The making of an AI news anchor—and its implications

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Abstract

This summer saw a months-long strike that pitted writers and performers against major Hollywood studios. A particularly fraught point of contention centered around the use (or not) of generative AI (also known as synthetic media or deepfakes). Aiming to find common ground, so that AI and writers can co-exist, the writers’ settlement requires that AI-generated material cannot be used to undermine a writer’s credit, and its use must be disclosed to writers. Protections for performers include that studios give fair compensation to performers for the use of digital replicas and for the labor unions and studios to meet twice a year to assess developments and implications of generative AI. A CNN anchor was all too easy to deepfake with a few simple AI tools. Image credit: Mike Coppola/Getty Images for CNN. As the strike winds down, and as governments consider appropriate worker protections, many continue to grapple with the potential impact of generative AI on creative professions: In particular, what is it, what is it capable of, and what will it be capable of? While entertainers consider the implications and policymakers slowly ruminate over the details, it’s already clear that the technology is exceptionally convincing. We—a computer science college student and a computer and information sciences professor—demonstrate here, in collaboration with a major TV network, that it’s relatively easy (and getting easier) to create an AI replica of not only a performer, but a figure that people trust to report the truth about world events. One clear takeaway: It’s time to stop mulling over future implications and to start considering near-term impacts, as well as potential regulations and controls. A Different Kind of Disruptor Generative AI refers to text, image, audio, or video that has been automatically synthesized by an AI-powered system (1). From only a text prompt, this technology can, for example, write a TV script; from only a text prompt, it can create a photo-realistic image; from only a few minutes of audio, it can clone an actor’s voice; and generative AI can insert a person’s likeness into anything ranging from a blockbuster movie to a newscast. Even in these early days, this technology can create stunningly realistic and creative content. The next generation will surely improve in terms of power, realism, and accessibility. Technological disruptions are, of course, not new. What makes this latest technological revolution different than others is that generative AI is trained on massive amounts of human-generated content in order to emulate human creativity (2). Because these systems wouldn’t exist without harvesting vast amounts of human-created data, this potential disruption to labor markets is qualitatively different than, for example, the disruption to bank tellers brought about by online banking and ATMs or the disruption to travel agencies brought about by online search and booking. Mimicking a News Anchor In collaboration with CNN, we attempted to create the opening of an AI-themed episode of The Whole Story, hosted by Anderson Cooper. The short script was not AI generated, but the rest of Cooper was. Here, we describe how, over a two-month period, using primarily open-source software, a first-year undergraduate student (the first author) was able to create an AI news anchor compelling enough to air on network television. The creation of an AI anchor consisted of two main parts: 1) generate an audio recording of the anchor reading a script; and 2) starting with a previously recorded video clip of the anchor, replace the audio and modify the anchor’s mouth and lower face to be consistent with this new audio (a so-called lip-sync deepfake). In describing these two steps, we intentionally omit some of the details as to how we accomplished this, so as not to further equip malicious actors. But we should be clear: None of these details required any significant innovations. The audio was created by first extracting examples of the anchor’s voice from 17 minutes of YouTube videos. These audio clips were used to clone the anchor’s voice using ElevenLabs’ commercial voice-cloning tool (3). Once cloned, we were able to generate an audio clip in the anchor’s voice from a text file containing the desired script. The deepfake came to life in four sequential steps: 1) The anchor’s head was automatically localized throughout each frame of the source video; and 2) a cropped video of just the anchor’s head was then provided, along with the generated audio, to the open-source VideoRetalking (4, 5). For each video frame, this neural-based system generated a new mouth region consistent with the new audio; 3) the resulting video was then provided to the open-source CodeFormer (6, 7). For each video frame, this generative adversarial network (GAN) enhanced the quality of the generated mouth by, for example, deblurring and removing obvious misshapen or absent teeth; and 4) the original face in each frame of the source video was replaced with the newly generated face. Lastly, a few preprocessing and postprocessing customizations refined the appearance of the final deepfake. Using this approach, we created an AI-generated version of Anderson Cooper reading parts of the opening of the December 3rd 2023 episode of The Whole Story. (See https://cnnpressroom.blogs.cnn.com/2023/11/27/cnns-nick-watt-investigates-ai-and-the-future-of-humanity-for-the-whole-story-with-anderson-cooper/.) We did run into some limitations. Large camera or head motions, for example, made it difficult to maintain temporal coherence, and unusual words in the script occasionally led to visually obvious errors in the shape of the mouth. In addition, the type of lip-sync deepfake we created works best when there is a large corpus of videos from which to select. As a result, right now, those with a large digital footprint—like Anderson Cooper—are more vulnerable to having their likeness copied. We have little doubt, however, that these limitations will soon be overcome with the next generation of software tools—tools that, even over the two months we worked on this project, evolved at a surprisingly fast pace. From Mimicking to Creating Generative AI can not only copy; it can also produce entirely new content and people. For example, image-based generative AI can produce highly realistic and customizable images of people who never existed. We created the person in Fig. 1 by prompting an open-source image generator (8) with “a photo of a trusted middle-aged female news anchor sitting at a news desk.” Fig. 1. This news anchor isn’t real. AI creations like this one are exceedingly easy to craft. Image credit: “A photo of a trusted middle-aged female news anchor sitting at a news desk,” image generated by Stable Diffusion on August 27, 2023 (https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0). Bringing this person—whom we named Sarah Connor—to life required only a few more steps. We next asked ChatGPT to write an opening line for our news anchor, yielding “Good evening ladies and gentlemen. I’m Sarah Connor, and this is your daily dose of Future Flash.” We then generated a voice using ElevenLabs customized voice generation (3) with the prompt “middle-aged, women, American, trusted news-anchor,” yielding a voice for our news anchor reading the AI-generated script. Lastly, we used the open-source Spline Motion Model (9) to animate the body and head of the single image and then used the same lip-sync deepfake pipeline as described above to bring this news anchor to life. (For audio and video generated, see https://github.com/matyasbohacek/AI-news-anchor.) This whole-cloth creation of an AI anchor took us two days to complete. Although the final video is not of the same quality as the AI version of Anderson Cooper, there’s little doubt that it will soon be possible to create highly realistic AI performers. The creation of an AI-generated news anchor is not hypothetical. As early as 2018, China’s state news agency Xinhua has been experimenting with AI-powered news anchors (10). There are clear incentives for a network to move from human- to AI-powered anchors. Some seem reasonable, such as the appearance and voice of AI anchors being customized to appeal to different markets. Other implications are more worrisome, as networks may simply decide to prioritize profits and eliminate their human workforce or push consistent and customized state-sanctioned narratives. From Lost Jobs to Disinformation Generative AI is also being weaponized by disinformation peddlers who co-opt the likenesses of trusted anchors and celebrities to push lies, conspiracies, and financial scams (11). Here, using the likenesses of trusted and well-known personalities is particularly powerful. Thanks to the past two decades of social media and the pressure to put content online, news anchors and celebrities have unknowingly provided their voices and likenesses for anyone to capture and exploit, now and forever. Without the use of deepfakes, disinformation has already sowed uncertainty and polarization around everything from our COVID response to our climate change response, our understanding of geopolitical conflicts, and the integrity of elections around the world (12). The introduction of deepfakes of news anchors to this already problematic landscape is sure to only exacerbate our ability to reason about a complex and fast-moving world. The doctrine of “fair use”—codified into the US Copyright Act of 1976—originated in common law during the 18th and 19th centuries and was designed to both protect creators and not be overly rigid so as to stifle innovation and creativity. In the same way, creators should have some say in how or whether their work is used to train AI systems. This can be accomplished, for example, by inserting into content an imperceptible and resilient digital watermark (13) that can specify everything from copyright ownership to the rules governing its use for training AI systems. There will, of course, be bad actors who choose to ignore these rules. But technological audits of their systems could reveal the presence of training data used without permission (14). Some regulatory guidelines can also mitigate disruptions. The US Senate, for example, has proposed the NO FAKES Act. If passed, this bill would create new federal intellectual property protections against the misappropriation of a person’s voice or likeness, prohibiting the unauthorized use of digital replicas without the informed consent of the depicted individuals. And the recent Digital Services Act passed by the European Union seeks to hold social media platforms accountable for promoting dangerous disinformation. There is good evidence that the public is uneasy with the role of generative AI in the creative sector. A recent Yahoo Entertainment/YouGov poll (15) found that 61% of US respondents believe that more AI-generated actors is a bad idea, with 29% unsure; and 63% believe that more AI-written scripts is a bad idea, with 28% unsure. Similarly, the US Copyright Office is hearing from creators asking for protection from generative AI (16). Real Risks We have focused our discussion on news anchors because they continue to hold a unique position of familiarity and trust in our society. Many other creators, however, are likely to see disruption to their sectors, including writers, editors, photographers, graphic artists, actors, voice-actors, musicians, and the enormous ecosystem that supports these creators. We believe there is a path forward that allows creators to benefit from their past labor, protect their future livelihoods, and leverage this latest exciting technology. In addition to the impact on existing performers, we may also soon see the resurrection of historical figures from Walter Cronkite to Hollywood greats like Sidney Poitier and Katharine Hepburn, raising thorny issues of right to publicity (17). Importantly, this technology will not reside only with well-resourced networks and Hollywood studios, but will soon become fully democratized. As such, the disruption to the entertainment industry, not to mention the news business, may not stop with writers and performers. The very studios advocating for the use of AI may also find themselves replaced by small teams that will use AI to write and produce content, which can then be distributed and monetized on social media. As we have shown, these risks are not hypothetical. Generative AI technology is here, becoming increasingly more powerful, and seeing widespread adoption. The time to discuss implications and interventions regarding disruption to the workforce to large-scale disinformation campaigns is now.

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News Feature: The genuine problem of fake news

M Mitchell Waldrop (2017)

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The muse in the machine

David Adam (2023)

As generative AI gets more inventive, what are the implications for human creativity? Artists have long looked to muses for inspiration. Model and actress Pattie Boyd inspired songs from both George Harrison and Eric Clapton. Oscar Wilde’s love for Lord Alfred Douglas encouraged Wilde to pen his famous plays. Even William Shakespeare opened Henry V with an earnest cry for creative help: “O for a Muse of fire, that would ascend the brightest heaven of invention.” Fast-improving AI is an impressive tool that could offer new ways to create, but is also a flawed newcomer that could mislead users and even denigrate the creative process. Image credit: Dave Cutler (artist). Enter machines. Can a computer act as a muse? Can an algorithm create art? To find out, artists and writers are among those experimenting with machine-learning computer models, which are trained on centuries of human works and can produce their own works on demand. From the high-profile language bot ChatGPT to visual-art generators like DALL-E, results suggest that artificial intelligence (AI) can now mimic human creativity at the touch of a button. Or can it? As researchers, artists, and others assess the fast-improving AI technology’s capabilities and shortcomings, they’re seeing an impressive tool that could offer new ways to create—but also a flawed newcomer that could mislead users and even denigrate the creative process. While some experts point to apparent examples of computerized creativity, others argue that AI technology will never match the human brain. “AI creativity is telling us more about our own creativity than anything else,” says Marcus du Sautoy, a mathematician at the University of Oxford (Oxford, UK) and author of the 2019 book The Creativity Code: How AI Is Learning to Write, Paint and Think. “It is a new telescope on the huge creative output that we have produced to date.” Creativity is notoriously difficult to define. Is it enough for a machine to rearrange words if it does not appreciate them? Does modeling, mimicking, and combining existing artistic styles count as doing something new? When does a computer’s note-by-note demonstration of its mastery of the mathematical roots of music tip from predictable to enjoyable? Those are philosophical as much as scientific questions. But, prompted by the rapid progress and public interest in what algorithms can achieve, mathematicians, psychologists, and AI experts are working to answer them. Flavors of Creativity Assessing creativity itself, whether human or machine, takes a little lateral thinking. Like many in the field, du Sautoy borrows a seminal concept first introduced by computer scientist Margaret Boden in 1998, which breaks down creativity into three types (1). The first, which Boden calls combinatorial creativity, involves the novel combination of familiar ideas. Generations of poets and writers have used this to find fame with a neat image or analogy. Think William Wordsworth and “I wandered lonely as a cloud.” The second, exploratory creativity, takes what already exists and pushes the boundaries to extend the limits of what was done or seen before. French painter Claude Monet exploited new pigments to visualize the way light fell on water lilies and, in doing so, helped launch Expressionism. Almost all human creativity is exploratory, Boden says. Her third branch of creativity is rarer and more mysterious. Called transformational creativity, it breaks rules, changes the game, and demands to be assessed on its own terms. Who says eyes must be painted on either side of the nose? Not Pablo Picasso. Or picture the impact of David Bowie as Ziggy Stardust performing Starman for the first time on television in 1972. du Sautoy argues that AI has achieved all three creativity types. “Midjourney and DALL-E, I think, could be regarded as interesting examples of combinational creativity—the power of AI guided by humans to mix language and visuals to create something surprising,” he says. Both programs generate images from textual and often abstract descriptions. Want to design a lawnmower? Here are a thousand pictures of possibilities, one of which is shaped like a dinosaur and another that is made of fruit. AIs are also well-placed for exploratory creativity, he says, because their training data often hide untapped potential. A music generator called The Continuator does this for jazz. It analyzes notes played by an improvising musician in real time and then continues to play in the same style, exploring new possibilities within that existing framework. For a case of AI and transformational creativity, du Sautoy points to a much-discussed round of the Chinese game Go, played in 2016 between Lee Sedol, an 18-time world champion, and AlphaGo, an algorithm developed by the company DeepMind. Go requires two players to alternately place black or white stones on a 19 × 19 grid, each trying to surround—and so capture—the stones of their opponent. For centuries, Go masters tended to place early stones on the board’s outer four lines. That’s a way to gain short-term territorial control while anticipating play shifting toward the center in later moves. But in move 37 of that game, AlphaGo broke with this orthodoxy and placed its stone on the fifth line in. It might not sound like much, but commentators and Sedol were staggered. Even AlphaGo knew it was doing something extraordinary, calculating the odds of a human player making that move as 10,000 to 1 against. It proved a masterstroke and, some 50 moves later, tipped the balance and sealed the win for the machine. “Transformational creativity is the tough one, where something new breaks the old system,” du Sautoy says. “I would say that Move 37 had that quality because it challenged the previous system of playing, with a radical new move.” Under the Hood How do the machines do it? Even the most creative algorithms can work only with the material on which they are trained. But these machines, known as generative AI, come in several types that apply what they learn from training data in subtly different ways. Language bots like ChatGPT typically employ a type of neural network called a transformer, which finds and learns statistical patterns in the order of words on millions of pages of online text. To create an essay, poem, or slide presentation, it computes what the next word should be, based on all the words that have come before (using all those millions of texts on which it’s been trained). The model also has something called a self-attention mechanism, which allows it to pick out the most important features of a user’s request. For example, if one asks a language model to describe how “a car is driving down the street when it gets hit by a truck,” then self-attention helps the algorithm identify that the word “it” in that request refers to the car, and not the street. That’s something the human mind assumes because a street being hit by a truck doesn’t make sense—but the AI doesn’t know that. Visual art AIs such as DALL-E tend to use different technology. Called latent diffusion models, these systems compress and manipulate data from existing images to find mathematical ways to generate other images from random noise. Among many other types of creative AIs, du Sautoy says some of the most successful are called Generative Adversarial Networks. These combine a generator model, which produces works based on a training dataset, with a discriminator model that must try to distinguish “fake” outputs (those made by the generator) from “real” (original examples similar to those in the training data). The two compete. While the generator offers fake samples that resemble real ones, the discriminator tries to spot the AI-generated outputs. Over time, the generator learns how to make its own output closer to the originals. “The feedback loop in the algorithm means that the algorithm is growing and learning as it plays and creates,” du Sautoy says. But although AI can do things we don’t inspect or instruct, in all cases, computer creativity can only follow human creativity, du Sautoy stresses. “AI needs our data to get going,” he says, “so it could never really get started without our creative output.” Anxious Artists As creative computers push into what has previously been a very human domain, their new-found abilities—and how far they might be able to go in the future—are provoking concern and controversy in some quarters. Earlier this year, ethicists even warned that generative AIs risked “the collapse of the creative process” because they devalue art (2). The debate has real-world implications. The UK Supreme Court is currently considering whether creative AIs should be granted intellectual property rights to their inventions. Physicist and entrepreneur Stephen Thaler wants to name his machine DABUS as an inventor on patent applications for an emergency beacon and a food container based on mathematical fractals. As the owner of the AI, Thaler argues that he would also own its patents by default. South Africa’s patent office agreed, issuing DABUS a patent in 2021, noting that it was “autonomously generated by an artificial intelligence.” Other patent-granting bodies, including that of the United States, refused because they require a human inventor. “It is a co-writer with a tendency to go off the rails, but sometimes it was fascinating to let it go off on a tangent a little, collect its ramblings, and piece together a part of a story out of them.” —Yudhanjaya Wijeratne Practitioners in science and education, meanwhile, worry that researchers and students could deliberately conceal the creative role of an AI. Shortly after the ChatGPT version of the model (since improved with a newer version called GPT-4) was released in November 2022, researchers at Northwestern University in Evanston, Illinois, showed that it could create convincing text for fake scientific abstracts, a third of which were plausible enough to fool human reviewers asked to identify them (3). One reason that the fake abstracts were convincing was that the AI knew how large an invented patient cohort should be, says Catherine Gao, a physician-scientist who led the work. For an invented study on the common condition of hypertension, ChatGPT included tens of thousands of patients in the cohort, whereas a study on monkeypox (a much rarer disease) had a much smaller number of participants. Many scientific journals subsequently published warnings to potential authors about using Large Language Models (LLMs) to help write submissions. Although PNAS and Nature, for example, now require authors to declare in research paper acknowledgments or “materials and methods” sections any help from AI language models, Science went further and banned any AI-generated content, including figures and graphs. The journal demands original work, said Science Editor-in-Chief Holden Thorp. “The word ‘original’ is enough to signal that text written by ChatGPT is not acceptable: It is, after all, plagiarized from ChatGPT,” Thorp wrote in a January 2023 editorial (4). Matthew Cobb, a zoologist at the University of Manchester in the UK, investigated ChatGPT’s abilities after growing concerned that students could submit the AI’s output in online exams that ask for text-based answers. He asked the AI to write answers on topics including the conflict between science and religion and the parenting behavior of birds. His worry was justified—up to a point. ChatGPT often produced answers that deserved a passing grade, he says. Still, rather than appearing creative, Cobb found that the language had the feel of generic boilerplate text. Artificial Amateurs Can AI help write quality creative fiction with the right prompts? To find out, experts at Google last year gave 13 professional writers the opportunity to work with its LLM, called LaMDA. The writers, who included Ken Liu, a multi-award-winning fantasy writer, and Robin Sloan, author of the 2012 bestselling novel Mr. Penumbra’s 24-Hour Bookstore, were given access to the AI for 9 weeks and asked to use it to craft a story (5). (The resulting stories can be read here: https://wordcraft-writers-workshop.appspot.com/.) “One of our goals was to help with the creative process,” says Daphne Ippolito, a senior research scientist at Google Brain, an AI division of the company. “Not to replace writers, but there’s parts of the writing process that are laborious, are boring or hard, like if you have writer’s block. And we really wanted to try to address some of these pain points.” The study broke down creative writing into separate tasks, from idea generation and writing sentences to looking up facts and suggesting words and items according to a specific theme, such as rabbit breeds and their magical qualities. “It was almost like having a constant brainstorming partner always there to bounce ideas off,” says Wole Talabi, a Nigerian author who took part in the study. “I would put in one or two sentences and then ask it to tell me what happens next. Even if I hated everything it suggested, it kept me thinking through the different alternatives. So I never got stuck.” Yudhanjaya Wijeratne, an author in Sri Lanka, also found the AI to be a useful prompt. “It is a co-writer with a tendency to go off the rails, but sometimes it was fascinating to let it go off on a tangent a little, collect its ramblings, and piece together a part of a story out of them.” He sees some pretty big potential. “I strongly suspect, that with a little bit of prompt engineering, we can actually co-write an entire novel this way.” Despite these positives, Ippolito says that most of the writers were disappointed with the creative aspects of the algorithm. “They expected it to be better at generating interesting stylistic things,” she says. The Google algorithm, like other language models, rarely surprised or produced something unexpected. “They don't really generate weird text. If they generate weird texts, it's probably because they made a mistake,” she says. “And the sort of weird things that a human writer writes is what makes their writing different.” Without that human quality, fictional stories written by AIs alone tend to stand out for their low quality. Already, science fiction and literary magazines have complained about receiving hundreds of hopeless algorithm-penned tales from would-be contributors. Another weakness of the algorithm, Ippolito says, was that it was just too nice. That goes for other language models as well, especially those that have been publicly released. “They bias the models to be agreeable and to agree with whatever the human says,” Ippolito says. “But if I ask, ‘is my story good?’, I don't want the answer to always be ‘yes.’ If my story is bad, I want the model to answer that the story is bad and explain why it's bad.” Other forms of bias in language models affect creativity as well. Early versions were trained on the full range of (often unpleasant) content available online and could easily be coaxed into making anti-Semitic or racist comments. So most developers now actively train models to avoid a range of topics. That’s good for their reputation, but less useful for a writer who might want to engage with the darker side of human nature. “The software seemed very reluctant to generate people doing mean things,” one of the authors told Google. These sensitivities mean that LLMs have whole categories of human experience that are off-limits, says Katherine Elkins, who works on AI and creativity at Kenyon College in Gambier, Ohio. “Drugs, sex, murder, violence—all the great stuff of novelists is filtered out,” she says. “So, we’re really not seeing what it’s capable of.” For Elkins, there is no doubt that AI can be creative. “I think the harder question,” she says, “is when we come to art.” Ghost in the Machine? Whether or not to judge creative works produced by a machine as art goes beyond assessment of the finished product, Elkins says. It also raises the issue of intentionality. “When I look at my students training to be artists, I think that they feel that the art that they're making is translating their lived experience—that there is an intention to make art behind it,” she says. “And obviously DALL-E doesn't have that kind of lived experience.” Or does it? “The tricky thing with all of this, right, is that it’s been trained on all of our art that has come out of that experience,” Elkins adds. Visual arts AIs have proven especially controversial recently, as artists discover that their works were used to train algorithms without their knowledge or consent. Online images often come with useful descriptions and captions that help the AI learn to associate the words and pictures—and then generate new images from text prompts. Some artists have fought back against the machines, launching a copyright lawsuit over the use of images and the ability to reproduce unique styles. “Humans are going to want to defend their territory,” Elkins says. But the stable diffusion mechanism at the heart of the visual arts AIs could make proving plagiarism difficult, she adds. Think of how a drop of food coloring or ink dropped into a glass of water spreads and diffuses into random patterns. “Well, here we're starting with the glass of water, with the ink already diffused, and it's like going in reverse,” she says. “So, there are no originals; there’s no plagiarism.” Some AI pictures have won prizes and sold for big money. In one now-infamous case, a Colorado artist submitted a Midjourney-produced image to an art contest and won. But because of the way these works are generated, many AI experts are reluctant to call the output of such models “art.” “I'm quite reserved about calling AI creative or at least comparing it to what artists do, because I know what these models look like from inside,” says Imke Grabe, a machine-learning researcher at the IT University of Copenhagen (Copenhagen, Denmark). “They lack an understanding of how the world works. And I think that’s a huge part of working as an artist.” For du Sautoy, this is where intentionality is key—and currently missing from machines. “I think that intention in AI creativity will happen, but I believe that will be a signifier of an emerging AI consciousness,” he says. “Once an AI has an inner world, it will be compelled to share this with others, and that will lead to the drive to demonstrate that something is going on inside the AI.” Derivative Designs A common argument against AIs being creative is that they draw heavily on the data on which they were trained. But, as Ippolito at Google Brain points out, writers, artists, and musicians have always done this. “If you look at the famous classical composers, Tchaikovsky steals from the composers who came before him; Bach steals from the composers who came before him,” she says. Arguably, creative works of all types are derivative, at least to some extent. Indeed, even a writer as creative as Shakespeare doused his muse of fire to routinely steal plotlines and scenes from other writers. “So, are we holding models to a higher standard for creativity, and for borrowing from the past, than humans?” Ippolito says. Perhaps these generative AI algorithms aren’t squashing human creativity—just pointing out its inherent limits. These arguments have been brought into sharper focus recently by the release of GPT-4 to ChatGPT subscribers in March, which, according to those who have seen it, offers a more sophisticated writer than its freely available predecessor. “It does seem like it has a longer attention, which means it can keep coherence and write longer,” says Annette Vee, an English professor at the University of Pittsburgh in Pennsylvania, who studies the intersection of writing and computation. “And it has a better sense of humor, which means that it processes context and cultural things a little bit better.” The updated chatbot also comes with an interesting new feature: It can analyze and describe images, including why they are funny. “The fact that it can translate the visual input into text along with all of these cultural things about humor is actually pretty impressive,” she says. OpenAI hasn’t revealed many details on the improved version, but Vee says the new algorithm is likely merging image models with text models. Such impressive exploits will continue to provoke both amazement and consternation from writers, artists, and researchers of all stripes, as they reconsider what it means to generate a creative work. “Creativity is a moving target, where people kind of very quickly accept, okay, computers can do this, meaning this is not an example of creativity,” says Michal Kosinski, a computational psychologist at Stanford University in California. “We shouldn’t be judging everything against human standards.”

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Towards robust blind face restoration with codebook lookup transformer

S Zhou, K. CHAN, C. Li … (2022)

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Hany Farid

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Journal ID (nlm-ta): Proc Natl Acad Sci U S A

Journal ID (iso-abbrev): Proc Natl Acad Sci U S A

Journal ID (publisher-id): PNAS

Title: Proceedings of the National Academy of Sciences of the United States of America

Publisher: National Academy of Sciences

ISSN (Print): 0027-8424

ISSN (Electronic): 1091-6490

Publication date (Electronic, pub): 27 December 2023

Publication date (Print, pub): 2 January 2024

Publication date PMC-release: 27 December 2023

Volume: 121

Issue: 1

Electronic Location Identifier: e2315678121

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[1] ^a Electrical Engineering and Computer Science, Stanford University, Stanford, CA 94305

[2] ^b University of California, Berkeley, CA 94720

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¹To whom correspondence may be addressed. Email: hfarid@ 123456berkeley.edu .

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Publisher ID: 202315678

DOI: 10.1073/pnas.2315678121

PMC ID: 10769834

PubMed ID: 38150500

SO-VID: f840a084-8da6-4199-8660-3f172e73db65

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News Feature: The genuine problem of fake news

The muse in the machine

Towards robust blind face restoration with codebook lookup transformer

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