# data[].attributes.body
AI helps give people mental health treatment

# data[].attributes.body
Lumen: real time lighting
Nanite: automating polygon rendering depending on distance
World Partition
Animation Tools
Sculpting
Asset Library
Metahumans

# data[].attributes.attachment_link.description
UE5 will empower you to realize next-generation real-time 3D content and experiences with greater freedom, fidelity, and flexibility than ever before.

# data[].attributes.headline
AI turns text descriptions into images by getting destructive 

# data[].attributes.subject
AI turns text descriptions into images by getting destructive 

# data[].attributes.attachment_link.readable_text
Section:

News

Technology

EARLY last year, artificial intelligence company OpenAI unveiled software with the surprising ability to create accurate images from text captions - even obscure inventions such as "an armchair in the shape of an avocado". The company has now released a new, sleeker version that can produce even better results.

Last year's program - called DALL-E - was a large AI model that had been trained on a huge set of images with associated captions. In recent years, most progress in AI has come from this sort of approach; training with ever more data on ever larger computers.

However, this makes the AIs expensive, unwieldy and hungry for resources. Hossein Malekmohamadi at De Montfort University in Leicester, UK, says this approach has been akin to "burning cash for research" in recent years.

The new GLIDE model is much less resource-hungry, partly because it uses an alternative approach that has come of age in the past year or so, called a diffusion model. The network is still trained using images, but it handles them differently. It gradually and deliberately destroys them by adding noise.

A pristine image has a layer of noise added that degrades it slightly, and then more noise is added and so on, until the image is pure chaos. The AI, known as a neural network, watches this process and consequently learns how to reverse it. It can then begin with an input that is nothing but noise and efficiently work towards a photorealistic image - effectively un-destroying a new image into existence.

When applied to a situation in which an AI creates images from text descriptions, this approach is far more efficient in terms of computer power than the approach used in DALL-E.

What's more, the results are of higher quality. In a test of the software's performance, human judges preferred GLIDE's images over those from DALL-E 87 per cent of the time in terms of photorealism, and 69 per cent of the time based on how well they matched the text input (arxiv.org/abs/2112.10741).

"GLIDE is a new AI that effectively un-destroys a new image into existence"

Although each GLIDE image still takes 15 seconds to create on an A100 graphics processing unit (GPU) that costs upwards of £10,000, the work represents an important step forward, says Malekmohamadi. "I'm glad to see that this kind of research direction is leading toward a smaller model that could be trained on less powerful GPUs," he says.

The method of destroying data to train the AI may seem counter-intuitive. "You take an image that's pristine and clear and you take it all the way down to the point where it's completely unrecognisable; [the AI] is in fact learning the opposite, which is taking something that's completely unrecognisable and 'restoring' it back to pristine condition," says Mark Riedl at the Georgia Institute of Technology in Atlanta. He believes that AI and diffusion models such as GLIDE will have a big impact on photo editing. "Photoshop will become neural," he says.

The OpenAI researchers, who weren't available for interview, say in their paper that GLIDE can find it hard to produce realistic images for complex prompts. To try to solve this, they added the ability to edit the initial images. Users can ask GLIDE to create "a cosy living room" and then select a region of the resultant picture and ask for more details, such as "a painting of a corgi on the wall". Riedl believes that this sort of process will one day be seen in commercial software.

PHOTO (COLOR): Pictures created by the GLIDE software from descriptive text

~~~~~~~~

By Matthew Sparkes

Copyright of New Scientist is the property of New Scientist Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.

# data[].attributes.attachment_link.title
AI turns text descriptions into images by getting destructive

# data[].attributes.attachment_link.description
Section: News Technology EARLY last year, artificial intelligence company OpenAI unveiled software with the surprising ability to create accurate images from text captions - even obscure inventions such as "an armchair in the shape of an avocado". The company has now released a new, sleeker version that can produce even better results.

# data[].attributes.body
Laws and legislation regarding the regulations of AI

# data[].attributes.attachment_link.title
EBSCO Publishing Service Selection Page - Ehost2

# data[].attributes.body
how AI is helping in making fusion power

# data[].attributes.attachment_link.readable_text
Section:

News

Energy

Tech firm DeepMind has created a neural network that keeps the super-hot plasma inside a fusion reactor under control, reports Matthew Sparkes

FUSION reactors promise cheap, abundant and relatively clean energy - if we can get them to work. Now, thanks to artificial intelligence firm DeepMind, fusion researchers are one step closer to extracting power from plasma that is hotter than the sun.

DeepMind worked with scientists at the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, to create a neural network capable of controlling the magnetic fields within EPFL's Variable Configuration Tokamak (TCV) fusion reactor.

These magnetic fields are essential for keeping the plasma generated by the reactor safely contained. If the plasma touches the walls of the reactor, it rapidly cools, stifling the reaction and potentially causing significant damage to the vessel.

Researchers at the TCV previously used 19 magnetic coils, each controlled by a separate algorithm that monitored the interior of the reactor thousands of times a second with a host of sensors. DeepMind instead created a single neural network to control all the coils at once, automatically learning which voltages needed to be supplied to them to best contain the plasma.

The team trained the AI on a precise digital simulation of the reactor before conducting experiments on the real machine. Ultimately, it was able to successfully contain the plasma for around 2 seconds, which is approaching the limits of the reactor - TCV can only sustain the plasma in a single experiment for up to 3 seconds, after which it needs 15 minutes to cool down. The record for fusion reactors is only 5 seconds, set recently by the Joint European Torus in the UK.

As well as controlling the plasma, the AI was able to shape it and move it around within the reactor. New plasma shapes may bring efficiency or stability improvements in new fusion reactors such as ITER, which is currently being built in France. It will be the world's largest tokamak, a doughnut-shaped reactor, when complete in 2025. The AI was also able to control two separate beams of plasma at once (Nature, doi.org/gpg9dn).

Federico Felici at EPFL says that although there are many theoretical approaches that could be used to contain the plasma with a magnetic coil, scientists have tried-and-tested strategies. But the AI surprised the team with its novel approach to forming those same plasma shapes with the coils.

"This AI algorithm, [through] reinforcement learning, chose to use the TCV coils in a completely different way, which still more or less generates the same magnetic field," says Felici. "So it was still creating the same plasma as we had expected, but it just used the magnetic coils in a completely different way because it had complete freedom to explore the whole operational space. So people were looking at these experimental results about how the coil currents evolve and they were pretty surprised."

Gianluca Sarri at Queen's University Belfast, UK, says that AI is key to the future of control systems for fusion devices, which are currently aiming to sustain a reaction that produces more power than is consumed.

"Once this is done, this is not the end of the story. Then you have to make it a power plant," he says. "And this AI is, in my opinion, the only way forward. There are so many variables, and a small change in one of them can cause a big change in the final output. If you try to do it manually, it's a very lengthy process."

To make fusion reactors efficient, practical power sources, physicists need to increase the ratio between the pressure of the plasma and the power of the magnetic fields containing it, a value that is known as beta, says Howard Wilson at the University of York, UK.

"The plasma writhes and wiggles and tries to escape the clutches of the magnetic fields"

"The plasma writhes and wiggles and tries to escape the clutches of the magnetic fields, and as one is pushing up that beta parameter, one is having to work harder and harder to get the control that one needs to just hold the plasma there," he says. "The further you push the plasma, the more chance you just suddenly lose it."

Wilson believes these AI experiments show promise for containing plasma in "extreme geometries", which could lead to improvements in stability or efficiency. "It makes the risky parameter space less risky to operate in, but also opens up new parameter space that we can go into and explore," he says.

PHOTO (COLOR): The TCV fusion reactor and, inset, plasma inside the device

~~~~~~~~

By Matthew Sparkes

Copyright of New Scientist is the property of New Scientist Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.

# data[].attributes.attachment_link.description
Section: News Energy Tech firm DeepMind has created a neural network that keeps the super-hot plasma inside a fusion reactor under control, reports Matthew Sparkes FUSION reactors promise cheap, abundant and relatively clean energy - if we can get them to work.

# data[].attributes.headline
Inside The Deepfake Technology That`s Making Fans Do A Double-Take 

# data[].attributes.subject
Inside The Deepfake Technology That`s Making Fans Do A Double-Take 

# data[].attributes.attachment_link.readable_text
SAVANNAH GUTHRIE (08:32:49): Now, here`s the thing Carson was telling us about yesterday, and we`re still talking about it. Those freakishly realistic deepfake videos on Tom Cruise on TikTok. Everybody spent the whole weekend thinking Tom Cruise is in the TikTok now. CARSON DALY (08:32:59): Yeah.

SAVANNAH GUTHRIE (08:33:00): He`s like fourteen.

CARSON DALY (08:33:01): I know. It`s not him though. NBC`s Tom Costello joins us with a closer look at two things, how the technology works and why videos using it have raised some concerns. So, Tom, IF it really is you, Good morning.

TOM COSTELLO (08:33:12): Yeah. Well, I get-- you know, people mistake me for Tom Cruise all the time. But you`re absolutely-- what are you laughing at? You`re absolutely right. You know, this kind of technology is used all the time in the movies and in film when an actor dies, right? And they`ve got to finish out the filming. You go on Snapchat these days, you go on Instagram, and you can manipulate your face. But this artificial intelligence is now changing and evolving so rapidly creating very realistic deepfakes.

(Begin VT) (08:33:39): (Excerpt from TikTok video)

TOM COSTELLO (08:33:41): But it`s not the real thing. Videos recently making the rounds on TikTok have users dumbfounded that the man in these clips isn`t actually Tom Cruise. It`s just the latest example of so-called deepfake technology, which uses software to morph someone`s face into someone else`s. A YouTube video last month had Spider-Man fans doing a double-take.

(08:34:05): (Excerpt from YouTube video)

TOM COSTELLO (08:34:06): Superimposing Tom Holland, who currently plays the web slinger, on the face of Tobey Maguire, from when he donned the famous blue and red jumpsuit nearly two decades ago. And last December, the UK`s Channel 4 took some heat over their annual satirical alternative Christmas message from the Queen using deepfake technology.

(08:34:26): (Excerpt from YouTube video)

TOM COSTELLO (08:34:31): Not all deepfakes are created equal, and one giveaway may be the voice. Cyber experts like Theresa Payton say the face is critical.

THERESA PAYTON (08:34:40): Deepfake technology is a very hard time doing sort of the typical crinkle of the eyes when somebody smiles You have the crinkle of the eyes when someone is smiling and talking.

TOM COSTELLO (08:34:48): Other areas to watch, ears and earrings, long hair, and whether the mouth and audio appear in sync. Characteristics that stood out to me two years ago when Payton used a technology to put someone else`s face on one of my live reports. And later, merged two videos of me to change my words.

(08:35:08): And it is, as you know, a single engine plane.

(08:35:11): The technology has improved by leaps and bounds, even in the last two years since we talked. Still, she says, how we watch these videos can affect how believable they really are.

THERESA PAYTON (08:35:20): Oftentimes, deepfake technology has a hard time staying in high fidelity on larger screens.

TOM COSTELLO (08:35:28): Recently, the ancestry site, MyHeritage, started offering what they`re calling deep nostalgia, a version of the technology. Getting mixed reaction over this image of famed abolitionist Frederick Douglass. While the technology is often used for humor and social media, not everyone has good intentions. Manipulated videos of Nancy Pelosi, Barack Obama and Donald Trump have all made the rounds. During the pandemic, with so many of us relying on video conferencing, deepfakes have the potential to create confusion and disinformation.

CLINT WATTS (08:36:00): Troublesome is where there`s not a strong media environment, to push back against the deepfakes. TOM COSTELLO (08:36:06): Clint Watts testified before Congress on the issue of deepfakes back in 2019 where he warned fake videos could be used by adversaries to create mistrust.

CLINT WATTS (08:36:16): The good news is there`s only a few people that can make deepfakes that are that sophisticated. The bad news, people fall for much less sophisticated ones.

(End VT)

TOM COSTELLO (08:36:25): Yeah. So in the case of the Tom Cruise video, Clint Watts is saying that, in fact, it was a Belgian special effects artist who had access to incredibly sophisticated technology. He is the one who made that Tom Cruise video. Listen, when you`re looking at videos and you`re thinking, is it real? Does it make sense? Context is key. Does it make sense for them to say this or that? And if it doesn`t, then you should start questioning it. Guys, back to you.

CARSON DALY (08:36:49): So, Tom, can anybody be susceptible to this? And is there any way to push back?

TOM COSTELLO (08:36:53): So the more videos there are of you online or out there, period, the more susceptible you are. So all of you guys there in the studio are going to be more susceptible, seriously, than a typical person on the street, right? One technology maybe to watermark videos so that there`s a digital watermark on it. But right now, the technology to detect deepfakes, it`s really not very good yet.

SAVANNAH GUTHRIE (08:37:16): Wow.

CARSON DALY (08:37:17): Tom, thank you for that, I think.

AL ROKER (08:37:19): Yeah.

CARSON DALY (08:37:21): I think Craig said it yesterday, the technology is only going to get better.

CRAIG MELVIN (08:37:23): Yeah.

SAVANNAH GUTHRIE (08:37:23): Yeah.

CARSON DALY (08:37:23): Someone`s caught on video doing something illegal, they can just claim in court. That`s a deepfake.

SAVANNAH GUTHRIE (08:37:26): But I think what Tom say, it`s like in the end, it`s a real low-tech solution. Common sense.

CRAIG MELVIN (08:37:31): Yes.

SAVANNAH GUTHRIE (08:37:31): You watch something and you`re like, did they- - I can`t believe they would say that, they probably didn`t.

AL ROKER (08:37:35): Or do that.

CRAIG MELVIN (08:37:35): Remember that time, it was a couple years ago, there was this ad going around and you were like pushing some product.

SAVANNAH GUTHRIE (08:37:39): Oh, yeah, yeah, yeah.

SHEINELLE JONES (08:37:40): Oh, that`s right.

SAVANNAH GUTHRIE (08:37:41): Yeah. That was a-- it wasn`t deepfake. But, yeah, they were like, Savannah`s leaving for a skincare line.

CRAIG MELVIN (08:37:45): Right.

SAVANNAH GUTHRIE (08:37:46): Hilarious.

CRAIG MELVIN (08:37:46): I was like, wow, she does have great skin. I didn`t know she was leaving the show.

SAVANNAH GUTHRIE (08:37:50): Yeah.

CRAIG MELVIN (08:37:50): When we come back, your weekend entertainment guide. As more movie theaters open up, some big hits head to streaming services, options literally for the entire family. Jeremy`s standing by. He`ll join us. But first, this is TODAY on NBC.

(ANNOUNCEMENTS)

END

Content and programming copyright 2021 NBCUniversal. ALL RIGHTS RESERVED. Transcription Copyright 2021 ASC Services II Media, LLC. All materials herein are protected by United States copyright law and may not be reproduced, distributed, transmitted, displayed, published or broadcast without the prior written permission of ASC Services II Media, LLC. You may not alter or remove any trademark, copyright or other notice from copies of the content.

# data[].attributes.attachment_link.title
Inside The Deepfake Technology That`s Making Fans Do A Double-Take

# data[].attributes.attachment_link.description
SAVANNAH GUTHRIE (08:32:49): Now, here`s the thing Carson was telling us about yesterday, and we`re still talking about it. Those freakishly realistic deepfake videos on Tom Cruise on TikTok. Everybody spent the whole weekend thinking Tom Cruise is in the TikTok now. CARSON DALY (08:32:59): Yeah. SAVANNAH GUTHRIE (08:33:00): He`s like fourteen.

# data[].attributes.body
Protests the idea that AI may never be able to fully replace people in the creative process. (The Turing test)

# data[].attributes.attachment_link.readable_text
Deciding whether artificial intelligence can one day make great art demands a deep dive into human consciousness, says Marcus du Sautoy

IN OCTOBER 2018, a portrait of Edmond Belamy sold at Christie's in New York for $432,500, nearly 45 times its maximum estimated price. Nothing that out of the ordinary, perhaps. Except Belamy didn't exist. He was the fictitious product of the artist's mind - and the mind that created him wasn't even human.

Signed in the corner by a formula that is part of the algorithm that created it, the portrait was the first artwork made by artificial intelligence brought to auction. There have been many similar seeming breakthroughs in AI creativity. In 2017, an AI wrote a continuation of the Harry Potter books by using machine learning to analyse the first seven volumes of J. K. Rowling's output. The music for US singer Taryn Southern's 2018 album I AM AI was bigged up as having been composed and produced entirely by machines. Back in 2016, SACEM, a French professional association in charge of artists' rights, was the first to acknowledge an algorithm, the Artificial Intelligence Virtual Artist or AIVA, as a composer.

It fits into a common theme that anything we can do, AI can do - and probably better. But it is worth looking under the hood of all these creative outputs to understand how much the machines really are doing, and how much is just hype. Answering the question of whether AI can be creative isn't easy - and raises fundamental questions about the nature and origins of human creativity.

Ever since the 1840s, when Ada Lovelace became obsessed with the possibility that Charles Babbage's Analytical Engine, a proposed mechanical computer, could do more than simple computations, we have been contemplating the idea that it isn't just biological life that may be creative. Recognising that music is an art form similar to mathematics in its manipulation of pattern, Lovelace speculated "the engine might compose elaborate and scientific pieces of music of any degree of complexity or extent".

It is fairly easy to discount or at least qualify many claims of AI creativity today. Just as, at the turn of the millennium, companies wishing to make it in the tech boom would indiscriminately tag.com on the end of their names, today businesses and individuals are using the labels "AI" or "Deep" to jump on a bandwagon. Much of what they are doing involves little more than data science and statistical number-crunching, and requires a lot of human intervention. The Harry Potter "writer", for example, relied on a statistical analysis of J. K. Rowling's existing oeuvre to suggest possibilities for the next words, but a human still chose which words to use. A new tale created by humans with the aid of some computational data science just isn't as good a story, however. Similarly, Southern got more press traction for her album by bigging up the novelty of a contribution from AI.

That isn't to say that there aren't some striking examples of AI potentially demonstrating true creativity. Take move 37 of the second game in the titanic battle of Go between the human champion Lee Sedol and the DeepMind algorithm AlphaGo in March 2016. Lee had already lost the first game, but many commentators felt this was because he had tried to play unconventionally to disrupt AlphaGo's dependence on learning from previous games. But in the second game, it was AlphaGo that tore up the rule book.

Having made the 36th move, Lee had retired for a quick cigarette break. Not requiring the same stimulation, AlphaGo thought a while and then asked its human representative to place a black stone on the line five steps in from the edge of the board. Conventional wisdom says that during the early part of a Go game you play stones only on the outer four lines, and so prepare the ground for an assault on the central part of the board later.

Lee flinched when he returned and took in the move. But as the game played out, rather than being a mistake, that stone turned out to be the key to establishing control of the board, ensuring AlphaGo its second victory. Human competitors have since aped AlphaGo's tactic to establish a competitive advantage. The AI's discovery taught the world a new way to play an ancient game.

For me, this clears some of the key hurdles AI must leap if it is to be deemed truly creative. A basic definition of a creative act might be one that is new, surprising and has value. A computer can easily be programmed to produce novel outputs, but those second two criteria are more challenging. Who is being surprised, and how does one decide value?

The value of AI might come not so much in making machines that act like humans, but stopping humans acting like machines

Move 37 certainly surprised the Go experts, and ultimately it had value: it won the game. It is easier to determine value in a game situation than in other creative spheres, however. An AI's worth is generally judged by its ability to solve problems, but creating art isn't a problem-solving activity. The monetary value of the Edmond Belamy portrait came about partly because it was created by an AI, not through some independent assessment of its artistic value. It is rather like Marcel Duchamp's notorious artwork Fountain, submitted signed R. Mutt to a US art display in 1917. Consisting of a urinal lying on its back, it was valuable less for what it was, than for the questions it raised about what we mean by art.

The AlphaGo story demonstrates another way AI can help to create a sort of value, one that does apply in other areas of creative endeavour. It comes not so much in making machines that act like creative humans, but in stopping creative humans behaving like machines. We can get terribly stuck in our ways of thinking: as a Go player, your master would have slapped your wrist if you placed an early stone on the fifth line. An AI's unprejudiced exploration of the terrain, meanwhile, can sometimes reveal new pinnacles of achievement. You may be at the top of Snowdon, thinking you have reached the ultimate height, but that is because you don't know Mount Everest exists.

Many of the examples of music created by AI are still stuck in the foothills, comprising poor pastiches of Mozart or Beethoven's works. But there are examples where code has helped us cross the valley to more interesting peaks. The Continuator, a jazz improvisor designed by François Pachet, director of the Spotify Creator Technology Research Lab, provides another example of how AI can help us escape the straitjacket of creative conventions. He trained his algorithm on the music played by jazz musicians. One of the standard modes of jazz is a call and response in which one player riffs and a second reacts. By analysing how one phrase of music mutated into another, the Continuator came up with its own responses to the riffs that make up a jazz musician's sound world.

When musicians improvised with the Continuator, they were amazed. The algorithm was passing a kind of musical Turing test, responding in a way indistinguishable from a human improvisor. And its responses weren't simply a mash-up of what had gone before: the musicians could hear the Continuator playing things that were recognisably connected with their style of performance, but which they never thought possible. It was taking the music to a new and unexpected level. It was as if the humans were playing in a huge hall but only in one small, illuminated corner - then the AI had thrown the lights on and shown them the whole space available to them. Pachet is currently working on Brazyle, an AI that aims to do something similar for Brazilian music.

Although novelty, surprise and value are three key components to measure if AI is being creative, I think a fourth element must also be introduced if we are going to herald real creativity in AI: originality of a truly independent nature. Lovelace herself raised it all those years ago as she wrote about Babbage's machine. "It is desirable to guard against the possibility of exaggerated ideas that might arise as to the powers of the Analytical Engine," she wrote. "The Analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform." Ultimately, she believed, you couldn't get more out of the machine than you had put in.

This raises a crucial question: how much of any AI's "creativity" is the creativity of the human coder rather than the code? Cameras heralded a new age of human creativity that can be seen displayed today in art museums across the world. But no one assigns the camera any part in the act of creativity.

But that is an imperfect analogy. Humans are machines running the instructions of a code, our DNA. Our parents are responsible for that code, yet we don't regard ourselves as a mere vessel for the creativity of our parents. Part of how a child differentiates itself from its parents comes from its unique interaction with its environment. This interaction also shapes our creative abilities.

And that is exactly what is happening with AI. Machine learning allows code to change, mutate and update itself based on its interaction with new data: inputs from the environment. In creative terms, the potential result is shown by a work from the artist Ian Cheng displayed at the Serpentine Gallery in London in March 2018. He started off with six artificial life forms, all called BOB and all written with the same code. But the parameters of each BOB's code mutated depending on interactions with gallery visitors. At the end of the show, after months of different interactions, the six BOBs were very different beasts.

How much are creative machines really doing - and how much is just hype?

This shift from top-down to bottom-up coding gives code the chance to assert an independence from its architect. You could argue that Cheng was still the ultimate creative author, because he was the one who gave the code the possibility to evolve. But as the decisions made by code based on its interactions with the environment become harder and harder for its programmer or others to rationalise and explain, this standpoint becomes more questionable.

This captures a quality of creativity that perhaps has got lost in modern definitions of the term that stress novelty and the creation of value. These have their origins in self-help books written by the advertising executive Alex Osborn in the 1940s that aimed to realise creativity in individuals and so help them make money. Before this commercial attitude took over, creative activity was more about capturing our attempts to understand being in the world.

Machine learning taps into this earlier take on creativity, in that its output is an original expression of a machine's interactions with an emerging digital world. But something fundamental is still missing: intentionality. What is driving the AI to blurt out a creative product? A human. Someone presses the print button, a person often chooses which algorithmic outputs to put in front of another human being. In that sense, AI doesn't display the same intentionality in creativity as humans do. Can it ever?

To answer that question, we must first ask what drives our own urge for artistic creation. And for me, that is bound up with the hard problem of consciousness: the difficulty of explaining the true nature of felt experience in ourselves and other sentient beings. Because it is impossible to get inside each other's heads to experience what another person's pain or ecstasy feels like, we create works of art as a kind of functional MRI scan to reveal our conscious world and share it with others. A novel or a musical composition or a painting is our best way to help gain access to another person's mind.

I can't prove it, but I wonder whether true creativity and consciousness emerged at the same time in the human species. Perhaps only when we had consciousness did we start to wonder what was going on in the minds of others and want to share our own internal worlds - and begin to express ourselves creatively. If so, I think that true creativity in machines will only happen when they have a conscious world they want to convey to us.

I suspect that moment will be reached, but probably only in the distant future. When it does arrive, however, machine consciousness is likely to be very different from our own. And it will be AIs' acts of artistic creativity that will be the best vehicle for accessing the strange world of what it is to be a conscious machine.

The creativity code

Hear Marcus du Sautoy talk about AI at New Scientist Live newscientistlive.com

Watch a video interview with Marcus du Sautoy on AI creativity at newscientist.com/marcusinterview

~~~~~~~~

By Marcus du Sautoy

Reported by Author

Copyright of New Scientist is the property of New Scientist Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.

# data[].attributes.attachment_link.description
Deciding whether artificial intelligence can one day make great art demands a deep dive into human consciousness, says Marcus du Sautoy IN OCTOBER 2018, a portrait of Edmond Belamy sold at Christie's in New York for $432,500, nearly 45 times its maximum estimated price. Nothing that out of the ordinary, perhaps.

# data[].attributes.headline
A Moral Framework Will Protect Humans from the Dangers of Artificial Intelligence

# data[].attributes.subject
A Moral Framework Will Protect Humans from the Dangers of Artificial Intelligence

# data[].attributes.attachment_link.readable_text
Document controls

Main content

The robot's eyes open, as if it's waking up for the first time. It looks around, confusion and wonder in its lifelike eyes. It brings a hand up to its face, giving a glance that says it's never seen itself before. The robot's name is Ameca, and the world met it in a video released late last month. When Elon Musk saw a similar robot made by the same company a few days later, he declared: "Real androids are coming." A longtime critic of artificial intelligence, Musk speaks for the countless people who fear a future defined by this beyond-cutting-edge technology.

A Potential Threat

That fear has been around for more than 50 years, when Stanley Kubrick depicted AI as a mortal threat to mankind in 2001: A Space Odyssey. (Similar anxieties can be seen earlier, in Mary Shelley's Frankenstein.) It has gained ground as AI has developed, and machines are now increasingly demonstrating the ability to create and execute complex tasks without human interaction or direction-the definition of artificial intelligence. A rapidly growing number of people now earnestly wonder: What will AI do to society?

It's an evocative, important question. But the answer will depend on the answer to a more important question altogether: Who is building AI? Artificial intelligence will do great evil or great good depending on the beliefs of those who make it. And right now, it is primarily being built by tech leaders with little to no understanding of or respect for the morality, virtues, and wisdom of the Judeo-Christian tradition. The greatest threat with AI isn't all-powerful robots. It's amoral people.

The threat of artificial intelligence is increasingly unmistakable. It could be used to exploit people economically, subverting our God-given call to work. It could be applied in ways that censor free speech, stifling the voices that God has granted us. It could replace human sensibility and compassion with robotic expediency, suffocating the rational soul that reflects God's image. There are already AI-driven robots capable of reproducing; will they and their spawn inherit the earth?

These possibilities have led many to predict that artificial intelligence will end life as we know it. Elon Musk has said that adopting AI is like "summoning the devil." Stephen Hawking declared that it may "replace humans altogether." And many other scientific and social leaders have spoken in the same vein, saying that AI is among "the new tools of oppression" and likening it to "children playing with a bomb." Hardly a day goes by without another apocalyptic prediction.

Serving Humanity

But this vision of artificial intelligence is far too pessimistic. As with all technology, artificial intelligence is the creation of people-thinking beings who can order AI according to certain principles, if we so choose. We can design it to respect and augment reason, empower and improve the productivity of workers, transform how we protect and uplift the most vulnerable among us, and generally serve humanity instead of making servants of us. AI has incredible potential to free us from much drudgery, help cure disease, and create a healthier, wealthier, and happier world.

Ensuring that artificial intelligence serves people is not simple. It requires tech leaders with properly formed consciences-men and women grounded in virtue, who understand what humanity is and what human beings need. Put another way, good AI depends on morally good AI creators. If we hope to maintain it as a tool and ourselves as its master, not the other way around, those who build this technology must understand right, wrong, and the foundational principles on which human flourishing depends.

Yet the opposite is happening right now. The tech elite possess extraordinary technical education, yet they are extraordinarily uneducated in history, philosophy, literature, and theology. As I saw building tech businesses in both Silicon Valley and Seattle, tech leaders weren't trained in the liberal arts. They weren't taught to think critically about tough ethical questions. And they weren't even instructed in how properly to accept the concept of truth (with a capital T), instead preferring a vague spirituality. They fancy themselves freethinkers and yet, cut off from the philosophical foundations of freedom, their thought is profoundly constrained and uniform. Their consciences are generally not properly formed.

Elitism and Immorality

The result is what C. S. Lewis described in the Abolition of Man: "We castrate and bid the geldings be fruitful." Freed from any concept of Judeo-Christian morality, today's tech leaders envision separating mind from body in AI-driven computers (https://www.firstthings.com/web-exclusives/2018/03/your-mind-uploaded-in-a-computer-would-not-be-you). They are also developing a quasi-religious hope in the so-called "metaverse," where AI determines what we see, hear, learn, and love. Where will Silicon Valley stop? Nowhere, because nothing eternal or ethical is holding the tech elite back from doing, designing, and destroying whatever they please, using the unsurpassed power of artificial intelligence. The zeitgeist is their conscience, and if it tells them to do something, they will comply.

If we reach a future where artificial intelligence overruns and rules us, the blame will lay with the human intelligence that thought such a future was not just possible but worth pursuing. AI is already rushing down that dangerous road, driven by people who see no error in their ways or ethical barriers blocking their path. Their elitism and amorality make it easier to envision a day when AI does the terrible things we only talk about now.

Such a powerful tool cannot be left in such hands, and to society's credit, calls are growing to rein in AI before it's too late. To date, most commentators have emphasized the need for principles to guide AI's development, but that presupposes the existence of people who are capable of creating and implementing such a framework. Those of us who are grounded in Judeo-Christian principles must design AI accordingly.

A Time-Tested Moral Code

That means innovation, entrepreneurship, and, ultimately, building AI that reflects and is restrained by a time-tested moral code. Isaac Asimov's "Three Laws of Robotics" are a useful starting point, but they aim only to prevent artificial intelligence from physically harming human beings. There is a bigger need for a broader definition of harm, which includes the emotional, economic, and ethical damage that AI could do if not properly guided. Creating and implementing that definition must be the work of morally trained people.

Our goal should be nothing less than an AI "Magna Carta." Such a framework would firmly establish the primacy of humanity vis-à-vis artificial intelligence and establish enumerated rights that AI and its developers cannot violate. The list must include speech, privacy, and, broadly speaking, life, liberty, and the pursuit of happiness. Moreover, reflecting the demands of basic morality, such a Magna Carta should restrict AI from injuring the family, enabling immoral activity like lying, pushing people toward dependency, promoting pornography and human trafficking, and becoming a weapon of war.

Yet for any of this to work, principled, ethical individuals with well-formed consciences must be in the driver's seat of AI's development, which they currently are not. Nor is it hopeful to see the Biden administration developing an "AI Bill of Rights," since the White House is run by ideologically minded people who are nearly as radicalized as Silicon Valley. Either moral people will harness artificial intelligence's tremendous potential for good or amoral people will continue to develop this technology for increasingly evil ends. Those who are blessed with a strong moral foundation must accept the burden to shape AI for the better. And we must do it soon.

Source Citation

# data[].attributes.attachment_link.title
A Moral Framework Will Protect Humans from the Dangers of Artificial Intelligence

# data[].attributes.attachment_link.description
The robot's eyes open, as if it's waking up for the first time. It looks around, confusion and wonder in its lifelike eyes. It brings a hand up to its face, giving a glance that says it's never seen itself before. The robot's name is Ameca, and the world met it in a video released late last month.

# data[].attributes.body
displays what it is used for and how it is 100% free

# data[].attributes.attachment_link.description
Search the world's information, including webpages, images, videos and more. Google has many special features to help you find exactly what you're looking for.

# data[].attributes.headline
New AUDIO DEEPFAKES Give You any Celebrity Voice

# data[].attributes.subject
New AUDIO DEEPFAKES Give You any Celebrity Voice

# data[].attributes.body
The video explores how accessible audio deepfakes are and how people can be easily manipulated into believing it's authentic, proving how far technology has come

# data[].attributes.attachment_link.title
New AUDIO DEEPFAKES Give You any Celebrity Voice

# data[].attributes.body
intelligent algorithms that mimic the learning and problem-solving functions of the human mind. Almost every game has some sort of AI

# data[].attributes.attachment_link.readable_text
Section:

News

ELCOME to the fifth part of the Future London STEM Project - our series for young people that considers the impact of science, technology, engineering and maths on our world, and explains how you can get involved. Every fortnight we share a STEM challenge for you to try at home, and meet inspiring people who tell us about their STEM jobs and explain how they developed their skills through school, university and beyond.

Today we're working with the Game AI Research Group at Queen Mary University of London and taking a look at artificial intelligence and the key role it plays in the creation of video games. We're also meeting Lucrezia Del Zompo who uses artificial intelligence in her job and in her studies.

Mike Cook, 33, a research fellow working in Queen Mary's Game AI Research Group, has helped us with our latest

STEM Challenge, in which we show you how to never lose at noughts and crosses - and how to simulate how

AI can do the same thing - using just a piece of paper with a set of instructions.

In our third STEM challenge, a magic card trick, we learned how computer algorithms work: how by following a series of logical steps, they guarantee a desired outcome to solving a problem.

AI is often used to describe machines or computers that mimic the learning and problem-solving functions of the human mind. Devices with AI are known as "intelligent agents" - capable of assessing their environment and taking actions to achieve specific goals. They do this by using algorithms that not only follow the same series of steps every time to solve a problem, but are capable of learning by a process of trial and error.

Mike says: "Imagine that you were trying to bake a cake but parts of the recipe were missing. We can bake a cake every day and change the recipe a little each time and see how it tasted. Over time, we'd get closer to a cake that tasted good.

"Those are the kinds of processes where we like to apply AI. Ones where our algorithm doesn't just do the same thing, but has a way of improving its solution each time it tries and fails."

If you've played a video game, then you will have played against the game's AI in single-player mode. The AI will be programmed to behave differently, depending on the game. "In a game like The Sims, we want our AI to act more like humans," explains Mike. "We want it to be emotional, make mistakes, to create dramatic stories. "Whereas in a game like Dark Souls [for ages 16+], which is about being an adventurer, we want the AI to be difficult but we don't want it to always beat us. We want it to be predictable so we can learn its movements and work out how to defeat it."

Almost every game we play has some kind of AI, says Mike. Sometimes you notice the AI because it's an enemy, for instance. But not all AI controls characters in games. Some of it is making the game design adapt to things we do.

Mike explains what that means in an online multiplayer game like Fortnite: "Here these intelligent algorithms try to guess where you're going to move or shoot next, so if there's a problem with your internet connection, it still feels to you like the correct thing happened."

AI is also used in video games for "procedural generation", which is designing the content, the levels and worlds players inhabit. "One of the big examples of this is Minecraft. Minecraft worlds are created by procedural generation and it's the AI doing the game design," Mike says.

Jason Deans ? Catch up with The STEM Project and all our challenges at standard.co.uk/futurelondon/stem

Copyright of Evening Standard is the property of Evening Standard Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.

# data[].attributes.attachment_link.title
Make your next move: video games, algorithms and the A-to-Z of AI

# data[].attributes.attachment_link.description
Section: News ELCOME to the fifth part of the Future London STEM Project - our series for young people that considers the impact of science, technology, engineering and maths on our world, and explains how you can get involved.

# data[].attributes.headline
How a 16 y/o made "the best horror short film in years"

# data[].attributes.subject
How a 16 y/o made "the best horror short film in years"

# data[].attributes.body
a 16 year old VFX artist, Kane Parsons, used the free program "blender" to create a viral video/trend

# data[].attributes.attachment_link.readable_text
Imagine being 16 years old and directing the best horror short film in years.

Everyone is talking about this new horror short! Have you seen it?

It's called The Backrooms. It was uploaded to YouTube on Jan. 7 and is a nine-minute film directed by Kane Parsons, who also goes by Kane Pixels. This kid is a talented VFX artist too and used his skills as a filmmaker to explore liminal spaces and cosmic horror, a world based on popular internet horror stories.

So what is this film about? The short follows a camera operator who falls over while shooting a film. He slips through a crack in reality and wakes up in an abandoned building on another plane. There's a horrible dial-up sound, a creepy message telling him to stay still, and long corridors that cast dark shadows. He's stuck in a maze of hallways with no way out. This is creepier than many recent big-budget horror films, and it's such a simple concept.

Watch the full film by Kane Pixels here, and let's talk after.

How Was The Backrooms Made?

This has to be one of the most original horror pieces I've seen in years, and knowing it was done by a 16-year-old blew my mind. Kane obviously has a boatload of talent, and is a name to watch.

The film is a mix of live-action video clips and CGI, which we believe is done in Blender and Adobe After Effects, Kane's tools of choice on his other videos.

As fellow YouTuber Night Mind pointed out in their reaction video, the amount of render power needed to create a film this detailed would be massive. And Kane's ability to create that realistic handheld camera shake/VHS look in his animations is incredible.

What Are the Backrooms?

The heart of the film is the exploration of liminal spaces. There is actually a community on Reddit dedicated to exploring them. That's where I learned that liminal space is a physical representation of "the time between the 'what was' and the 'next.' It is a place of transition, waiting, and not knowing."

The Backrooms started as a "creepypasta" a few years ago when 4chan users created a story for a creepy photo of a yellow-tinged room. Since then, the idea of this strange, endless space has haunted horror fans.

In Kane's short, there's a new monster that reminded me of The Babadook or Slender Man -something you don't really need mythos for, it just is terrifying as hell to see. I swear it felt like my heart was beating out of my chest for the hour after I watched.

What Kane has done with his short film, which has actually expanded into a series of shorts, has added a layer of worldbuilding as backstory for this internet legend. In his version of the Backrooms, the liminal space is explained as a government experiment gone awry. The fictional " Async Research Facility" was trying to create an infinite, liminal location to solve the problem of limited storage and living spaces in the 1980s, but something went horribly wrong.

This worldbuilding creates almost an interactive element as viewers are taken through strange clips and images that reveal bits and pieces of this imagined alternate history. The creation of the Backrooms, for instance, "caused" the real San Fransisco earthquake of 1989-an idea suggested in one of Kane's unlisted videos titled "collateral.mov."

It's clear that Kane is not only a talented director, but knows how to hook an audience with engaging storytelling, too.

This online series is being updated now, so you still have a chance to catch up and learn more alongside other viewers.

Did you watch? Was it the best horror short you've seen in years?

Are you Kane Parsons? We love you, hit us up to be on the podcast! Reach out!

Let us know your thoughts in the comments.

# data[].attributes.attachment_link.title
'The Backrooms' Is the Viral Horror Short Shaking the Internet Up

# data[].attributes.attachment_link.description
Imagine being 16 years old and directing the best horror short film in years. Everyone is talking about this new horror short! Have you seen it? It's called The Backrooms. It was uploaded to YouTube on Jan. 7 and is a nine-minute film directed by Kane Parsons, who also goes by Kane Pixels.

# data[].attributes.headline
Billie Eilish proved anyone can access Grammy-winning gear

# data[].attributes.subject
Billie Eilish proved anyone can access Grammy-winning gear

# data[].attributes.body
Billie Eilish made her album with just her and her brother using entirely Garageband, an app that comes free with an Apple device.

# data[].attributes.attachment_link.readable_text
Singer Billie Eilish was on the Grammy's red carpet pulling Ellen Degeneres underwear out of a flowery bag when she got the news. A woman nearby, apparently having just gotten word, held up a phone to Eilish's face and said, "You just won Best Pop Vocal."

The singer went on to sweep up four of the biggest awards that night, making her the first female artist to do so. Her debut album When We All Fall Asleep, Where Do We Go? was crowned Best Album of the Year, while the song "Bad Guy" won Song of the Year and Record of the Year.

Eilish, at 18, had achieved something more prolific performers armed with top notch studios, dedicated teams of sound engineers and high end equipment had not. The capper? She had done so by working with her older brother Finneas O'Connell, out of a small bedroom studio in their parents' home.

O'Connell, also known by his stage name FINNEAS, said he preferred recording in that space because it offered more natural light than recording studios. He that "they tend to be lifeless and without any natural light, so I wanted to record wherever we lived." Cost was also a factor. "We just don't want to be bound to a studio to who we'd have to pay untold sums to."

The production setup was relatively simple, too. According to a Pro Sound Network interview with O'Connell, all there was in this bedroom was a pair of $200 Yamaha HS5 nearfield monitors with an H8S subwoofer ($450), a Universal Audio Apollo 8 interface and Apple's Logic Pro X. Oh, and a bed, of course, against one of the walls, on which Eilish sat to record with an Audio Technica AT2020 mic (at least, in the early days). As an amateur mixer myself, it was exciting to know that the duo use the exact same mic I own to record my vocals.

The tight, closed setting lent the album an intimate vibe that made it feel like Eilish's at-times breathy, feathery vocals were being whispered right into your ears. The stems (that is, individual layers of instruments and music) were then sent to mix engineer Rob Kinelski to compile.

All that is the work that resulted in When We Fall Asleep... winning the coveted Album of the Year title at this year's Grammys, as well as the award for Best Engineered Album, Non-Classical. O'Connell himself earned the Producer of the Year nod and altogether the album's impact is monumental. Now, more than ever, anyone could be an EGOT. Thanks to the democratization of music and video recording equipment, not to mention the high-powered software needed to create intricate pop productions, the barriers to entry into the world of awards ceremonies and red carpet shows have all but crumbled. Not that long ago, Pro Tools was the audio editing software du jour, and required specialized hardware. These days, every Mac or iPhone ships with GarageBand, and you can get Logic Pro for about $200.

"This is for all the kids who make music in their bedrooms," O'Connell said while accepting the Song of the Year award, reminding the world not only of the pair's brand of relatability but also of how big dreams of winning a Grammy could be within reach.

It's not just in the world of music-making that the proliferation of high-quality, relatively affordable equipment and software has made it easier to produce award-winning material. The Ringer's "NBA Desktop" won a Sports Emmy in May for Outstanding Digital Innovation. That show is shot entirely on a single-camera, single-frame setup, cutting in captures from host Jason Concepcion's computer desktop. It's a simple production process with basic equipment. While I hesitate to liken an established outlet like The Ringer to some random person in a bedroom, the achievement goes to show that you don't need an elaborate set, massive budgets or frenzied entourages to produce award-winning content.

In recent years, it's become much easier to reach a large audience. YouTube, SoundCloud and even Amazon's Kindle self-publishing platform have pulled some of that power out of the clutches of old-school production houses and broadcast networks, placing it in the hands of anyone with an email address. The rise of Instagram and Twitter culture gave any individual with a decent camera and a good joke the ability to market themselves and control their own message, eliminating the need for pesky agents and publicists.

Now, as cameras, microphones, mixers and editing software get better and more affordable, pretty much anyone can create top-notch material. Audio engineer Matt Jordan, Pro Audio category manager for music gear marketplace Reverb, told Engadget that "the barrier of entry to becoming a great engineer or producer is lower than ever." Second-hand gear through outlets like Reverb also help put pro-level equipment within reach by offering them for fractions of retail prices.

That's not to say that we've reached a point where anyone who wants to make a high-quality podcast or YouTube show can afford to do so. You'd still need access to a relatively powerful computer and some gear, which would cost at least a few hundred dollars. But in just the last five years, the creative landscape has changed significantly. It won't be long before the next Billie Eilish stuns the world with a unique work of art produced in a noisy dorm room or musty garage.

# data[].attributes.attachment_link.title
Billie Eilish proved anyone can access Grammy-winning gear | Engadget

# data[].attributes.attachment_link.description
Singer Billie Eilish was on the Grammy's red carpet pulling Ellen Degeneres underwear out of a flowery bag when she got the news. A woman nearby, apparently having just gotten word, held up a phone to Eilish's face and said, "You just won Best Pop Vocal."

# data[].attributes.body
Made from scratch using blender and online tutorials

# data[].attributes.headline
Live and shape your favourite stories and games with 5G and AI

# data[].attributes.subject
Live and shape your favourite stories and games with 5G and AI

# data[].attributes.body
The future of video games with 5G, AI-based gaming, Interactive Storytelling, eSports, etc

# data[].attributes.attachment_link.readable_text
Important User Information: Remote access to EBSCO's databases is permitted to patrons of subscribing institutions accessing from remote locations for personal, non-commercial use. However, remote access to EBSCO's databases from non-subscribing institutions is not allowed if the purpose of the use is for commercial gain through cost reduction or avoidance for a non-subscribing institution.

# data[].attributes.attachment_link.description
Important User Information: Remote access to EBSCO's databases is permitted to patrons of subscribing institutions accessing from remote locations for personal, non-commercial use. However, remote access to EBSCO's databases from non-subscribing institutions is not allowed if the purpose of the use is for commercial gain through cost reduction or avoidance for a non-subscribing institution.

# data[].attributes.body
The video displays them making a generated human make TikTok/Influencer videos based on AI learning and how they discover how it is oddly human as it goes on to mostly talk about their issues in life...as a computer

# data[].attributes.attachment_link.title
I Made the World’s First 100% Artificial Influencer and it got Weird

# data[].attributes.body
Programming AI for video games: how programmers bring game characters to life, what AI tries to make objects/characters in the world behave, etc

# data[].attributes.attachment_link.readable_text
PROGRAMMING ARTIFICIAL INTELLIGENCE FOR VIDEO GAMES

A ghost chases you through a maze. Chomp that energy pill, Pac-Man! Whew, the ghost ran off. How do ghosts in "Pac-Man" and other game characters know when to chase and when to flee? They're smart! Well, no, but they do have a form of artificial intelligence (AI). A programmer wrote computer code, or a set of rules, to act as their brains.

Game AI code tells characters where to go and what to do based on what's happening in the game. These characters have to act smart-but the game also has to be fun. When developers find just the right mix of fun and brains, you get game heaven.

AI programmers use many different methods to bring game characters to life. A finite state machine (FSM) is a tool that allows a character to cycle through several different behaviors. For example, the ghosts in Pac-Man switch back and forth between chasing Pac-Man and wandering the maze. If Pac-Man eats one of the energy pills, then the ghosts run away. The ghosts have only three states: chase, wander, and run away.

Game developers also sometimes use state machines to describe the combat cycle in an action game. Enemies start out searching for the player and then attack; when they get hurt, they take cover or run away. If they're still alive, they search for the player again.

Damian Isla is an AI programmer who has worked on many games, including the "Halo" series and "Destiny." Right now, he's working on a new game called "Te Flame in the Flood." He typically uses a tool called a behavior tree to write the code for the combat cycle. It's a series of lists arranged from high priority to low priority, says Isla. In this project, the bad guys are wolves. Te top item on a wolf's list of priorities might be "I want to stay alive," followed by "I want to attack the player," and finally, if nothing more important is going on, "I want to hang out by the coconut tree."

Once the wolf decides it wants to attack the player, that behavior has its own list of priorities. Number one could be "bite the player," but she could be too far away. So the next behavior is "approach the player," but what if the wolf can't find her? Then it has to "search for the player." And each of those actions breaks down into a list of smaller tasks as well. If the wolf needs to search for the player, then it has to decide on a direction to walk.

AI characters have to be smart-a wolf that walks into a rock or freezes in the middle of a battle looks stupid and makes the game less interesting. But the real goal for game programmers is not super-brainy characters. Tey want you to have fun playing.

"Smart is often not fun and fun is often not smart," says Isla. Imagine what would happen if you played a racing game in which the other cars always took the perfect route around the track. They'd be playing it smart, but you'd never have a chance to win the game! How fun is that? Isla explains that programmers will actually put mistakes into the AI on purpose. For example, if enemies are searching for the player, "Sometimes it's fun to let them get confused," says Isla. When the enemy loses track of you, then you have an opportunity to launch a sneak attack or get away.

Other times, all it takes to make the game fun is one bark. A "bark" is a word or phrase that characters shout out during a game. For example, an enemy might say, "I need reinforcements!" or "I'm gonna flank him!" It sounds as if the AI characters are planning something smart and complicated, but in reality no special behavior goes along with the words. The player just hears the bark and imagines that the enemy is doing something interesting.

Isla explains that, often, the most important behaviors to get right are the ones that make an AI character seem lifelike. Right now, he's working on a system that will handle how the characters in his new game move around. Te wolves, for example, need to move like they are stalking prey. Ideally, players should feel like they are being stalked even if the scary wolf images are replaced with plain gray boxes. Isla says, "I think I could create a compelling stalking AI by having [the boxes] follow you in a creepy, slow, aggressive way."

The player's experience of the game is the key. If she jumps when a wolf tries to bite her or shouts "Yes!" when she defeats an enemy, then the AI has succeeded.

~~~~~~~~

By Kathryn Hulick

Kathryn Hulick is married to a former video game AI programmer. She would like to thank him for his help with this article and for all those gaming tips.

Copyright of Muse is the property of Cricket Media and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.

# data[].attributes.attachment_link.description
PROGRAMMING ARTIFICIAL INTELLIGENCE FOR VIDEO GAMES A ghost chases you through a maze. Chomp that energy pill, Pac-Man! Whew, the ghost ran off. How do ghosts in "Pac-Man" and other game characters know when to chase and when to flee? They're smart! Well, no, but they do have a form of artificial intelligence (AI).

# data[].attributes.headline
Interview: "With AI, you might unlock some of the secrets about how life works" 

# data[].attributes.subject
Interview: "With AI, you might unlock some of the secrets about how life works" 

# data[].attributes.body
Solving AI to use as tool to help understand the world and make a positive impact, DeepMind's recent successes, how it can help us find out about biology that traditional approaches can't, Criticism, breakthroughs on scientific challenges, and other applications.

# data[].attributes.attachment_link.readable_text
IN THE run up to the match, Lee Sedol was feeling confident. He was about to play an artificial intelligence that had been trained to play the board game Go. But as one of the world's best players of the game himself, Lee thought he would easily win. "I thought AlphaGo was based on probability calculation and it was merely a machine," he said at the time. Even after he lost the first game of the match, Lee believed it was just because AlphaGo had made no mistakes. Then, in the now infamous move 37 of the second game, the AI seemed to be rewriting the rules of Go and played a move no human would have dreamed of. Lee, who ultimately lost the match 4-1, was dumbstruck: "This move was really creative and beautiful," he said.

This contest marked a pivotal moment in the development of AI, and Demis Hassabis was one of the main people responsible for it. In 2010, he co-founded the research company DeepMind and began working on AIs that could play games better than people. At the time, Go was considered too hard for artificial intelligence to master. It has more possible moves than there are atoms in the universe. Yet the win over Lee in 2016 catapulted DeepMind - and Hassabis - to worldwide fame. AlphaGo's victory was the biggest moment in AI since IBM's Deep Blue defeated world chess champion Garry Kasparov in 1997.

Since then, Hassabis's firm, which is now a subsidiary of Alphabet, Google's parent company, has been honing its algorithms and looking beyond games. In November, its new AI, AlphaFold, cracked the devilishly hard problem of predicting protein structures. Proteins are strings of amino acids that fold themselves into incredibly intricate shapes and serve as the biochemical tools that make living things tick. Working out the shape of a protein usually requires years of experiments. AlphaFold does it without any. Hassabis believes this is just the beginning of what AI can do for science.

Demis Hassabis: Our vision is to solve artificial intelligence. What we mean by that is to fundamentally understand intelligence and recreate it artificially, and then use that as a tool to help us understand the world and make a positive impact. Part of that is about advancing the pace of scientific discovery. The Hubble telescope allows us to see further into the cosmos, and that's how I see AI, as a generalised Hubble telescope that can help us make a lot more progress in many areas of science.

We take a lot of inspiration from neuroscience. I'm both a neuroscientist and a computer scientist; my first degree was in computer science, my PhD was in neuroscience. We've tried to bring both together, being inspired by the capabilities the human mind has and how it operates.

There are so many projects going on. We try to identify problems in science that have a cross section of factors that we like. There has got to be a clear objective to optimise, and some training data. The other thing we look for is impact: is this a challenge that, if we solved it, would unlock whole new branches of scientific endeavour? Protein folding has all those characteristics. We're also looking at quantum chemistry, pure mathematics and areas of physics and materials science.

I think proteins are incredibly exquisite. They are like little bio-machines, transporting nutrients around your body. They are switches, they are motors, they are little factories. From a mathematical and computer science point of view, it's intriguing to see that biology at that level starts looking digital and almost computational code-like. There are only about 100,000 proteins we know the structure of. That's a tiny fraction of the 200 million or so proteins that are out there in nature.

If you could solve protein folding, then you could probably accelerate drug discovery. You might start unlocking some of the secrets about how life works much faster than you can with experiments, which are slow, painstaking and difficult. You could better understand disease. I think one of the big transformations in science over the next 10 to 20 years is more and more biology being understandable in this computational way.

A lot of science's low-hanging fruit that could be addressed by an Einstein-like lone genius was done in the early parts of last century. Now we are left with more complex systems that don't yield themselves to simple laws. Up until now, biology has been regarded as too chaotic and too complex to apply physics approaches to it, but perhaps computer science and AI can help bridge that gap. That's exactly where I think the AI sweet spot is.

You could think of it as having the world's best research assistant on tap for you. They can find patterns, pull together disparate bits from different papers and find connections between them. It would still require the human scientist to come up with the goal and why that is an important goal. A lot of work that PhD students do is not the most fascinating or creative. If you had automated systems that take out some of the drudgery, you could free up the students to think about creative solutions.

No, but I think it's a reasonable criticism. We developed AlphaZero, which can play any two-player game with no modification, but it has to learn from scratch each time. Its knowledge doesn't transfer. Some people would say: that's fine, you've got a general algorithm. Others find it unsatisfactory.

I would like that if an AI learns chess, it shouldn't be a complete zero at Go; something should transfer over. Humans do this extremely well because obviously we cannot afford to start from scratch at every task. That's called transfer learning and no one's cracked it yet with an AI. It's a super-active area of research and something I'm personally working on. Perhaps there are a couple more modules we need, like episodic memory and attention.

It seems like the brain has at least two computational modes. You've got a cortex, which is quite slow at learning and needs quite a lot of examples, but it's very stable. On the other hand, you've got a hippocampus, which is what I studied for my PhD. It's a critical part of your brain and it is a superfast learner. That's how you remember what you had for lunch yesterday; it's your hippocampus doing that.

You might ask why don't we just have one giant hippocampus. Fast seems better. Actually, the rat, which is pretty clever for its size, has a brain that is basically one ginormous hippocampus. But the problem is - this is my conjecture - if you have just a fast learner, and you learn something new, it's very volatile. By learning how to ride a bike, you might wipe out your piano-playing expertise. Because it's got to be fast, you don't have much control over what gets overwritten.

You don't want your whole brain to be that volatile if you're trying to build knowledge on top of knowledge. But on the other hand, you don't want to have to encounter the lion at the watering hole at dusk more than once before you learn that's really dangerous. So you need both systems and, presumably, some kind of transfer function between the two. That's partially what happens when we sleep.

Potentially. We do work on that. If you look at our early work getting AI to play Atari video games, memory replay is one of the key reasons it works. Not only does our AI play the game, it replays key aspects of what it did back to itself. You could call it sleep, but, obviously, it's not actually sleeping. It's in an offline mode.

I would say somewhere about halfway. But there are different schools of thought. Some people think we as a community with deep learning and reinforced learning are not on the right track, so there'll be some brick wall. Others on the pro side say, look, we have everything we need. It's now just a question of scaling up: more computers, more data and we'll eventually pop out a human-level general AI.

I think both camps are wrong. My view is that what we have invented so far is on the right track and is super-useful. But I don't think we have all the ingredients. There will be further leaps, maybe only a handful, that are required.

It came in a bit too early for AI to be of much real help for this particular outbreak. God forbid, if there's another one in a few years' time, I would expect AI to play a bigger part.

The genetic sequences of the proteins in the virus were sequenced early on by Chinese researchers and some of the structures had been experimentally determined. But for a dozen or so of the proteins involved, we did not know the structures. At the time, we already thought our AlphaFold system was pretty accurate but it hadn't been battletested, so we didn't want to make big claims about the structures of those proteins. But we also thought we might be sitting on something that could be useful for the scientific community. So we immediately published our best models of those understudied proteins.

"What we have invented so far is on the right track. But I don't think we have all the ingredients"

One of our most famous pieces of work we did for Google was to control the cooling systems in the data centres that everybody uses when you watch YouTube and so on. We managed to save 30 per cent of the power that those data centres use by more efficiently switching on and off the fans and the pumps and all the other amazing equipment that is in these data centres. So that's obviously huge for cost-saving, but also for the environment.

You should be able to apply the same types of optimisation to other buildings or even a national grid. That would save a lot of energy. In the future, AI might contribute to things like material design or protein design, so that we have proteins that can break down waste plastics or create renewable biofuel.

I hope to see dozens of breakthroughs on grand scientific challenges, and then many products, services and advances that are enabled because of that. I would be very surprised if that doesn't happen in the next 10 years. We hope to be a big part of all that in a lot of different branches of science, from renewable energy - things like fusion - to quantum chemistry, material design and finding cures for diseases.

I probably shouldn't have mentioned that. But yes, I feel like there are some interesting areas of fusion that I think are amenable to AI, to do with the control of the plasma for instance. I have probably said too much, but in future we may have something to discuss about that.

I have a rather strange schedule, because my work and my life are all intermeshed. I have a two-day set-up. So during the day, I manage the company and do meetings. In the evenings, I start again. I'm a very nocturnal person, so I sit down again around 10 pm and go into the small hours of the morning. And that's when I write my research papers, read about and look into different areas, research them. That's my quiet time, and I love it. And I do that seven days a week.

Yes, I know, which is bad. I don't know how many more years I can work like this. And I know very well how bad it is for your brain, so I do try to get 6 hours at least, but I sometimes fail. On the weekends, I try to catch up by lying in, although that's not as good as getting a regular amount of sleep. I'm working on that, the sleep problem.

PHOTO (COLOR): The structures of proteins are fiendishly complex. It usually takes years of experimental work to deduce them

PHOTO (COLOR): One of DeepMind's early successes was at the board game Go

PHOTO (COLOR)

~~~~~~~~

By Demis Hassabis and Timothy Revell

Copyright of New Scientist is the property of New Scientist Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.

# data[].attributes.attachment_link.title
Interview: "With AI, you might unlock some of the secrets about how life wo...: EBSCOhost

# data[].attributes.attachment_link.description
IN THE run up to the match, Lee Sedol was feeling confident. He was about to play an artificial intelligence that had been trained to play the board game Go. But as one of the world's best players of the game himself, Lee thought he would easily win.

# data[].attributes.headline
AI learns game thanks to English prompts 

# data[].attributes.subject
AI learns game thanks to English prompts 

# data[].attributes.body
AI can play games better than humans and can even learn in plain English.

# data[].attributes.attachment_link.description
Sign in with your credentials to access the EBSCOhost premium information resources provided by your subscribing institution.

# data[].attributes.headline
Epic Games' Unreal Engine launches Women Creators Program in India to bring more women into the spotlight

# data[].attributes.subject
Epic Games' Unreal Engine launches Women Creators Program in India to bring more women into the spotlight

# data[].attributes.body
More opportunities world wide

# data[].attributes.attachment_link.readable_text
Mar. 26�Epic Games' Unreal Engine has announced, 'The Women Creators Program', which will select some talented women producers and VFX professionals from India to participate in a four week-long virtual training and mentoring program. The program, starting April 19, will select 15 women to be mentored by former Unreal fellowship graduates along with Unreal Engine evangelists.

The main idea behind this program is to inspire and train women creators and form a community of creators.

The applications for Women Creators' Program are now open and those interested can apply through this link. The deadline is April 10.

The Women Creators Program program was announced through a panel discussion with women creators in partnership with ASIFA India on March 25.

This program has evangelists and women mentors on the panel include � Vani Balgam, Creative Producer-Dancing Atoms; Charuvi Agarwal, Founder and DirectorCDL; Marta Ampudia Dalmau; Arvind Neelakantan, Tech Evangelist at Epic Games; Dean Reinhard, Technical Account Manager at Epic Games and Karan Parikh, Founder- Green Rain Studios and Unreal Authorized Instructor partner for Epic Games.

The workshop training will cover the VFX industry's relevant themes like Animation and DCC workflows, World Building and Terrain Tools, Lighting and Post Processing, Material Editor, Sequence, Livelink, and Blueprints.

___ (c)2021 the Hindustan Times (New Delhi) Visit the Hindustan Times (New Delhi) at www.hindustantimes.com Distributed by Tribune Content Agency, LLC.

Copyright of Hindustan Times is the property of Hindustan Times. The copyright in an individual article may be maintained by the author in certain cases. Content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. Source: Hindustan Times, Mar 26, 2021
Item: 2W613177186

# data[].attributes.attachment_link.title
Epic Games' Unreal Engine launches Women Creators Program in India to bring...: Points of View Reference Center Home

# data[].attributes.attachment_link.description
Mar. 26�Epic Games' Unreal Engine has announced, 'The Women Creators Program', which will select some talented women producers and VFX professionals from India to participate in a four week-long virtual training and mentoring program. The program, starting April 19, will select 15 women to be mentored by former Unreal fellowship graduates along with Unreal Engine evangelists.

# data[].attributes.headline
China: Determined to dominate cyberspace AI

# data[].attributes.subject
China: Determined to dominate cyberspace AI

# data[].attributes.body
How China is "chasing dominance" in AI, noth for private and military reasons to be a global cyber power and how they are doing it

# data[].attributes.attachment_link.title
China: Determined to dominate cyberspace and AI.

# data[].attributes.body
What they are, history, how it became the subject of research in times of concerns and how it has raised serious ethical problems

# data[].attributes.attachment_link.readable_text
Introduction

Algorithms-crucial to the field of computer science since the advent of computing-took on even greater importance during the first decades of the twenty-first century as technology companies explored new applications for algorithms within subfields such as artificial intelligence and machine learning. During the 2000s and 2010s, algorithms came to be used for a wide range of purposes, including serving social media users with relevant content, analyzing and identifying images of human faces, predicting criminal activity, and assessing job applicants.

At the same time, however, a debate arose regarding how best to manage the ethical implications of algorithmic technologies, which at times raised moral quandaries and made questionable decisions based on biased data. Some individuals and organizations concerned about those ethical ramifications have argued that technology companies and their programmers should take the lead in promoting ethical algorithm design and use and in combatting bias through means such as human-led audits. Some, on the other hand, have asserted that the technology industry cannot regulate itself and that the US government must intervene and regulate algorithms to ensure that they are being designed and implemented in an ethical manner.

Algorithm: A sequence of instructions that a computer follows to perform a given task.

Artificial intelligence (AI): Computer technology designed to perform intelligence-based tasks, including analyzing data and making decisions, that have historically been carried out by humans.

Facial recognition technology: Computer technology designed to detect human faces and, in many cases, match those faces with images of faces stored in a database for the purpose of determining or confirming the identities of the individuals in question.

Machine learning: A phenomenon in which computer algorithms learn from the data they process and change accordingly, without direct instruction by human programmers.

Predictive policing: The use of various means, including algorithm-based technologies, to predict crimes and prevent them from occurring.

Quote from "Ethical algorithm design should guide technology regulation." The Brookings Institution Artificial Intelligence and Emerging Technology (AIET) report by Michael Kearns and Aaron Roth, January 13, 2020. Photo courtesy Pixabay.

The term algorithm originated in mathematics and was subsequently brought into widespread use within the field of computer science. It refers to a sequence of instructions a computer follows to perform a specific task. The concept of computer algorithms predates the physical creation of computers in the modern sense, arising as early as the mid-nineteenth century in the form of early programs written for proposed-though not yet built-computing devices. Perhaps the most famous of those early algorithms was a program written by the English mathematician Ada Lovelace, created for use with the Analytical Engine computing device proposed by inventor Charles Babbage. Lovelace's algorithm, published in 1843 as a note in her annotated translation of an Italian paper on the Analytical Engine, was designed to calculate the numbers known as Bernoulli numbers.

As computer technology developed from theoretical to physical, algorithms designed for computing devices became increasingly complex, rendering computers capable of performing a variety of complicated tasks. One of the first recorded uses of algorithms with computers was by Alan Turing, a mathematician best known for breaking the Enigma code during World War II who published a computational theory to explain the patterns seen in nature. Turing's goal to capture general processes led to the creation of the Turing Machine, which in turn led the way for general purpose computers. Those machines were the first computers able to execute arbitrary sets of instructions. Following those early successes, computer scientists made significant strides in the areas of computer technology and artificial intelligence (AI)-algorithm-based computer technology that performs intelligence-based tasks, such as analyzing information and making decisions, that have historically been carried out within human minds. Some AI technologies function based on specific instructions coded by programmers, while others fall within the realm of machine learning, in which algorithms process large sets of data, learn from that data, and adapt accordingly without further human instruction.

During the early twenty-first century, algorithms came to play crucial roles in both programs and services used regularly by the public and in more experimental technologies under development. In the 2000s, social media services, such as Facebook and Twitter, began to use algorithms to determine which posts would appear in a user's newsfeed and in what order, as well as to identify trending content. Algorithms were also created to supply users of a content-providing service, such as a music- or video-streaming application, with relevant content. The music-streaming service Spotify, for instance, uses algorithms to recommend new music to a user based on that individual's past listening habits, while the video-streaming website YouTube uses algorithms to recommend videos related to one that the user has just watched.

Another common use of algorithms is the analysis and tagging of images. The service Google Photos, for example, could use algorithms to determine that a specific image was a photograph of a cat and could then tag the photograph accordingly. Image-analyzing algorithms were also key to the development of facial-recognition technology, which can locate a human face in an image, analyze its features, and compare that face against images of faces in a central database to find a match. Such technology came into regular use both as a personal security feature, enabling users to unlock their mobile devices with their faces rather than passcodes or fingerprints, and as a form of surveillance and policing technology, allowing authorities to identify individuals of interest based on sources such as security camera footage.

In addition to using facial-recognition technology, law enforcement and corrections officials in some areas experimented with a practice known as predictive policing, in which algorithms were used to predict the areas in which crimes were most likely to occur, which individuals were likely to commit crimes, and which imprisoned individuals seeking parole were likely to commit further crimes if released.

Some technological innovations based on AI and machine technologies earned praise and demonstrated the potential of algorithms to increase the efficiency, accuracy, and profitability of certain processes for companies and convenience or better quality of life for users. However, their emergence into the mainstream likewise called attention to the ethical drawbacks of some such technologies. Content-serving algorithms, such as those used by Facebook and Twitter, drew criticism for exerting undue control over users' newsfeeds and thereby further polarizing the public politically, and Facebook's algorithm to highlight trending news stories struggled to discern factual articles from so-called fake news. Facial recognition programs made more errors when trying to identify women and people of color than they did when identifying White men, a phenomenon that prompted researchers to investigate the diversity of the datasets used when training such programs and to speak out against the use of facial recognition technology in policing, as flawed programs could produce false matches. Predictive policing also drew criticism, with some scholars and advocacy groups asserting that the algorithmic approach to law enforcement was based on flawed data, disproportionately targeted Black people and residents of poor neighborhoods, and ultimately perpetuated systemic racism. Additional technologies that presented ethical challenges included an experimental recruiting algorithm tested by the retailer Amazon, which penalized applications that included the word women's. Ethical problems also arose around the use of algorithms in medicine and finance.

In light of growing concerns regarding the ethical implications of AI, machine learning, and other algorithm-based technologies, the ethics and implicit biases of algorithms became the subject of extensive research throughout the United States, including at colleges and universities such as the Massachusetts Institute of Technology (MIT), Harvard University, and Stanford University. Researchers affiliated with such institutions not only looked into the causes and ramifications of algorithmic bias, but also explored means of mitigating such biases and tackling the numerous other ethical quandaries the continuously developing AI industry raised.

In response to the increased emphasis on ethics, a number of major technology companies developed boards or committees dedicated to ethics, particularly regarding their AI research. Microsoft founded its AI and Ethics in Engineering and Research (AETHER) Committee in 2017. Google launched an Advanced Technology External Advisory Council (ATEAC) to oversee its AI work in March 2019 but disbanded the council less than two weeks later. Social media companies also began ethics-oriented self-regulation initiatives, with Facebook selecting the first group of members of its Oversight Board, a group focused primarily on content-moderation disputes, in 2020.

Although scholars, organizations, and industry leaders with a diverse array of perspectives widely agreed that the use of algorithm-driven technology raised serious ethical concerns, opinions regarding the best means of addressing those concerns were split. Many individuals and trade organizations within the technology industry argued that technology companies should take the lead in addressing the issue and could regulate their own products and practices. In addition to recommending that companies perform regular audits to detect bias or other ethical issues in their technologies, some proponents of industry-led reform suggested that the technology industry could also benefit from greater diversity in staffing, which might help mitigate implicit racial or gender bias. By contrast, some experts argued that industry self-regulation was ineffective and that existing means of government oversight were incapable of keeping up with the ever-changing technological landscape. Such individuals argued that the US government should introduce new regulations to protect the public from the misuse of algorithms and to ensure that further developments in AI, machine learning, facial recognition technology, and related areas would be implemented ethically.

These essays and any opinions, information, or representations contained therein are the creation of the particular author and do not necessarily reflect the opinion of EBSCO Information Services.

Joy Crelin is a freelance writer and editor based in Wethersfield, Connecticut. She holds a bachelor of fine arts in writing, literature, and publishing from Emerson College.
Bibliography 1 Brogan, Jacob. "What's the Deal with Algorithms?" Slate, 2 Feb. 2016, slate.com/technology/2016/02/whats-the-deal-with-algorithms.html. Accessed 22 Feb. 2021. 2 DeAngelis, Stephen F. "Artificial Intelligence: How Algorithms Make Systems Smart." Wired, Sept. 2014, www.wired.com/insights/2014/09/artificial-intelligence-algorithms-2. Accessed 22 Feb. 2021. 3 Heaven, Will Douglas. "Predictive Policing Algorithms Are Racist. They Need to Be Dismantled." MIT Technology Review, 17 July 2020, www.technologyreview.com/2020/07/17/1005396/predictive-policing-algorithms-racist-dismantled-machine-learning-bias-criminal-justice. Accessed 22 Feb. 2021. 4 Kearns, Michael. "How to Build an Ethical Algorithm." Interview by Dan Costa. PCMag, 27 Feb. 2020, www.pcmag.com/news/how-to-build-an-ethical-algorithm. Accessed 22 Feb. 2021. 5 Kearns, Michael, and Aaron Roth. "Ethical Algorithm Design Should Guide Technology Regulation." Brookings, 13 Jan. 2020, www.brookings.edu/research/ethical-algorithm-design-should-guide-technology-regulation. Accessed 22 Feb. 2021. 6 Kearns, Michael, and Aaron Roth. "Who Is Responsible for Biased and Intrusive Algorithms?" Knowledge@Wharton, University of Pennsylvania, 2 Dec. 2019, knowledge.wharton.upenn.edu/article/who-is-responsible-for-biased-and-intrusive-algorithms. Accessed 22 Feb. 2021. 7 Kowalkiewicz, Marek. "How Did We Get Here? The Story of Algorithms." Towards Data Science, 10 Oct. 2019, towardsdatascience.com/how-did-we-get-here-the-story-of-algorithms-9ee186ba2a07. Accessed 19 Mar. 2021. 8 Rainie, Lee, and Janna Anderson. "Code-Dependent: Pros and Cons of the Algorithm Age." Pew Research Center, 8 Feb. 2017, www.pewresearch.org/internet/2017/02/08/code-dependent-pros-and-cons-of-the-algorithm-age. Accessed 22 Feb. 2021.
~~~~~~~~

By Joy Crelin

Copyright of Points of View: Ethics of Algorithms is the property of Salem Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.

# data[].attributes.attachment_link.description
Introduction Algorithms-crucial to the field of computer science since the advent of computing-took on even greater importance during the first decades of the twenty-first century as technology companies explored new applications for algorithms within subfields such as artificial intelligence and machine learning.

# data[].attributes.headline
How A Pennsylvania Mother Allegedly Used Deepfake Technology To Manipulate Photos Of Girls On Her Daughter`s Cheerleading Team To Get Them In Trouble 

# data[].attributes.subject
How A Pennsylvania Mother Allegedly Used Deepfake Technology To Manipulate Photos Of Girls On Her Daughter`s Cheerleading Team To Get Them In Trouble 

# data[].attributes.attachment_link.readable_text
CRAIG MELVIN (08:04:04): Now that disturbing case involving deepfake videos that are manipulated to make it look like someone did or said something they did not. A Pennsylvania mother is facing criminal charges this morning, accused of using deepfakes to harass a member of her daughter`s cheerleading squad. NBC`s Stephanie Gosk has all the details.

(Begin VT)

STEPHANIE GOSK (08:04:26): It`s a troubling new take on cyberbullying. A mom in Pennsylvania is accused this morning of using so-called deepfakes to anonymously harass members of her daughter`s cheerleading team.

MADI HIME (08:04:37): I think I was just so much, like, in shock with everything going on, like I couldn`t really comprehend, like, what was going on.

STEPHANIE GOSK (08:04:44): Madi Hime says after getting several threatening calls and texts from unknown numbers, some even saying she should take her own life. Her cheer coaches told Madi they received this video that supposedly showed her vaping. She and her mom shared it with us. They say it`s fake.

MADI HIME (08:05:02): I felt like if I said to someone, like, no one-- no one would trust me. They have the video on proof, even though the video wasn`t real.

STEPHANIE GOSK (08:05:09): According to court documents, images from Madi`s social media were also doctored to make it appear like she was nude and drinking. Her mother turned to police.

JENNIFER HIME (08:05:19): I didn`t know how to protect her from that. I didn`t know who to protect her from. There were a lot of sleepless nights.

STEPHANIE GOSK (08:05:26): Authorities say behind it all was Rafaela Spone, whose own daughter is a teammate of Hime`s on the Victory Vipers cheer squad. Spone is now charged three counts each of cyber harassment of a child and harassment. ROBERT BIRCH (Attorney for Accused Mom) (08:05:38): My client denies what they`ve charged her with, and we are going to aggressively fight this.

STEPHANIE GOSK (08:05:44): In a statement, the Victory Vipers said in part their group ".has always promoted a family environment, and we are sorry for all individuals involved." Deepfakes have made headlines before with manipulated videos like this one showing what appears to be Tom Cruise.

(08:06:00): (Excerpt from TikTok)

STEPHANIE GOSK (08:06:01): But they`re not real. They`re altered through software. The district attorney in Pennsylvania fears this is the start of a dangerous trend.

MAN (08:06:09): We`ve always taken for granted that a photo is a photo, a pic-- a video is a video. We can`t take that for granted any longer.

STEPHANIE GOSK (08:06:17): A disturbing new tool that has this community demanding justice.

(08:06:22): For TODAY, Stephanie Gosk, NBC News.

(End VT)

CRAIG MELVIN (08:06:25): We could use a boost.

SAVANNAH GUTHRIE (08:06:26): Yeah.

HODA KOTB (08:06:26): Okay.

CRAIG MELVIN (08:06:26): We could use a boost.

HODA KOTB (08:06:27): Well, I think I got you covered right here. Okay. So there`s a sheriff`s deputy in Florida and he was checking out a noise complaint from somebody who thought that a dance studio was being used as an illegal nightclub. Well, turned out, everything was legit. A dance group known as The Pensacola Prancing Pirouettes, they were practicing their routine. So Deputy Roman Brissett showed up, he went up to the dancers and he wanted them to explain what was going on. Well, the deputy asked if they were any good. So they said, well, are you any good? Here`s what happened.

(08:06:55): (Excerpt from internet video)

HODA KOTB (08:07:02): Deputy Brissett, he took up the challenge and he showed them. Okay. By the way, the women had no idea he actually belonged to a student dance group--

SAVANNAH GUTHRIE (08:07:09): Yes, he did.

HODA KOTB (08:07:09): --when he was in college twenty years ago.

CRAIG MELVIN (08:07:12): Wow.

SAVANNAH GUTHRIE (08:07:13): If he is not in their next performance--

CRAIG MELVIN (08:07:15): Wow.

SAVANNAH GUTHRIE (08:07:15): --I mean--

CRAIG MELVIN (08:07:16): Wow. Okay.

HODA KOTB (08:07:19): Perfect.

CRAIG MELVIN (08:07:19): Didn`t-- that`s not how I thought-- I didn`t see that going there.

SAVANNAH GUTHRIE (08:07:22): Yeah.

CRAIG MELVIN (08:07:23): All right. Coming up, we`ll take you inside music`s other big night.

SAVANNAH GUTHRIE (08:07:26): I was going to say that wasn`t the biggest night right there? How women really owned the evening at the Grammy Awards, historic wins, unforgettable performances. We`ll tell you everything about right after this.

(ANNOUNCEMENTS)

END

Content and programming copyright 2021 NBCUniversal. ALL RIGHTS RESERVED. Transcription Copyright 2021 ASC Services II Media, LLC. All materials herein are protected by United States copyright law and may not be reproduced, distributed, transmitted, displayed, published or broadcast without the prior written permission of ASC Services II Media, LLC. You may not alter or remove any trademark, copyright or other notice from copies of the content.

# data[].attributes.attachment_link.title
How A Pennsylvania Mother Allegedly Used Deepfake Technology To Manipulate ...: Points of View Reference Center Home

# data[].attributes.attachment_link.description
CRAIG MELVIN (08:04:04): Now that disturbing case involving deepfake videos that are manipulated to make it look like someone did or said something they did not. A Pennsylvania mother is facing criminal charges this morning, accused of using deepfakes to harass a member of her daughter`s cheerleading squad.

# data[].attributes.attachment_link.title
James Dean may be resurrected for a new film, decades after he died

# data[].attributes.attachment_link.description
Legendary actor James Dean may be brought back to life using special effects in a movie titled "Back to Eden." Related NFTs may also be put on sale.

# data[].attributes.headline
Deepfakes and the New disinformation War

# data[].attributes.subject
Deepfakes and the New disinformation War

# data[].attributes.body
What they are, how they are used, potential, fruad, countering them, laws, accessability

# data[].attributes.attachment_link.readable_text
A picture may be worth a thousand words, but there is nothing that persuades quite like an audio or video recording of an event. At a time when partisans can barely agree on facts, such persuasiveness might seem as i. it could bring a welcome clarity. Audio and video recordings allow people to become firsthand witnesses of an event, sparing them the need to decide whether to trust someone else's account of it. And thanks to smartphones, which make it easy to capture audio and video content, and social media platforms, which allow that content to be shared and consumed, people today can rely on their own eyes and ears to an unprecedented degree.

Therein lies a great danger. Imagine a video depicting the Israeli prime minister in private conversation with a colleague, seemingly revealing a plan to carry out a series of political assassinations in Tehran. Or an audio clip of Iranian officials planning a covert operation to kill Sunni leaders in a particular province of Iraq. Or a video showing an American general in Afghanistan burning a Koran. In a world already primed for violence, such recordings would have a powerful potential for incitement. Now imagine that these recordings could be faked using tools available to almost anyone with a laptop and access to the Internet -- and that the resulting fakes are so convincing that they are impossible to distinguish from the real thing.

Advances in digital technology could soon make this nightmare a reality. Thanks to the rise of "deepfakes" -- highly realistic and difficult-to-detect digital manipulations of audio or video -- it is becoming easier than ever to portray someone saying or doing something he or she never said or did. Worse, the means to create deepfakes are likely to proliferate quickly, producing an ever-widening circle off actors capable off deploying them for political purposes. Disinformation is an ancient art, off course, and one with a renewed relevance today. But as deepfake technology develops and spreads, the current disinformation wars may soon look like the propaganda equivalent off the era off swords and shields.

Deepfakes are the product off recent advances in a form off artificial intelligence known as "deep learning," in which sets off algorithms called "neural networks" learn to infer rules and replicate patterns by sifting through large data sets. (Google, for instance, has used this technique to develop powerful image-classification algorithms for its search engine.) Deepfakes emerge from a specific type off deep learning in which pairs off algorithms are pitted against each other in "generative adversarial networks," or GANs. In a GAN, one algorithm, the "generator," creates content modeled on source data (for instance, making artificial images off cats from a database off real cat pictures), while a second algorithm, the "discriminator," tries to spot the artificial content (pick out the fake cat images). Since each algorithm is constantly training against the other, such pairings can lead to rapid improvement, allowing GANS to produce highly realistic yet fake audio and video content.

This technology has the potential to proliferate widely. Commercial and even free deepfake services have already appeared in the open market, and versions with alarmingly few safeguards are likely to emerge on the black market. The spread off these services will lower the barriers to entry, meaning that soon, the only practical constraint on one's ability to produce a deepfake will be access to training materials -- that is, audio and video off the person to be modeled -- to feed the GAN. The capacity to create professional-grade forgeries will come within reach off nearly anyone with su.cient interest and the knowledge off where to go for help.

Deepfakes have a number off worthy applications. Modified audio or video off a historical figure, for example, could be created for the purpose off educating children. One company even claims that it can use the technology to restore speech to individuals who have lost their voice to disease. But deepfakes can and will be used for darker purposes, as well. Users have already employed deepfake technology to insert people's faces into pornography without their consent or knowledge, and the growing ease off making fake audio and video content will create ample opportunities for blackmail, intimidation, and sabotage. The most frightening applications off deepfake technology, however, may well be in the realms off politics and international affairs. There, deepfakes may be used to create unusually effective lies capable off inciting violence, discrediting leaders and institutions, or even tipping elections.

Deepfakes have the potential to be especially destructive because they are arriving at a time when it already is becoming harder to separate fact from fiction. For much off the twentieth century, magazines, newspapers, and television broadcasters managed the flow off information to the public. Journalists established rigorous professional standards to control the quality off news, and the relatively small number off mass media outlets meant that only a limited number off individuals and organizations could distribute information widely. Over the last decade, however, more and more people have begun to get their information from social media platforms, such as Facebook and Twitter, which depend on a vast array off users to generate relatively unfiltered content. Users tend to curate their experiences so that they mostly encounter perspectives they already agree with (a tendency heightened by the platforms' algorithms), turning their social media feeds into echo chambers. These platforms are also susceptible to so-called information cascades, whereby people pass along information shared by others without bothering to check i. it is true, making it appear more credible in the process. The end result is that falsehoods can spread faster than ever before.

These dynamics will make social media fertile ground for circulating deepfakes, with potentially explosive implications for politics. Russia's attempt to influence the 2016 U.S. presidential election -- spreading divisive and politically inflammatory messages on Facebook and Twitter -- already demonstrated how easily disinformation can be injected into the social media bloodstream. The deepfakes off tomorrow will be more vivid and realistic and thus more shareable than the fake news off 2016. And because people are especially prone to sharing negative and novel information, the more salacious the deepfakes, the better.

The use off fraud, forgery, and other forms off deception to influence politics is nothing new, off course. When the USS Maine exploded in Havana Harbor in 1898, American tabloids used misleading accounts of the incident to incite the public toward war with Spain. The anti-Semitic tract Protocols of the Elders of Zion, which described a fictional Jewish conspiracy, circulated widely during the first half off the twentieth century. More recently, technologies such as Photoshop have made doctoring images as easy as forging text. What makes deepfakes unprecedented is their combination off quality, applicability to persuasive formats such as audio and video, and resistance to detection. And as deepfake technology spreads, an ever-increasing number off actors will be able to convincingly manipulate audio and video content in a way that once was restricted to Hollywood studios or the most well-funded intelligence agencies.

Deepfakes will be particularly useful to nonstate actors, such as insurgent groups and terrorist organizations, which have historically lacked the resources to make and disseminate fraudulent yet credible audio or video content. These groups will be able to depict their adversaries -- including government officials -- spouting inflammatory words or engaging in provocative actions, with the specific content carefully chosen to maximize the galvanizing impact on their target audiences. An a.liate off the Islamic State (or ....), for instance, could create a video depicting a U.S. soldier shooting civilians or discussing a plan to bomb a mosque, thereby aiding the terrorist group's recruitment. Such videos will be especially difficult to debunk in cases where the target audience already distrusts the person shown in the deepfake. States can and no doubt will make parallel use off deepfakes to undermine their nonstate opponents.

Deepfakes will also exacerbate the disinformation wars that increasingly disrupt domestic politics in the United States and elsewhere. In 2016, Russia's state-sponsored disinformation operations were remarkably successful in deepening existing social cleavages in the United States. To cite just one example, fake Russian accounts on social media claiming to be a.liated with the Black Lives Matter movement shared inflammatory content purposely designed to stoke racial tensions. Next time, instead off tweets and Facebook posts, such disinformation could come in the form off a fake video off a white police off cer shouting racial slurs or a Black Lives Matter activist calling for violence.

Perhaps the most acute threat associated with deepfakes is the possibility that a well-timed forgery could tip an election. In May 2017, Moscow attempted something along these lines. On the eve of the French election, Russian hackers tried to undermine the presidential campaign off Emmanuel Macron by releasing a cache of stolen documents, many off them doctored. That effort failed for a number off reasons, including the relatively boring nature off the documents and the effects off a French media law that prohibits election coverage in the 44 hours immediately before a vote. But in most countries, most off the time, there is no media blackout, and the nature off deepfakes means that damaging content can be guaranteed to be salacious or worse. A convincing video in which Macron appeared to admit to corruption, released on social media only 24 hours before the election, could have spread like wildfire and proved impossible to debunk in time.

Deepfakes may also erode democracy in other, less direct ways. The problem is not just that deepfakes can be used to stoke social and ideological divisions. They can create a "liar's dividend": as people become more aware off the existence off deepfakes, public figures caught in genuine recordings off misbehavior will find it easier to cast doubt on the evidence against them. (I. deepfakes were prevalent during the 2016 U.S. presidential election, imagine how much easier it would have been for Donald Trump to have disputed the authenticity off the infamous audiotape in which he brags about groping women.) More broadly, as the public becomes sensitized to the threat off deepfakes, it may become less inclined to trust news in general. And journalists, for their part, may become more wary about relying on, let alone publishing, audio or video off fast-breaking events for fear that the evidence will turn out to have been faked.

There is no silver bullet for countering deepfakes. There are several legal and technological approaches -- some already existing, others likely to emerge -- that can help mitigate the threat. But none will overcome the problem altogether. Instead off full solutions, the rise off deepfakes calls for resilience.

Three technological approaches deserve special attention. The first relates to forensic technology, or the detection off forgeries through technical means. Just as researchers are putting a great deal off time and effort into creating credible fakes, so, too, are they developing methods off enhanced detection. In June 2018, computer scientists at Dartmouth and the University at Albany, ..NÕ, announced that they had created a program that detects deepfakes by looking for abnormal patterns off eyelid movement when the subject off a video blinks. In the deepfakes arms race, however, such advances serve only to inform the next wave off innovation. In the future, GANs will be fed training videos that include examples off normal blinking. And even i. extremely capable detection algorithms emerge, the speed with which deepfakes can circulate on social media will make debunking them an uphill battle. By the time the forensic alarm bell rings, the damage may already be done.

A second technological remedy involves authenticating content before it ever spreads -- an approach sometimes referred to as a "digital provenance" solution. Companies such as Truepic are developing ways to digitally watermark audio, photo, and video content at the moment off its creation, using metadata that can be logged immutably on a distributed ledger, or blockchain. In other words, one could effec tively stamp content with a record off authenticity that could be used later as a reference to compare to suspected fakes.

In theory, digital provenance solutions are an ideal fix. In practice, they face two big obstacles. First, they would need to be ubiquitously deployed in the vast array off devices that capture content, including laptops and smartphones. Second, their use would need to be made a precondition for uploading content to the most popular digital platforms, such as Facebook, Twitter, and YouTube. Neither condition is likely to be met. Device makers, absent some legal or regulatory obligation, will not adopt digital authentication until they know it is affordable, in demand, and unlikely to interfere with the performance off their products. And few social media platforms will want to block people from uploading unauthenticated content, especially when the first one to do so will risk losing market share to less rigorous competitors.

A third, more speculative technological approach involves what has been called "authenticated alibi services," which might soon begin emerging from the private sector. Consider that deepfakes are especially dangerous to high-profile individuals, such as politicians and celebrities, with valuable but fragile reputations. To protect themselves against deepfakes, some off these individuals may choose to engage in enhanced forms off "lifelogging" -- the practice off recording nearly every aspect off one's life -- in order to prove where they were and what they were saying or doing at any given time. Companies might begin offering bundles off alibi services, including wearables to make lifelogging convenient, storage to cope with the vast amount off resulting data, and credible authentication off those data. These bundles could even include partnerships with major news and social media platforms, which would enable rapid confirmation or debunking off content.

Such logging would be deeply invasive, and many people would want nothing to do with it. But in addition to the high-profile individuals who choose to adopt lifelogging to protect themselves, some employers might begin insisting on it for certain categories off employees, much as police departments increasingly require off cers to use body cameras. And even i. only a relatively small number off people took up intensive lifelogging, they would produce vast repositories off data in which the rest off us would find ourselves inadvertently caught, creating a massive peer-to-peer surveillance network for constantly recording our activities.

I. these technological fixes have limited upsides, what about legal remedies? Depending on the circumstances, making or sharing a deepfake could constitute defamation, fraud, or misappropriation off a person's likeness, among other civil and criminal violations. In theory, one could close any remaining gaps by criminalizing (or attaching civil liability to) specific acts -- for instance, creating a deepfake off a real person with the intent to deceive a viewer or listener and with the expectation that this deception would cause some specific kind off harm. But it could be hard to make these claims or charges stick in practice. To begin with, it will likely prove very difficult to attribute the creation off a deepfake to a particular person or group. And even i. perpetrators are identified, they may be beyond a court's reach, as in the case off foreign individuals or governments.

Another legal solution could involve incentivizing social media platforms to do more to identify and remove deepfakes or fraudulent content more generally. Under current U.S. law, the companies that own these platforms are largely immune from liability for the content they host, thanks to Section 230 off the Communications Decency Act off 1996. Congress could modify this immunity, perhaps by amending Section 230 to make companies liable for harmful and fraudulent information distributed through their platforms unless they have made reasonable efforts to detect and remove it. Other countries have used a similar approach for a different problem: in 2017, for instance, Germany passed a law imposing stiffines on social media companies that failed to remove racist or threatening content within 24 hours off it being reported.

Yet this approach would bring challenges off its own. Most notably, it could lead to excessive censorship. Companies anxious to avoid legal liability would likely err on the side off policing content too aggressively, and users themselves might begin to self-censor in order to avoid the risk oÉ having their content suppressed. It is far from obvious that the notional benefits off improved fraud protection would justify these costs to free expression. Such a system would also run the risk off insulating incumbent platforms, which have the resources to police content and pay for legal battles, against competition from smaller firms.

The unavoidable conclusion is that deepfakes spell trouble. As interest in the technology mounts, deepfakes will improve in quality, become ever cheaper and easier to make, and be disseminated widely through both commercial services and the black markets off the Dark Web, where deepfake production is likely to emerge as a service for hire. The result will be a rising tide off false yet highly realistic audio and video content, ready to be weaponized for political purposes and spread on social media.

But although deepfakes are dangerous, they will not necessarily be disastrous. Detection will improve, prosecutors and plaintiffs will occasionally win legal victories against the creators oÉ harmful fakes, and the major social media platforms will gradually get better at flagging and removing fraudulent content. And digital provenance solutions could, i. widely adopted, provide a more durable fix at some point in the future.

In the meantime, democratic societies will have to learn resilience. On the one hand, this will mean accepting that audio and video content cannot be taken at face value; on the other, it will mean fighting the descent into a post-truth world, in which citizens retreat to their private information bubbles and regard as fact only that which flatters their own beliefs. In short, democracies will have to accept an uncomfortable truth: in order to survive the threat off deepfakes, they are going to have to learn how to live with lies.

PHOTO (COLOR): True lies: stills of a deepfake video of Barack Obama created by researchers in 2017

~~~~~~~~

By Robert Chesney, James A. Baker III Chair and Director of the Robert Strauss Center for International Security and Law at the University of Texas at Austin. and Danielle Citron, Morton and Sophia Macht Professor of Law at the University of Maryland. Affiliate Fellow at the Yale Information Society Project.

ROBERT CHESNEY is James A. Baker III Chair and Director of the Robert Strauss Center for International Security and Law at the University of Texas at Austin.

DANIELLE CITRON is Morton and Sophia Macht Professor of Law at the University of Maryland and A.iliate Fellow at the Yale Information Society Project.

The contents of Foreign Affairs are protected by copyright. © 2004 Council on Foreign Relations, Inc., all rights reserved. To request permission to reproduce additional copies of the article(s) you will retrieve, please contact the Permissions and Licensing office of Foreign Affairs.

# data[].attributes.attachment_link.description
Sign in with your credentials to access the EBSCOhost premium information resources provided by your subscribing institution.

# data[].attributes.headline
Deepfakes a new Weapon of Crass Destruction

# data[].attributes.subject
Deepfakes a new Weapon of Crass Destruction

# data[].attributes.body
UAE cracking down on Deepfakes, which "can enable anyone to become posers" and how it is very accessible and is a great concern

# data[].attributes.attachment_link.readable_text
Alvin Cabral

Jul. 9-DUBAI - Whether we like it or not, advancements in innovation present an equal opportunity for the yin and yang of society. Photoshop, for example, made Microsoft Paint look like child's play - and that same notion is applicable to how new tech can enable anyone to become posers.

The UAE's latest move on cracking down on deepfakes is a stark reminder that nobody is safe from an ingenious method of duping people, from illicit financial gain to political swaying and cyberbullying to plain misinformation.

Deepfakes, or synthetic media, are, in layman's terms, audio or video content doctored with digitally-altered likenesses to make them look genuine. The technique isn't new, but powerful new tech like artificial intelligence and machine learning have made it, most of the time, extremely difficult to discern.

When Photoshop grew to become the holy grail of digital alteration, it also became a playground for fakers. Adobe, the software's creator, did roll out a metadata programme last year to help ID fakes.

With videos, it gets trickier. The masses, thanks to the influence of social media, can easily be reeled in, unwillingly baited into the no-no of not verifying sources before sharing content. For the corporate world, it gets even dicier: The bigger the stakes, the better weapons are launched by bad actors.

More alarmingly, deepfake videos online are spiking at an annual rate of 900 per cent, according to the World Economic Forum.

A Carnergie Endowment for International Peace study showed that in 2019, US residents lost $667 million via fake phone calls to impersonate the government or relatives in distress, among others, forcing them to transfer funds. Corporates face potentially - realistically, really - billions in damages, not to mention the harm it will do to their brands.

AI, despite its promise for progress, is also equally playing a huge role in spreading fake content. Its democratisation and increasing ease of use, while made with good intentions, inevitably comes with unintended consequences. It is a given that the sources of these deepfakes can be tracked down, but the general idea is that the tech-for-all ideology makes it all the more easier to get into the action.

"Even unskilled operators could download the requisite software tools and, using publicly-available data, create increasingly convincing counterfeit content," analyst Laurie Harris wrote in a study for the Congressional Research Service, the US Congress' think-tank.

Organisations and watchdogs themselves must also be equipped to keep pace. Research firm Gartner, in a recent report, cited that, by 2023, 20 per cent of successful account takeover hacks will use deepfakes to "socially engineer users to turn over sensitive data or move money into criminal accounts".

"Countering synthetic media in the financial system will require new technologies, institutional practices and education in the financial sector," Carnegie fellow Jon Bateman said.

Last year's hotly-contested US elections dragged deepfakes further into the spotlight. Facebook banned deepfake videos ahead of the polls, well over a year after a doctored video of a 'drunk' House Speaker Nancy Pelosi surfaced, which was tweeted out by then-president Donald Trump. The social media network took down that video but, curiously, didn't do the same for a deepfake involving its own CEO, Mark Zuckerberg.

Even world leaders have fallen victim - Queen Elizabeth in 1995, former British prime minister Tony Blair in 1998 and former Cuban leader Fidel Castro in 2003. They were technically prank calls, but the whole idea holds up: Deepfakes are the next step in disinformation and economic damage.

"Financially-motivated extortion and social engineering, and influence operations aimed at destabilising democracies, are just the start," Trend Micro security research vice-president Rik Ferguson said in a report aptly titled Weaponised deepfakes are getting closer to reality.

The question is, as Ferguson pointed out in closing, "what can we do about it?"

- alvin@khaleejtimes.com

___ (c)2021 the Khaleej Times (Dubai, United Arab Emirates) Visit the Khaleej Times (Dubai, United Arab Emirates) at www.khaleejtimes.com Distributed by Tribune Content Agency, LLC.

Copyright of Khaleej Times (7/2008 to 4/2009) is the property of Khaleej Times (7/2008 to 4/2009). The copyright in an individual article may be maintained by the author in certain cases. Content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. Source: Khaleej Times (7/2008 to 4/2009), Jul 09, 2021
Item: 2W63368640460

# data[].attributes.attachment_link.title
Deepfakes a new weapon of crass destruction

# data[].attributes.attachment_link.description
Alvin Cabral Jul. 9-DUBAI - Whether we like it or not, advancements in innovation present an equal opportunity for the yin and yang of society. Photoshop, for example, made Microsoft Paint look like child's play - and that same notion is applicable to how new tech can enable anyone to become posers.

# data[].attributes.body
What they are, how they are used, concerns from government

# data[].attributes.attachment_link.readable_text
BILL WHITAKER: You may never have heard the term "synthetic media"--more commonly known as --but our military, law enforcement and intelligence agencies certainly have. They are hyper-realistic video and audio recordings that use artificial intelligence and deep learning to create fake content or . The U.S. government has grown increasingly concerned about their potential to be used to spread disinformation and commit crimes. That`s because the creators of deepfakes have the power to make people say or do anything, at least on our screens. Most Americans have no idea how far the technology has come in just the last four years or the danger, disruption and opportunities that come with it.

(Begin VT)

TOM CRUISE ( Deepfake; TikTok): You know I do all my own stunts, obviously. I also do my own music.

BILL WHITAKER (voiceover): This is not Tom Cruise. It`s one of a series of hyper-realistic deepfakes of the movie star that began appearing on the video-sharing app TikTok earlier this year.

TOM CRUISE ( Deepfake; TikTok): Hey, what`s up TikTok?

BILL WHITAKER (voiceover): For days, people wondered if they were real, and if not, who had created them.

TOM CRUISE ( Deepfake; TikTok): It`s important.

BILL WHITAKER (voiceover): Finally, a modest, thirty-two-year-old Belgian visual effects artist named Chris Ume, stepped forward to claim credit.

CHRIS UME: We believed as long as we`re making clear this is a parody, we`re not doing anything to harm his image. But after a few videos, we realized like, this is blowing up; we`re getting millions and millions and millions of views.

BILL WHITAKER (voiceover): Ume says his work is made easier because he teamed up with a Tom Cruise impersonator whose voice, gestures and hair are nearly identical to the real McCoy. Ume only deepfakes Cruise`s face and stitches that onto the real video and sound of the impersonator.

TOM CRUISE ( Deepfake; TikTok): That`s where the magic happens.

BILL WHITAKER (voiceover): For technophiles, DeepTomCruise was a tipping point for deepfakes.

TOM CRUISE ( Deepfake; TikTok): Still got it.

BILL WHITAKER: How do you make this so seamless?

CHRIS UME: It begins with training a deepfake model, of course. I have all the face angles of Tom Cruise, all the expressions, all the emotions. It takes time to create a really good deepfake model.

BILL WHITAKER: What do you mean training the model? How do you train your computer?

CHRIS UME: Training means it`s going to analyze all the images of Tom Cruise, all his expressions, compared to my impersonator. So the computer`s going to teach itself: when my impersonator is smiling, I`m going to recreate Tom Cruise smiling. And that`s-- that`s how you train it.

BILL WHITAKER: Using video from the CBS News archives, Chris Ume was able to train his computer to learn every aspect of my face, and wipe away the decades. This is how I looked thirty years ago. He can even remove my mustache. The possibilities are endless and a little frightening.

CHRIS UME: I see a lot of mistakes in my work. But I don`t mind it, actually, because I don`t want to fool people. I-- I just want to show them what`s possible.

BILL WHITAKER: You don`t want to fool people.

CHRIS UME: No. I want to entertain people, I want to raise awareness, and I want-- and I want to show where it`s all going.

NINA SCHICK: It is without a doubt one of the most important revolutions in the future of human communication and perception. I would say it`s analogous to the birth of the internet.

BILL WHITAKER (voiceover): Political scientist and technology consultant Nina Schick wrote one of the first books on deepfakes. She first came across them four years ago when she was advising European politicians on Russia`s use of disinformation and social media to interfere in democratic elections.

What was your reaction when you first realized this was possible and was going on?

NINA SCHICK: Well, given that I was coming at it from the perspective of disinformation and manipulation in the context of elections, the fact that AI can now be used to make images and video that are fake, that look hyper realistic, I thought, well, from a disinformation perspective, this is a game-changer.

BILL WHITAKER (voiceover): So far, there`s no evidence deepfakes have changed the game in a U.S. election. But earlier this year, the FBI put out a notification warning that ".Russian and Chinese. actors are using synthetic profile images."--creating deepfake journalists and media personalities to spread anti-American propaganda on social media.

LIEUTENANT GENERAL ROBERT ASHLEY: So, how do you get deepfakes--

BILL WHITAKER (voiceover): The U.S. military, law enforcement and intelligence agencies have kept a wary eye on deepfakes for years. At this 2019 hearing, Senator Ben Sasse of Nebraska asked if the U.S. is prepared for the onslaught of disinformation, fakery and fraud.

SENATOR BEN SASSE: When you think about the catastrophic potential to public trust and to markets that could come from deepfake attacks, are we organized in a way that we could possibly respond fast enough?

DAN COATS: We clearly need to be more agile. It poses a major threat to the United States and something that the intelligence community needs to be restructured to address.

BILL WHITAKER (voiceover): Since then, technology has continued moving at an exponential pace while U.S. policy has not. Efforts by the government and big tech to detect synthetic media are competing with a community of deepfake artists who share their latest creations and techniques online. Like the internet, the first place deepfake technology took off was in pornography. The sad fact is the majority of deepfakes today consist of women`s faces, mostly celebrities, superimposed onto pornographic videos.

NINA SCHICK: The first use case in pornography is just a harbinger of how deepfakes can be used maliciously in many different contexts, which are now starting to arise.

BILL WHITAKER: And they`re getting better all the time?

NINA SCHICK: Yes. The incredible thing about deepfakes and synthetic media is the pace of acceleration when it comes to the technology. And by five to seven years, we are basically looking at a trajectory where any single creator--so a YouTuber, a TikToker--will be able to create the same level of visual effects that is only accessible to the most well-resourced Hollywood studio today.

BILL WHITAKER (voiceover): The technology behind deepfakes is artificial intelligence, which mimics the way humans learn. In 2014, researchers for the first time used computers to create realistic-looking faces using something called Generative Adversarial Networks, or GANs.

NINA SCHICK: So you set up an adversarial game where you have two AIs combating each other to try and create the best fake synthetic content. And as these two networks combat each other, one trying to generate the best image, the other trying to detect where it could be better, you basically end up with an output that is increasingly improving all the time.

BILL WHITAKER (voiceover): Schick says the power of generative adversarial networks is on full display at a website called ThisPersonDoesNotExist.com

NINA SCHICK: Every time you refresh the page, there`s a new image of a person who does not exist.

BILL WHITAKER (voiceover): Each is a one-of-a-kind, entirely AI-generated image of a human being who never has, and never will, walk this Earth.

NINA SCHICK: You can see every pore on their face. You can see every hair on their head. But now imagine that technology being expanded out not only to human faces, in still images, but also to video, to audio synthesis of people`s voices and that`s really where we`re heading right now.

BILL WHITAKER: This is mind-blowing.

NINA SCHICK: Yes.

BILL WHITAKER: What-- what`s the positive side of this?

NINA SCHICK: The technology itself is neutral. So just as bad actors are, without a doubt, going to be using deepfakes, it is also going to be used by good actors. So first of all, I would say that there`s a very compelling case to be made for the commercial use of deepfakes.

BILL WHITAKER (voiceover): Victor Riparbelli is CEO and co-founder of Synthesia, based in London, one of dozens of companies using deepfake technology to transform video and audio productions.

VICTOR RIPARBELLI: The way Synthesia works is that we`ve essentially replaced cameras with code, and once you`re working with software, we do a lot of things that you wouldn`t be able to do with a normal camera. We`re still very early, but this is going to be a fundamental change in how we create media.

WOMAN (Synthesia): This video was, of course, generated by Synthesia.

BILL WHITAKER (voiceover): Synthesia makes and sells digital avatars, using the faces of paid actors to deliver personalized messages in sixty-four languages and allows corporate CEOs to address employees overseas.

(Woman speaking foreign language; Synthesia)

(Excerpt from Just Eat ad; Synthesia)

BILL WHITAKER (voiceover): Synthesia has also helped entertainers like Snoop Dogg go forth and multiply. This elaborate TV commercial for European food delivery service Just Eat cost a fortune.

(Excerpt from Just Eat ad; Synthesia)

VICTOR RIPARBELLI: Just Eat has a subsidiary in Australia, which is called Menulog. So what we did with our technology was we switched out the word Just Eat for Menulog.

(Excerpt from Just Eat ad; Synthesia)

VICTOR RIPARBELLI: And all of a sudden they had a localized version for the Australian market without Snoop Dogg having to do anything.

BILL WHITAKER: So he makes twice the money, huh?

VICTOR RIPARBELLI: Yeah.

BILL WHITAKER (voiceover): All it took was eight minutes of me reading a script on camera for Synthesia to create my synthetic talking head, complete with my gestures, head and mouth movements. Another company, Descript, used AI to create a synthetic version of my voice--

( Deepfake): This is Bill Whitaker`s synthetic voice, based on--

(Voiceover): --with my cadence, tenor and syncopation.

(Deepfacke): This is the result. The words you`re hearing were never spoken by the real Bill into a microphone or to a camera. He merely typed the words into a computer and they come out of my mouth.

(Voiceover): It may look and sound a little rough around the edges right now, but as the technology improves, the possibilities of spinning words and images out of thin air are endless.

( Deepfake): I`m Bill Whitaker. I`m Bill Whitaker. I`m Bill Whitaker.

Wow. And the head, the eyebrows, the mouth, the way it moves.

VICTOR RIPARBELLI: It`s-- it`s all synthetic.

BILL WHITAKER: I could be lounging at the beach and say, folks, you know, I`m not going to come in today. But you can use my avatar to do the work.

VICTOR RIPARBELLI: Maybe in a few years.

BILL WHITAKER: Don`t-- don`t-- don`t tell me that. I-- I`d be tempted.

TOM GRAHAM: I think it will have a big impact.

BILL WHITAKER (voiceover): The rapid advances in synthetic media have caused a virtual gold rush. Tom Graham, a London-based lawyer who made his fortune in cryptocurrency, recently started a company called Metaphysic with none other than Chris Ume, creator of DeepTomCruise. Their goal: develop software to allow anyone to create Hollywood-caliber movies without lights, cameras, or even actors.

TOM GRAHAM: As the hardware scales and as the models become more efficient, we can scale up the size of that model to be an entire Tom Cruise; body, movement and everything.

BILL WHITAKER: Well, talk about disruptive. I mean, are you going to put actors out of jobs?

TOM GRAHAM: I think it is a great thing if you`re a well-known actor today because you may be able to let somebody collect data for you to create a version of yourself in the future where you could be acting in movies after you have deceased. Or you could be the director, directing your younger self in a movie or something like that.

BILL WHITAKER (voiceover): If you are wondering how all of this is legal, most deepfakes are considered protected free speech. Attempts at legislation are all over the map. In New York, commercial use of a performer`s synthetic likeness without consent is banned for forty years after their death. California and Texas prohibit deceptive political deepfakes in the lead-up to an election.

NINA SCHICK: There are so many ethical, philosophical gray zones here that we really need to think about.

BILL WHITAKER: So, how do we as a society grapple with this?

NINA SCHICK: Just understanding what`s going on. Because a lot of people still don`t know what a deepfake is, what synthetic media is, that this is now possible. The counter to that is, how do we inoculate ourselves and understand that this kind of content is coming and exists without being completely cynical? Right? How do we do it without losing trust in all authentic media?

BILL WHITAKER (voiceover): That`s going to require all of us to figure out how to maneuver in a world where seeing is not always believing.

(End VT)

ANNOUNCER: When any video can be faked, how do you prove when one is real--

NINA SCHICK: If everything can be faked, then anything can be denied.

ANNOUNCER: --at 60minutesovertime.com.

(ANNOUNCEMENTS)

END

Content and programming Copyright MMXXI CBS Broadcasting Inc. ALL RIGHTS RESERVED. Copyright 2021 ASC Services II Media, LLC. All materials herein are protected by United States copyright law and may not be reproduced, distributed, transmitted, displayed, published or broadcast without the prior written permission of ASC Services II Media, LLC. You may not alter or remove any trademark, copyright or other notice from copies of the content.

Source: 60 Minutes (CBS News), Oct 10, 2021
Item: 32U1069706386CB7

# data[].attributes.attachment_link.description
BILL WHITAKER: You may never have heard the term "synthetic media"--more commonly known as --but our military, law enforcement and intelligence agencies certainly have. They are hyper-realistic video and audio recordings that use artificial intelligence and deep learning to create fake content or . The U.S.