While quantum computing has a long way to go, it can open tantalizing new doors for the field of genomics. herstockart/iStock via Getty Images Plus Decades after researches first sequenced the human genome, scientists throughout the world are still working to understand it. Despite diligent global efforts to link uncommon variations in DNA sequences with human disease, progress has been slow, in large part due to limitations in scientific understanding and in part due to limitations in computational technologies. Artificial intelligence has the potential to help scientists decipher the millions of genetic variations present in the genomes of different people in order to identify which ones lead to disease and which ones do not. In order to fully exploit the power of AI, however, scientists need to compare the genomes of thousands or tens of thousands of people. This task not only requires intense computational effort, it is also prone to error and will take years to complete. Quantum computing has the potential to facilitate that process. We are researchers with a long-standing interest in finding ways to use genetics in the clinic and developing new technologies to study the human genome. Combining quantum computing with AI has the potential to accelerate genomic analysis far beyond traditional methods. For time-sensitive medical conditions, faster decoding of genetic information can directly inform urgent treatment decisions and, in some cases, be lifesaving. Conventional vs. quantum computing In conventional computing, individual bits of information – binary digits, also called bits – can represent only two states: namely, 0 and 1. However, the qubits used in quantum computing can have more than two distinct states. Adding qubits together increases the number of states exponentially. The power of quantum computers lies in being able to check all the possibilities at once for problems with large numbers of variables, rather than one at a time like even the fastest possible classical computer must do. This allows quantum computers to solve certain types of problems, such as factoring large numbers for today’s encryption schemes and performing combinatorial optimization to find the best route through a large number of points. Quantum computers work much differently from the computer you’re likely using to read this article. Still, quantum computing is currently in its infancy. Despite the enormous potential of this technology, computer scientists are dealing with challenges related to its scalability, error correction, hardware development and the setting of standards. There are also significant time and cost constraints associated with ameliorating these challenges. Experts in the field estimate that it may be at least a decade before quantum computing will be truly useful outside of the laboratory. Bigger and better data analysis If researchers are able to overcome these challenges, combining AI and quantum computing may not only enable scientists and clinicians to better understand the human genome but also to leverage that understanding to improve patient care. Currently, researchers are able to use AI to analyze genomic data in combination with limited amounts of other biological information, such as gene activity, epigenomics, RNA signatures and protein function. Quantum computing could allow AI to process increasingly more massive and highly detailed datasets. This might look like integrating large-scale genetic, protein and spatial datasets with clinical, demographic and real-time physiological data. This systems-level approach enables a more comprehensive and accurate understanding of complex biological systems beyond DNA sequence alone that could be used to improve public health. In other words, quantum computing could make it possible to sequence a patient’s genome and combine that information with other information about how their body works at the molecular level to improve the accuracy of diagnoses and determine the best course of treatment in hours instead of months. Challenges in access and privacy Like many burgeoning technologies, combining AI with quantum computing has inherent and inescapable challenges. In particular, there are several ethical issues related to healthcare access. One will be the cost. New technologies are typically expensive and that will likely widen the gap between those who can afford the best healthcare and those who cannot. Anticipating these costs and finding preemptive creative solutions is necessary to allow everyone to benefit equally. While there are likely many approaches to reducing out-of-pocket expenses for healthcare, federal legislation could mandate affordable or free genetic information-based care to those in greatest financial need. Similar to the 2008 Genetic Information Nondiscrimination Act, which prohibits discrimination based on genetics, a new law could prohibit healthcare providers from withholding genetic information-based care from those who cannot afford it. Biological data inherently comes with a privacy risk. Tek Image/Science Photo Library Another challenge will be availability. These technologies will likely first be available at only the top medical centers in the country, which traditionally have the research funding and the cadre of skilled scientists and clinicians needed to develop new diagnostic methods and treatments. Consequently, the latest advances in health technology will be unavailable to people who physically or financially cannot travel to receive the best medical care. A combination of telemedicine, centralized laboratories and shared data could potentially help make new technologies more accessible. There are also privacy concerns intrinsic to sharing personal health data. Truly anonymizing personal information remains a challenge, and privacy concerns are likely to prevent some people from taking advantage of potentially lifesaving technologies. One approach that may quell these fears is a model called federated blockchain governance. This approach involves sharing control of a blockchain, which is a digital ledger used to track transactions, among a small group of institutions rather than a single entity or the general public. Limiting the number of trusted curators of genetic data reduces the risk of privacy violations or security breaches and subsequently increases the chance that patient data will remain private. Improving public health Despite these challenges, combining advances in quantum computing and AI has the potential to significantly drive innovation and improve public health. When scientists and clinicians are able to accurately identify the genetic basis of disease and potential risk factors, they will not only be able to develop better treatments but also help patients and healthcare providers know what symptoms to look for among those predisposed to certain conditions. Taken together, this knowledge can improve public health, reduce the cost of healthcare and improve quality of life. The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Elon Musk and Sam Altman are going to court over OpenAI’s future Elon Musk and OpenAI CEO Sam Altman head to trial this week in a case with sweeping consequences. Ahead…

NaNs don’t crash your training — they quietly destroy it. The post PyTorch NaNs Are Silent Killers — So I Built a 3ms Hook to Catch Them at the Exact Layer appeared first on Towards Data Science.

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A table tennis robot has outperformed elite players in recent evaluations. The robot, called Ace, marks a significant step toward artificial intelligence (AI) systems that can operate in fast, uncertain, real-world environments. In the tests, the autonomous robot won three out of five matches against elite players – competitive athletes with over ten years’ experience and an average of 20 hours weekly training. The robot, developed by Sony AI, lost both matches against players in professional Japanese leagues, but did win a game against one of them. The system is described in detail in a recent paper published in Nature. AI has spent decades mastering games. It has repeatedly outperformed the best humans in everything from complex video games like StarCraft II to chess – where modern programs now far exceed human ratings. Landmark systems such as Deep Blue and AlphaGo have confirmed that, given clear rules and enough data, AI can achieve superhuman performance. But these victories all shared one key feature: they happened in controlled, digital environments. At first glance, table tennis might seem like an unusual benchmark for artificial intelligence. In reality, it is one of the most demanding imaginable. The ball can travel faster than 20 metres per second, giving players less than half a second to react. On top of that, spin introduces enormous complexity. A ball rotating at extreme speeds can curve mid-air and rebound unpredictably off the table. For humans, interpreting spin is largely intuitive. For robots, it has been a longstanding obstacle. This robot can beat you at table tennis (Nature) Earlier table tennis robotic systems such as Forpheus, developed by Japanese company Omron, addressed this by simplifying the game – using controlled ball launchers, limiting movement, or ignoring spin altogether. More recent iterations have aimed for interaction, but still operate under constrained conditions. Ace does none of this. It plays with standard equipment, on a regulation table, against human opponents who are free to use the full range of shots. How Ace works Ace’s performance relies on three key innovations: how it sees the world, how it decides what to do, and how it carries out those actions. First, let’s deal with how Ace sees the world. Traditional cameras struggle with fast motion, often producing a blur or missing critical details. Ace instead uses three “event-based” vision sensors, which detect changes in light rather than capturing full images at fixed intervals. These are complemented by nine high-speed cameras that track the environment, including the opponent and their racket. Together, these systems enable high-speed gaze control (the technology that enables a robot to direct its sensors to focus on specific things) and allow the robot to follow the ball with exceptional real-time precision. By tracking markings on the ball, where professional players can generate spin approaching 9,000 revolutions per minute (rpm), the system can estimate spin in real time, something that has long challenged robotic systems. How Ace’s gaze control system works (Sony AI and Nature). The second important innovation is how Ace decides what to do. Knowing where the ball is going is only half the problem; the robot must also respond instantly. Ace uses deep reinforcement learning, trained in simulation over millions of virtual rallies, including self-play. It continuously generates movement commands for its multi-jointed robotic arm, recalculating trajectories every few tens of milliseconds while avoiding collisions with the table or itself. The third innovation is how Ace carries out its actions. To match the speed of human elite players, the robot is built around a high-performance arm combining two prismatic (sliding) and six revolute (rotational) joints. This enables rapid sideways motion and precise striking. There is both a table tennis racket and a mechanism for ball handling, allowing one-armed serves. Crucially, the system is engineered for high-speed interaction: lightweight structures and optimised actuation (the mechanisms in a robot that convert energy into mechanical force) allow Ace to return balls at speeds approaching 20 metres per second. This enables sustained, competitive rallies with skilled human players. Ace makes a split section change when the ball hits the net (Sony AI and Nature). What makes this particularly notable is the transition from simulation to reality. Many AI systems perform well in virtual environments but fail when exposed to real-world noise and uncertainty. Ace demonstrates that this “sim-to-real” gap can be meaningfully reduced. One moment during a rally with an elite player illustrates the way that Ace has leapt over this gap. When a predicted ball trajectory suddenly changed after clipping the net, Ace reacted almost instantly, returning the shot and winning the point. What makes Ace particularly significant is therefore not just its performance, but its ability to operate reliably under real-world uncertainty. Why this matters beyond sport A robot returning high-speed topspin shots may be entertaining, but the implications go far beyond table tennis. In manufacturing, for example, robots are typically confined to highly structured tasks. The real challenge is adaptability, handling irregular objects, responding to variation. This is particularly relevant for next-generation robots operating in unstructured environments. To function effectively in homes, hospitals or construction sites, robots must be able to predict, adapt and respond to constantly changing conditions. The same predictive and control capabilities that allow Ace to respond to unpredictable shots could enable more flexible, responsive automation. Most industrial robots are kept behind safety barriers because they cannot respond to unexpected human behaviour. Zhu Difeng There are also implications for human–robot interaction. Most industrial robots are kept behind safety barriers because they cannot react quickly or reliably enough to unexpected human behaviour. Ace operates at the edge of human reaction time, suggesting a future where robots can safely collaborate with people in shared spaces. More broadly, this work represents a shift in what AI is expected to do. The next frontier is not just intelligence in abstract problem-solving, but intelligence embedded in the physical world. The gap between simulations and reality needs filling, and this is a big step forward. Amazon’s new robot has a sense of touch, but it’s not here to replace humans What humans still do better Professional players were still able to exploit Ace’s limitations – particularly in reach, speed, and the ability to handle extreme or highly deceptive shots. This highlights that intelligence is not just about prediction and control, but also about physical embodiment. Humans combine perception, movement and strategy in ways that remain difficult to replicate. Interestingly, systems like Ace may end up enhancing human performance rather than replacing it. As one former Olympic player observed after facing the robot, seeing it return seemingly impossible shots suggests humans might be capable of more than previously thought. Kartikeya Walia receives funding from Innovate UK, UKRI. He does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

The bidirectional design has some clear advantages for a working taxi.

The differences between our brains and Neanderthals' were likely cosmetic.

We recently spoke with Tiffany Luck, a partner at New Enterprise Associates about the increasing relevance of vertical AI, how startups can carve out durable advantages in a world dominated by platform giants, and more.

New York Times: As Russia throttles popular apps and intermittently cuts off internet access, ordinary citizens, politicians, TV stars, and others criticize the restrictions  —  From beauty influencers to the token political opposition, Russians are openly questioning President Vladimir V. Putin's moves to hamstring access.

On this week’s Big Interview podcast, actor-director Ben McKenzie talks about the rise of crypto, why he finds it dangerous, and why it benefits from having a mysterious creator.

Derek B. Johnson / CyberScoop: Gartner: US states issued $3.45B in privacy-related fines to companies in 2025, a total larger than the last five years combined, driven by new privacy laws  —  The increase is being driven by powerful privacy laws in states like California, new interstate partnerships and a renewed focus on the privacy impacts of AI and automation.

Ashley Carman / Bloomberg: Spotify reports Q1 revenue up 14% YoY to €4.5B, MAUs up 12% YoY to 761M, above 759M est., Premium subscribers up 9% YoY to 293M, and a €715M operating income  —  Spotify Technology SA, the music streaming leader, added slightly more users than analysts' expected in the first quarter …

When a company with US$15.5 million in annual revenue debuts on a stock exchange and its market capitalisation briefly hits US$10 billion, the obvious question is: what do investors know that the financials don’t show yet? In Lightelligence’s case, the answer is optical interconnect and the growing conviction that conventional copper wiring between AI chips […] The post Lightelligence’s 400% debut is a bet that AI’s next bottleneck is the optical interconnect appeared first on AI News.

Kakao Mobility has set out plans to develop Level 4 autonomous driving technologies in-house as part of its physical AI strategy. Kim Jin-kyu, vice president and head of Kakao Mobility’s Physical AI division, presented the roadmap at the 2026 World IT Show conference at COEX in Seoul. His session focused on autonomous driving services built […] The post Kakao Mobility details Level 4 autonomous driving roadmap for physical AI appeared first on AI News.

The Guardian: Australia unveils a draft News Bargaining Incentive scheme that imposes a 2.25% levy on local revenues for platforms; Meta says the government is “simply wrong”  —  Google also rejects need for reform after Albanese government reveals draft news bargaining incentive scheme

Samantha Kelly / Bloomberg: Match Group invests $100M in Seattle-based Sniffies, a web-only platform for queer men with ~3M MAUs worldwide, paving the way for a possible acquisition  —  Match Group Inc., whose dating-app portfolio includes brands like Hinge, Tinder and Match.com, is investing $100 million in Sniffies …

PLUS: Set up AI teammates with ChatGPT Workspace Agents

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WIRED spoke with Bloomberg’s chief technology officer about the big, chatbot-style changes coming to the iconic platform for traders.