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This June, in the political battle leading up to the 2024 US presidential primaries, a series of images were released showing Donald Trump embracing one of his former medical advisers, Anthony Fauci. In a few of the shots, Trump is captured awkwardly kissing the face of Fauci, a health official reviled by some US conservatives for promoting masking and vaccines during the COVID-19 pandemic.

It was obvious that they were fakes, says Hany Farid, a computer scientist at the University of California, Berkeley, and one of many specialists who examined the pictures. On close inspection of three of the photos, Trumps hair is strangely blurred, the text in the background is nonsensical, the arms and hands are unnaturally placed and the details of Trumps visible ear are not right. All are hallmarks for now of generative artificial intelligence (AI), also called synthetic AI.

Science and the new age of AI: a Nature special

Such deepfake images and videos, made by text-to-image generators powered by deep learning AI, are now rife. Although fraudsters have long used deception to make a profit, sway opinions or start a war, the speed and ease with which huge volumes of viscerally convincing fakes can now be created and spread paired with a lack of public awareness is a growing threat. People are not used to generative technology. Its not like it evolved gradually; it was like boom, all of a sudden its here. So, you dont have that level of scepticism that you would need, says Cynthia Rudin, an AI computer scientist at Duke University in Durham, North Carolina.

Dozens of systems are now available for unsophisticated users to generate almost any content for any purpose, whether thats creating deepfake Tom Cruise videos on Tik Tok for entertainment; bringing back the likeness of a school-shooting victim to create a video advocating gun regulation; or faking a call for help from a loved one to scam victims out of tens of thousands of dollars. Deepfake videos can be generated in real time on a live video call. Earlier this year, Jerome Powell, chair of the US Federal Reserve, had a video conversation with someone he thought was Ukrainian President Volodymyr Zelenskyy, but wasnt.

The quantity of AI-generated content is unknown, but it is thought to be exploding. Academics commonly quote an estimate that around 90% of all Internet content could be synthetic within a few years1. Everything else would just get drowned out by this noise, says Rudin, which would make it hard to find genuine, useful content. Search engines and social media will just amplify misinformation, she adds. Weve been recommending and circulating all this crap. And now were going to be generating crap.

Although a lot of synthetic media is made for entertainment and fun, such as the viral image of Pope Francis wearing a designer puffer jacket, some is agenda-driven and some malicious including vast amounts of non-consensual pornography, in which someones face is transposed onto another body. Even a single synthetic file can make waves: an AI-generated image of an explosion at the US Pentagon that went viral in May, for example, caused the stock market to dip briefly. The existence of synthetic content also allows bad actors to brush off real evidence of misbehaviour by simply claiming that it is fake.

Peoples ability to really know where they should place their trust is falling away. And thats a real problem for democracy, says psychologist Sophie Nightingale at Lancaster University, UK, who studies the effects of generative AI. We need to act on that, and quite quickly. Its already a huge threat. She adds that this issue will be a big one in the coming year or two, with major elections planned in the United States, Russia and the United Kingdom.

AI-generated fakes could also have huge impacts on science, say some experts. They worry that the rapidly developing abilities of generative AI systems could make it easier for unscrupulous researchers to publish fraudulent data and images (see Scammed science at the end of this article).

For now, some synthetic content contains give-away clues such as images featuring people with six fingers on one hand. But generative AI is getting better every day. Were talking about months until people cant tell the difference with the naked eye, says Wael Abd-Almageed, an information scientist and computer engineer at the University of Southern California in Los Angeles.

An overlay of synthetic faces (left) shows more regularity than one of real images (right).Credit: Hany Farid

All of this has researchers scrambling to work out how to harness the deepfake powers of AI for good, while developing tools to guard against the bad. There are two prongs of technological defence: proactively tagging real or fake content when it is generated; and using detectors to catch fakes after publication. Neither is a perfect solution, but both help by adding hurdles to fakery, says Shyam Sundar, a psychologist and founder of the Media Effects Research Laboratory at Pennsylvania State University in University Park. If youre a dedicated malicious actor, you can certainly go quite far. The idea is to make it difficult for them, he says.

Technology will be crucial in the short term, says Nightingale, but then longer term, maybe we can think more about education, regulation. The European Union is leading the way globally with its AI Act, which was passed by the parliament this June and is awaiting decisions by the two other branches of the EU government. Were going to learn important lessons from it for sure, says Nightingale, whether they get it right or not.

For researchers, generative AI is a powerful tool. It is being used, for example, to create medical data sets that are free of privacy concerns, to help design medicinal molecules and to improve scientific manuscripts and software. Deepfakes are being investigated for their use in anonymising participants of video-based group therapy; creating custom avatars of physicians or teachers that are more compelling for viewers; or allowing for improved control conditions in social-science studies2. Im more hopeful than concerned, says Sundar. I think its transformative as a technology.

But with the spectre of rampant misuse, researchers and ethicists have attempted to lay down rules for AI, including the 2018 Montreal Declaration for the Responsible Development of Artificial Intelligence and the 2019 Recommendation on Artificial Intelligence from the Organisation for Economic Co-operation and Development. An initiative called the Partnership on AI, a non-profit organization that includes major industry partners, fosters dialogue on best practices although some observers and participants have questioned whether it has had any impact.

AI and science: what 1,600 researchers think

All advocate for the principles of transparency and disclosure of synthetic content. Companies are picking that up: in March, for example, TikTok updated its community guidelines to make it mandatory for creators to disclose use of AI in any realistic-looking scene. In July, seven leading technology companies including Meta, Microsoft, Google, OpenAI and Amazon made voluntary commitments to the White House to mark their AI-generated content. And in September, Google declared that starting in mid-November, any AI-generated content used in political ads will have to be declared on its platforms, including YouTube.

One way to tag synthetic images is to watermark them by altering the pixels in a distinctive way thats imperceptible to the eye but notable on analysis. Tweaking every nth pixel so that its colour value is an even number, for example, would create a watermark but a simple one that would disappear after almost any image manipulation, such as applying a colour filter. Some watermarks have been criticized for being too easy to remove. But deeper watermarks can, for instance, insert a wave of dark-to-light shading from one side of an image to the other and layer it on top of several more such patterns, in a way that cant be wiped away by fiddling with individual pixels. These watermarks can be difficult (but not impossible) to remove, says Farid. In August, Google released a watermark for synthetic images, called SynthID, without revealing details about how it works; its unclear yet how robust it is, says Farid.

The companion idea to watermarking is to tag a files metadata with secure provenance information. For photography, such systems start when a photo is taken, with software on the camera device that ensures that an images GPS and time stamps are legitimate, and that the image isnt a photo of another photo, for example. Insurance underwriters use such systems to validate images of assets and damages, and the news agency Reuters has trialled authentication technology to validate photos of the war in Ukraine.

The Coalition for Content Provenance and Authenticity (C2PA), which brings together key industry groups in technology and publishing, released a first version of a set of technical specifications in 2022 for how systems should track provenance information for both synthetic and real imagery. Plenty of C2PA-compliant tools that embed, track and verify provenance data are now available, and many corporate commitments such as Microsofts say they will follow C2PA guidelines. C2PA is going to be very important; its going to help, says Anderson Rocha, an AI researcher at the University of Campinas in Brazil.

Systems that track image provenance should become the workhorse for cutting down the sheer number of dubious files, says Farid, who is on the C2PA steering committee and is a paid consultant for Truepic, a company in San Diego, California, that sells software for tracking authentic photos and videos. But this relies on good actors signing up to a scheme such as C2PA, and things will slip through the cracks, he says. That makes detectors a good complementary tool.

AI will transform science now researchers must tame it

Academic labs and companies have produced many AI-based classifiers. These learn the patterns that can distinguish AI-made media from real photos, and many systems have reported that they can spot fakes more than 90% of the time, while falsely identifying real images as fakes only 1% or less of the time. But these systems can often be defeated3. A bad actor can tweak images so that the detector is more likely to be wrong than right, says Farid.

AI-based tools can be paired with other techniques that lean on human insights to unravel the fake from the real. Farid looks for clues such as lines of perspective that dont follow the rules of physics. Other signs are more subtle. He and his colleagues found that facial profiles made by StyleGAN generators, for example, tend to place the eyes in the exact same position in the photo4, providing a hint as to which faces are fakes. Detectors can be given sophisticated algorithms that can, for example, read a clock somewhere in the photo and check to see whether the lighting in the image matches the recorded time of day. Tech company Intels FakeCatcher analyses videos by looking for expected colour changes in the face that arise from fluctuations in blood flow. Some detectors, says Rocha, look for distinctive noise patterns generated by light sensors in a camera, which so far arent well replicated by AI.

Experts worry about the influence of deepfake videos, such as this one of Barack Obama.Credit: AP via Alamy

The battle between fake-makers and fake-detectives is fierce. Farid recalls a paper by his former student Siwei Lyu, now a computer scientist at the University at Buffalo, New York, that highlighted how some AI videos featured people whose two eyes blinked at different rates5. Generators fixed that problem in weeks, he says. For this reason, even though Farids lab publishes the vast majority of its work, he releases code only on a case-by-case basis to academics who request it. Abd-Almageed takes a similar approach. If we release our tool to the public, people will make their own generation methods even more sophisticated, he says.

Several detection services that have public user interfaces have sprung up, and many academic labs are on the case, including the DeFake project at the Rochester Institute of Technology in New York and the University at Buffalos DeepFake-o-meter. And the US Defense Advanced Research Projects Agency (DARPA) launched its Semantic Forensics (SemaFor) project in 2021, with a broad remit of unearthing the who, what, why and how behind any generative file. A team of nearly 100 academics and corporate researchers have worked together under SemaFor to create more than 150 analytics, says the projects head, Wil Corvey. The bulk are detection algorithms that can be used in isolation or together.

AI tools as science policy advisers? The potential and the pitfalls

Because there are a huge number of both generators and detectors, and every case is different, reported accuracy rates vary wildly. And the arms race between them means that the situation is constantly changing. But for many media types, current success rates are poor. For generated text, a review this year of 14 detection tools found that all were neither accurate nor reliable6. For video, a high-profile competition in 2020 was won by a system that was only about 65% accurate3 (see also go.nature.com/3jvevoc). For images, Rocha says that if the generator is well known, detectors can easily be more than 95% accurate; but if the generator is new or unknown, success rates typically plummet. Using multiple detectors on the same image can increase the success rate, says Corvey.

He adds that detecting whether something is synthetic is only one part of the puzzle: as more users rely on AI to tweak their content, the more important question is not how much of this is synthetic? but rather why was this made?, he says. For this reason, an important part of SemaFors work is to determine the intent behind fakes, by attributing the media to a creator and characterizing its meaning. A parallel DARPA project, the Influence Campaign Awareness and Sensemaking (INCAS), is attempting to develop automated tools to detect the signals of mass misinformation campaigns that might or might not be supported by AI fakery.

SemaFor is now in the third and final stage of its project, in which Corvey is focusing on reaching out to potential users such as social-media sites. We have outreach to a number of companies including Google. To our knowledge, none have taken or are running our algorithms on a constant basis on-site, he says. Meta has collaborated with researchers at Michigan State University in East Lansing on detectors, but hasnt said how it might use them. Farid works with the employment-focused platform LinkedIn, which uses AI-based detectors to help weed out synthetic faces that support fraudulent accounts.

Abd-Almageed is in favour of social-media sites running detectors on all images on their sites, perhaps publishing a warning label on images flagged with a high percentage chance of being fake. But he had no luck when he discussed this a couple of years ago with a company that he would not name. I told a social network platform, take my software and use it, take it for free. And they said, if you cant show me how to make money, we dont care, says Abd-Almageed. Farid argues, however, that automated detectors arent well suited to this kind of use: even a 99% accurate tool would be wrong one time out of 100, which he thinks would completely erode public confidence. Farid argues that detection should be targeted at intensive, human-led investigations of specific cases, rather than trying to police the entire Internet.

Many argue that companies such as publishers and social-media sites will need legislation to push them into responsible behaviour. In June, the European Parliament passed a draft law that would strictly regulate high-risk uses of AI and enforce disclosure of content generated by such tools. The world is watching, because the EU has taken the lead on this, says Nightingale. But experts disagree widely about the acts merits and whether it might quash innovation. In the United States, a few AI bills are pending, including one aimed at preventing deepfakes of intimate images and one about the use of AI in political advertising, but neither is certain to pass.

There is one point that experts agree on: improving tech literacy will help to stop society and democracy from drowning in fakes. We need to get the word out to make people aware of whats happening, says Rocha. When they are informed about it, they can take action. They can demand education in schools.

Even with all the technological and social tools at our disposal, Farid says, its a losing battle to stop or catch all fakery. But its OK, because even in defeat I will have taken this out of the hands of the average person, he says. Then, as with counterfeit money, it will still be possible to fool the world with generative AI fakes but much harder.

Science isnt immune to the problem of AI-generated fakery. One concern is the integrity of biomedical images such as scans, microscopy images and western blots a standard technique in which distinctive bands are created by proteins of various molecular weights as they spread across a gel. Fraudsters have long faked such images using Photoshop or other image-manipulation software, but that is often detectable by a trained eye or by computers that check for image duplication. Generating images entirely with AI presents a bigger detection problem.

Last year, Rongshan Yu and his colleagues at Xiamen University, China, created scientific images using synthetic AI to see how easily it can be done7. They trained one generative AI program to create new western blot images from a training data set of 3,000 images; and used another to insert features of oesophageal cancer into an image of a non-cancerous intestine. They then tested how convincing the western blots were by inviting three specialists to try to spot one fake in a set of four images; two of the experts performed worse than chance, and the third fared better by spotting a visual clue to do with the smoothness between the image and the background. A computer system did slightly better than the specialists.

In a larger study of 14,000 original western blot images and 24,000 synthetic ones, made using four different generators, Anderson Rocha at the University of Campinas, Brazil, and his colleagues found that AI detectors trained on large data sets achieved accuracies of more than 85% (ref. 8). This is just the beginning, where we showed its feasible. Its possible to do way more than that, Rocha says. He and his team have a paper under review that extends their methods beyond western blots, he says.

No one Nature spoke to could provide proof that AI is being used by academics to beef up their papers or funding applications, but experts say its likely. Wael Abd-Almageed, an information scientist and computer engineer at the University of Southern California in Los Angeles, says he knows of specific cases in which academics have used AI to synthesize fakes, but declined to divulge details. Researchers investigating fake paper mills have said they have seen AI-generated images.

I dont know any case yet of a retracted paper that used synthetic creation to illustrate the results in that paper, says Rocha, who is working with the blog Retraction Watch on this issue. But its just a matter of time, I guess.

Publishers are on alert, says Bernd Pulverer, chief editor of the journal EMBO Reports. We are certainly concerned, he says. In the arms race between generative AI and detection tools, he says, we will struggle to detect well-executed AI image manipulation. It is all too easy to weaponize existing AI tools at every level of biological data.

Watermarking technologies could help, Pulverer adds. This idea has been explored previously for medical images not to prevent AI alterations, but to protect privacy and prevent any kind of tampering. Pulverer says he plans to work with instrument makers and image-processing experts to discuss the feasibility of watermarking or provenance-tagging scientific imagery.

Beyond developing technological tools against fraud, he says, the key is to encourage efforts to reproduce data in science. Attempting to replicate results could be the ultimate way to catch out fakery.

Originally posted here:

How to stop AI deepfakes from sinking society and science - Nature.com

Im very sensitive to the notion that on AI we shouldnt do that, he continued, but if we try to overreach, we may come up with goose eggs meaning nothing.

Congress sat on the regulatory sidelines throughout the rise of the internet and social media, only to later discover widespread concerns including data privacy, hate speech, election interference, misinformation and market dominance. Even after years of tense hearings and legislative proposals, Warner acknowledges our record in social media is a big fat zippo.

He worries lawmakers will suffer a similar fate with artificial intelligence by trying to mitigate its full spectrum of risks with a single law comprehensive legislation that others, including Sen. Todd Young (R-Ind.) also doubt is realistic. Instead, Warners been selling his colleagues on first tackling narrowly focused issues: the potential for AI-generated deepfakes to disrupt elections and financial markets.

Where Im at on the regulatory front is we probably cant solve it all at once, Warner said. But where are the two most immediate areas where AI could have an almost existential threat tomorrow?

He said hes considering new regulations that would perhaps address concerns about bias or require labels for AI-generated deepfakes though Warner said he has reservations about allowing companies to apply labels.

Hes also weighing an increase in penalties under existing laws when AI is used to undermine elections or markets. But who might pay those penalties when technology is abused the tech company or their users has been a sore point for Congress. A law created at the dawn of the internet, known as Section 230, has largely shielded tech companies from liability for their users actions.

Even the biggest advocates of Section 230, in my conversations with them up here on the Hill, have said they dont expect Section 230 to carry over to AI, Warner said.

A targeted bill would still struggle to clear a sharply divided Congress, especially one that deals with election security, Warner said. But he argues it stands a better chance than some of the more sweeping ideas being considered, including the notion of creating a federal agency to oversee AI. Warner said hes not against that idea, but with a Republican-controlled House, I wouldnt put all my eggs in that basket.

Attempts to regulate technology with ties to China, in particular the video-sharing app TikTok, offer another cautionary tale, Warner said. Legislation that Warner introduced earlier this year with Senate Minority Whip John Thune (R-S.D.) that would give the Commerce Department more oversight of foreign-owned tech firms, called the RESTRICT Act, S. 686 (118), was lining up senators two by two, like Noahs Ark and had the White Houses blessing before stalling amid political attacks earlier this year.

Warner said he has less anxiety today about China dominating AI than he did a year ago, though concerns remain about Beijing using the technology to advance its military and intelligence operations. But if Congress cannot come to a bipartisan agreement on how to combat national security concerns posed by Chinese technology, he said, then the prospect for comprehensive AI legislation looks grim.

Its so important, this is more on the politics side than the substance side, to at least show we can do something now, Warner said. Even if industry and other groups think thats all Congress will do, he added, I will take that risk because weve been so pathetic on social media. Weve got to show that we can actually put some markers down that have the force of law.

Annie Rees contributed to this report.

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Warner on AI regulation: 'We probably can't solve it all at once' - POLITICO

Can machines sniff out the presence of life on other planets? Well, to some extent, they already are.

Sensors onboard spacecraft exploring other worlds have the capability to detect molecules indicative of alien life. Yet, organic molecules that hint at intriguing biological processes are known to degrade over time, making their presence difficult for current technology to spot.

But now, a newly developed method based on artificial intelligence (AI) is capable of detecting subtle differences in molecular patterns that indicate biological signals even in samples hundreds of millions of years old. Better yet, the mechanism offers results with 90% accuracy, according to new research.

In the future, this AI system could be embedded in smarter sensors on robotic space explorers, including landers and rovers on the moon and Mars, as well as within spacecraft circling potentially habitable worlds like Enceladus and Europa.

"We began with the idea that the chemistry of life differs fundamentally from that of the inanimate world; that there are 'chemical rules of life' that influence the diversity and distribution of biomolecules," Robert Hazen, a scientist at the Carnegie Institution for Science in Washington D.C. and co-author of the new study, said in a statement. "If we could deduce those rules, we can use them to guide our efforts to model life's origins or to detect subtle signs of life on other worlds."

Related: NASA hopes humanoid robots can help us explore the moon and Mars

The new method relies on the premise that chemical processes that govern the formation and functioning of biomolecules differ fundamentally from those in abiotic molecules, in that biomolecules (like amino acids) hold on to information about the chemical processes that made them. This is likely to hold true for alien life, too, according to the new study.

On any world, life may produce and use higher quantities of a select few compounds to function on a daily basis. This would differentiate them from abiotic systems and it is these differences that can be spotted and quantified with AI, the researchers said in the statement.

The team first trained the machine learning algorithm with 134 samples, of which 59 were biotic and 75 were abiotic. Next, to validate the algorithm, the data was randomly split into a training set and a test set. The AI method successfully identified biotic samples from living things like shells, teeth, bones, rice, human hair as well as from ancient life preserved in certain fossilized fragments made of things like coal, oil and amber.

The tool also identified abiotic samples including chemicals like amino acids that were created in a lab as well as carbon-rich meteorites, according to the new study.

Almost immediately, the new AI method can be used to study the 3.5 billion-year-old rocks in the Pilbara region in Western Australia, where the world's oldest fossils are thought to exist. First found in 1993, these rocks were thought to be fossilized remains of microbes akin to cyanobacteria, which were the first living organisms to produce oxygen on Earth.

If confirmed, the bacteria's presence so early in Earth's history would mean the planet was friendly towards thriving life much earlier than previously thought. However, those findings have remained controversial, as research repeatedly pointed out that the evidence can also be due to pure geological processes having nothing to do with ancient life. Perhaps AI holds the answer.

This research is described in a paper published Monday (Sept. 25) in the journal Proceedings of the National Academy of Sciences.

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New AI algorithm can detect signs of life with 90% accuracy. Scientists want to send it to Mars - Space.com

By Emily Gaines Buchler

Artificial intelligence (AI) chatbots like ChatGPT can solve math problems, draft computer code, write essays, and create digital artall in mere seconds. But the knowledge and information spewed by the large language models are not always accurate, making fact-checking a necessity for anyone using it.

Since its launch in November 2022 by OpenAI, ChatGPT has kicked off a flurry of both excitement and concern over its potential to change how students work and learn. Will AI-powered chatbots open doors to new ways of knowledge-building and problem solving? What about plagiarism and cheating? Can schools, educators, and families do anything to prepare?

To answer these and other questions, three experts from Johns Hopkins University came together on Sept. 19 for "Could AI Upend Education?", a virtual event open to the public and part of the Johns Hopkins Briefing Series. The experts included James Diamond, an assistant professor in the School of Education and faculty lead of Digital Age Learning and Educational Technology Programs; Daniel Khashabi, an assistant professor of computer science in the Whiting School of Engineering; and Thomas Rid, a professor of strategic studies in the School of Advanced International Studies and the director of the Alperovitch Institute for Cybersecurity Studies. Lanie Rutkow, vice provost for interdisciplinary initiatives and a professor of health policy and management in the Bloomberg School of Public Health, mediated the conversation.

Here are five takeaways from the discussion:

"The sudden introduction of any new technology into an educational setting, especially one as powerful as [a chatbot with AI], rightly raises concerns," Diamond says. " There are concerns about plagiarism and cheating, [and] a reduced effort among some learners to solve problems and build their own understandings. There are also real concerns about AI perpetuating existing biases and inaccuracies, as well as privacy concerns about the use of technology."

"ChatGPT is a superpower in the classroom, and like power in general, it can either be used for good or for bad," Rid said.

"If we look at human knowledge as an ocean, [then] artificial intelligence and large language models allow us to navigate the deep water more quickly, but as soon as we get close to the ground or shore, the training material in the model is shallow, [and the bot] will start to hallucinate, or make things up. So reliability is a huge problem, and we have to get across to students that they cannot trust the output and have to verify and fact-check."

"[With new and emerging generative AI,] there are some really powerful implications for personalized learning [and] easing work burdens," Diamond said. "There's the potential to foster deeper interest and topics among students. There's also the potential of using [these tools] to create new materials or generate draft materials that learners build off and [use to] explore new ways to be creative."

"You can [use various programs to] identify to what extent what portions of a particular generation [or, say, essay] have been provided by the [large language] model," Khashabi said. "But none of these are robots. None of them are 100% reliable. There are scenarios under which we can say that with some high degree of confidence something has been generated, but for the next few years, as a technologist, I would say, 'Don't count on those.'"

"Parents and caretakers can sit next to their kid and explore a technology like ChatGPT with curiosity, openness, and a sense of wonder, [so] their kids see these tools as something to explore and use [in an experimental way] to create," Diamond said.

"Educators can have discussions with students about what might compel a learner to cheat. [They] can start to develop their students' AI literacy to help them understand what the technology is, what it can and cannot do, and what they can do with it."

"It really is essential that all stakeholdersparents, students, classroom teachers, school administrators, policymakerscome together and have discussions about how this technology is going to get used," Diamond said. "If we don't do that, then we'll wind up in a situation where we have the technology dictating the terms."

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Johns Hopkins experts advise educators to embrace AI and ChatGPT - The Hub at Johns Hopkins

Csar de la Fuente, PhD

In an era peppered by breathless discussions about artificial intelligencepro and conit makes sense to feel uncertain, or at least want to slow down and get a better grasp of where this is all headed. Trusting machines to do things typically reserved for humans is a little fantastical, historically reserved for science fiction rather than science.

Not so much for Csar de la Fuente, PhD, the Presidential Assistant Professor in Psychiatry, Microbiology, Chemical and Biomolecular Engineering, and Bioengineering in Penns Perelman School of Medicine and School of Engineering and Applied Science. Driven by his transdisciplinary background, de la Fuente leads the Machine Biology Group at Penn: aimed at harnessing machines to drive biological and medical advances.

A newly minted National Academy of Medicine Emerging Leaders in Health and Medicine (ELHM) Scholar, among earning a host of other awards and honors (over 60), de la Fuente can sound almost diplomatic when describing the intersection of humanity, machines and medicine where he has made his wayensuring multiple functions work together in harmony.

Biology is complexity, right? You need chemistry, you need mathematics, physics and computer science, and principles and concepts from all these different areas, to try to begin to understand the complexity of biology, he said. That's how I became a scientist.

Since his earliest days, de la Fuente has been fascinated by what he calls the intricate wonders of biology. In his late teens, for his undergraduate degree, de la Fuente immersed himself in microbiology, physics, mathematics, statistics, and chemistry, equipping himself with the necessary tools to unravel those biological mysteries.

In his early twenties, determined to understand biology at a fundamental level, de la Fuente decided to pursue a PhD, relocating to Canada from Spain. Overcoming language and cultural barriers, he embraced the challenges and opportunities that lay before him, determined to become a scientist.

His PhD journey centered around programming and digitizing the fundamental workings of biological systems. He specialized in bacteria, the simplest living biological system, as well as proteins and peptides, the least programmable of biomolecules and the workhorses of biology that perform every task in lifeliterally, from moving your mouth while speaking, to blinking your eyes while reading this.

Although his research was successful, the landscape of using machines for biology remained uncharted. Upon completing his PhD, de la Fuente noted that technology (at the time) still did not exist to manipulate peptides in any programmable way. I felt dissatisfied with the available technologies for programming biology, which relied on slow, painstaking, and unpredictable trial-and-error experimentation. Biology remained elusive in terms of programmability.

De la Fuente was then recruited by MIT in 2015, at the time a leading home for AI research. However, AI had not yet been applied to biology or molecules. While computers were already adept at recognizing patterns in images and text, de la Fuente saw an opportunity to train computers for applications in biology, connecting the ability for computers to process the massive amounts of data that was becoming increasingly available.

His focus was to incorporate computational thinking into his work, essentially infusing AI into biologyparticularly to discover new antibiotics.

The motivation behind that is antibiotic resistance, de la Fuente said, adding that bacteria that have developed resistance to known antibiotics kill over one million people per year, projected to grow to 10 million deaths annually by 2050 as resistant strains spread. Making advances in this hugely disinvested area and coming up with solutions to this sort of critical problem has been a huge motivation for me and for our team.

The typical timeline for discovering antibiotics is three to six years using conventional methods, but de la Fuentes work in recent years has bucked that trend. With some of the algorithms that his group has developed, what used to take three to six years can now be done in days, or even hours. The potential antibiotic compounds they have identified need more evaluation before they are ready for clinical testing in humans. Even so, the accelerated rate of antibiotic discovery remains a point of pride for de la Fuentes lab.

This work launched the emerging field of AI for antibiotic discovery, following a pioneering study with his colleagues that led to the design of the first antibiotic using AI. That led de la Fuente to joining Penn as a Presidential Assistant Professor, a post he holds today. Since then, much of his work has focused on pioneering computational and experimental methods to search inside the human bodys own proteins for unknown but potentially useful molecules. By discovering them, his team could learn to manufacture them and use them as templates for antibiotic development.

In 2021, we performed the first ever exploration of the human proteomethe set of all proteins in the human bodyas a source of antibiotics, he said. We found them encoded in proteins of the immune system, but also in proteins from the nervous system and the cardiovascular system, digestive systemall throughout our body.

Just this summer, de la Fuente continued to derive antibiotic discovery from a curious source of inspiration that has been extinct for tens of thousands of years.

Recently, de la Fuentes team applied machine learning to explore the proteomes not just of living humans like us, but of extinct organisms (think: Neanderthals and Denisovans) to find potential new antibiotics, launching the field of what they call molecular de-extinction" and providing a new framework for thinking about drug discovery. But when asked about what he sees as the future of harnessing machines for human benefit, de la Fuente is remarkably honest when asked about what surprises him about his field.

I've been working in the antibiotics field for a long time, and it has become a sort of under-invested area of research. Sometimes it feels like theres only a couple of us out there doing this work, so it feels weird sometimes, he said. With remarkable advances in machine and artificial intelligence in the last half decade, any new support may not be human but machine.

That combination between machine intelligence and human ingenuity, I think, will be part of the future and were going to see a lot of meaningful and important research coming out from that intersection. I believe we are on the cusp of a new era in science where advances enabled by AI will help control antibiotic resistance, infectious disease outbreaks, and future pandemics.

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Meet the AI Expert Using Machines to Drive Medical Advances - Penn Medicine