A novel vision sensor inspired by the neural transmission mechanisms of the human brain has been developed to efficiently and accurately extract object outlines even under fluctuating lighting environments. This advancement promises to significantly

In this Q&A, get a glimpse into the future of artificial intelligence (AI) and computer vision through the lens of longtime Unity user Gerard Espona, whose robot digital twin project was featured in the Made with Unity: AI series. Working as simulation lead at Luxonis, whose core technology makes it possible to embed human-level perception into robotics, Espona uses his years of experience in the industry to weigh in on the current state and anticipated progression of computer vision.During recent years, computer vision (CV) and AI have become the fastest-growing fields both in market size and industry adoption rate. Spatial CV and edge AI have been used to improve and automate repetitive tasks as well as complex processes.This new reality is thanks to the democratization of CV/AI. Increasingly affordable hardware access, including depth perception capability as well as improvements in machine learning (ML), has enabled the deployment of real solutions on edge CV/AI systems.Spatial CV using edge AI enables depth-based applications to be deployed without the need of a data center service, and also allows the user to preserve privacy by processing images on the device itself.Along with more accessible hardware, software and machine learning workflows are undergoing important improvements. Although they are still very specialized and full of technical challenges, they have become much more accessible, offering tools that allow users to train their own models.Within the standard ML pipeline/workflow, large-scale edge computing and deployment can still pose issues. One of the biggest general challenges is to reduce the costs and timelines currently required to create and/or improve machine learning models on real-world applications. In other words, the challenge is how to manage all these devices to enable a smooth pipeline for continuous improvement.Also, the implicit limitations in terms of compute processing need extra effort on the final model deployed on the device (that is, apps need to be lightweight, performant, etc.). That said, embedded technology evolves really fast, and each iteration is a big leap in processing capabilities.Spatial CV/AI is a field that still requires a lot of specialization and systems. Workflows are often complicated and tedious due to numerous technical challenges, so a lot of time is devoted to smoothing out the workflow instead of focusing on value-added tasks.Creating datasets (collecting and filtering images and videos), annotating the images, preprocessing/augmentation process, training, deploying and closing the feedback loop for continuous improvement is a complex process. Each step of the workflow is technically difficult and usually involves time and financial cost, and more so for systems working in remote areas with limited connectivity.At Luxonis, we help our customers build and deploy solutions to solve and automate complex tasks at scale, so we’re facing all these issues directly. Our mission, “Robotic vision made simple,” provides not only great and affordable depth-capable hardware, but also a solid and smooth ML pipeline with synthetic datasets and simulation.Another important challenge is the work that needs to be done on the interpretability of models and the creation of datasets from an ethical, privacy and bias point of view.Last but not least, global chip supply issues are making it difficult to get the hardware into everybody’s hands.Data-centric AI is potentially useful when a working model is underperforming. Investing a large amount of time to optimize the model often leads to almost zero real improvement. Instead, with data-centric AI the investment is in analysis, cleaning and improving of the dataset.Usually when a model is underperforming, the issue is within the dataset itself, as there is not enough data for the model to outperform. This could be the result of two possible reasons: 1) the model needs a much larger amount of data, which is difficult to collect in the real world, or 2) the model doesn’t have enough examples of rare cases, which take a lot of time to happen in the real world.In both situations, synthetic datasets could help.Thanks to Unity’s computer vision tools, it is very easy to create photorealistic scenes and randomize elements like materials, light conditions and object placement. The tools come with common labels like 2D bounding boxes, 3D bounding boxes, semantic and instance segmentation, and even human body key points. Additionally, these can be easily extended with custom randomizers, labelers and annotations.Almost any task you want to automate or improve using edge CV/AI very likely involves detecting people for obvious safety and security reasons. It’s critical to guarantee user safety around autonomous systems or robots when they’re working, requiring models to be trained on data about humans.That means we need to capture a large amount of images, including information like poses and physical appearance, that are representative of the entire human population. This task raises some concerns about privacy, ethics and bias when starting to capture real human data to train the model.Fortunately, we can use synthetic datasets to mitigate some of these concerns using human 3D models and poses. A very good example is the work done by the Unity team with PeopleSansPeople.PeopleSansPeople is a human-centric synthetic dataset creator using 3D models and standard animations to randomize human body poses. Also, we can use a Unity project template, to which we add our own 3D models and poses to create our own human synthetic dataset.At Luxonis, we’re using this project as the basis for creating our own human synthetic dataset and training models. In general, we use Unity’s computer vision tools to create large and complex datasets with a high level of customization on labelers, annotations and randomizations. This allows our ML team to iterate faster with our customers, without needing to wait for real-world data collection and manual annotation.Since the introduction of transformer architecture, CV tasks are more accessible. Generative models like DALL-E 2 could also be used to create synthetic datasets, and NeRF as a neural approach to generate novel point of views of known objects and scenes. It’s clear all these innovations are catching the attention of audiences.On the other hand, having access to better annotation tools and model zoos and libraries with pre-trained, ready-to-use models are helping drive wide adoption.One key element contributing to the uptick in computer vision use is the fast evolution of vision processing units (VPUs) that currently allow users to perform model inferences on device (without the need for any host) at 4 TOPS of processing power (current Intel Movidius Myriad X). The new generation of VPUs promises a big leap in capabilities, allowing even more complex CV/AI applications to be deployed on edge.Any application related to agriculture and farming always captures my attention. For example, there is now a cow tracking and monitoring CV/AI application using drones.Our thanks to Gerard for sharing his perspective with us – keep up with his latest thoughts on LinkedIn and Twitter. And, learn more about how Unity can help your team generate synthetic data to improve computer vision model training with Unity Computer Vision.

Once again, Sam Altman wants to show you the future. The CEO of OpenAI is standing on a sparse stage in San Francisco, preparing to reveal his next move to an attentive crowd. “We needed some way for identifying, authenticating humans in the age of AGI,” Altman explains, referring to artificial general intelligence. “We wanted a way to make sure that humans stayed special and central.” The solution Altman came up with is looming behind him. It’s a white sphere about the size of a beach ball, with a camera at its center. The company that makes it, known as Tools for Humanity, calls this mysterious device the Orb. Stare into the heart of the plastic-and-silicon globe and it will map the unique furrows and ciliary zones of your iris. Seconds later, you’ll receive inviolable proof of your humanity: a 12,800-digit binary number, known as an iris code, sent to an app on your phone. At the same time, a packet of cryptocurrency called Worldcoin, worth approximately $42, will be transferred to your digital wallet—your reward for becoming a “verified human.” Altman co-founded Tools for Humanity in 2019 as part of a suite of companies he believed would reshape the world. Once the tech he was developing at OpenAI passed a certain level of intelligence, he reasoned, it would mark the end of one era on the Internet and the beginning of another, in which AI became so advanced, so human-like, that you would no longer be able to tell whether what you read, saw, or heard online came from a real person. When that happened, Altman imagined, we would need a new kind of online infrastructure: a human-verification layer for the Internet, to distinguish real people from the proliferating number of bots and AI “agents.”And so Tools for Humanity set out to build a global “proof-of-humanity” network. It aims to verify 50 million people by the end of 2025; ultimately its goal is to sign up every single human being on the planet. The free crypto serves as both an incentive for users to sign up, and also an entry point into what the company hopes will become the world’s largest financial network, through which it believes “double-digit percentages of the global economy” will eventually flow. Even for Altman, these missions are audacious. “If this really works, it’s like a fundamental piece of infrastructure for the world,” Altman tells TIME in a video interview from the passenger seat of a car a few days before his April 30 keynote address.Internal hardware of the Orb in mid-assembly in March. Davide Monteleone for TIMEThe project’s goal is to solve a problem partly of Altman’s own making. In the near future, he and other tech leaders say, advanced AIs will be imbued with agency: the ability to not just respond to human prompting, but to take actions independently in the world. This will enable the creation of AI coworkers that can drop into your company and begin solving problems; AI tutors that can adapt their teaching style to students’ preferences; even AI doctors that can diagnose routine cases and handle scheduling or logistics. The arrival of these virtual agents, their venture capitalist backers predict, will turbocharge our productivity and unleash an age of material abundance.But AI agents will also have cascading consequences for the human experience online. “As AI systems become harder to distinguish from people, websites may face difficult trade-offs,” says a recent paper by researchers from 25 different universities, nonprofits, and tech companies, including OpenAI. “There is a significant risk that digital institutions will be unprepared for a time when AI-powered agents, including those leveraged by malicious actors, overwhelm other activity online.” On social-media platforms like X and Facebook, bot-driven accounts are amassing billions of views on AI-generated content. In April, the foundation that runs Wikipedia disclosed that AI bots scraping their site were making the encyclopedia too costly to sustainably run. Later the same month, researchers from the University of Zurich found that AI-generated comments on the subreddit /r/ChangeMyView were up to six times more successful than human-written ones at persuading unknowing users to change their minds.  Photograph by Davide Monteleone for TIMEBuy a copy of the Orb issue hereThe arrival of agents won’t only threaten our ability to distinguish between authentic and AI content online. It will also challenge the Internet’s core business model, online advertising, which relies on the assumption that ads are being viewed by humans. “The Internet will change very drastically sometime in the next 12 to 24 months,” says Tools for Humanity CEO Alex Blania. “So we have to succeed, or I’m not sure what else would happen.”For four years, Blania’s team has been testing the Orb’s hardware abroad. Now the U.S. rollout has arrived. Over the next 12 months, 7,500 Orbs will be arriving in dozens of American cities, in locations like gas stations, bodegas, and flagship stores in Los Angeles, Austin, and Miami. The project’s founders and fans hope the Orb’s U.S. debut will kickstart a new phase of growth. The San Francisco keynote was titled: “At Last.” It’s not clear the public appetite matches the exultant branding. Tools for Humanity has “verified” just 12 million humans since mid 2023, a pace Blania concedes is well behind schedule. Few online platforms currently support the so-called “World ID” that the Orb bestows upon its visitors, leaving little to entice users to give up their biometrics beyond the lure of free crypto. Even Altman isn’t sure whether the whole thing can work. “I can see [how] this becomes a fairly mainstream thing in a few years,” he says. “Or I can see that it’s still only used by a small subset of people who think about the world in a certain way.” Blania (left) and Altman debut the Orb at World’s U.S. launch in San Francisco on April 30, 2025. Jason Henry—The New York Times/ReduxYet as the Internet becomes overrun with AI, the creators of this strange new piece of hardware are betting that everybody in the world will soon want—or need—to visit an Orb. The biometric code it creates, they predict, will become a new type of digital passport, without which you might be denied passage to the Internet of the future, from dating apps to government services. In a best-case scenario, World ID could be a privacy-preserving way to fortify the Internet against an AI-driven deluge of fake or deceptive content. It could also enable the distribution of universal basic income (UBI)—a policy that Altman has previously touted—as AI automation transforms the global economy. To examine what this new technology might mean, I reported from three continents, interviewed 10 Tools for Humanity executives and investors, reviewed hundreds of pages of company documents, and “verified” my own humanity. The Internet will inevitably need some kind of proof-of-humanity system in the near future, says Divya Siddarth, founder of the nonprofit Collective Intelligence Project. The real question, she argues, is whether such a system will be centralized—“a big security nightmare that enables a lot of surveillance”—or privacy-preserving, as the Orb claims to be. Questions remain about Tools for Humanity’s corporate structure, its yoking to an unstable cryptocurrency, and what power it would concentrate in the hands of its owners if successful. Yet it’s also one of the only attempts to solve what many see as an increasingly urgent problem. “There are some issues with it,” Siddarth says of World ID. “But you can’t preserve the Internet in amber. Something in this direction is necessary.”In March, I met Blania at Tools for Humanity’s San Francisco headquarters, where a large screen displays the number of weekly “Orb verifications” by country. A few days earlier, the CEO had attended a $1 million-per-head dinner at Mar-a-Lago with President Donald Trump, whom he credits with clearing the way for the company’s U.S. launch by relaxing crypto regulations. “Given Sam is a very high profile target,” Blania says, “we just decided that we would let other companies fight that fight, and enter the U.S. once the air is clear.” As a kid growing up in Germany, Blania was a little different than his peers. “Other kids were, like, drinking a lot, or doing a lot of parties, and I was just building a lot of things that could potentially blow up,” he recalls. At the California Institute of Technology, where he was pursuing research for a masters degree, he spent many evenings reading the blogs of startup gurus like Paul Graham and Altman. Then, in 2019, Blania received an email from Max Novendstern, an entrepreneur who had been kicking around a concept with Altman to build a global cryptocurrency network. They were looking for technical minds to help with the project. Over cappuccinos, Altman told Blania he was certain about three things. First, smarter-than-human AI was not only possible, but inevitable—and it would soon mean you could no longer assume that anything you read, saw, or heard on the Internet was human-created. Second, cryptocurrency and other decentralized technologies would be a massive force for change in the world. And third, scale was essential to any crypto network’s value. The Orb is tested on a calibration rig, surrounded by checkerboard targets to ensure precision in iris detection. Davide Monteleone for TIMEThe goal of Worldcoin, as the project was initially called, was to combine those three insights. Altman took a lesson from PayPal, the company co-founded by his mentor Peter Thiel. Of its initial funding, PayPal spent less than $10 million actually building its app—but pumped an additional $70 million or so into a referral program, whereby new users and the person who invited them would each receive $10 in credit. The referral program helped make PayPal a leading payment platform. Altman thought a version of that strategy would propel Worldcoin to similar heights. He wanted to create a new cryptocurrency and give it to users as a reward for signing up. The more people who joined the system, the higher the token’s value would theoretically rise. Since 2019, the project has raised $244 million from investors like Coinbase and the venture capital firm Andreessen Horowitz. That money paid for the $50 million cost of designing the Orb, plus maintaining the software it runs on. The total market value of all Worldcoins in existence, however, is far higher—around $12 billion. That number is a bit misleading: most of those coins are not in circulation and Worldcoin’s price has fluctuated wildly. Still, it allows the company to reward users for signing up at no cost to itself. The main lure for investors is the crypto upside. Some 75% of all Worldcoins are set aside for humans to claim when they sign up, or as referral bonuses. The remaining 25% are split between Tools for Humanity’s backers and staff, including Blania and Altman. “I’m really excited to make a lot of money,” ” Blania says.From the beginning, Altman was thinking about the consequences of the AI revolution he intended to unleash. (On May 21, he announced plans to team up with famed former Apple designer Jony Ive on a new AI personal device.) A future in which advanced AI could perform most tasks more effectively than humans would bring a wave of unemployment and economic dislocation, he reasoned. Some kind of wealth redistribution might be necessary. In 2016, he partially funded a study of basic income, which gave $1,000 per-month handouts to low-income individuals in Illinois and Texas. But there was no single financial system that would allow money to be sent to everybody in the world. Nor was there a way to stop an individual human from claiming their share twice—or to identify a sophisticated AI pretending to be human and pocketing some cash of its own. In 2023, Tools for Humanity raised the possibility of using the network to redistribute the profits of AI labs that were able to automate human labor. “As AI advances,” it said, “fairly distributing access and some of the created value through UBI will play an increasingly vital role in counteracting the concentration of economic power.”Blania was taken by the pitch, and agreed to join the project as a co-founder. “Most people told us we were very stupid or crazy or insane, including Silicon Valley investors,” Blania says. At least until ChatGPT came out in 2022, transforming OpenAI into one of the world’s most famous tech companies and kickstarting a market bull-run. “Things suddenly started to make more and more sense to the external world,” Blania says of the vision to develop a global “proof-of-humanity” network. “You have to imagine a world in which you will have very smart and competent systems somehow flying through the Internet with different goals and ideas of what they want to do, and us having no idea anymore what we’re dealing with.”After our interview, Blania’s head of communications ushers me over to a circular wooden structure where eight Orbs face one another. The scene feels like a cross between an Apple Store and a ceremonial altar. “Do you want to get verified?” she asks. Putting aside my reservations for the purposes of research, I download the World App and follow its prompts. I flash a QR code at the Orb, then gaze into it. A minute or so later, my phone buzzes with confirmation: I’ve been issued my own personal World ID and some Worldcoin.The first thing the Orb does is check if you’re human, using a neural network that takes input from various sensors, including an infrared camera and a thermometer. Davide Monteleone for TIMEWhile I stared into the Orb, several complex procedures had taken place at once. A neural network took inputs from multiple sensors—an infrared camera, a thermometer—to confirm I was a living human. Simultaneously, a telephoto lens zoomed in on my iris, capturing the physical traits within that distinguish me from every other human on Earth. It then converted that image into an iris code: a numerical abstraction of my unique biometric data. Then the Orb checked to see if my iris code matched any it had seen before, using a technique allowing encrypted data to be compared without revealing the underlying information. Before the Orb deleted my data, it turned my iris code into several derivative codes—none of which on its own can be linked back to the original—encrypted them, deleted the only copies of the decryption keys, and sent each one to a different secure server, so that future users’ iris codes can be checked for uniqueness against mine. If I were to use my World ID to access a website, that site would learn nothing about me except that I’m human. The Orb is open-source, so outside experts can examine its code and verify the company’s privacy claims. “I did a colonoscopy on this company and these technologies before I agreed to join,” says Trevor Traina, a Trump donor and former U.S. ambassador to Austria who now serves as Tools for Humanity’s chief business officer. “It is the most privacy-preserving technology on the planet.”Only weeks later, when researching what would happen if I wanted to delete my data, do I discover that Tools for Humanity’s privacy claims rest on what feels like a sleight of hand. The company argues that in modifying your iris code, it has “effectively anonymized” your biometric data. If you ask Tools for Humanity to delete your iris codes, they will delete the one stored on your phone, but not the derivatives. Those, they argue, are no longer your personal data at all. But if I were to return to an Orb after deleting my data, it would still recognize those codes as uniquely mine. Once you look into the Orb, a piece of your identity remains in the system forever. If users could truly delete that data, the premise of one ID per human would collapse, Tools for Humanity’s chief privacy officer Damien Kieran tells me when I call seeking an explanation. People could delete and sign up for new World IDs after being suspended from a platform. Or claim their Worldcoin tokens, sell them, delete their data, and cash in again. This argument fell flat with European Union regulators in Germany, who recently declared that the Orb posed “fundamental data protection issues” and ordered the company to allow European users to fully delete even their anonymized data. (Tools for Humanity has appealed; the regulator is now reassessing the decision.) “Just like any other technology service, users cannot delete data that is not personal data,” Kieran said in a statement. “If a person could delete anonymized data that can’t be linked to them by World or any third party, it would allow bad actors to circumvent the security and safety that World ID is working to bring to every human.”On a balmy afternoon this spring, I climb a flight of stairs up to a room above a restaurant in an outer suburb of Seoul. Five elderly South Koreans tap on their phones as they wait to be “verified” by the two Orbs in the center of the room. “We don’t really know how to distinguish between AI and humans anymore,” an attendant in a company t-shirt explains in Korean, gesturing toward the spheres. “We need a way to verify that we’re human and not AI. So how do we do that? Well, humans have irises, but AI doesn’t.”The attendant ushers an elderly woman over to an Orb. It bleeps. “Open your eyes,” a disembodied voice says in English. The woman stares into the camera. Seconds later, she checks her phone and sees that a packet of Worldcoin worth 75,000 Korean won (about $54) has landed in her digital wallet. Congratulations, the app tells her. You are now a verified human.A visitor views the Orbs in Seoul on April 14, 2025. Taemin Ha for TIMETools for Humanity aims to “verify” 1 million Koreans over the next year. Taemin Ha for TIMEA couple dozen Orbs have been available in South Korea since 2023, verifying roughly 55,000 people. Now Tools for Humanity is redoubling its efforts there. At an event in a traditional wooden hanok house in central Seoul, an executive announces that 250 Orbs will soon be dispersed around the country—with the aim of verifying 1 million Koreans in the next 12 months. South Korea has high levels of smartphone usage, crypto and AI adoption, and Internet access, while average wages are modest enough for the free Worldcoin on offer to still be an enticing draw—all of which makes it fertile testing ground for the company’s ambitious global expansion. Yet things seem off to a slow start. In a retail space I visited in central Seoul, Tools for Humanity had constructed a wooden structure with eight Orbs facing each other. Locals and tourists wander past looking bemused; few volunteer themselves up. Most who do tell me they are crypto enthusiasts who came intentionally, driven more by the spirit of early adoption than the free coins. The next day, I visit a coffee shop in central Seoul where a chrome Orb sits unassumingly in one corner. Wu Ruijun, a 20-year-old student from China, strikes up a conversation with the barista, who doubles as the Orb’s operator. Wu was invited here by a friend who said both could claim free cryptocurrency if he signed up. The barista speeds him through the process. Wu accepts the privacy disclosure without reading it, and widens his eyes for the Orb. Soon he’s verified. “I wasn’t told anything about the privacy policy,” he says on his way out. “I just came for the money.”As Altman’s car winds through San Francisco, I ask about the vision he laid out in 2019: that AI would make it harder for us to trust each other online. To my surprise, he rejects the framing. “I’m much more [about] like: what is the good we can create, rather than the bad we can stop?” he says. “It’s not like, ‘Oh, we’ve got to avoid the bot overrun’ or whatever. It’s just that we can do a lot of special things for humans.” It’s an answer that may reflect how his role has changed over the years. Altman is now the chief public cheerleader of a $300 billion company that’s touting the transformative utility of AI agents. The rise of agents, he and others say, will be a boon for our quality of life—like having an assistant on hand who can answer your most pressing questions, carry out mundane tasks, and help you develop new skills. It’s an optimistic vision that may well pan out. But it doesn’t quite fit with the prophecies of AI-enabled infopocalypse that Tools for Humanity was founded upon.Altman waves away a question about the influence he and other investors stand to gain if their vision is realized. Most holders, he assumes, will have already started selling their tokens—too early, he adds. “What I think would be bad is if an early crew had a lot of control over the protocol,” he says, “and that’s where I think the commitment to decentralization is so cool.” Altman is referring to the World Protocol, the underlying technology upon which the Orb, Worldcoin, and World ID all rely. Tools for Humanity is developing it, but has committed to giving control to its users over time—a process they say will prevent power from being concentrated in the hands of a few executives or investors. Tools for Humanity would remain a for-profit company, and could levy fees on platforms that use World ID, but other companies would be able to compete for customers by building alternative apps—or even alternative Orbs. The plan draws on ideas that animated the crypto ecosystem in the late 2010s and early 2020s, when evangelists for emerging blockchain technologies argued that the centralization of power—especially in large so-called “Web 2.0” tech companies—was responsible for many of the problems plaguing the modern Internet. Just as decentralized cryptocurrencies could reform a financial system controlled by economic elites, so too would it be possible to create decentralized organizations, run by their members instead of CEOs. How such a system might work in practice remains unclear. “Building a community-based governance system,” Tools for Humanity says in a 2023 white paper, “represents perhaps the most formidable challenge of the entire project.”Altman has a pattern of making idealistic promises that shift over time. He founded OpenAI as a nonprofit in 2015, with a mission to develop AGI safely and for the benefit of all humanity. To raise money, OpenAI restructured itself as a for-profit company in 2019, but with overall control still in the hands of its nonprofit board. Last year, Altman proposed yet another restructure—one which would dilute the board’s control and allow more profits to flow to shareholders. Why, I ask, should the public trust Tools for Humanity’s commitment to freely surrender influence and power? “I think you will just see the continued decentralization via the protocol,” he says. “The value here is going to live in the network, and the network will be owned and governed by a lot of people.” Altman talks less about universal basic income these days. He recently mused about an alternative, which he called “universal basic compute.” Instead of AI companies redistributing their profits, he seemed to suggest, they could instead give everyone in the world fair access to super-powerful AI. Blania tells me he recently “made the decision to stop talking” about UBI at Tools for Humanity. “UBI is one potential answer,” he says. “Just giving [people] access to the latest [AI] models and having them learn faster and better is another.” Says Altman: “I still don’t know what the right answer is. I believe we should do a better job of distribution of resources than we currently do.” When I probe the question of why people should trust him, Altman gets irritated. “I understand that you hate AI, and that’s fine,” he says. “If you want to frame it as the downside of AI is that there’s going to be a proliferation of very convincing AI systems that are pretending to be human, and we need ways to know what is really human-authorized versus not, then yeah, I think you can call that a downside of AI. It’s not how I would naturally frame it.” The phrase human-authorized hints at a tension between World ID and OpenAI’s plans for AI agents. An Internet where a World ID is required to access most services might impede the usefulness of the agents that OpenAI and others are developing. So Tools for Humanity is building a system that would allow users to delegate their World ID to an agent, allowing the bot to take actions online on their behalf, according to Tiago Sada, the company’s chief product officer. “We’ve built everything in a way that can be very easily delegatable to an agent,” Sada says. It’s a measure that would allow humans to be held accountable for the actions of their AIs. But it suggests that Tools for Humanity’s mission may be shifting beyond simply proving humanity, and toward becoming the infrastructure that enables AI agents to proliferate with human authorization. World ID doesn’t tell you whether a piece of content is AI-generated or human-generated; all it tells you is whether the account that posted it is a human or a bot. Even in a world where everybody had a World ID, our online spaces might still be filled with AI-generated text, images, and videos.As I say goodbye to Altman, I’m left feeling conflicted about his project. If the Internet is going to be transformed by AI agents, then some kind of proof-of-humanity system will almost certainly be necessary. Yet if the Orb becomes a piece of Internet infrastructure, it could give Altman—a beneficiary of the proliferation of AI content—significant influence over a leading defense mechanism against it. People might have no choice but to participate in the network in order to access social media or online services.I thought of an encounter I witnessed in Seoul. In the room above the restaurant, Cho Jeong-yeon, 75, watched her friend get verified by an Orb. Cho had been invited to do the same, but demurred. The reward wasn’t enough for her to surrender a part of her identity. “Your iris is uniquely yours, and we don’t really know how it might be used,” she says. “Seeing the machine made me think: are we becoming machines instead of humans now? Everything is changing, and we don’t know how it’ll all turn out.”—With reporting by Stephen Kim/Seoul. This story was supported by Tarbell Grants.Correction, May 30The original version of this story misstated the market capitalization of Worldcoin if all coins were in circulation. It is $12 billion, not $1.2 billion.

Artificial Intelligence (AI) has been making significant strides over the past few years, with the emergence of Large Language Models (LLMs) marking a major milestone in its growth. With such widespread adoption, feeling left out of this revolution is not uncommon. One way an individual can stay updated with the latest trends is by reading books on various facets of AI. Following are the top AI books one should read in 2025. Deep Learning (Adaptive Computation and Machine Learning series) This book covers a wide range of deep learning topics along with their mathematical and conceptual background. It also provides information on the different deep learning techniques used in various industrial applications. Python: Advanced Guide to Artificial Intelligence This book helps individuals familiarize themselves with the most popular machine learning (ML) algorithms and delves into the details of deep learning, covering topics like CNN, RNN, etc. It provides a comprehensive understanding of advanced AI concepts while focusing on their practical implementation using Python. Machine Learning (in Python and R) for Dummies This book explains the fundamentals of machine learning by providing practical examples using Python and R. It is a beginner-friendly guide and a good starting point for people new to this field. Machine Learning for Beginners Given the pace with which machine learning systems are growing, this book provides a good base for anyone shifting to this field. The author talks about machine intelligence’s historical background and provides beginners with information on how advanced algorithms work. Artificial Intelligence: A Modern Approach This is a well-acclaimed book that covers the breadth of AI topics, including problem-solving, knowledge representation, machine learning, and natural language processing. It provides theoretical explanations along with practical examples, making it an excellent starting point for anyone looking to dive into the world of AI. Human Compatible: Artificial Intelligence and the Problem of Control The book discusses the inevitable conflict between humans and machines, providing important context before we advocate for AI. The author also talks about the possibility of superhuman AI and questions the concepts of human comprehension and machine learning. The Alignment Problem: Machine Learning and Human Values This book talks about a concept called “The Alignment Problem,” where the systems we aim to teach, don’t perform as expected, and various ethical and existential risks emerge. Life 3.0: Being Human in the Age of Artificial Intelligence The author of this book talks about questions like what the future of AI will look like and the possibility of superhuman intelligence becoming our master. He also talks about how we can ensure these systems perform without malfunctioning. The Coming Wave: Technology, Power, and the Twenty-First Century’s Greatest Dilemma This book warns about the risks that emerging technologies pose to global order. It covers topics like robotics and large language models and examines the forces that fuel these innovations. Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning “Artificial Intelligence Engines” dives into the mathematical foundations of deep learning. It provides a holistic understanding of deep learning, covering both the historical development of neural networks as well as modern techniques and architecture while focusing on the underlying mathematical concepts. Neural Networks and Deep Learning This book covers the fundamental concepts of neural networks and deep learning. It also covers the mathematical aspects of the same, covering topics like linear algebra, probability theory, and numerical computation. Artificial Intelligence for Humans This book explains how AI algorithms are used using actual numeric calculations. The book aims to target those without an extensive mathematical background and each unit is followed by examples in different programming languages. AI Superpowers: China, Silicon Valley, and the New World Order The author of this book explains the unexpected consequences of AI development. The book sheds light on the competition between the USA and China over AI innovations through actual events. Hello World: Being Human in the Age of Algorithms The author talks about the powers and limitations of the algorithms that are widely used today. The book prepares its readers for the moral uncertainties of a world run by code. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World This book talks about the concept of the “Master algorithm,” which is a single, overarching learning algorithm capable of incorporating different approaches. Applied Artificial Intelligence: A Handbook for Business Leaders “Applied Artificial Intelligence” provides a guide for businesses on how to leverage AI to drive innovation and growth. It covers various applications of AI and also explores its ethical considerations. Additionally, it sheds light on building AI teams and talent acquisition.  Superintelligence: Paths, Dangers, Strategies This book asks questions like whether AI agents will save or destroy us and what happens when machines surpass humans in general intelligence. The author talks about the importance of global collaboration in developing safe AI. We make a small profit from purchases made via referral/affiliate links attached to each book mentioned in the above list. If you want to suggest any book that we missed from this list, then please email us at asif@marktechpost.com Shobha KakkarShobha is a data analyst with a proven track record of developing innovative machine-learning solutions that drive business value.Shobha Kakkarhttps://www.marktechpost.com/author/shobha-kakkar/Hugging Face Introduces a Free Model Context Protocol (MCP) Course: A Developer’s Guide to Build and Deploy Context-Aware AI Agents and ApplicationsShobha Kakkarhttps://www.marktechpost.com/author/shobha-kakkar/13 Free AI Courses on AI Agents in 2025Shobha Kakkarhttps://www.marktechpost.com/author/shobha-kakkar/OpenAI Just Announced API Access to o1 (Advanced Reasoning Model)Shobha Kakkarhttps://www.marktechpost.com/author/shobha-kakkar/OpenAI Just Released Sora: The Most Awaited AI Video-Generation Tool

NVIDIA is working with companies worldwide to build out AI factories — speeding the training and deployment of next-generation AI applications that use the latest advancements in training and inference. The NVIDIA Blackwell architecture is built to meet the heightened performance requirements of these new applications. In the latest round of MLPerf Training — the 12th since the benchmark’s introduction in 2018 — the NVIDIA AI platform delivered the highest performance at scale on every benchmark and powered every result submitted on the benchmark’s toughest large language model (LLM)-focused test: Llama 3.1 405B pretraining. The NVIDIA platform was the only one that submitted results on every MLPerf Training v5.0 benchmark — underscoring its exceptional performance and versatility across a wide array of AI workloads, spanning LLMs, recommendation systems, multimodal LLMs, object detection and graph neural networks. The at-scale submissions used two AI supercomputers powered by the NVIDIA Blackwell platform: Tyche, built using NVIDIA GB200 NVL72 rack-scale systems, and Nyx, based on NVIDIA DGX B200 systems. In addition, NVIDIA collaborated with CoreWeave and IBM to submit GB200 NVL72 results using a total of 2,496 Blackwell GPUs and 1,248 NVIDIA Grace CPUs. On the new Llama 3.1 405B pretraining benchmark, Blackwell delivered 2.2x greater performance compared with previous-generation architecture at the same scale. On the Llama 2 70B LoRA fine-tuning benchmark, NVIDIA DGX B200 systems, powered by eight Blackwell GPUs, delivered 2.5x more performance compared with a submission using the same number of GPUs in the prior round. These performance leaps highlight advancements in the Blackwell architecture, including high-density liquid-cooled racks, 13.4TB of coherent memory per rack, fifth-generation NVIDIA NVLink and NVIDIA NVLink Switch interconnect technologies for scale-up and NVIDIA Quantum-2 InfiniBand networking for scale-out. Plus, innovations in the NVIDIA NeMo Framework software stack raise the bar for next-generation multimodal LLM training, critical for bringing agentic AI applications to market. These agentic AI-powered applications will one day run in AI factories — the engines of the agentic AI economy. These new applications will produce tokens and valuable intelligence that can be applied to almost every industry and academic domain. The NVIDIA data center platform includes GPUs, CPUs, high-speed fabrics and networking, as well as a vast array of software like NVIDIA CUDA-X libraries, the NeMo Framework, NVIDIA TensorRT-LLM and NVIDIA Dynamo. This highly tuned ensemble of hardware and software technologies empowers organizations to train and deploy models more quickly, dramatically accelerating time to value. The NVIDIA partner ecosystem participated extensively in this MLPerf round. Beyond the submission with CoreWeave and IBM, other compelling submissions were from ASUS, Cisco, Dell Technologies, Giga Computing, Google Cloud, Hewlett Packard Enterprise, Lambda, Lenovo, Nebius, Oracle Cloud Infrastructure, Quanta Cloud Technology and Supermicro. Learn more about MLPerf benchmarks.

Humans learn the norms, values and behaviors of society from each other — and Bernt Børnich, founder and CEO of 1X Technologies, thinks robots should learn like this, too. “For robots to be truly intelligent and show nuances like being careful around your pet, holding the door open for an elderly person and generally behaving like we want robots to behave, they have to live and learn among us,” Børnich told the AI Podcast. 1X Technologies is committed to building fully autonomous humanoid robots, with a focus on safety, affordability and adaptability. Børnich explained how 1X Technologies uses a combination of reinforcement learning, expert demonstrations and real-world data to enable its robots to continuously learn and adapt to new situations. NEO, the company’s upcoming robot, can perform household tasks like vacuuming, folding laundry, tidying and retrieving items. It’s built with operational safety at its core, using tendon-driven mechanisms inspired by the human musculoskeletal system to achieve low energy consumption. Børnich highlights the potential for robots to enhance human productivity by helping handle mundane tasks, freeing people up to focus more on interpersonal connections and creative activities. Learn more about the latest in physical AI and robotics at NVIDIA GTC Paris, which takes place from June 10-12. Register to attend humanoid-related sessions, including: “An Introduction to Humanoid Robots” for a deep dive into NVIDIA Isaac GR00T “How Physical AI Is Shaping the Next Generation of Industrial Robots” to learn how robotics leaders are making robots more intuitive and effective across industries Time Stamps 05:18 – 1X Technologies’ approach to robot safety. 11:36 – How world models enable robots to search backwards from the goal. 16:51 – How robots can free humans up for more meaningful activities. 22:29 – NEO answers the door so Børnich can interview a candidate. You Might Also Like…  How World Foundation Models Will Advance Physical AI With NVIDIA’s Ming-Yu Liu AI models that can accurately simulate and predict outcomes in physical, real-world environments will enable the next generation of physical AI systems. Ming-Yu Liu, vice president of research at NVIDIA and an IEEE Fellow, explains the significance of world foundation models — powerful neural networks that can simulate physical environments. Roboflow Helps Unlock Computer Vision for Every Kind of AI Builder Roboflow’s mission is to make the world programmable through computer vision. By simplifying computer vision development, the company helps bridge the gap between AI and people looking to harness it. Cofounder and CEO Joseph Nelson discusses how Roboflow empowers users in manufacturing, healthcare and automotive to solve complex problems with visual AI. Imbue CEO Kanjun Qiu on Transforming AI Agents Into Personal Collaborators Kanjun Qiu, CEO of Imbue, explores the emerging era where individuals can create and use their own AI agents. Drawing a parallel to the PC revolution of the late 1970s and ‘80s, Qiu discusses how modern AI systems are evolving to work collaboratively with users, enhancing their capabilities rather than just automating tasks.

Next week, the former president of “Grant Theft Auto” maker Rockstar North launches his first title since leaving the Take-Two Interactive-owned video game developer and opening his own studio, Build A Rocket Boy: the AAA narrative-driven action-adventure thriller “MindsEye.” Published by IOI Partners, the team behind the “Hitman” franchise, the Unreal Engine 5-built game will debut June 10 across PlayStation 5, Xbox Series X and S, and on PC via Steam and Epic Games Store with a $59.99 price tag for the standard edition. Related Stories Set in the near-futuristic city of Redrock, “MindsEye” puts players into the role of Jacob Diaz, a former soldier haunted by fragmented memories from his mysterious MindsEye neural implant, as he uncovers a conspiracy involving rogue AI, corporate greed, an unchecked military, and a threat so sinister that it endangers the very survival of humanity. Popular on Variety But the base story isn’t the biggest draw for “MindsEye,” which includes Build A Rocket Boy’s proprietary Game Creation System, that enables players to, well, “craft anything in their minds eye.” Per the studio, “Players can craft their own experiences using all of the ‘MindsEye’ assets, creating everything from custom missions to entirely new scenarios within the game’s expansive, richly detailed world. Whether you’re designing a high-speed chase through Redrock’s bustling cityscapes or a stealth mission in its industrial outskirts, it is designed to be intuitive and easy to use, ensuring that players of all skill levels can bring their imagination to life.” Benzies’ Edinburgh-based Build A Rocket Boy has promised “fresh premium content” will rollout monthly for the game, including regular releases of new missions, challenges and game assets. While “MindsEye” is the first title from Benzies since he launched BARB after leaving Rockstar in 2016 (Benzies was the lead “Grand Theft Auto” developer across the third through fifth games in the franchise, as well as “Grand Theft Auto Online,” and was in a legal battle with parent company Take Two over unpaid royalties from 2016 until 2019), it’s just step one in the prolific producer’s plan to shake up the gaming industry. “At Build A Rocket Boy, our vision goes far beyond a single title,” Benzies told Variety. “‘MindsEye’ is the first episode and central story around which ever-expanding interconnected episodes will span. We’re already working on future episodes, which will introduce alternate realities while maintaining it’s core themes of hope, redemption, and the intrigue of civilizations past and future, drawing from the lore and multiverse concepts.” See Variety‘s full interview with Benzies below, including the inevitable comparisons that will be drawn between “MindsEye” and the aesthetic of the “GTA” franchise, and his hopes for Rockstar Games’ highly anticipated and much-delayed “GTA 6.” Where did the concept for “MindsEye” come from? I pull a lot of inspiration from the real world. Watching the actions of humans – their foibles and their virtues. Watching the advancement of technology and how we adapt, or indeed, do not adapt. We’ve been moving to an automated world for many years now, and the impact on humans, especially with recent advancements in AI, which serves as good fodder for a story and even better for a video game. I think we all have this little nagging feeling about how humans and AI will blend together in the future—will it go smoothly, or will it turn sinister? We’re fans of all different types of media, and we’ve drawn influence from cinematic visionaries like Ridley Scott, Paul Greengrass, Christopher Nolan, and J.J. Abrams, and films like “The Bourne Identity,” “Memento,” and TV series “Lost” — they’re all exploring memory, perception, and control in their own ways. So, while we nod to those influences here and there, we wanted to build something that feels fresh, grounded in today’s world, but still asking the kinds of questions that have always made this genre powerful. With your “GTA” roots, obvious comparisons are already being drawn between the style and aesthetic of that franchise and “MindsEye.” Comparisons will always be made—it’s the way human beings pigeonhole concepts. But “MindsEye” isn’t built to fit into anyone else’s box. Many games share the same core elements: cars, guns, cities, and charismatic characters, and differentiation is even tougher in today’s entertainment landscape. Streaming, social media, and on-demand binge culture have fractured attention spans, and consumer mindshare is a brutal battlefield for all IP. Our industry continues to celebrate each other’s breakthroughs, and I’m proud that our collective innovation is advancing the medium of gaming, even if our paths diverge. As an independent studio we have the freedom to break ground in experimental new ways and the challenge is balancing innovation with familiarity—too much “new” risks alienating fans, too much “same” feels stale. It’s about nailing what makes your game’s world feel alive and urgent. “MindsEye” is about consequence and connection—it’s cinematic, reactive, and meant to feel like a world you’re not just playing in, but able to create in it too. We’re excited to see what they’ve crafted with “GTA VI ,” and I can’t wait to play it as a consumer for the first time. They’re always delivering something new, unique and at a scale that very few can pull off. What does MindsEye represent in BARB’s larger vision and long-term strategy? Are you plotting this out as a multi-game franchise or your first standalone? At Build A Rocket Boy, our vision goes far beyond a single title. “MindsEye” is the first episode and central story around which ever-expanding interconnected episodes will span. We’re already working on future episodes, which will introduce alternate realities while maintaining it’s core themes of hope, redemption, and the intrigue of civilizations past and future, drawing from the lore and multiverse concepts. It’s the future of entertainment to allow active participation so players feel like they have agency and can immerse themselves in our world as they want to. We are introducing three products in one game that will revolutionize AAA-quality interactive gaming and storytelling: “MindsEye” narrative story, Play.MindsEye, and Build.MindsEye. In our tightly crafted action-noir, “MindsEye” narrative story we have rips in time accessed through portals at strategic points throughout the game – so while you play as Jacob Diaz on his personal journey, players can also explore side stories and delve deeper into the backstories of characters they encounter along the way. In this way we are delivering companion content at the same time as the anchor content, weaving a rich narrative tapestry which will continue to evolve and expand giving greater depth to characters so you understand their personality and motivations. How do digital products Play.MindsEye (formerly named Arcadia) and Build.MindsEye (formerly Everywhere) tie in to plans for “MindsEye” and what BARB wants to offer gamers? In this new era of entertainment, where streaming platforms, boom-and-bust games, and an on-demand culture dominate, we’re pushing things in a new direction—with an interface that simplifies how we consume not just games, but all forms of entertainment. Consumers are moving away from 2D browsing into fully 3D, immersive experiences. Put simply, we’re shifting from passive interaction to active participation. As with all new products, things evolve. Arcadia was originally envisioned as our creation platform, but as we continued developing “MindsEye” and building out BARB’s ecosystem, it naturally grew into something more focused— Play.MindsEye and Build.MindsEye. Play delivers cinematic, high-intensity gameplay with missions and maps that constantly evolve. Build gives players intuitive tools to create their own content—no technical skills required, just imagination and intent. For BARB to fully realize our vision, we had to beta test our creation system with a community of builders in real-time and started with Everywhere while we were in stealth mode developing MindsEye. How did you settle on IOI as publishing partner? We’ve always found the way IOI handled the “Hitman” franchise interesting. They are one of the few publishers that have taken their single-player IP and increased their player count and amplified their community culture over time. From a technology point of view, their one executable approach for all of their content is very smart, and we always planned to have a similar approach, which encouraged us to join forces. This interview has been edited and condensed.

Editor’s note: This blog, originally published on October 28, 2024, has been updated. Financial losses from worldwide credit card transaction fraud are projected to reach more than $403 billion over the next decade. The new NVIDIA AI Blueprint for financial fraud detection can help combat this burgeoning epidemic — using accelerated data processing and advanced algorithms to improve AI’s ability to detect and prevent credit card transaction fraud. Launched this week at the Money20/20 financial services conference, the blueprint provides a reference example for financial institutions to identify subtle patterns and anomalies in transaction data based on user behavior to improve accuracy and reduce false positives compared with traditional methods. It shows developers how to build a financial fraud detection workflow by providing reference code, deployment tools and a reference architecture. Companies can streamline the migration of their fraud detection workflows from traditional compute to accelerated compute using the NVIDIA AI Enterprise software platform and NVIDIA accelerated computing. The NVIDIA AI Blueprint is available for customers to run on Amazon Web Services, with availability coming soon on Dell Technologies and Hewlett Packard Enterprise. Customers can also use the blueprint through service offerings from NVIDIA partners including Cloudera, EXL, Infosys and SHI International. Businesses embracing comprehensive machine learning (ML) tools and strategies can observe up to an estimated 40% improvement in fraud detection accuracy, boosting their ability to identify and stop fraudsters faster and mitigate harm. As such, leading financial organizations like American Express and Capital One have been using AI to build proprietary solutions that mitigate fraud and enhance customer protection. The new AI Blueprint accelerates model training and inference, and demonstrates how these components can be wrapped into a single, easy-to-use software offering, powered by NVIDIA AI. Currently optimized for credit card transaction fraud, the blueprint could be adapted for use cases such as new account fraud, account takeover and money laundering. Using Accelerated Computing and Graph Neural Networks for Fraud Detection Traditional data science pipelines lack the compute acceleration to handle the massive data volumes required for effective fraud detection. ML models like XGBoost are effective for detecting anomalies in individual transactions but fall short when fraud involves complex networks of linked accounts and devices. Helping address these gaps, NVIDIA RAPIDS — part of the NVIDIA CUDA-X collection of microservices, libraries, tools and technologies — enables payment companies to speed up data processing and transform raw data into powerful features at scale. These companies can fuel their AI models and integrate them with graph neural networks (GNNs) to uncover hidden, large-scale fraud patterns by analyzing relationships across different transactions, users and devices. The use of gradient-boosted decision trees — a type of ML algorithm — tapping into libraries such as XGBoost, has long been the standard for fraud detection. The new AI Blueprint for financial fraud detection enhances the XGBoost ML model with NVIDIA CUDA-X Data Science libraries including GNNs to generate embeddings that can be used as additional features to help reduce false positives. The GNN embeddings are fed into XGBoost to create and train a model that can then be orchestrated. In addition, NVIDIA Dynamo-Triton, formerly NVIDIA Triton Inference Server, boosts real-time inferencing while optimizing AI model throughput, latency and utilization. NVIDIA CUDA-X Data Science and Dynamo-Triton are included with NVIDIA AI Enterprise. Leading Financial Services Organizations Adopt AI During a time when many large North American financial institutions are reporting online or mobile fraud losses continue to increase, AI is helping to combat this trend. American Express, which began using AI to fight fraud in 2010, leverages fraud detection algorithms to monitor all customer transactions globally in real time, generating fraud decisions in just milliseconds. Using a combination of advanced algorithms, one of which tapped into the NVIDIA AI platform, American Express enhanced model accuracy, advancing the company’s ability to better fight fraud. European digital bank bunq uses generative AI and large language models to help detect fraud and money laundering. Its AI-powered transaction-monitoring system achieved nearly 100x faster model training speeds with NVIDIA accelerated computing. BNY announced in March 2024 that it became the first major bank to deploy an NVIDIA DGX SuperPOD with DGX H100 systems, which will help build solutions that support fraud detection and other use cases. And now, systems integrators, software vendors and cloud service providers can integrate the new NVIDIA blueprint for fraud detection to boost their financial services applications and help keep customers’ money, identities and digital accounts safe. Explore the NVIDIA AI Blueprint for financial fraud detection and read this NVIDIA Technical Blog on supercharging fraud detection with GNNs. Learn more about AI for fraud detection by visiting the AI Summit at Money20/20, running this week in Amsterdam. See notice regarding software product information.

Paradromics, a brain-computer interface startup, inserted its brain implant in a person—briefly—in an early test of its technology.

How Deepfakes Are Created Generative AI models enable the creation of highly realistic fake media. Most deepfakes today are produced by training deep neural networks on real images, video or audio of a target person. The two predominant AI architectures are generative adversarial networks (GANs) and autoencoders. A GAN consists of a generator network that produces synthetic images and a discriminator network that tries to distinguish fakes from real data. Through iterative training, the generator learns to produce outputs that increasingly fool the discriminator¹. Autoencoder-based tools similarly learn to encode a target face and then decode it onto a source video. In practice, deepfake creators use accessible software: open-source tools like DeepFaceLab and FaceSwap dominate video face-swapping (one estimate suggests DeepFaceLab was used for over 95% of known deepfake videos)². Voice-cloning tools (often built on similar AI principles) can mimic a person’s speech from minutes of audio. Commercial platforms like Synthesia allow text-to-video avatars (turning typed scripts into lifelike “spokespeople”), which have already been misused in disinformation campaigns³. Even mobile apps (e.g. FaceApp, Zao) let users do basic face swaps in minutes⁴. In short, advances in GANs and related models make deepfakes cheaper and easier to generate than ever. Diagram of a generative adversarial network (GAN): A generator network creates fake images from random input and a discriminator network distinguishes fakes from real examples. Over time the generator improves until its outputs “fool” the discriminator⁵ During creation, a deepfake algorithm is typically trained on a large dataset of real images or audio from the target. The more varied and high-quality the training data, the more realistic the deepfake. The output often then undergoes post-processing (color adjustments, lip-syncing refinements) to enhance believability¹. Technical defenses focus on two fronts: detection and authentication. Detection uses AI models to spot inconsistencies (blinking irregularities, audio artifacts or metadata mismatches) that betray a synthetic origin⁵. Authentication embeds markers before dissemination – for example, invisible watermarks or cryptographically signed metadata indicating authenticity⁶. The EU AI Act will soon mandate that major AI content providers embed machine-readable “watermark” signals in synthetic media⁷. However, as GAO notes, detection is an arms race – even a marked deepfake can sometimes evade notice – and labels alone don’t stop false narratives from spreading⁸⁹. Deepfakes in Recent Elections: Examples Deepfakes and AI-generated imagery already have made headlines in election cycles around the world. In the 2024 U.S. primary season, a digitally-altered audio robocall mimicked President Biden’s voice urging Democrats not to vote in the New Hampshire primary. The caller (“Susan Anderson”) was later fined $6 million by the FCC and indicted under existing telemarketing laws¹⁰¹¹. (Importantly, FCC rules on robocalls applied regardless of AI: the perpetrator could have used a voice actor or recording instead.) Also in 2024, former President Trump posted on social media a collage implying that pop singer Taylor Swift endorsed his campaign, using AI-generated images of Swift in “Swifties for Trump” shirts¹². The posts sparked media uproar, though analysts noted the same effect could have been achieved without AI (e.g., by photoshopping text on real images)¹². Similarly, Elon Musk’s X platform carried AI-generated clips, including a parody “Ad” depicting Vice-President Harris’s voice via an AI clone¹³. Beyond the U.S., deepfake-like content has appeared globally. In Indonesia’s 2024 presidential election, a video surfaced on social media in which a convincingly generated image of the late President Suharto appeared to endorse the candidate of the Golkar Party. Days later, the endorsed candidate (who is Suharto’s son-in-law) won the presidency¹⁴. In Bangladesh, a viral deepfake video superimposed the face of opposition leader Rumeen Farhana onto a bikini-clad body – an incendiary fabrication designed to discredit her in the conservative Muslim-majority society¹⁵. Moldova’s pro-Western President Maia Sandu has been repeatedly targeted by AI-driven disinformation; one deepfake video falsely showed her resigning and endorsing a Russian-friendly party, apparently to sow distrust in the electoral process¹⁶. Even in Taiwan (amidst tensions with China), a TikTok clip circulated that synthetically portrayed a U.S. politician making foreign-policy statements – stoking confusion ahead of Taiwanese elections¹⁷. In Slovakia’s recent campaign, AI-generated audio mimicking the liberal party leader suggested he plotted vote-rigging and beer-price hikes – instantly spreading on social media just days before the election¹⁸. These examples show that deepfakes have touched diverse polities (from Bangladesh and Indonesia to Moldova, Slovakia, India and beyond), often aiming to undermine candidates or confuse voters¹⁵¹⁸. Notably, many of the most viral “deepfakes” in 2024 were actually circulated as obvious memes or claims, rather than subtle deceptions. Experts observed that outright undetectable AI deepfakes were relatively rare; more common were AI-generated memes plainly shared by partisans, or cheaply doctored “cheapfakes” made with basic editing tools¹³¹⁹. For instance, social media was awash with memes of Kamala Harris in Soviet garb or of Black Americans holding Trump signs¹³, but these were typically used satirically, not meant to be secretly believed. Nonetheless, even unsophisticated fakes can sway opinion: a U.S. study found that false presidential ads (not necessarily AI-made) did change voter attitudes in swing states. In sum, deepfakes are a real and growing phenomenon in election campaigns²⁰²¹ worldwide – a trend taken seriously by voters and regulators alike. U.S. Legal Framework and Accountability In the U.S., deepfake creators and distributors of election misinformation face a patchwork of tools, but no single comprehensive federal “deepfake law.” Existing laws relevant to disinformation include statutes against impersonating government officials, electioneering (such as the Bipartisan Campaign Reform Act, which requires disclaimers on political ads), and targeted statutes like criminal electioneering communications. In some cases ordinary laws have been stretched: the NH robocall used the Telephone Consumer Protection Act and mail/telemarketing fraud provisions, resulting in the $6M fine and a criminal charge. Similarly, voice impostors can potentially violate laws against “false advertising” or “unlawful corporate communications.” However, these laws were enacted before AI, and litigators have warned they often do not fit neatly. For example, deceptive deepfake claims not tied to a specific victim do not easily fit into defamation or privacy torts. Voter intimidation laws (prohibiting threats or coercion) also leave a gap for non-threatening falsehoods about voting logistics or endorsements. Recognizing these gaps, some courts and agencies are invoking other theories. The U.S. Department of Justice has recently charged individuals under broad fraud statutes (e.g. for a plot to impersonate an aide to swing votes in 2020), and state attorneys general have considered deepfake misinformation as interference with voting rights. Notably, the Federal Election Commission (FEC) is preparing to enforce new rules: in April 2024 it issued an advisory opinion limiting “non-candidate electioneering communications” that use falsified media, effectively requiring that political ads use only real images of the candidate. If finalized, that would make it unlawful for campaigns to pay for ads depicting a candidate saying things they never did. Similarly, the Federal Trade Commission (FTC) and Department of Justice (DOJ) have signaled that purely commercial deepfakes could violate consumer protection or election laws (for example, liability for mass false impersonation or for foreign-funded electioneering). U.S. Legislation and Proposals Federal lawmakers have proposed new statutes. The DEEPFAKES Accountability Act (H.R.5586 in the 118th Congress) would, among other things, impose a disclosure requirement: political ads featuring a manipulated media likeness would need clear disclaimers identifying the content as synthetic. It also increases penalties for producing false election videos or audio intended to influence the vote. While not yet enacted, supporters argue it would provide a uniform rule for all federal and state campaigns. The Brennan Center supports transparency requirements over outright bans, suggesting laws should narrowly target deceptive deepfakes in paid ads or certain categories (e.g. false claims about time/place/manner of voting) while carving out parody and news coverage. At the state level, over 20 states have passed deepfake laws specifically for elections. For example, Florida and California forbid distributing falsified audio/visual media of candidates with intent to deceive voters (though Florida’s law exempts parody). Some states (like Texas) define “deepfake” in statutes and allow candidates to sue or revoke candidacies of violators. These measures have had mixed success: courts have struck down overly broad provisions that acted as prior restraints (e.g. Minnesota’s 2023 law was challenged for threatening injunctions against anyone “reasonably believed” to violate it). Critically, these state laws raise First Amendment issues: political speech is highly protected, so any restriction must be tightly tailored. Already, Texas and Virginia statutes are under legal review, and Elon Musk’s company has sued under California’s law (which requires platforms to label or block deepfakes) as unconstitutional. In practice, most lawsuits have so far centered on defamation or intellectual property (for instance, a celebrity suing over a botched celebrity-deepfake video), rather than election-focused statutes. Policy Recommendations: Balancing Integrity and Speech Given the rapidly evolving technology, experts recommend a multi-pronged approach. Most stress transparency and disclosure as core principles. For example, the Brennan Center urges requiring any political communication that uses AI-synthesized images or voice to include a clear label. This could be a digital watermark or a visible disclaimer. Transparency has two advantages: it forces campaigns and platforms to “own” the use of AI, and it alerts audiences to treat the content with skepticism. Outright bans on all deepfakes would likely violate free speech, but targeted bans on specific harms (e.g. automated phone calls impersonating voters, or videos claiming false polling information) may be defensible. Indeed, Florida already penalizes misuse of recordings in voter suppression. Another recommendation is limited liability: tying penalties to demonstrable intent to mislead, not to the mere act of content creation. Both U.S. federal proposals and EU law generally condition fines on the “appearance of fraud” or deception. Technical solutions can complement laws. Watermarking original media (as encouraged by the EU AI Act) could deter the reuse of authentic images in doctored fakes. Open tools for deepfake detection – some supported by government research grants – should be deployed by fact-checkers and social platforms. Making detection datasets publicly available (e.g. the MIT OpenDATATEST) helps improve AI models to spot fakes. International cooperation is also urged: cross-border agreements on information-sharing could help trace and halt disinformation campaigns. The G7 and APEC have all recently committed to fighting election interference via AI, which may lead to joint norms or rapid response teams. Ultimately, many analysts believe the strongest “cure” is a well-informed public: education campaigns to teach voters to question sensational media, and a robust independent press to debunk falsehoods swiftly. While the law can penalize the worst offenders, awareness and resilience in the electorate are crucial buffers against influence operations. As Georgia Tech’s Sean Parker quipped in 2019, “the real question is not if deepfakes will influence elections, but who will be empowered by the first effective one.” Thus policies should aim to deter malicious use without unduly chilling innovation or satire. References: https://www.security.org/resources/deepfake-statistics/. https://www.wired.com/story/synthesia-ai-deepfakes-it-control-riparbelli/. https://www.gao.gov/products/gao-24-107292. https://technologyquotient.freshfields.com/post/102jb19/eu-ai-act-unpacked-8-new-rules-on-deepfakes. https://knightcolumbia.org/blog/we-looked-at-78-election-deepfakes-political-misinformation-is-not-an-ai-problem. https://www.npr.org/2024/12/21/nx-s1-5220301/deepfakes-memes-artificial-intelligence-elections. https://apnews.com/article/artificial-intelligence-elections-disinformation-chatgpt-bc283e7426402f0b4baa7df280a4c3fd. https://www.lawfaremedia.org/article/new-and-old-tools-to-tackle-deepfakes-and-election-lies-in-2024. https://www.brennancenter.org/our-work/research-reports/regulating-ai-deepfakes-and-synthetic-media-political-arena. https://firstamendment.mtsu.edu/article/political-deepfakes-and-elections/. https://www.ncsl.org/technology-and-communication/deceptive-audio-or-visual-media-deepfakes-2024-legislation. https://law.unh.edu/sites/default/files/media/2022/06/nagumotu_pp113-157.pdf. https://dfrlab.org/2024/10/02/brazil-election-ai-research/. https://dfrlab.org/2024/11/26/brazil-election-ai-deepfakes/. https://freedomhouse.org/article/eu-digital-services-act-win-transparency. 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