteurs du cloud au pied du mur

Une solution qui a "  l'avantage d'être conforme by design. C'est la seule façon d'être conforme au RGPD ", soulignait Bertrand Pailhès, directeur des technologies et de l'innovation à la Cnil. En effet, la technique à elle seule ne suffit pas : un cadre légal et réglementaire cohérent est également indispensable pour assurer la confiance dans le cloud. Et de ce point de vue, l'UE semble avoir pris le taureau par les cornes. "  L'Europe a mis un principe assez fort qui, je pense, fait consensus, qui est de dire la protection des données des Européens doit être garantie en tous lieux et en tout temps  ", se félicitait encore Bertrand Pailhès. Le RGPD, très protecteur des données personnelles, est ainsi élevé au rang de norme de facto sur laquelle les autres régions du monde sont invitées à s'aligner.

C'est le sens de l'arrêt " Schrems II ", que la Cour de justice de l'Union européenne (CJUE) a rendu le 16 juillet 2020. Estimant que la protection des données personnelles aux États-Unis n'était pas au niveau, la CJUE a cassé le "  Privacy Shield  ", le régime de transferts de données qui existait entre Washington et Bruxelles. Problème : aucun accord de substitution n'a été mis en place et le marché n'est pas prêt, selon le directeur des technologies et de l'innovation à la Cnil.

"  La décision de la CJUE "Schrems II", c'est à partir du 17 juillet. À partir de demain, il est interdit de transférer les données vers les États-Unis. Il n'y a pas de période de mise en conformité ", déplorait Bertrand Pailhès lors du FIC. "  C'est parfois complètement irréaliste de penser que parce qu'un juge à Luxembourg a décidé que le marché n'était pas conforme aux droits fondamentaux, tout le monde allait se mettre d'accord avec ça ", ajoutait-il. Et le régulateur doit dans ce cas faire preuve de souplesse le temps qu'assez "  de solutions alternatives (sic) émergent  ".

Micmac législatif

Le législateur européen est donc plein de bonnes intentions, mais celles-ci ne se heurtent parfois au réel... A moins que ce ne soient des "  questions réglementaires qui soient, d'une certaine manière, complètement opposées les unes aux autres ", relevait Peter Sund, pour qui "  il est toujours délicat d'arriver à un équilibre  " entre des objectifs contradictoires. Ainsi, la protection des données personnelles et les pouvoirs accordés à la police et à la justice pour enquêter sont-ils antinomiques. Il a pris l'exemple du dispositif baptisé " un meilleur Internet pour les enfants ", destiné à lutter contre la pédopornographie.

Pour ce faire, la Commission européenne prévoit que les autorités puissent avoir accès aux photos et autres matériels hébergés par les fournisseurs de services en ligne, notamment les messageries chiffrées. Alors que la sécurité du cloud repose en bonne partie sur le chiffrement des données, cette mesure le ferait voler en éclats. " Cela risque de semer la confusion et de créer une situation qui va à l'encontre des objectifs du cloud  ", s'inquiétait Peter Sund, tout en reconnaissant l'importance de la lutte contre la pédocriminalité.

De fait, soulignait Bertrand Pailhès, les États-Unis se sont appuyés sur une législation robuste pour développer le cloud : "  Elle a été lancée il y a dix ans, elle est hyper complète, le secteur est hyper réglementé et ça, et ça a permis en fait à l'écosystème américain du cloud d'émerger, parce qu'il avait des règles claires sur ce qui était attendu de lui.  "

" L'Europe doit créer des champions "

Ce cadre favorable n'est que l'une des expressions d'une volonté politique forte, a abondé Solange Viegas Dos Reis : "  quand on voit aujourd'hui les champions du cloud comme les Américains ou les Chinois, on se rend compte que ce sont des champions qui se sont renforcés sur leur marché domestique avec un très fort soutien étatique, en ayant des marchés publics, en requérant des financements et des soutiens à la recherche.  "

Une volonté qui a longtemps fait défaut de ce côté de l'Atlantique, tant le credo libéral (aucun coup de pouce ni entrave au marché) de la Commission restait fort. Mais cela pourrait changer : "  nous soutenons le "Buy European Act", qui permettrait d'avoir un soutien concret, financier, de donner à tous les acteurs du cloud les moyens nécessaires à leur développement  ", lançait encore Solange Viegas Dos Reis.

Il serait temps que les lignes bougent, estimait d'ailleurs le CEO d'Orange Cyberdefense France. "  L'Europe doit créer des champions dans ce domaine ", plaidait Hugues Foulon, qui soulignait de plus que la question ne s'arrêtait pas là. Le cloud, ce n'est pas que des data centers, ce sont aussi des solutions logicielles en évolution rapide, de la cybersécurité, de la maintenance, bref, tout un environnement. Et selon lui, "  l'Europe n'a pas tout à fait conscience de ces gros enjeux de formation ". Le continent manque encore de développeurs et d'experts en tout genre et sans ces compétences, "  ça sera compliqué de faire marcher un écosystème cloud stratégiquement autonome  ".

"  On va avoir du mal à créer "from scratch" un écosystème aussi performant que celui des leaders du marché. Je pense que c'est une aventure qui se compte plutôt en décennies  ", avertissait Hugues Foulon, qui plaidait pour une approche pragmatique, à l'exemple du partenariat entre " Bleu " et Microsoft. Sera-t-il même un jour possible de faire sans les GAFAM ? Les professionnels européens veulent y croire.

# data[].attributes.attachment_link.title
[FIC 2023] Course d'obstacles pour le cloud souverain européen

# data[].attributes.attachment_link.description
Malgré des avancées remarquables, le cloud européen semble à la peine face à l'écrasante domination des GAFAM. L'analyse qu'en ont fait régulateurs, professionnels du cloud et de la cybersécurité lors d'une table ronde au FIC 2023 ne vient guère dissiper cette impression. Avis de tempête sur le cloud européen ?

# data[].attributes.headline
SAP und Siemens kritisieren Data Act der EU

# data[].attributes.subject
SAP und Siemens kritisieren Data Act der EU

# data[].attributes.body
SAP und Siemens kritisieren in einem Schreiben an die EU-Kommission den Data Act. Das geplante EU-Gesetz soll unter anderem Usern das Recht einräumen, über die Verwendung der Daten zu bestimmen, die von ihren Geräten generiert werden. Zudem sollen Cloud-Services interoperabel werden. "Es besteht die Gefahr, dass die europäische Wettbewerbsfähigkeit untergraben wird, indem Datenaustausch – einschliesslich Kern-Know-how und Konstruktionsdaten – vorgeschrieben wird", heisst es im Schreiben. Unterzeichnet haben es neben SAP und Siemens unter anderem das deutsche Medizintechnikunternehmen Brainlab, das Softwareunternehmen Datev und der Verband Digitaleurope.

# data[].attributes.attachment_link.title
SAP und Siemens kritisieren Data Act der EU

# data[].attributes.attachment_link.description
Täglich kostenlose News, Trends, Nachrichten und Hintergründe aus der Schweizer und der internationalen Informatik. Für Anwenderunternehmen und Anbieter.

# data[].attributes.headline
The Disconnect on Undersea Cable Security

# data[].attributes.subject
The Disconnect on Undersea Cable Security

# data[].attributes.body
Geopolitical tension is climbing steadily in the fiber-optic submarine cable sector. Cables are an invaluable critical infrastructure that transmit 99 percent of all intercontinental digital traffic and more than $10 trillion worth of financial transactions each day. The U.S. Congress is in the process of passing legislation to curb foreign cable construction, highlighting a key difference in the perception of security risk to subsea cables between U.S. government officials (and security analysts) and industry owners, operators, and manufacturers.

# data[].attributes.attachment_link.readable_text
Editor's Note: The fiber-optic submarine cable sector is a vital, but often ignored, part of the world's critical infrastructure. Many members of Congress and the U.S. government, however, see the risks to subsea cables quite differently than cable owners and manufacturers. My Brookings colleague Joseph Keller examines this disconnect, suggesting ways that the policy community can protect and advance this critical industry.

Daniel Byman

***

Geopolitical tension is climbing steadily in the fiber-optic submarine cable sector. Cables are an invaluable critical infrastructure that transmit 99 percent of all intercontinental digital traffic and more than $10 trillion worth of financial transactions each day. The U.S. Congress is in the process of passing legislation to curb foreign cable construction, highlighting a key difference in the perception of security risk to subsea cables between U.S. government officials (and security analysts) and industry owners, operators, and manufacturers.

Industry stakeholders have prioritized common security risks endangering commercial competition, revenue, and network integrity but have been less engaged on the potential for cyberattacks and other vulnerabilities that could be exploited by malicious actors. By contrast, U.S. officials and the intelligence community have focused almost exclusively on the acute national security threats posed by tampering and espionage, without lending comparable weight to infrastructure protection and resilience. These independent and contrasting motivations may imperil the global information chain and hinder technological innovation, presumably mirroring the often inharmonious ambitions of government and private industry.

To avoid future constraints on global telecommunications caused by this disconnect, stakeholders must bridge the gulf between them. Developing public-private partnerships, building consensus around shared standards of submarine cable risk and protection, and increasing U.S. prioritization of federal subsea communications cable activities will reduce potential disruptions-an essential foundation for international stakeholder collaboration supporting a resilient wide area network.
Geopolitical Pressure Prompts Cable Security Concerns
In April 2020, the Trump administration signed an executive order formalizing the Committee for the Assessment of Foreign Participation in the United States Telecommunications Services Sector, informally known as Team Telecom, which was officially tasked with bolstering the Federal Communications Commission's (FCC's) capacity to evaluate foreign participation in U.S. telecommunications activities. The order punctuated the government's efforts to address national security concerns associated with licenses for the landing and operation of undersea cables on U.S. soil.

The United States avoids both direct and indirect association with China around telecommunications. Regulators view preventing interconnection with foreign adversaries as an essential measure in defending communications and data transmitted through cables from adversaries. They contend that shared cable infrastructure with foreign ownership and untrusted equipment tied to China introduces risks to citizens' privacy and the potential for foreign intelligence agencies to intercept sensitive information. U.S. security officials have required concessions on business agreements on national security grounds to eliminate partnerships with Beijing-backed players, referencing the threats posed by their involvement.

The committee's initial mandate was to evaluate applications for new cable systems that would connect to the United States from another country and instances in which foreign ownership exceeds 10 percent, and it has recommended denying applications with ties to political regimes deemed in misalignment with U.S. values. Since the committee's establishment just three years ago, the number of permit applications has surged and the committee's practices have become increasingly mired in bureaucracy. Team Telecom now reviews all applications even in situations without foreign ownership and takes much longer to process recommendations. At its inception, estimates cited the expectation of a 120-day process, but now multiple stages of questions and screening can occur even before that 120-day clock begins, extending decision timelines by more than nine months. The burden of compliance weighs heavily on the applicant and often at a significant financial cost. The committee justifies this more thorough inspection by stating that it further protects data transmitting over undersea cables, a concern that is seen largely as being driven by increasing friction between the United States and China.

Team Telecom specifically targets Chinese-owned submarine cables and equipment, taking steps to block or reroute Chinese cables, restrict ownership of cables by Chinese companies, and prohibit use of some Chinese equipment and services. The committee has recommended that licensees diversify cable interconnection points across Asia to avoid a direct linking to Hong Kong, among other decisions. Other Chinese telecom operators have felt compelled to withdraw their involvement from submarine cable projects, sensing an impasse produced by escalating opposition.

In March, the U.S. House of Representatives passed the Undersea Cable Control Act, which, if passed by the Senate, would prohibit the export of cable technologies and equipment to China in an attempt to prevent foreign adversaries from acquiring the means to build and operate subsea communication cables. The legislation may fragment the internet ecosystem-China recently announced plans to develop a $500 million subsea cable system that would circumvent connections with the United States to link Asia, the Middle East, and Europe. It and other similar proposals may perpetuate economic and political roadblocks between the United States and China, with implications for other countries and to the potential detriment of international connectivity.
Industry Safeguards Against Expected Hazards
Industry stakeholders have expressed concerns about cable protection and resilience, but they don't perceive the same security risk to cables as policymakers in Washington. The subsea cable industry landscape includes a broad cast of characters: state governments, industry associations, fiber manufacturers, small private cable companies, local telecom companies, and cloud service providers such as Google, Amazon, and Meta. These communities steer the planning, funding, building, and maintenance of 1.4 million kilometers of cable globally.

Leaders in the fiber-optic cable sector acknowledge the vulnerabilities of cables and want to safeguard their investments and deliver for their customers. Data consistently shows that cable disruption s have been caused almost exclusively by natural susceptibilities and accidents involving humans. Fishing- and anchoring-related activities pose the main threat, accounting for approximately 70 percent of the more than 200 cable faults each year (though this varies by geography and region).

International frameworks and standards have shaped this industry perception by focusing predominantly on guarding the physical integrity of cables. The International Cable Protection Committee (ICPC) suggests best practices in protection and security of undersea cables. The United Nations has proposed good practices for responsible state behavior and protection of critical infrastructure against attack, and the Organization for Economic Cooperation and Development (OECD) offers guidance for the governance of infrastructure. These organizations have not dedicated much public effort to concerns about cyber vulnerabilities.
Government Regulators Versus Bad Actors
Meanwhile, the U.S. national security community and intelligence researchers have outlined extensive and valid risks to subsea cables from malign actors. Their analyses point to the threat of authoritarian influence, vulnerabilities to cyberwarfare, and the need to join allies in countering potential dangers posed by Russia and China. The federal government acknowledges threats to undersea telecom cables and has developed a series of actions to protect against security vulnerabilities. But some observers still believe that the United States has fallen short in fortifying the undersea communications cable system.

Undersea cable networks are now seen as a principal component of U.S.-China technological competition, which has been reinforced by declining diplomatic relations that, for some observers, foreshadows a new cold war. The protection and resilience of the global internet could become collateral damage in this great power competition. At the same time, it appears that little to no dialogue occurs among U.S. security, regulation, and industry communities. Avoiding policies and practices that stunt or fragment global connectivity will be a challenging task in this competitive geopolitical environment, but it will be next to impossible without clear and open discussion among these stakeholders.
Bridging Security Echo Chambers
Complications stemming from a lack of engagement between U.S. government regulators and industry actors are bound to reach a breaking point. With the growing global population, the digitization that has resulted from the coronavirus pandemic, and a booming business ecosystem, the number of cables is expected to double by 2030. What's missing at this moment is a shared sense of common ground, and this lack of consensus and collaboration may jeopardize global network expansion. Overly stringent regulation and protocols can impede investment projects. Instead, engagement and collaboration must unite industry and government.

To support the advancements of a global internet, the United States must recognize its weakening centrality in international cable capacity and expand relations with submarine cable communities across the globe. There are concrete steps that U.S. regulators and policymakers can take toward this end.

Convene an interdisciplinary group of government regulators, security experts, and industry leaders. A joint task force, with regular interaction and dialogue, should be created as a forum for stakeholders across industry, security, and regulatory sectors. The Cybersecurity and Infrastructure Security Agency's Joint Cyber Defense Collaborative (JCDC), which brings together cyber practitioners worldwide, may serve as a model for building a security-minded group that encompasses subsea owners, operators, and manufacturers in order to improve U.S. cable security and operations. JCDC convenes organizations and operators from across the public and private sectors, including state, local, and international government participants. Additionally, it operates with existing information-sharing hubs and groups to ensure the rapid dissemination of actionable information across the broader community. Independent of, but connected to this cross-sector group, the Communication Security, Reliability, and Interoperability Council-a federal advisory committee -has advised the FCC to convene an interagency working group to improve oversight and coordination of cables and tech. Appointing a clear point of contact on both the industry and federal sides would also simplify communication between parties.

Collaborate on enhanced security measures, developing shared risk management and mitigation frameworks. It's necessary for stakeholders to define and agree on approaches to manage and mitigate risk to undersea cables. Collectively, U.S. regulators and industry representatives must draft a mutually agreeable set of cable risks and vulnerabilities with corresponding best practices for cable network protection and resilience. Activities could include conducting regular audits of cables with industry and security experts, offering education and security training for submarine networks, and establishing an information sharing and analysis center for subsea cable networks similar to the organization that monitors financial services networks. Stakeholders should circulate promising research and foundational best practices from the ICPC, OECD, and UN, and this guidance could be integrated by U.S. federal agencies like the National Institute of Standards and Technology for cable protection and security baselines.

Demonstrate deeper commitment to industry actions to provide security for undersea cables. Prioritizing resources, improving bureaucratic efficiency, and increasing the predictability of federal cable operations will facilitate industryprogressandinnovation while preserving security protocols. Team Telecom has acquired a reputation for being inflexible and lacking industry perspective, and the U.S. security review procedure comes across as a "black box" to many-including the Senate Homeland Security and Governmental Affairs Committee, which called the process "opaque" and some decisions "arbitrary." Reforming these review processes can improve the transparency of cable permitting and licensing. Team Telecom should hire staff with technical knowledge, as the evaluation process would benefit greatly from individuals with engineering proficiency. Greater regulatory clarity would also repair the erosion of trust, improve predictability, and decrease anxiety that is harming U.S. competitiveness. Delays in timelines for approval undercut efforts to create a rigorous yet efficient pipeline for cable permitting. Congress also has a role to play. It must better prioritize fiber-optic cable activities and should consult industry experts when drafting legislation. Congress can also delegate authority to a lead agency to coordinate cable security with industry leaders, establish a formal regulatory framework to improve cable security, increase U.S. investments in cable repair vessels and the U.S. Cable Security Fleet, and support cyber research to advance cable protections.

***

Subsea cables are a growing U.S. national security risk, but collaboration among stakeholders will help confront the challenges that keep policymakers and industry stakeholders up at night. The U.S. government aspires to balance diplomatic, economic, and national security interests with the international growth of a robust and resilient submarine cable ecosystem. They can't do it alone. Industry actors-from local governments, to private cable companies, to cloud computing giants-will be necessary partners in achieving these interrelated objectives.

# data[].attributes.attachment_link.title
The Disconnect on Undersea Cable Security

# data[].attributes.attachment_link.description
Editor's Note: The fiber-optic submarine cable sector is a vital, but often ignored, part of the world's critical infrastructure. Many members of Congress and the U.S. government, however, see the risks to subsea cables quite differently than cable owners and manufacturers.

# data[].attributes.headline
The CHIPS and Science Act won't build inclusive innovation ecosystems on its own

# data[].attributes.subject
The CHIPS and Science Act won't build inclusive innovation ecosystems on its own

# data[].attributes.body
Since the early 1980s, the U.S. has embraced a predominantly laissez faire approach to economic development. Recently, however, there has been a momentous shift to embrace place-based

industrial policy and an intentional effort to reshore “critical industries” through a multitrillion-dollar wave of legislation, including the CHIPS and Science Act (CHIPS), Inflation Reduction Act (IRA), and Infrastructure Investment and Jobs Act (IIJA). Congress justified the pivot with the geopolitical urgency to compete with China, the growing need to control risk and escalating costs in global supply chains, and the challenge of addressing rising economic inequality among left-behind people and places.

# data[].attributes.attachment_link.readable_text
Since the early 1980s, the U.S. has embraced a predominantly laissez faire approach to economic development. Recently, however, there has been a momentous shift to embrace place-based industrial policy and an intentional effort to reshore "critical industries" through a multitrillion-dollar wave of legislation, including the CHIPS and Science Act (CHIPS), Inflation Reduction Act (IRA), and Infrastructure Investment and Jobs Act (IIJA). Congress justified the pivot with the geopolitical urgency to compete with China, the growing need to control risk and escalating costs in global supply chains, and the challenge of addressing rising economic inequality among left-behind people and places.

Our declining and relatively low levels of public investment in education and innovation compared to major competitors have enabled China and other countries to rise to a dominant position in many critical industries, which has consequences for our economic growth, national security, and global influence. Among the recent bills, CHIPS represents the most direct investment in innovation ecosystems, with an explicit focus on domestic semiconductor production, electric battery manufacturing, quantum computing, and other basic research, development, and commercialization activities.

Notably, CHIPS explicitly acknowledges that diversity and inclusion in science, technology, engineering, and math (STEM) fields will be key to strengthening U.S. innovation. Acknowledging this, in itself, is a significant step, because the U.S. population is becoming more racially and ethnically diverse over time, yet STEM labor markets are riddled with inequality. STEM jobs typically pay well and diversity contributes to higher levels of innovation, but these jobs disproportionately exclude women as well as Black and Latino or Hispanic people, especially in the highest-paying occupations. Research estimates that our failure to address racial and gender inequities in the labor market (who has access to what jobs) hampers growth in aggregate market output per capita by 20% to 40%, and similar racial and gender gaps in the innovation process inhibit GDP growth per capita by 2.7%.

As Brookings Metro's Xavier de Souza Briggs recently wrote about with co-author Madeline Janis, the recent wave of federal investments has tremendous potential to increase access to high-quality jobs for underrepresented groups -but whether it will succeed in benefitting workers and communities will depend on the details of implementation. Building on this argument, we look more closely at the CHIPS and Science Act and its ambitious goals to diversify talent in STEM and U.S. innovation ecosystems more broadly.

We argue that the fragmented, piecemeal nature of CHIPS investments will not be enough to address long-standing structural inequalities in the education and training, invention, and commercialization stages of innovation, especially in the most marginalized regions of the U.S. Although CHIPS makes significant, necessary investments, successful implementation will also require intentional federal coordination, regional capacity building and collaboration, and careful planning to ensure that the preconditions for leveling the playing field across all stages of innovation are in place.

How racial and gender gaps inhibit the innovation ecosystem

Innovation requires a number of different actors, organizations, and institutions to work together, even if indirectly, to generate new ideas and technologies and, subsequently, commercialize and apply them in industry. U.S. innovation ecosystems are traditionally configured with five major sets of actors: founders, universities, government entities, venture capitalists, and corporations.

Innovations arise through investments, education, training, and research-the foundational activities that support the innovation ecosystem through the stakeholders. In turn, venture capitalists and other investors infuse capital to support innovations that can be commercialized within the ecosystem, thereby funding scalable new ventures. The hope is that these ventures will rapidly grow, employ talent, and contribute to economic growth and further innovation (the "virtuous cycle").

Yet, as researchers Lisa D. Cook, Janet Gerson, and Jennifer Kuan found, there are significant gender and racial gaps in the innovation process, which lead to innovation ecosystems that are highly skewed toward particular founders and ideas. Cook and Gerson offered a breakdown of the various gaps that take shape in each stage of innovation: education and training, invention, and commercialization.

The National Science Foundation (NSF) has historically been the lead federal agency focused on STEM inclusion efforts, such as tracking and broadening participation of women and Black and Latino or Hispanic people in STEM programs and university research. But despite decades of effort to increase the number of women and underrepresented groups in STEM careers, gender and racial gaps persist. Broader sociocultural factors appear to play a role in continued occupational segregation, such as gendered preferences for certain occupations, decisions about work-life balance, and individuals' beliefs about their capabilities and belonging in STEM fields. This suggests a need for more examination of how these opportunity gaps are reproduced in both education and training systems and the labor market, and that additional interventions (such as the CHIPS investments) and new approaches are necessary to engage the full potential of American talent.

The U.S. education and training systems have a long history of racial and income-based segregation, and access to quality education and quality job opportunities are heavily shaped by one's ZIP code and family wealth rather than raw talent and hard work. Most STEM jobs require college degrees, and while STEM degree production has been expanding rapidly, the racial and gender composition of STEM degree recipients has been much slower to change. Permanent residents and temporary visa holders from other countries make up a high share of STEM graduate students and faculty, but U.S. citizens in the field remain predominantly white and male.

The U.S. largely has a "college for all" approach to getting students into STEM careers, yet the high cost of a college education limits access. Most other industrialized countries have paid, work-based pathways into STEM jobs-options that lead to a postsecondary degree or an equivalent qualification. Without multiple such pathways to quality jobs, the innovation ecosystem in the U.S. will likely remain skewed.

Gender and racial gaps persist into the invention and commercialization stages of the innovation process. There are lower rates of patenting for women and underrepresented groups, as well as diminished opportunities to access organizational resources to foster innovation, mentorship, and leadership opportunities in organizations. This lack of exposure to innovation and commercialization opportunities results in what researchers have called "lost Einsteins," as women, Black and Latino or Hispanic people, and low-income groups continue to be underrepresented among star inventors.

In the commercialization stage, gender and racial gaps are reinforced by unequal access to business and professional networks, mentoring resources, capital, and information. White men control 93% of venture capital dollars, which contributes to a continued lack of diversity in the types of founders and ideas that receive venture capital. In 2022, female founders received 1.9% of all venture dollars, Black founders received 1%, and Latino or Hispanic founders received 1.5%. The overwhelming majority of venture capital funding went to white, male founders.

Unless the implementation of the new wave of federal investments intentionally identifies and devotes resources to rectifying these gaps across every stage of innovation (what we refer to as a "holistic" approach), success will be hard to achieve.

New federal legislation sets bold inclusion goals but offers mostly piecemeal solutions

CHIPS (alongside other legislation) has an explicit and bold focus on directing federal funding to institutions, people, and places that have historically received relatively low levels of public and private investment. The legislation allocates funding at each stage of the innovation process and explicitly states in Title III, Subtitle A that Congress seeks to increase technology transfer, engage the full potential of our talent, incorporate diverse perspectives into the innovation ecosystem, and invest in basic science and research and development. Congress was clear that it considers all of these factors to be important drivers of the nation's ability to innovate and stay competitive.

Despite these bold goals and acknowledgement of barriers to inclusion, CHIPS leaves a lot of the details of implementation vague. Allocation of funding and authority for implementation appear piecemeal, focusing on particular aspects of the innovation cycle in isolation rather than strengthening connections between them. The table below provides some examples of how CHIPS lacks specificity in how these pieces can work together cohesively.

Avoiding overly prescriptive legislation can help enable flexibility, but it can also be problematic: Implementation policies and approaches can be easily changed when a new administration comes to power, and they rely heavily on agency leadership taking on the risk of leading transformational change efforts that require coordination across multiple agencies and funding streams.

There are a few levers that federal agencies have for implementation, but they tend to be compliance or evaluative in nature rather than focused on ensuring that the preconditions for success are in place. For example, the Department of Commerce's Notice of Funding Opportunity regarding the construction of semiconductor fabrication facilities requires applicants requesting over $150 million to provide a plan to ensure the availability of child care for their facility and construction workers. However, placing the onus on applicants when major swaths of the U.S. are child care deserts may result in a situation where communities and companies that were already well-resourced have an advantage, thus contradicting the goals of inclusion.

In addition, although CHIPS focuses on workforce development as a legislative priority, it does not provide much direct funding to the Department of Labor, which administers the publicly funded workforce system. Instead, it funds other agencies with much less infrastructure and expertise in workforce development, such as the Department of Commerce and Department of Energy. And the legislation is largely silent about what "workforce training" actually means in practice, including clarifying the duration and quality of training that will be needed to address the scale of inclusion challenges in STEM talent pipelines.

CHIPS investments targeting the commercialization processes also appear fragmented and unspecified. Individual entrepreneurs and founders still have to navigate networks they may not be a part of in order to get connected to capital, mentors, bid opportunities, and resources. A few innovation stakeholders-such as universities, accelerators, and venture capitalists-can play an outsized role in connecting people, capital, and resources. But those who lack the networks to connect to such stakeholders, such as people in rural areas, will continue to face disproportionate obstacles to success.

What's needed for systemic change?

The legislative priorities set out in CHIPS are positive and signal a major policy shift in the U.S. Yet addressing structural inequities-particularly as they relate to STEM education and unlocking innovation opportunities-requires thinking about the whole innovation pathway simultaneously rather than specific elements in isolation.

The leaders across the innovation ecosystem who are tasked with implementing CHIPS (and other major federal and state investments) have a remarkable opportunity and responsibility to ensure that the law actually improves inclusion in the innovation ecosystem. If they are successful, their efforts could help spark a new era of innovation and shared prosperity.

However, their success will depend on being able to strategically coordinate across funding streams and agencies that tend to operate in silos. Successful implementation will also require a level of regional capacity and coordination that does not already exist in most regions and will likely take many years to fully realize through the legislation's various regional centers and hubs.

Leaders will need to focus their coordination efforts on aligning communication channels, incentives, compliance processes, and resources in ways that position marginalized communities and underserved communities for success. For example, providing grants for more inclusive STEM education in K-12 schools in marginalized communities is necessary, but it is not sufficient to achieve better outcomes if, for example, there are still too few options after high school that seem feasible and affordable or if workplaces are still unwelcoming to female, Black, and Latino or Hispanic workers. Additionally, coordination activities need to be prioritized and sustained across different presidential administrations, and successful inclusion will need to be strategically and intentionally addressed upfront rather than treated as a box-checking exercise.

There are several factors that will help lay a strong foundation for successful implementation in the education and training, invention, and commercialization stages of innovation, many of which the legislation does not explicitly address. Leaders should consider the following:

A national career outreach and education campaign to address widespread information gaps about STEM careers, starting a business in STEM fields, and how to prepare for these opportunities.

Reducing degree requirements in hiring, career advancement, and procurement.

Increasing the use of credit for prior learning, competency-based education, and automatic credit transfer policies.

Digital literacy broadly integrated into K-20 curricula to reflect how technologies are shaping every job and industry.

More inclusive earn-and-learn options to attain a STEM degree, designed around the needs of working learners who cannot afford student debt and who prefer experiential learning.

More robust anti-discrimination monitoring and enforcement, including adequate resources for the Equal Employment Opportunity Commission to assess and respond to bias in hiring and promotions.

Attention to the behavioral and structural dimensions of how university- and graduate-level researchers are selected and retained in academia, in addition to the resources in CHIPS (scholarships, fellowships, traineeships, and post-hoc requirements embedded in research grants).

Closer attention to the duration, quality, wraparound services, and outcomes of training to ensure they lead to better jobs.

Diversity commitments by institutional and limited partners who already direct investments into entrepreneurship ecosystems via private equity on behalf of beneficiaries (such as pension funds, endowments, and charities).

Required diversity riders in venture capital term sheets and good governance in actors that support innovation and startups.

Congress has acknowledged that investments in American talent are critical for the country's global competitiveness, growth, and innovation. And the new legislative priorities signal a willingness to address long-standing occupational segregation, discrimination, and underinvestment in rural and marginalized communities. Yet it is unlikely that these entrenched problems can be addressed without a clear, cross-cutting vision.

The scale and specificity of the solutions should match the scale and specificity of the challenges. Ultimately, we will need long-term, holistic solutions over multiple administrations to level the playing field and cultivate a more equitable innovation economy. Although CHIPS may not necessarily get us there alone, with more clarity around the implementation vision and an intentional focus on coordination, capacity, and the preconditions necessary for success, it does have potential to better position our talent, business community, and regions to thrive and compete in the digital age.

# data[].attributes.attachment_link.title
The CHIPS and Science Act won't build inclusive innovation ecosystems on its own

# data[].attributes.attachment_link.description
Since the early 1980s, the U.S. has embraced a predominantly laissez faire approach to economic development. Recently, however, there has been a momentous shift to embrace place-based industrial policy and an intentional effort to reshore "critical industries" through a multitrillion-dollar wave of legislation, including the CHIPS and Science Act (CHIPS), Inflation Reduction Act (IRA), and Infrastructure Investment and Jobs Act (IIJA).

# data[].attributes.headline
Artificial Intelligence in Government - Committee on Homeland Security & Governmental Affairs

# data[].attributes.subject
Artificial Intelligence in Government - Committee on Homeland Security & Governmental Affairs

# data[].attributes.body
FULL COMMITTEE HEARING

# data[].attributes.attachment_link.provider_name
Committee on Homeland Security & Governmental Affairs

# data[].attributes.attachment_link.title
Artificial Intelligence in Government - Committee on Homeland Security & Governmental Affairs

# data[].attributes.body
As the cyber insurance market stabilizes, the number of clients buying coverage continues to increase. Read on to find out why — and what comes next. Despite the challenges in the cyber insurance market over the past two years — which included steep pricing increases, tighter terms and conditions, and intensified scrutiny from underwriters — the percentage of clients buying coverage continued to climb in 2022. Looking back on 2022 and ahead into 2023, we see a number of trends, including:

Cyber insurance pricing increases moderated for the fifth consecutive quarter, rising 11% on average in the US in the first quarter of 2023, compared to 28% in the prior quarter.

The largest companies, those with greater than $1 billion in annual revenues, continue to be far more likely to purchase cyber insurance.

Innovative, data-backed analysis allows clients to better understand and prioritize the impact of cyber risk controls.

Over the past two years, many cyber insurers have focused on potentially catastrophic cyber risk, including fallout from geopolitical conflicts and corresponding nation state activity, changing policy exclusions, and the possible impact from single points of failure.

# data[].attributes.headline
White House Meeting with CEOs on Advancing Responsible Artificial Intelligence Innovation

# data[].attributes.subject
White House Meeting with CEOs on Advancing Responsible Artificial Intelligence Innovation

# data[].attributes.body
Vice President Harris and senior Administration officials met with CEOs of four American companies at the forefront of AI innovation to share concerns about the risks associated with AI. President Biden dropped by the meeting to underscore that companies have a fundamental responsibility to make sure their products are safe and secure before they are deployed or made public.
The President and Vice President were clear that in order to realize the benefits that might come from advances in AI, it is imperative to mitigate both the current and potential risks AI poses to individuals, society, and national security. These include risks to safety, security, human and civil rights, privacy, jobs, and democratic values.

# data[].attributes.attachment_link.readable_text
Today, Vice President Harris and senior Administration officials met with CEOs of four American companies at the forefront of AI innovation to share concerns about the risks associated with AI. President Biden dropped by the meeting to underscore that companies have a fundamental responsibility to make sure their products are safe and secure before they are deployed or made public.

The President and Vice President were clear that in order to realize the benefits that might come from advances in AI, it is imperative to mitigate both the current and potential risks AI poses to individuals, society, and national security. These include risks to safety, security, human and civil rights, privacy, jobs, and democratic values.

Given the role these CEOs and their companies play in America's AI innovation ecosystem, Administration officials also emphasized the importance of their leadership, called on them to model responsible behavior, and to take action to ensure responsible innovation and appropriate safeguards, and protect people's rights and safety. This includes taking action consistent with the Biden-Harris Administration's Blueprint for an AI Bill of Rights and the AI Risk Management Framework.

The meeting also included frank and constructive discussion on three key areas: the need for companies to be more transparent with policymakers, the public, and others about their AI systems; the importance of being able to evaluate, verify, and validate the safety, security, and efficacy of AI systems; and the need to ensure AI systems are secure from malicious actors and attacks.

Administration officials and CEOs agreed that more work is needed to develop and ensure appropriate safeguards and protections, and CEOs committed to continue engaging with the Administration to ensure the American people are able to benefit from AI innovation.

Today's meeting was part of a broader, ongoing effort to engage with advocates, companies, researchers, civil rights organizations, not-for-profit organizations, communities, international partners, and others on critical AI issues. This effort builds on the considerable steps the Administration has taken to date to promote responsible innovation and risk mitigation in AI. This includes additional actions announced this morning, the Blueprint for an AI Bill of Rights and related executive actions, the AI Risk Management Framework, and a roadmap for standing up a National AI Research Resource.

Participants

Vice President Kamala Harris

Gina Raimondo, Secretary of Commerce

Arati Prabhakar, Assistant to the President and Director of the Office of Science and Technology Policy

Jeff Zients, Assistant to the President and Chief of Staff

Bruce Reed, Assistant to the President and Deputy Chief of Staff

Mike Donilon, Assistant to the President and Senior Advisor

Jake Sullivan, Assistant to the President for National Security Affairs

Susan Rice, Assistant to the President for Domestic Policy

Lael Brainard, Assistant to the President and Director of the National Economic Council

Stuart Delery, Assistant to the President and White House Counsel

Sam Altman, CEO of OpenAI

Dario Amodei, CEO of Anthropic

Satya Nadella, Chairman and CEO of Microsoft

Sundar Pichai, CEO of Google and Alphabet

###

# data[].attributes.attachment_link.title
Readout of White House Meeting with CEOs on Advancing Responsible Artificial Intelligence Innovation | The White House

# data[].attributes.attachment_link.description
Today, Vice President Harris and senior Administration officials met with CEOs of four American companies at the forefront of AI innovation to share concerns about the risks associated with AI. President Biden dropped by the meeting to underscore that companies have a fundamental responsibility to make sure their products are safe and secure before they...

# data[].attributes.headline
The NSA Is Warning AI Startups: 'China Is Coming For You'

# data[].attributes.subject
The NSA Is Warning AI Startups: 'China Is Coming For You'

# data[].attributes.body
The National Security Agency is telling U.S. tech companies to beware Chinese attempts to steal their AI technology, while the Pentagon’s intelligence chief is warning about what China might do with the new tools.
“We think much about the ability of what AI is going to do for us in the future. One of the things that we have communicated very clearly to a number of the U.S. companies is the importance of securing the intellectual property that you have invested within this. This type of capability because this will be a target of our adversaries,” Gen. Paul Nakasone told the House Armed Services cyber and intelligence subcommittee on Thursday.
While the head of the NSA and Cyber Command didn’t mention China by name, he didn’t have to. Like his predecessors, Nakasone frequently describesChina as the top nation-state threat to U.S. intellectual property.

# data[].attributes.attachment_link.readable_text
The National Security Agency is telling U.S. tech companies to beware Chinese attempts to steal their AI technology, while the Pentagon's intelligence chief is warning about what China might do with the new tools.

"We think much about the ability of what AI is going to do for us in the future. One of the things that we have communicated very clearly to a number of the U.S. companies is the importance of securing the intellectual property that you have invested within this. This type of capability because this will be a target of our adversaries," Gen. Paul Nakasone told the House Armed Services cyber and intelligence subcommittee on Thursday.

While the head of the NSA and Cyber Command didn't mention China by name, he didn't have to. Like his predecessors, Nakasone frequently describes China as the top nation-state threat to U.S. intellectual property.

The world has been captivated by the rise of new public-facing large language model artificial intelligence programs like ChatGPT, which can provide complex, human-like answers to a wide number of prompts. These generative pretrained transformers have proven widely useful for everything from writing research papers and recipes to finding Chinese weather balloons. But many of them achieve their miraculous effects by simply pulling information off of the open internet- information that is not always correct. Additionally, as much as 11 percent of the data that employees put into ChatGPT and other new AI engines to get answers is proprietary or confidential, according to a recent analysis from software company Cyberhaven.

Intelligence agencies will have to be cautious in employing such models and be certain of the providence of the data that feeds them, Ronald Moultrie, defense undersecretary for intelligence, told lawmakers. But the intelligence community also has to be more attuned to how adversaries might use similar AI engines to corrupt open-source data or create disinformation.

"We know our adversaries are considering or already thinking about large data models if you will, and how they may use that against us, Moultrie said. "Our...being able to identify when something is actually an AI-generated outcome is something that's gonna be very important to us, too."

The Chinese government is spending heavily to become the world leader in AI by 2030.

At Thursday's hearing, Rep. Jeff Jackson, D-N.C., asked whether China had more to gain from the recent wave of AI developments than the United States.

"Seems like now that the genie is out of the bottle, it's less of an advantage to us than it will be to our adversaries, who are so far behind us that it lets them catch up to us more quickly," Jackson said. "It's an incremental gain for us, but it may be an exponential game for our adversaries."

Answered Moultrie: "​​I think we can talk more about that in closed session."

# data[].attributes.attachment_link.title
The NSA Is Warning AI Startups: 'China Is Coming For You'

# data[].attributes.attachment_link.description
The National Security Agency is telling U.S. tech companies to beware Chinese attempts to steal their AI technology, while the Pentagon's intelligence chief is warning about what China might do with the new tools. "We think much about the ability of what AI is going to do for us in the future.

# data[].attributes.headline
Who Gets the Algorithm? The Bigger TikTok Danger

# data[].attributes.subject
Who Gets the Algorithm? The Bigger TikTok Danger

# data[].attributes.body
Controversy surrounding TikTok, the popular Chinese company-owned social media platform, has continued to give rise to impasse in recent weeks. Just days after the Biden administration issued a divestiture-or-ban ultimatum to the company and Beijing firmly opposed a forced sale, TikTok CEO Shou Zi Chew testified in Congress to try to save the app’s U.S. operations.
The main concern the U.S. government has with TikTok is the potential access the Chinese government may have to U.S. user data—including journalists’ whereabouts—by having oversight of the app. Chew, trying to bypass the standoff, touted TikTok’s Project Texas, an ongoing effort to establish a data security entity, overseen by Oracle and governed by independent directors, to administer future access to Americans’ data and content moderation. But few lawmakers seemed convinced.

# data[].attributes.attachment_link.readable_text
Controversy surrounding TikTok, the popular Chinese company-owned social media platform, has continued to give rise to impasse in recent weeks. Just days after the Biden administration issued a to the company and , TikTok CEO Shou Zi Chew to try to save the app's U.S. operations.

The main concern the U.S. government has with TikTok is the potential access the Chinese government may have to U.S. user data-including -by having oversight of the app. Chew, trying to bypass the standoff, touted TikTok's , an ongoing effort to establish a data security entity, overseen by Oracle and governed by independent directors, to administer future access to Americans' data and content moderation. But few lawmakers seemed convinced.

The only way out of this impasse is for TikTok to cease to be Chinese owned, which would more effectively cut off the CCP's access to user data, or cease to operate in the U.S., which would prevent Beijing from exploiting the app's knowledge and intelligence on the American people.

Why TikTok's Algorithm Is a National Security-Not Just Personal Privacy-Threat

Not even seven years old, TikTok is one of the most popular social media platforms in the world. The secret sauce for its meteoric success is the app's recommendation engine-a machine algorithm able to repeatedly show customized videos that keep the user hooked. It's like a personal chef who intimately understands you and keeps making what you want the most at every meal, including food that you don't even know you like. To appreciate how hard it is to create such a successful recommendation engine, just think about the number of uninteresting songs or movies your streaming services pick for you.

As with every successful big-data algorithm, TikTok's recommendation engine is so addictive because it collects and learns from an aggressive amount of user information. According to a by Internet 2.0, a cybersecurity company, TikTok has twice as many trackers inside its app as other social media platforms like Facebook and Instagram do, and it tops the other 22 apps analyzed in the amount of privacy risks it exposes users to. (TikTok is followed by VKontakte, the Russian social media app, on this metric.) These trackers, technically known as software development kits (SDKs), are snippets of source code developed in-house or by third parties that facilitate the functioning of the app. While all apps use SDKs and they can be legitimate tools, some of these trackers are more intrusive than others in peeping into private information about the user or the device that has downloaded the app, including, in TikTok's case, monitoring user behavior like the existence of those features on its app, although it claimed that it used the information only for debugging.

The TikTok app presents a demand-forecasting dilemma. Different from other social media apps like Facebook and Twitter, which display a scrollable feed of content from the user's social network, TikTok presents one-and only one-video at a time from somewhere on the platform that the app thinks the user might like. The user needs to decide whether and for how long to watch that video before the next one-and the next decision point-appears. Many dating apps take a similar approach; a user swipes left or right before seeing another potential match. A user in this type of situation is essentially making a purchasing decision every few seconds by "buying" some content in exchange for their time-the time that could have been spent seeing other content or doing something else.

When TikTok observes enough of these "purchases"-and there are a lot of such because the videos tend to be short-the algorithm quickly acquires a lot of actionable information to back out the user's preferences, just like a mathematical formula in Afriat's world. The algorithm might figure out whether a user is a strict Catholic or a secular Jew, likes fancy cars or shiny shoes, or how motivated they are by one social issue versus another. This is what in economics is called revealed preferences-people's desires and curiosities exposed by their actions-but on a big-data steroid.

That's why tackling data privacy and content manipulation on the app alone would be missing the point. Even if the CCP was barred from accessing user data or manipulating content on the platform going forward, access to an already well-trained algorithm would still be useful in other venues of Beijing's influence operations, such as mainstream news and other social media platforms.

Knowing that someone prefers a pill wrapped in cheese to one coated in sugar can help persuade them to swallow it. Similarly, given TikTok's success, it's not at all unreasonable to believe that propaganda operatives in Beijing could use the insights behind the algorithm to design messaging strategies that more effectively appeal to U.S. politicians and the American public-even in other news and social media venues. At a minimum, being able to feed users what they like allows the app to at times slip in what they may not agree with, steering their views away from, for example, supporting Ukraine or strengthening ties with Taiwan, or influencing how they vote in elections.

Stopping China's Big Brother

To the extent that TikTok's algorithm has gained intelligence on the American people, and given the indications that the CCP has a strong grip on Chinese companies like its parent ByteDance, the app has already likely done some damage to U.S. national security. The company's engineering team that designed the algorithm is with ByteDance in Beijing, and at least one TikTok executive is to be reporting directly to ByteDance, bypassing TikTok's CEO.

More importantly, ByteDance has already offered up its secret sauce to the CCP. Last August, China's cyber regulators made a that scores of Chinese tech giants, including ByteDance, had shared with the Chinese government details about their supposedly secret algorithms-prized machine learning models that are already trained and have so far stood the test of market competition-to comply with that governs recommendation engines. Feeding a government user data feeds it for a day. Feeding a government the ability to understand users feeds it for a lifetime. China's Big Brother understands that all too well.

That presents a different problem. It's one thing to stop the CCP from targeting particular users or censoring certain content on the platform; TikTok that Project Texas's plan to let Oracle host its user data and algorithm in the United States would achieve that. But it's another to cut Beijing off from the intimate knowledge about the American people the algorithm has already acquired. ByteDance and presumably the Chinese authorities already have the algorithm. The CCP would be shut out of the algorithm only if it permanently deletes its copy of the secret sauce-a hard-pressed "if" -or when the American people's preferences evolve so much in the future that Beijing's knowledge becomes out of date.

Even if either scenario is true, Beijing could still recover much of TikTok's secret algorithm if it's able to interact with it. For example, software tools like this often include application programming interfaces (APIs) that take queries from outside the system and return what the algorithm thinks are the answers, even though the algorithm may stay in the backend during the interaction. One way to hack the system is to exploit such an API.

To get a sense of this type of capability, suppose TikTok has an API that takes any proposed video for any targeted user as the query and gives a personalized rating for the video as the output. Because TikTok's algorithm seeks to custom-recommend videos users like the most, it could quite accurately foresee the user's reactions to the proposed video. An API like this could also compare two proposed videos by how popular they are among the entire user base. If, hypothetically, strict Catholics who happen to like fancy cars are also interested in the Trump indictment lawsuits, a querier could also find that out by asking the API questions.

Think of this process as a personal chef being willing to answer someone's hypothetical, but telling, questions about their client's palate. Suppose one asks the personal chef what their client will eat on a Friday, and the chef says "salmon." If one asks about a few more Fridays and keeps getting fish as Easter approaches, one could infer that the client is probably Catholic. So even if the CCP or ByteDance no longer has the TikTok algorithm under Project Texas, they could still easily retrieve at least the contour of it using an API.

Here's why divestiture can make a meaningful difference. TikTok's defenders might argue that if Oracle has control over the algorithm and its APIs, it can prevent Chinese authorities from reverse-engineering the technology. Sure, but only in wishful thinking. Project Texas doesn't change the fact that TikTok remains the owner of the app, that ByteDance remains the owner of TikTok, or that the CCP still commands and controls ByteDance. Oracle would be able to more credibly commit to stopping Beijing's reach if it, instead of ByteDance, owned TikTok. But it doesn't.

The Department of Defense and its contractors surely keep a tight lid on their prized artificial intelligence applications-from warfare systems to cyber threat monitoring-and know better, one would assume, than to set up an API that would allow Beijing's prying eyes to "steal" the algorithms. And that's because ownership matters. Those contractors abide by U.S. laws, and the Department of Defense answers to the U.S. president-not the CCP's general secretary. TikTok is no defense contractor. To have such a strong incentive to firewall its algorithm from Beijing, it can't have a Chinese parent company.

TikTok's harm to U.S. national security was inflicted when its algorithm learned to "read" users' minds. But the silver lining is that people change, and whatever knowledge the CCP has gained through TikTok could eventually become obsolete if the CCP's access to the app is cut off.

# data[].attributes.attachment_link.title
Who Gets the Algorithm? The Bigger TikTok Danger

# data[].attributes.attachment_link.description
Controversy surrounding TikTok, the popular Chinese company-owned social media platform, has continued to give rise to impasse in recent weeks. Just days after the Biden administration issued a to the company and , TikTok CEO Shou Zi Chew to try to save the app's U.S. operations. The main concern the U.S.

# data[].attributes.body
The Chinese Communist Party’s (CCP’s) embrace of large-scale online influence operations and spreading of disinformation on Western social-media platforms has escalated since the first major attribution from Silicon Valley companies in 2019. While Chinese public diplomacy may have shifted to a softer tone in 2023 after many years of wolf-warrior online rhetoric, the Chinese Government continues to conduct global covert cyber-enabled influence operations. Those operations are now more frequent, increasingly sophisticated and increasingly effective in supporting the CCP’s strategic goals. They focus on disrupting the domestic, foreign, security and defence policies of foreign countries, and most of all they target democracies.

# data[].attributes.attachment_link.authors[].name
Albert Zhang, Tilla Hoja, Jasmine Latimore

# data[].attributes.headline
How politics and business are driving the AI arms race with China

# data[].attributes.subject
How politics and business are driving the AI arms race with China

# data[].attributes.body
In March, thousands of tech leaders—Elon Musk among them—signed an open letter asking artificial intelligence (AI) labs to stop developing next-generation training systems for at least six months. There is a precedent for such temporary pauses in other fields of research: In 2019, for example, scientists successfully called for a moratorium on any human gene editing that would pass along heritable DNA to genetically modified children.
While a pause in the field of AI is unlikely to happen, at least it means the United States is finally starting to realize the importance of regulating AI systems.

# data[].attributes.attachment_link.readable_text
In March, thousands of tech leaders-Elon Musk among them-signed an open letter asking artificial intelligence (AI) labs to stop developing next-generation training systems for at least six months. There is a precedent for such temporary pauses in other fields of research: In 2019, for example, scientists successfully called for a moratorium on any human gene editing that would pass along heritable DNA to genetically modified children.

While a pause in the field of AI is unlikely to happen, at least it means the United States is finally starting to realize the importance of regulating AI systems.

The reasons that a pause in AI won't happen are multifold-and are about more than just the research itself. Critics of the proposed pause argue that regulating or restricting AI would help China pull ahead in AI development, causing the United States to lose its military and economic edge. To be sure, the United States must keep its citizens secure. But failing to regulate AI or to coordinate with China in cases where that is in the United States' interest would endanger US citizens.

History shows us that this worry is more than just theoretical. As a presidential candidate, John F. Kennedy invented the " missile gap " narrative to make President Dwight D. Eisenhower seem weak on defense, claiming that the Soviet Union was overtaking the United States in nuclear missile deployment. Kennedy's rhetoric may have helped him politically but also hindered cooperation with the Soviet leadership. Historically, arms races are often driven more by domestic economics and politics than by rational responses to external threats.

China, which is actually regulating AI much more tightly than the United States or even the European Union and is likely to be hamstrung by US semiconductor export controls in the coming years, is far behind the United States in AI development. Much like the Cold War nuclear arms race, today's US-China AI competition is heavily influenced by domestic forces such as private interest groups, bureaucratic infighting, electoral politics, and public opinion. By better understanding these domestic forces, policy makers in the United States can minimize the risks faced by the United States, China, and the world.

Private interests. In the US-China AI competition, companies developing AI systems and promoting their own interests might lobby against domestic or international AI regulation. There is historical precedent for this. In 2001, the United States rejected a Protocol to strengthen the Biological Weapons Convention, in part because of pressure from the US chemical and pharmaceutical industries, which wanted to limit inspections of their facilities.

US AI companies appear to be aware of the risks of using their products. OpenAI's stated mission is "to ensure that artificial general intelligence benefits all of humanity." DeepMind's operating principles include a commitment to "act as responsible pioneers in the field of AI." DeepMind's founders have pledged not to work on lethal AI, and Google's AI Principles state that Google will not deploy or design AI for weapons intended to injure humans, or for surveillance that violates international norms.

However, there are already worrisome signs that commercial competition may undermine these commitments. Google, fearing that OpenAI's ChatGPT could replace its search engine, told employees it would "recalibrate" the amount of risk it is prepared to accept when deploying new AI systems. While not strictly relevant to international agreements, this move suggests that tech companies are willing to compromise on AI safety in response to commercial incentives.

Another potentially concerning development is the creation of links between AI startups and big tech companies. OpenAI partnered with Microsoft in January, and Google acquired DeepMind in 2014. Acquisition and partnership may limit the ability of AI startups to act in ways that lower risk. DeepMind and Google, for example, have clashed over the governance of DeepMind projects since their merger.

Lobbying may also raise risks. The big tech companies are experienced lobbyists: Amazon spent $21.4 million on lobbying in 2022, making it the 6th largest spender; Meta (the parent company of Facebook, Instagram, and WhatsApp) came in 10th with $19.2 million; and Alphabet (parent company of Google) was 19th with $13.2 million. Last year, big tech companies increased their donations to US foreign policy think tanks in an effort to promote the argument that stricter rules will harm their ability to compete with China.

In the future, suppliers of military AI systems might increase the chances of an AI arms race by lobbying for the development of more advanced weapons systems, or by opposing arms control agreements that would limit their future sales. This is probably a long way off. Analysis from the Brookings Institution-a nonprofit public policy organization-found that 95 percent of federal contracts from the last five years with "artificial intelligence" in the description were for professional, scientific, and technical services (essentially external funding for research and development). The same analysis found that there were 307 different vendors and 474 total contracts.

Taken together, this analysis suggests an immature market, with many smaller vendors focused on developing AI systems rather than on larger contracts for supplying hardware or software, which are more typical for military procurement. In the future, though, larger contracts for military AI and a more concentrated supplier base would probably mean increased lobbying by military AI suppliers-and increased chances of a military AI arms race.

Bureaucratic politics. There were many instances of bureaucratic politics exacerbating the Cold War nuclear arms race. As Slate columnist and author of several books on military strategy Fred Kaplan has described, the Air Force and the Navy repeatedly came up with new nuclear strategies and doctrines that would give them more of the nuclear weapons budget. For example, the Navy's think tank came up with "finite deterrence," which suggested that the United States could deter the Soviet Union by deploying a relatively small number of nuclear missiles on submarines, obviating the need for large numbers of nuclear bombers and missiles (which were operated by the Air Force).

Bureaucratic incentives often cause organizations to attempt to accumulate more resources and influence than is optimal from the perspective of the state. Although most cutting-edge AI development is currently carried out in the private sector, that could change. History suggests that as a technology's strategic importance and cost grow, the inclination and capacity for the state to exert control over its development and deployment will also grow.

There is another reason for concern about AI developed in or for the public sector-particularly the defense sector, despite the current private-sector dominance. As former US Navy Secretary Richard Danzig has written, military development and use of technology tends to be particularly risky, for several reasons: secrecy makes oversight and regulation more difficult; the unpredictability of warfare environments; and the adversarial, unconstrained nature of military operations. The military already accounts for a significant proportion of US government spending on AI.

Regardless of how the military uses AI, it is likely there will be resistance to any AI arms control initiatives. An arms control agreement almost always interferes with the interests of one or more groups within the defense establishment. Military support is particularly important for ratification, which is why President Kennedy had to abandon his push for a comprehensive test ban in the face of resistance from the Joint Chiefs of Staff.

Electoral politics and public opinion. The relationship between foreign policy and electoral politics is not straightforward. An influential paper published in 2005 found that US foreign policy "is most heavily and consistently influenced by internationally oriented business leaders, followed by experts," with some small influence for organized labor groups, and very weak or no influence from public opinion. (It should be noted that not all researchers agree with this finding, however: Many case studies and experiments have found that public opinion does influence decision makers in certain circumstances.)

Studies suggest public opinion is more important for high-salience issues-that is, issues that are seen as particularly noticeable or important. Public opinion does not come into play as much for issues that (rightly or wrongly) feel less relevant. For example, voters generally do not care much about trade policy: They do not know their political representatives' trade policy positions, so trade policy does not affect their voting behavior. According to the " Secret Congress " theory, which contends that it is much easier to pass legislation on topics that are more under the radar and consequently not politically salient, if AI policy issues were politically salient and the parties were divided on them, it would be much more difficult to pass regulation and treaties that would reduce risks from AI.

At the moment, AI is too esoteric to be politically salient, although this is starting to change. The electoral politics of AI policy are overshadowed by broader concerns about strategic competition with China. In the United States, elite opinion, business opinion, and public opinion have shifted toward the view that engagement with China has failed and a more confrontational approach is now required. Current US policy toward China-including accelerating US AI development and restricting Chinese AI progress-commands bipartisan support.

However, if one party becomes more hawkish on China-related policy issues, public opinion on AI might split accordingly, with supporters of the more hawkish party viewing cooperation on AI policy less favorably. This may have happened in the past with nuclear weapons. There is some evidence to suggest that Obama's 2009 Prague speech, in which he announced "America's commitment to seek the peace and security of a world without nuclear weapons," led to disarmament being associated with Obama personally. This polarized the issue of arms control and disarmament along partisan lines, making future policy making more difficult.

If AI policy issues do become politically salient, the history and political science literature suggest that electoral politics might impede arms control in a number of ways. For example, if arms control policy gets caught up in partisan politics, it becomes much harder to develop and implement, particularly given that treaty ratification requires a two-thirds majority in the Senate.

In the past, political groups have held dovish positions on some nuclear issues while holding hawkish positions on others. For example, the Nunn-Lugar Cooperative Threat Reduction program, which worked with the states of the former Soviet Union to dismantle and secure the legacies of the Cold War, had strong bipartisan support, even as arms control agreements faced resistance from many Republicans. Certain nuclear issues are idiosyncratic. For example, Iran issues are politicized in a different way than other nuclear issues, because of the link to Israel's security: Many otherwise liberal Democrats who are Jewish or represent heavily Jewish districts are hawkish on Iran. AI may turn out to be similar, with political cooperation on some aspects of AI policy and partisan gridlock on others.

Finally, it is worth noting that the large number of potential uses for AI means that AI will touch people's lives frequently and in significant ways. However, it is unlikely that these applications will cohere into a consistent pro- or anti-AI perspective. Public opinion on AI foreign policy will probably resemble other technology-related foreign policy issues-with the two major parties split according to their levels of hawkishness.

The United States has a tricky balance to strike. On the one hand, promoting AI development could create economic and social benefits, and the government has a duty to keep US citizens safe by maintaining technological superiority. On the other hand, if AI is not sufficiently well-regulated, and the United States and China can't cooperate where necessary, the whole world could be at risk.

Striking this balance is like walking a tightrope. Domestic forces threaten to knock the United States off balance.

# data[].attributes.attachment_link.title
How politics and business are driving the AI arms race with China

# data[].attributes.attachment_link.description
In March, thousands of tech leaders-Elon Musk among them-signed an open letter asking artificial intelligence (AI) labs to stop developing next-generation training systems for at least six months. There is a precedent for such temporary pauses in other fields of research: In 2019, for example, scientists successfully called for a moratorium on any human gene editing that would pass along heritable DNA to genetically modified children.

# data[].attributes.headline
Tracking hate speech targeting Ukrainians during Russia’s war in Ukraine

# data[].attributes.subject
Tracking hate speech targeting Ukrainians during Russia’s war in Ukraine

# data[].attributes.body
This research examines the use of hate speech and national slurs to dehumanise, call for violence against, and celebrate the death of Ukrainians. The data analysed indicates widespread hate speech targeting Ukrainians primarily on Twitter and Telegram. Further examination of these accounts indicates the content was further spread to YouTube and Facebook.

The research was accomplished by analysing data collected from Twitter and Telegram based on national slurs referencing Ukrainians in the context of Russia’s full-scale invasion of Ukraine. The dataset collected and analysed in the research resulted in 1585 accounts with more than 2500 interactions from Twitter, and 15655 results from more than 480 channels on Telegram.

This analysis shows that in comparison to prior years, February 2022 saw a surge in the use ofnational slurs and hate speech against Ukrainians on both Twitter and Telegram.

# data[].attributes.headline
L'outil de cyber-espionnage russe "le plus sophistiqué" a été neutralisé

# data[].attributes.subject
L'outil de cyber-espionnage russe "le plus sophistiqué" a été neutralisé

# data[].attributes.body
Les Etats-Unis ont annoncé mardi avoir neutralisé l’un des logiciels espions russes «les plus sophistiqués», à l’origine de nombreuses attaques contre des pays membres de l’Otan au cours des 20 dernières années. Ce logiciel, baptisé «Snake», a permis aux services de sécurité russes (FSB) de «voler des centaines de documents sensibles dans au moins 50 pays», en attaquant notamment les services informatiques de gouvernements, de médias ou de centres de recherches, selon un communiqué du ministère américain de la Justice.

# data[].attributes.attachment_link.readable_text
Le logiciel "Snake", neutralisé par Washington, a permis aux services de sécurité russes (FSB) de "voler des centaines de documents sensibles dans au moins 50 pays" pendant 20 ans.

Les Etats-Unis ont annoncé mardi avoir neutralisé l'un des logiciels espions russes "les plus sophistiqués", à l'origine de nombreuses attaques contre des pays membres de l'Otan au cours des 20 dernières années.

Ce logiciel, baptisé "Snake", a permis aux services de sécurité russes (FSB) de "voler des centaines de documents sensibles dans au moins 50 pays", en attaquant notamment les services informatiques de gouvernements, de médias ou de centres de recherches, selon un communiqué du ministère américain de la Justice.
Opération de haute technologie
"Grâce à une opération de haute technologie, qui a retourné ce logiciel malveillant russe contre lui-même, les forces de l'ordre américaines ont neutralisé l'un des outils de cyber-espionnage russe les plus sophistiqués", s'est félicitée la ministre adjointe de la Justice Lisa Monaco.

D'après les autorités américaines, le logiciel était guidé depuis une unité du FSB baptisée "Turla", située à Riazan, en Russie. Il pouvait identifier et voler des documents et rester non détecté de manière indéfinie. Sa spécificité: les agents de "Turla" exfiltraient ces données en utilisant le réseau mondial des ordinateurs infectés. En 2018, le ministère allemand des Affaires étrangères avait révélé avoir fait l'objet d'une attaque sans précédent attribuée par les médias au logiciel "Snake", aussi connu sous le nom de "Uroboros". Des victimes ont aussi été identifiées en Belgique, en Ukraine, aux Etats-Unis, en Suisse ou en Géorgie.
Persée et Méduse
"Snake" était connu des experts en cybersécurité depuis au moins dix ans. L'agence américaine de cyberdéfense Cisa situe sa date de création autour de 2003 et estime qu'il a fait l'objet de nombreuses mises à jour au cours du temps.

C'est "l'outil de cyber-espionnage le plus sophistiqué dans l'arsenal du FSB", estime aussi Cisa dans un document publié avec ses homologues britanniques, canadiens et australiens, en insistant sur le caractère furtif du logiciel. "Il a de manière étonnante très peu de bugs informatiques, ce qui est surprenant compte tenu de sa complexité", notent également ces agences.

Après avoir étudié pendant de longues années ce logiciel, la police fédérale américaine a réussi à créer un outil, baptisé "Persée", capable de communiquer avec "Snake" et de lui ordonner de s'éteindre sans mettre en cause l'ordinateur hôte. Il a été mis hors d'état de nuire lors de l'opération "Méduse" menée par le FBI, en coordination avec des partenaires étrangers, a précisé le ministre américain de la Justice Merrick Garland. "Nous allons continuer à renforcer nos défenses collectives contre les efforts de déstabilisation du régime russe", a-t-il promis.

# data[].attributes.attachment_link.title
L'outil de cyber-espionnage russe "le plus sophistiqué" a été neutralisé

# data[].attributes.attachment_link.description
Le logiciel "Snake", neutralisé par Washington, a permis aux services de sécurité russes (FSB) de "voler des centaines de documents sensibles dans au moins 50 pays" pendant 20 ans. Les Etats-Unis ont annoncé mardi avoir neutralisé l'un des logiciels espions russes "les plus sophistiqués", à l'origine de nombreuses attaques contre des pays membres de l'Otan au cours des 20 dernières années.

# data[].attributes.headline
Cyber Operations in Russia’s War against Ukraine

# data[].attributes.subject
Cyber Operations in Russia’s War against Ukraine

# data[].attributes.body
One year after Russia’s invasion of Ukraine, certain assumptions about the utility of cyber operations during wartime can now be put to the test. Russian cyber salvos opened this war, but they failed to achieve their objectives in the face of a resilient cyber defender. Joint cyber/conventional warfighting is still hard to implement due to its uncertain effects, the potential for spill-over, malware development cycles, and differing operational tempos. Cyber operations against Ukraine have not (yet) achieved major strategic effects in reducing Ukraine’s capacity to resist. Additionally, Russian information operations targeting Ukrainian and Western audiences fell on deaf ears. The greatest value of cyber operations therefore still appears to lie in their intelligence and reconnaissance functions.

# data[].attributes.attachment_link.title
Cyber Operations in Russia’s War against Ukraine

# data[].attributes.attachment_link.description
Uses, limitations, and lessons learned so far

# data[].attributes.headline
Evolving Cyber Operations and Capabilities

# data[].attributes.subject
Evolving Cyber Operations and Capabilities

# data[].attributes.body
Most networks can be breached, and most software has exploitable flaws. This can give unparalleled advantage to attackers, but the situation in Ukraine suggests that an energetic and thorough defense can prove more than adequate in matching this advantage. The Ukraine experience can guide decisions on cyber defense, and it suggests that adequate cyber defense will require different approaches, involve new actors, and be complex for nations to construct and coordinate. As part of the UK National Cyber Security Centre’s efforts to shape debate and discussion around cybersecurity issues, this collection of essays examines the war in Ukraine, with a view to the wider debate around the role and value of cyber capabilities.

# data[].attributes.attachment_link.title
Evolving Cyber Operations and Capabilities

# data[].attributes.attachment_link.description
The war in Ukraine shows that cyber defense requires a different approach with new actors and strategies that will be complex for nations to construct. This report examines cybersecurity in Ukraine, with a look to the larger debate around cyber capabilities and defense.

# data[].attributes.headline
Heikle Phase für das E-Patienten­dossier

# data[].attributes.subject
Heikle Phase für das E-Patienten­dossier

# data[].attributes.body
Bei der Einführung des elektronischen Patientendossiers (EPD) harzt es seit Jahren. In seiner aktuellen Form sei das EPD "gescheitert", hält beispielsweise der Regierungsrat des Kantons Thurgau fest. In einer Stellungnahme spricht er von "Konstruktionsfehlern" und wünscht sich, dass ein revidiertes EPD mit allen Mitteln rasch und entschlossen vorangetrieben wird. Es solle ein Produkt entstehen, dass in der Bevölkerung akzeptiert und positiv wahrgenommen wird. Frühestens 2027 tritt die umfassende Revision des Bundesgesetzes über das elektronische Patientendossier (EPDG) 2027 in Kraft. Eine Teilrevision soll den Zeitraum bis dahin überbrücken.

# data[].attributes.attachment_link.title
Heikle Phase für das E-Patienten­dossier

# data[].attributes.attachment_link.description
Das EPD kann in seiner aktuellen Form beinahe als gescheitert bezeichnet werden. Eine Revision soll für Schub sorgen, den Kantonen geht es aber zu langsam.

# data[].attributes.headline
Die Schweiz muss ihre KI-Hausaufgaben machen

# data[].attributes.subject
Die Schweiz muss ihre KI-Hausaufgaben machen

# data[].attributes.body
Nicht nur im Bundeshaus, auch auf Kantonsebene häufen sich parlamentarische Vorstösse zu Künstlicher Intelligenz. "Ich bin froh darum, dass aus den verschiedenen politischen Ecken Vorstösse kommen. Das zeigt, dass von links bis rechts die Dringlichkeit nach Klarheit im Umgang mit KI erkannt wird", erklärt Nationalrat Gerhard Andrey (Grüne/FR) gegenüber inside-it.ch. Entsprechend erhoffe er sich politische Mehrheiten, "um dereinst smarte Rahmenbedingungen verabschieden zu können".

# data[].attributes.attachment_link.title
"Die Schweiz muss ihre KI-Hausaufgaben machen"

# data[].attributes.attachment_link.description
Im Nationalrat wurde eine neue Interpellation eingereicht, die vom Bundesrat Antworten zu KI-Rechtssicherheit und Gesetzgebung fordert. Gerhard Andrey erklärt den Vorstoss.

# data[].attributes.body
Der Cyber Security Threat Radar von Swisscom deckt das Vorgehen der Cyberkriminellen auf und zeigt, in welche Angriffsmethoden sie ihre Energie stecken. Deutlich an Fahrt aufgenommen haben KI-basierte Angriffe. Aber es gibt auch noch andere Herausforderungen.

# data[].attributes.attachment_link.description
Der Cyber Security Threat Radar von Swisscom deckt das Vorgehen der Cyberkriminellen auf und zeigt, in welche Angriffsmethoden sie ihre Energie stecken. Deutlich an Fahrt aufgenommen haben KI-basierte Angriffe. Aber es gibt auch noch andere Herausforderungen.

# data[].attributes.headline
Bericht des Bundesrates zur Beurteilung der Bedrohungslage 

# data[].attributes.subject
Bericht des Bundesrates zur Beurteilung der Bedrohungslage 

# data[].attributes.body
Der Bundesrat hat an seiner Sitzung vom 10. Mai 2023 den jährlichen Bericht zur Beurteilung der Bedrohungslage gemäss Art. 70 NDG verabschiedet. Der Bericht wurde vom Nachrichtendienst des Bundes (NDB) erstellt und bezieht sich auf die im NDG genannten Bedrohungen sowie auf sicherheitspolitisch bedeutsame Vorgänge im Ausland.

# data[].attributes.body
Der neue Halbjahresbericht des Nationalen Zentrums für Cybersicherheit (NCSC) befasst sich mit den wichtigsten Cybervorfällen der zweiten Jahreshälfte 2022 in der Schweiz und international. Im Fokus werden die wichtigsten Punkte rund um die Cybersicherheit bei KMU beleuchtet.

Die Digitalisierung schreitet auch in kleinen und mittleren Unternehmen voran. Zahlreiche Computer sind über Netzwerkschnittstellen miteinander verbunden. Prozesse wie Bestellungsabwicklung, Planung, Produktion und Logistik greifen immer mehr ineinander und werden digital gesteuert. Dadurch steigt die Anzahl Systeme, die aus dem Internet zugänglich sind und somit bestmöglich geschützt werden sollten. Doch oftmals wird gerade bei KMU der Cybersicherheit noch zu wenig Beachtung geschenkt. Aus diesem Grund wird im aktuellen Halbjahresbericht der Fokus auf die Cybersicherheit bei KMU gelegt und die wichtigsten Punkte zum Schutz vor Cyberbedrohungen aufgezeigt. Zudem geben ein Unternehmen und eine Polizeibehörde Einblick in den Ablauf konkreter Cybervorfälle.