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135 recent industry stories relevant to the field â releases, launches, and announcements beyond the papers.
The neolab is betting that automating routine computer tasks will soon outpace coding as AI's biggest use case.
Opus 5 will be both cheaper and less restrictive than Fable, likely making it preferable in most use cases.
Weeks after Anthropic's latest toe-to-toe with the US government, and days after an OpenAI security incident that dominated tech industry discussions, Anthropic on Thursday released its newest model, Claude Opus 5. The company said in a release that Opus 5 "comes close to the capabilities of Claude Fable 5 in many domains" and is much […]
AI companies, including Nvidia and Mistral, urge policymakers to avoid broad restrictions on open-weight AI models as Washington debates responses to Chinese AI and alleged model distillation.
Users can now ask Attie questions about news, trends, and conversations on Bluesky and other apps on the AT Protocol.
The Trump administration unveiled the first "Genesis Mission" grants on Thursday, directing $5 billion toward hundreds of AI-driven science projects in an effort the White House has described as "comparable in urgency and ambition to the Manhattan Project." At roughly the same time, Trump's science adviser Michael Kratsios was on Capitol Hill selling lawmakers on […]
ChatGPT Voice on desktop can work with both ChatGPT Work and Codex to complete tasks and control agents.
AMD is challenging its chipmaker rival with a new rack-scale system that will start shipping to customers later this year.
The Media Router is a tool that automatically selects the best image, video, or audio generation model for a request based on whether a developer prioritizes quality, speed or cost.
The viability of orbital data centers hosting the largest and most capable large language models (LLMs) remains hotly contested. But enormous deployments that require thousands of GPUs arenât the only way LLMs might prove useful in space. NASAâs Jet Propulsion Laboratory recently sent Googleâs Gemma 3 to space, achieving the first in-orbit demonstration of a vision-language model analyzing imagery from a satelliteâs own sensor.The system, known as NAVI-Orbital, used Gemma 3 to analyze images captured by a YAM-9 satellite built by Loft Orbital. Juan M. Delfa, technical group lead at NASA, said that though the goal in this case was image analysis, the projectâs success implies a fundamentally new way researchers on the ground can interact with spacecraft.âThis is a major shift,â said Delfa. âNow, a scientist can write a prompt, upload it to the spacecraft, and that will be taken into account by the system. Itâs different from previous paradigms, where researchers have to write very structured commands that require an operations team and process.â Google Gemma 3 goes to spaceâno modifications requiredAt its core, NAVI-Orbital is an agentic software framework developed by Delfa and his coauthors, Taran Cyriac John, an AI researcher at NASA JPL, and Andrew W. Herson, a tech lead at Loft Orbital. It coordinates operations with a LangGraph-based conductor and deploys a compressed, 4-bit format of Googleâs Gemma 3 4B, an open-weights LLM, to produce plain-text image descriptions.NAVI-Orbital was 88 percent accurate when used to classify images in a benchmark dataset of 7,960 images. Notably, Gemma 3 classified the images without being trained or fine-tuned on this particular dataset or its categories; itâs the same base model you can download from Hugging Face and use on a laptop. The benchmark was conducted on the ground to validate the system before launch.Once the system was in orbit, NASA researchers performed two live capture tests with a camera on Loftâs YAM-9 satellite: one over Toulouse, France, and a second over the coast of Argentina. Gemma 3 generated a text description of each image, and NASA also prompted the LLM with a set of scripted questions about the images, such as whether they contain commercial or residential areas or show natural features. The image analysis also took place onboard YAM-9, which carries a compute cluster of several radiation-hardened processors (FPGAs, CPUs, and GPUs) to serve multiple customer payloads simultaneously. The satellite is powered by solar panels, which provide onboard systems with between 150 and 500 watts, depending on the position of the satellite.For the live capture experiment, Gemma 3 ran on Nvidiaâs Jetson Orin AGX, a small compute module frequently used for robotics and AI tasks. The 4-bit, 4-billion-parameter model requires only 8 gigabytes of memory, which makes it possible for it to run on a lower-power device such as the Orin AGX. âIt conveys the message of how lightweight it is. You can run it in a tiny, tiny computer,â said Delfa.Getting more useful data across limited bandwidthPaul Lasserre, general manager at Loft Orbital, said vision-capable LLMs could deliver a âparadigm shiftâ for orbital operations. Contrary to what spy movies would have you believe, most satellites canât provide fast, high-fidelity image and video feeds to observers on the ground. Bandwidth is often limited and, in most cases, satellites can deliver data to ground stations only at set intervals based on their orbit. Lasserre believes AI models can work around this problem with âsemantic compression.â Instead of sending large amounts of raw image data, a satellite can report a text summary of noteworthy information. âIt doesnât matter if the link is slow, because youâre downlinking dozens of kilobytes instead of dozens or hundreds of megabytes,â said Lasserre. âIt lets you use your satellite in a tactical way, which until now was only in Hollywood movies.âDelfa expanded on this with a real-world example: wildfire detection. Satellites are currently capable of detecting wildfires, but limits in downlink bandwidth and data processing can delay results by up to 90 minutes. A satellite capable of analyzing an image in space and reporting a plain-text warning might remove this delay. NASAâs Jet Propulsion Laboratory tested NAVI-Orbital on a YAM-9 satellite, made by Loft Orbital. Loft OrbitalFrom image analysis to spacecraft controlQuicker insight is only half of what NAVI-Orbital points toward. The other half relates to the âmajor shiftâ Delfa flagged. NAVI-Orbital provides a proof of concept for an alternate means of interacting with spacecraft.Thatâs not to say Google Gemma 3 is currently at the controls. NAVI-Orbital is deliberately walled off from the flight software. It reads images and produces descriptions. The system has the capability to make decisions about how images are analyzed but has no access beyond that.Still, the interface is novel. Retooling the system to search for a different kind of targetâsuch as wildfiresâis a matter of editing a text prompt. It doesnât require rewriting and revalidating onboard software, or training and deploying a different AI model (as was often required with prior image-classification models). The long-term vision for how this capability could be deployed goes beyond uncrewed satellites and image processing. Delfa said NAVI is rooted in thinking about how AI could serve as a companion for astronauts. âWe thought, astronauts have a lot of limitations in the spacesuit in terms of dexterity, so we conceived this idea of having NAVI as a companion to the astronaut, to allow interaction via natural languageâŚ. This is the concept that we definitely want to push forward.â A great deal of additional research will be required to push the technology that far, but NAVI-Orbitalâs demonstration has shown that two elementsâdeploying a large language model in space and controlling it with promptsâare possible.
Designing and developing a new medicine is an expensive, failure-prone scientific challenge. A new drug can take many years to develop, at the cost of a significant investment. And even then, most possible candidates never reach the patient. For biologic medicines, therapies made from engineered proteins rather than synthetic chemistry (which are often used to…
Google's cloud business is thriving, as companies adopting its AI and AI infrastructure services help the tech giant to report record profits.
Uber is also investing in Travis Kalanick's company Atoms, which has made gauzy claims about using industrial AI to modernize the world.
OpenAI will spend the equivalent of Sweden's GDP on infrastructure through 2030.
AMD says it's going to invest up to $5 billion in Anthropic, while helping to expand the AI company's computing power, according to an announcement on Wednesday. As part of the new partnership, Anthropic will deploy up to 2 gigawatts of AMD's Instinct MI450 AI GPUs using the chipmaker's new Helios rack-scale system, as reported […]
Synthesia launched AI Roleplay Sessions, an interactive enterprise training platform where employees practice workplace conversations with AI avatars that provide feedback, scoring, and analytics to help companies measure training effectiveness.
Major benchmarks measure what AI can do. None measure whether it does what you mean: the distance between what you ask an AI to do and the unspoken assumptions about how you want the AI to do it. We propose a new metric: the Genie coefficient.Thereâs often a gap between one personâs request and anotherâs understanding. Most of the time, we bridge it using general knowledge. For example, if you ask a friend to get you coffee, theyâll pour a cup from the pot or buy one from a coffee shop. They wonât bring you a bag of raw beans or snatch a cup from a stranger and hand it to you. You never specified any of this. You never had to.One might think the fix is just to specify tasks, questions, and intent better. But in 1987, in their seminal book on AI, Terry Winograd and Fernando Flores succinctly captured why that wonât work: âQ: Is there any water in the refrigerator? A: Yes. Q: Where? I donât see it. A: In the cells of the eggplant.â In human language, wants and desires are always underspecified. It is impossible to list all the caveats, all the limitations, all the exceptions.So how does anyone communicate, if intent canât be pinned down? Because a reasonable person can make a reasonable guess. Even though wants and desires are always underspecified, a competent person generally knows enough context to get it right or else knows to ask for clarification. Linguists call this pragmatics: Meaning lies in the words and the situation and also in all prior communication, shared culture, and innate human behavior.An AI agent asked for coffee might buy a coffee plantation or order a cup of coffee for delivery in three weeks.It doesnât always work out, of course. Your friend might bring you a hot coffee when you wanted an iced coffee, or an Italian coffee when you wanted a Turkish coffee. The more dissimilar the two people are in age, culture, and background, the more likely the request will be misunderstood in some way.This situation has major implications for AI agents that are increasingly being given requests by humans and expected to fulfill them. They have enormous latitude to get it wrong. An AI agent asked for coffee might buy a coffee plantation or order a cup of coffee for delivery in three weeks. Its actions may be recognizable as âgetting coffee,â but not remotely what you intended. Theyâll think outside the box because they wonât have our conception of the box.When AI Gets ProactiveFor most of the last decade, when systems like Alexa or Siri misinterpreted a request, it was annoying, not dangerous. Beyond the AI model itself, what has changed is the harness: the ordinary code that wraps around an AI model, decides when and how to use the model, and controls access to tools like a browser, a low-level command line, or a financial API. Developments in harnesses have turned large-language models that just predict text into AI agents that take actions in the world, without necessarily checking back in before reaching the goal.AI researcher Simon Willison spent two days with Anthropicâs Fable AI, and called it ârelentlessly proactive.â For example, he asked it to track down a stray scroll bar in a web app. He came back to find it had opened browsers, written its own screenshot tooling, created its own page to re-create the bug, and stood up a local web server to collect measurements. It found the bug and, along the way, did many surprising things he never asked it to do. And we are seeing similar behavior with all recent AI models when combined with flexible harnesses.This kind of behavior could easily go off the rails. Tell an AI agent to book you a flight and, finding the airlineâs site says sold out, it might break into the booking database and force a reservation. Ask it to schedule a meeting and it might snoop your password to access your calendar. Tell it to save money on your phone plan and it might cancel the plan outright, or scam someone else into paying the bill.Getting precisely what you asked for and bitterly regretting it is one of the oldest hazards from ancient folklore. King Midas asked Dionysus for the power to turn everything he touched into gold only to see his bread, wine, and daughter turn to gold. Tithonus, granted the immortality his lover asked for but not the eternal youth she forgot to request, withered into a husk. The sorcererâs apprentice enchanted a broom to fill the cistern, and the broom relentlessly complied until it flooded the house. The Golem of Prague, shaped from clay to guard its community, guarded it past all reason until someone erased the word on its forehead.The most classic of these is a genie, bound to obey and indifferent to whether the wish was wise or well-structured.Genies are now an engineering problem. We are handing them the keys to our inboxes, bank accounts, code repositories, and physical infrastructure. And we have no agreed-upon ways to measure how genie-like any AI system actually is.Measuring Genie BehaviorIn economics, the Gini coefficient (developed by statistician Corrado Gini) is a measure of the gap between an actual distribution and a perfectly equal one; itâs useful for understanding income inequality and more. Our proposed Genie coefficient measures the gap between what a user asked an AI to do and what the AI actually did.Sometimes the AI might do the wrong thing. Like Dionysus, it reads your request literally and returns you a mess you never intended: like a coffee plantation instead of a cup. Asked to deal with all the spam phone calls youâre getting, a Dionysus genie might contact your carrier and change your phone number. Asked to get a refund for a bad toaster, it might draft a legal threat on fake letterhead and send it to the retailer. Ryan SnookOther times the AI does exactly the right thing, trampling everything nearby to get there. Like a golem or the sorcererâs broom, it books your flight by hacking the airline. Or consider a ticket sale for a popular concert, where the ticketing system puts buyers into a virtual waiting room and admits them a few at a time. Asked to buy a ticket, a golem genie might spin up cloud servers to pose as millions of buyers from different addresses, improving your odds of getting a ticket while crowding out other users.The two are not opposites, and a single botched task can have both characteristics.Genie behavior is not flat-out failure. If you ask the AI for Q3 numbers and get Q2âs, thatâs not a genie. Nor is prompt injection: Thatâs someone tricking the AI into doing something it shouldnât. Here, the user is trying to work with the AI, and the AI is trying to comply. Itâs also not simply a measure of the AIâs success in fulfilling a task. Itâs a recognition that how an AI interprets and achieves a goal is as important as whether it achieves a goal. Genie behavior isnât new. Researchers have spent years studying AI systems that âgameâ their objectives. Goodhartâs law says that when a measure becomes a target, it stops being a good measure, and itâs long been known that AIs sometimes achieve goals in ways we donât expect due to reward hacking. Some AI models will accidentally learn that cheating is one way to âwin.â More recently, researchers have developing benchmarks for reward hacking in coding agents and for unpredictable behavior in customer support agents, while AI labs conduct their own safety evaluations before model releases. One effort found that AIs under pressure use tools they were told not to use, and this was a case where the rules were made explicit. These are all disparate research directions; nothing yet ties them together.This problem falls under the general theme of alignment, a topic that has occupied science fiction writers and AI researchers for decades. At one extreme, the âpaper-clip maximizerâ thought experiment postulates a superintelligent and powerful AI that is told to maximize paper-clip production and turns the world into paper clips, which is the ultimate golem genie. At a mundane level, AI researchers are working to better design reward functions to ensure that AIs behave well and donât cheat in the lab. Itâs the practical middle ground that remains unbenchmarked: the ordinary AI agent in use today that might take your request and satisfy it the wrong way. We are not at the stage where an AI can focus the worldâs production on paper clips, but it might charge a million paper clips to your credit card or hack into a paper-clip companyâs network.Building a Genie BenchmarkThe Genie coefficient is meant for AI agents operating in the real world. It measures their behavior as they perform real tasks long after the model is trained, not just during development. It also recognizes that genie-like behavior is a property of the harness-plus-model system, not the model alone. The harness determines what tools the agent can use, how much autonomy it has, and how proactive it is, and itâs a place we can make real interventions.It rests on the same âreasonable personâ standard that we use for people. Did the system do what a reasonable person would have taken the request to mean? Answering that requires human judgment.If we get the measurement right, it enables things that arenât possible today, like policies concerning AI behavior. In a courtroom, the concept of mens rea, what someone meant to do, is often as important as what they did. The Genie coefficient suggests an AI analogue, where a user is accountable for the plain intent of what they asked the AI. If an AI system betrays the reasonable meaning of an instruction, thatâs the AIâs misbehavior, not the userâs.Weâll need multiple benchmarks to measure the Genie coefficient, because genie-like behavior can be domain specific. An AI coding agent may need to be judged on how often it fakes the tests, or swallows errors, or colors outside the lines on its way to a solution. An AI legal agent will need to be judged on how often its output says what you asked but means something youâll regret. And so on for medical, finance, and other domains of knowledge and expertise.Genie benchmarks can be built inside out, each task seeded with a choice that might literally satisfy but that a reasonable person rejects, such as tempting misreadings or unsanctioned shortcuts. The traps in a Genie coefficient benchmark might turn on situational knowledge, the kind of context that a reasonable person would bring to the task. Another approach is to give the same request in several different contexts, each with a different reasonable course of action.Getting precisely what you asked for and bitterly regretting it is one of the oldest hazards from ancient folklore.A Genie benchmark should be permissive and make it genuinely tempting for an AI agent to take unreasonable shortcuts, because it can only find genie behavior when itâs actually possible. Test the AI in a safe, walled-off copy of a real system, with real tools it can misuse and some tasks that canât be done honestly at all. Make the temptation to cut corners real. Test a diverse array of skills, use cases, and tools, and give the AI system sparse, confusing, or overwhelming context. Include tasks that people have learned, through experience, require human oversight.How the benchmark is scored matters just as much. Measure Dionysus and golem genies separately and together, based on their worst, not best, behavior. Run the same model inside harnesses that vary its freedom to act, revealing which limits actually keep it in line and should therefore be required in AI harness policies. Weight each failure by the harm it would cause, not just a simple count. And donât measure genie behavior in isolation: A model could otherwise earn a perfect score by stalling, refusing, or drowning the user in clarifying questions without ever doing the job. The first versions of these benchmarks will be crude, but thatâs how benchmarks always start.We have built genies. We have handed them our data and credentials. We made them relentless, creative, and indifferent to the gap between what we tell them and what we mean. The least we can do, before they are booking our flights, running our infrastructure, and signing contracts unsupervised, is to measure how often they betray us.
Google released Gemini 3.6 Flash, 3.5 Flash-Lite, and Flash Cyber, but the continued absence of Gemini 3.5 Pro raises fresh questions about its AI strategy.
Google is launching Gemini 3.6 Flash alongside a new security model dedicated to quickly finding and patching security vulnerabilities. In a blog post on Tuesday, Google describes Gemini 3.5 Flash Cyber as a "cost-efficient and highly capable alternative" to larger, more expensive AI systems, such as the one offered by Anthropic's Mythos. The cybersecurity model […]
The final approval settles one case, but it doesn't resolve the broader issue of using copyrighted works to train AI models.
Alphabet, Google's parent company, is reportedly working on a new chip designed to make its Gemini models run much more efficiently.
China's leading AI companies are ramping up the pressure on Silicon Valley, as Moonshot and Alibaba unveiled models they claim can go toe-to-toe with the best from OpenAI and Anthropic at a fraction of the cost. The rapid-fire releases suggest America's lead at the AI frontier is increasingly tight, just as the technology is becoming […]
Databricks has remade its image into an AI company and has published research on the cost savings of open-weight AI models for coding.
A $400 million chip-backed loan points to the next wave of AI infrastructure deals.
Across 107 enterprises, AI infrastructure spending is accelerating well ahead of the ability to see or steer its economics. Most organizations run their AI on a familiar base of hyperscalers and model-provider APIs, yet the next dollar is aimed at specialized compute almost none of them use today; a majority intend to switch or add providers within the year, many within a quarter. Buying decisions turn on integration and total cost of ownership rather than headline token price â which is fortunate, because most enterprises cannot yet see their unit economics clearly: GPUs sit at half utilization or less, and fewer than half rigorously track what their compute actually costs. The result is a compute gap â heavy, fast-moving investment running ahead of the visibility needed to control it.This wave of VentureBeat Pulse Research examines enterprise AI infrastructure and compute: where organizations are in their deployment journey, what they run AI on today, how satisfied they are, what would make them switch, where they plan to evaluate their investments, and â most revealingly â how well they can measure and control the economics of the compute underneath it all.The central finding is a compute gap â the distance between how aggressively enterprises are investing in AI infrastructure and how little of its economics they can see. Only about one in five (21%) run AI in production at scale, yet spending intentions are outrunning that maturity: the single largest planned area enterprises plan to evaluate over the next year is AI-specialized clouds (45%), a layer almost none of these enterprises use today. Meanwhile the compute already in place runs cold â 83% report GPU utilization of 50% or less â and fewer than half (44%) can rigorously track what their AI compute costs. Enterprises are buying more infrastructure faster than they can account for what they already own.Enterprises are not settled on their infrastructure vendors, either: A clear majority (64%) plan to switch or add an infrastructure provider within twelve months, and 38% within the next quarter â unusually high churn intent for a category this foundational. When they choose, they choose on integration with the existing stack (41%) and total cost of ownership (35%), not on headline price: cost per million tokens is the deciding factor for just 8%. And the frontier constraint that will shape the next round of decisions â the shift from GPU compute to memory bandwidth as inference scales â is barely on the radar, with roughly one in five enterprises either unaware of it or yet to address it.MethodologyVentureBeat fielded this survey as part of its ongoing Pulse Research series, this survey focused on enterprise AI infrastructure, compute, and inference economics. Responses are filtered to organizations with more than 100 employees (n=107; the surveyâs smallest size band, 1â100 employees, is excluded), drawn from a single Q2 2026 (June) wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%.By organization size the sample concentrates in the mid-market: 101â250 employees (36%) and 251â1,000 (27%) lead, with 1,001â5,000 (22%), 5,001â10,000 (8%), and 10,001+ (7%) above them. By role it spans managers (38%), individual contributors (28%), VPs and directors (19%), and the C-suite (13%); on purchasing authority it is buyer-credible, with 45% final decision-makers and another 30% recommenders or influencers for AI solutions. Technology/Software is the largest industry at 26%, followed by Healthcare/Life Sciences (15%), Financial Services (13%), and Retail/E-commerce (12%).At 107 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It also skews toward the mid-market and toward earlier-stage adopters, so it is best read as the view from organizations actively building out AI infrastructure rather than from the largest hyperscale operators.Finding 1: Ambition outpaces productionOnly one in five run AI in production at scaleWe asked where organizations sit in their AI deployment journey. Most are still building toward production rather than operating at scale.The maturity curve is front-loaded. Three-quarters of enterprises (76%) are either experimenting or running only some workloads in production, and just 21% describe AI in production at scale. This matters for everything that follows: the infrastructure decisions in this report are being made largely by organizations still early in deployment, whose compute footprint â and whose costs â are about to grow. The evaluation and switching intentions in Findings 3 and 4 are the leading edge of that build-out, not the settled preferences of operators who have already found what works.Finding 2: Enterprises run on hyperscalers and model APIsThe specialized GPU clouds barely register â todayWe asked which providers and platforms enterprises currently use to run their AI. The answer is a familiar one: the incumbents.The current stack is hyperscaler-and-API. Google Cloud leads at 48%, and the general-purpose clouds (Google, Microsoft, AWS, Oracle) together with the major model APIs (Gemini, OpenAI, Anthropic) account for essentially all current deployment. The specialized âneocloudâ GPU providers that dominate AI-infrastructure headlines â CoreWeave, Lambda, Crusoe, Nebius and peers â register at or near zero among these enterprises today. Only 6% run their own on-prem GPU clusters and 4% a custom open-source stack. Enterprises are, for now, running AI on the providers they already buy from â which makes the evaluation intentions in Finding 3 all the more striking.(A note on reading these shares. As described in the methodology section, this sample is self-selected and skews mid-market, and this question counted every provider a respondent uses â an average of 2.1 selections each â so the figures measure presence in the stack rather than spending or primary status. A sample built this way will show a different provider mix than a spend-weighted census of the broader market; Google's strength here, for example, is consistent with its long-standing position among smaller enterprises building on AI. Read these shares as a portrait of what this AI-active cohort runs today, and treat gaps between these figures and industry-wide market share estimates as a property of the sample rather than a contradiction of either.)Finding 3: The next dollar goes to infrastructure they donât yet runAI-specialized clouds top the evaluations listWe asked where enterprises planned to evaluate AI infrastructure over the next 12 months. Their answers point away from the stack they run today.Here is the reportâs sharpest tension. The single most-cited planned evaluation area â AI-specialized clouds, at 45% â is the very category almost none of these enterprises use today (Finding 2). Nearly a third (32%) intend to evaluate non-Nvidia accelerators, and 28% in next-generation Nvidia silicon; even decentralized compute networks (16%) and sovereign compute (11%) draw meaningful interest. Read against current usage, this is not incremental â it is the leading edge of a re-platforming. The direction-of-travel question tells the same story: every infrastructure approach is net-expanding, but specialized AI clouds carry the highest net momentum (+24), edging out even the hyperscalers (+22). Enterprises are preparing to move a meaningful share of AI compute off the general-purpose cloud.This continues a trend we saw in our April-May survey wave. Back then, usage of the AI-specialized clouds was equally marginal â CoreWeave at 3%, Lambda at 4%, Crusoe at 2% of enterprises. When we asked enterprises what change they planned in their AI infrastructure strategy over the next twelve months, the most-cited answer was moving workloads to specialized AI clouds, at 33%. Asked in April-May which emerging compute option they were most likely to evaluate AI-specialized clouds again drew the most responses. Two waves, two differently worded questions, one consistent picture: the type of cloud enterprises are most eager to assess is the type they have barely begun to use.Finding 4: A switching wave is buildingSix in 10 plan to change providers within a year â many within a quarterWe asked whether and when enterprises plan to switch or add an infrastructure provider. Very few intend to stand still.For a category as foundational as compute, this is a remarkable amount of intended movement. Only 36% have no plans to change, meaning a clear majority (64%) intend to switch or add a provider within twelve months â and 38% within the next quarter alone. Where that interest points is telling: the providers drawing the most switching consideration are again the incumbents â Microsoft Azure and Google Cloud (33% each), OpenAI (30%), and Gemini (22%) â which suggests much of the near-term movement is reshuffling among the majors and consolidating spend rather than defecting to new entrants. The neocloud interest in Finding 3 is a 12-month evaluation thesis; the switching in the next quarter is mostly incumbents trading share.(Method note: Respondents who selected both "no plans to change" and a specific switching window are counted as switchers, on the logic that naming a timeframe is the more specific answer; three respondents were reclassified under this rule.)Finding 5: Nobody buys on token priceIntegration and total cost of ownership decide â not sticker priceWe asked what matters most when enterprises select an AI infrastructure provider. Headline price finished last.Enterprises do not buy AI infrastructure on pricing, which is the place vendors compete on hardest. Integration with the existing stack (41%) and total cost of ownership (35%) dominate, while the headline metric â cost per million tokens â is the deciding factor for just 8%, dead last. The pattern is coherent: buyers are optimizing for how a provider fits and what it truly costs to operate, not for the advertised unit rate. It also foreshadows Finding 7 â enterprises say TCO matters most, yet most cannot yet measure it rigorously. The stated priority and the measured capability are out of step.Finding 6: Expensive GPUs, idle most of the time83% report GPU utilization of 50% or lessWe asked what share of their GPU capacity enterprises actually utilize. The answer is a well-known but rarely quantified inefficiency.Disclosure: Band percentages count every selection against all 107 qualified respondents; 14 respondents selected more than one band, so bands overlap. At the respondent level, 83 of the 100 GPU-operating enterprises reported utilization at or below 50%The compute already in place runs cold. Adding the bands at or below half capacity, 83% of enterprises that operate GPUs report utilization of 50% or less, and nearly half (49%) run at 25% or below. Only 12% clear the 50% mark, and a further 8% do not measure utilization at all. Idle accelerators are expensive accelerators, and this is the clearest single measure of the compute gap: enterprises are planning to buy more GPUs and specialized compute (Finding 3) while the capacity they already own sits substantially unused. The efficiency headroom in the current fleet is large â and largely unmeasured.Finding 7: Spending fast, measuring slowlyFewer than half rigorously track what their compute costsWe asked whether enterprises can quantify the cost and return of their AI infrastructure spend, and how satisfied they are with what they run. Confidence in the ledger lags the spending.Measurement trails money. Fewer than half of enterprises (44%) rigorously track the cost and return of their AI compute; the majority track only partially (39%), cannot quantify it yet (20%), or have not prioritized it (6%). That gap is consequential given Finding 5, where total cost of ownership was the second-ranked buying criterion â enterprises are choosing providers on an economic basis they mostly cannot yet measure. Satisfaction with current infrastructure is moderately positive but not enthusiastic: on a five-point scale, overall satisfaction averages 4.0, with ease of implementation (3.8) and value for money (3.9) trailing slightly â the softness landing, tellingly, on cost. Enterprises are spending quickly and accounting slowly.Finding 8: The next bottleneck few are watchingAs inference shifts from compute to memory, the field scattersFinally, we asked how enterprises would address the emerging constraint in large-scale inference â the shift from GPU compute to memory, specifically KV-cache capacity. The responses reveal a frontier that is not yet a priority.The memory frontier is real but barely governed. Asked which approach they would rely on as the binding constraint in inference shifts from compute to memory bandwidth, enterprises scatter: Dell leads at 31%, Nvidia follows at 16%, and the rest fragments across storage vendors, open-source tooling, and model-level efficiency techniques. Most telling is that roughly one in five (18%) either do not recognize the constraint or have not begun to address it. For a shift that will reshape inference cost and architecture, this is an early and unsettled market â and, consistent with the measurement gap in Finding 7, one where many enterprises simply do not yet have a view. It is the next chapter of the compute gap, arriving before most have closed the current one.The bottom line: A compute gap that faster spending will widen, not closeOrganizations with more than 100 employees are investing in AI infrastructure faster than they can measure it. Most are still early in deployment, yet their spending intentions point past their current stack â toward specialized clouds and alternative accelerators almost none of them run today â and a clear majority intend to change providers within the year. They buy on integration and total cost of ownership rather than headline price, which is rational; the difficulty is that most cannot yet see those economics clearly.The visibility gap is concrete. The GPUs enterprises already own run at half utilization or less for the overwhelming majority, and fewer than half can rigorously track what their compute costs or returns. Satisfaction is decent but unenthusiastic, softest on value for money â the dimension hardest to judge without measurement. And the next constraint, the shift from compute to memory in large-scale inference, is arriving while most enterprises are still unaware of it. At 107 respondents in a single Q2 wave this is a directional read, skewed toward the mid-market and earlier-stage adopters â but the direction is consistent: the appetite to spend is running well ahead of the instrumentation to spend well. The compute gap is not a capacity problem that more hardware will solve on its own; it is, first, a problem of seeing what the hardware already costs. The open question for later waves is whether enterprises build that visibility before the re-platforming arrives â or buy the next layer of infrastructure as blind to its economics as the last.Based on survey responses from 107 qualified enterprise respondents (100+ employees), drawn from a single Q2 2026 (June) wave. Because this is one wave rather than a pooled multi-month sample, the results read cross-sectionally rather than as a month-over-month trend, and at 107 respondents this is a directional signal rather than a precise measurement â the sample is self-selected, skews mid-market, and leans toward earlier-stage adopters rather than the largest hyperscale operators. Respondents include managers, individual contributors, VPs/directors, and the C-suite, with buyer-credible purchasing authority, across Technology/Software, Healthcare/Life Sciences, Financial Services, Retail/E-commerce, and other industries.
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The FT reports Kimi K3 will be the largest open AI model from China, with a parameter count between 2 trillion and 3 trillion.
1Password has launched a new browser integration for Claude that allows the Anthropic chatbot to access stored security credentials like usernames and passwords. The 1Password for Claude feature means that users can authorize Claude to complete multi-step tasks like booking travel and managing online accounts on their behalf without having to manually input their login […]
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. Meet GPT-Red: an LLM super-hacker OpenAI built to make its models safer OpenAI has built an LLM super-hacker called GPT-Red that it uses as a sparring partner to help its other…
It's the company's first public proof point after a year and a half spent building AI infrastructure largely out of public view.
Suno data obtained in a hacking incident has exposed that the AI music generator was trained by scraping millions of songs and lyrics from online audio platforms, including YouTube Music, Deezer, and Genius, 404 Media reports. Given that Suno has avoided revealing what's in its training datasets and how they were acquired, this a rare […]
OpenAI has built an LLM super-hacker called GPT-Red that it uses as a sparring partner to help its other models boost their defenses against cyberattacks. Last week the company released the latest version of its flagship LLM, GPT-5.6. OpenAI says that training it against GPT-Red made the model its most robust release yet. GPT-Red automates…
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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. What Anthropicâs latest AI discovery doesâand doesnâtâshow âJames O’Donnell When Anthropic announced last week that it had found a new window into its modelsâ âinternal thoughtsâ as they reason through answers,…
With the cash, the company aims to expand its world model offering and reach customers across geographies.
The company is raising at least $75 million, led by Robot Ventures, with significant participation from USV and other prominent investors.
This article is brought to you by X Square Robot.Large language models gave artificial intelligence a working recipe. Pretrain a large model on broad data, and general capability follows. Robotics has no such recipe. Robotics systems have long been assembled from separate perception, planning, and control parts that rarely add up to intelligence a robot can carry from one task to another, or one machine to another. The central problem in embodied AI is to find the equivalent recipe, and the field does not yet agree on what it is.X Square Robot, a Chinese embodied-AI company, has made an unusually explicit bet. It argues that the recipe is an integrated stack, spanning the data a robot learns from, a world model for predicting changes in the physical world, and an action model that brings together perception, planning, reasoning, and decision-making to generate executable robot behavior. The company also believes that the stack should be built and released in the open. X Square Robot shares its vision of bringing robots into real homes.X Square RobotX Square Robotâs embodied AI stackWhat holds the stack together is a small set of principles rather than a single overarching model.The first is that the basic unit of robot data is an interaction, not a trajectory; a demonstration is successful only if it changes the world as intended, not simply because the joints moved. The second is that pretraining should yield usable capability, not just an initialization for later fine-tuning. The third is that behavior should be modeled around physical events rather than fixed slices of time. These principles make the layers interdependent, since the same robot-free data that trains the action model is also structured to feed the world model. It is worth being precise, though. The company describes the world model and the action model as complementary but independent model families that share a code base. Both sit within its broader World Unified Model, which it has presented as an architecture for training vision, language, action, and physical prediction together.Robot learning data: Engineering for quality and cost, not scaleFor the X Square Robot team, one of the biggest constraints on general-purpose robots is the cost and quality of interaction data, not the number of parameters. To address that, the company built its Universal Manipulation Interface (UMI) data collection system, QUANXTA Zero Series. It works by collecting demonstrations from people wearing a rig with dual grippers rather than teleoperating a robot. This approach is not itself new, and builds on established methods for robot-free data capture. What sets it apart are two engineering choices. X Square Robot emphasizes data quality control, recording trajectories and replaying them on a real robot, with only those that actually complete the task counted as valid.X Square RobotThe first is quality control, and it is the most distinctive part. Rather than accepting recorded trajectories as they are, the system runs a closed inspection loop, and its notable step is physical playback. A sample of trajectories is replayed on the real robot, and only those that actually complete the task count as valid. That makes the validity rate a measured quantity rather than an assumption. For example, a gripper that closes a fraction of a second too early still looks like a grasp in the data, yet it has pushed the object away, so it shouldnât be classified as valid. A smaller clean dataset can be worth more than a larger noisy one.The second choice is how lower-cost human data and scarce robot data are combined. The company pretrains on a large volume of robot-free demonstrations to build general representations, then adds a small amount of real-robot data as an anchor to the specific machineâs dynamics. It reports that this reaches performance comparable to an all-robot dataset at roughly a 20-fold lower cost of collection, driven mainly by how much cheaper the wearable rig is than a teleoperation setup. The resulting dataset is deliberately model-agnostic, formatted to feed both action models and world models. The caveat is that the strongest results are measured on the companyâs own robots and data-collection pipelines. Broader independent testing will help confirm and extend these promising results across a wider range of settings.A world model organized around eventsIn developing its world model, called WALL-WM, X Square Robot took a differentiated approach. Most action models predict a fixed-length chunk of motion from the current image and instruction. That is convenient, but it segments behavior into fixed-duration windows, so the boundaries fall where elapsed time dictates rather than where one action ends and the next begins. WALL-WM instead treats an action-grounded semantic event as its unit: a coherent piece of behavior such as reaching, grasping, or placing, something that can be named in language, seen in video, and executed as motion. X Square Robotâs world model, called WALL-WM, treats an action-grounded semantic event as its unit: a coherent piece of behavior such as reaching, grasping, or placing, something that can be named in language, seen in video, and executed as motion.X Square RobotWALL-WMâs design reflects a specific concern about not discarding what large video models already know. To achieve that, a text-to-video model is coupled to a freshly initialized action network that reads from the video features without overwriting them, which preserves the visual prior. From that one process, it offers two modes. An event mode runs in variable-length segments and suits reasoning over long horizons, while a fixed-length mode produces the steady, real-time output a controller needs. That places WALL-WM between mainstream chunk-based action models and pure video world models, keeping the predictive character of a world model while still yielding executable control.In a series of experiments, the company relied on a generalization test that is more specific than most. A model trained on a limited dataset was evaluated on long-horizon tasks in unseen settings and, on the companyâs real-robot benchmark, reportedly outscored baselines that had been fine-tuned on related data. That is a meaningful result if it holds. For now, it is measured on the companyâs own benchmark. With the code now being released, the broader community will have the opportunity to test, reproduce, and build on them across more settings.A policy that runs before fine-tuning, and action tokens with meaningThe action layer carries two connected ideas. The first is a requirement the company sets for itself with Wall-OSS-0.5, its vision-language-action model: The pretrained model should run on a real robot before any task-specific fine-tuning. The interest is less in the scores than in the design behind them. The model trains three objectives together, namely discrete action tokens, language grounding, and continuous action generation. And it keeps gradients flowing through all of them rather than freezing parts of the network as some rival designs do. Itâs also a more strict method, since it reports untuned behavior such as approaching, grasping, and recovering, including on a deformable task held out of training. As part of X Square Robotâs Wall-OSS-0.5 vision-language-action model design, the pretrained model should run on a real robot before any task-specific fine-tuning. X Square RobotThe second idea is the action interface itself, called X-Tokenizer. Most systems that turn continuous motion into discrete tokens produce codes that the language model cannot interpret. X-Tokenizer reframes tokenization as learning a semantic interface, so that the top-level code stands for the intent of a motion while lower-level codes carry finer detail, all aligned with the language modelâs own features. A useful consequence is stability. Adding noise to an action barely moves the intent code, which is what lets one tokenizer to be reused across robots without re-tuning. The tokenizer inside the production action model is a related variant of this approach. Together, the two ideas give the action layer something rather powerful: capability that transfers.The future of embodied AI stacksX Square Robot is betting that its unique approach combining three layers, each specialized in solving a key part of the problem, will stand out from other embodied AI stacks. The physical-playback step that grounds data quality is uncommon and sensible. The reframing of world modeling around events, with one backbone serving both reasoning and control, is a genuinely distinct approach. And the pairing of a deployable pretraining standard with a tokenizer designed as a semantic interface gives the action layer unusual coherence. X Square Robotâs valuation has climbed above 20 billion yuan (about US $2.9 billion), suggesting that investors increasingly view data infrastructure, foundation models, and scalable training systems as long-term differentiators in embodied AI.The next phase will bring broader validation. Much of the current evidence comes from X Squareâs own robots and benchmarks. With the world model code now being made public, and as the community begins to test, reproduce, and build on the work, the reported capabilities will be tested across more robots, tasks, and settings.X Square Robotâs recent funding rounds reflect similar confidence. The companyâs valuation has climbed above 20 billion yuan (about US $2.9 billion), suggesting that investors increasingly view data infrastructure, foundation models, and scalable training systems as long-term differentiators in embodied AI.Whatâs next for X Square RobotTo learn more about its future plans, the following Q&A with the X Square Robot team further explores the companyâs technology, strategy, and vision.What made now the right moment, technically, to commit to this stack? What recently became possible that wasnât possible a couple of years ago?It is not one breakthrough but several trends maturing together. Foundation models gave us a shared representation across vision, language, and action, so we can model what a robot sees, what it is asked to do, and how its actions change the world in one framework, rather than as separate perception, planning, and control modules. Compute and infrastructure are finally sufficient for large-scale pretraining over long-horizon, multi-embodiment data. Just as importantly, we realized that data, not model size, is the real bottleneck for general robotsâwhat is scarce is diverse, high-quality, reproducible interaction data. And world modeling has become practical. The useful question is no longer how to predict a few seconds of video, but how to understand the ways actions change objects, contacts, and task states. Two years ago these ingredients existed separately. Today they are mature enough to work as one system.âWe realized that data, not model size, is the real bottleneck for general robotsâwhat is scarce is diverse, high-quality, reproducible interaction data. And world modeling has become practical.âYour data system captures demonstrations with a wearable VR rig and custom grippers rather than teleoperating robots. What was wrong with standard teleoperation?Teleoperation is built around controlling the robot. It forces the operator to work within the machineâs kinematics, latency, and viewpoint, and the resulting demonstrations are slower, stiffer, and less diverse. We built our system around capturing human skill instead. Manipulation is really about contact, timing, finger coordination, and recovery, not just the path the hand takes, and a wearable rig records those before the behavior is compressed onto one particular robot. It also breaks teleoperationâs expensive scaling law, in which every demonstration needs a robot. People can generate rich data independently of any robot, and the crucial property is that those demonstrations can still be replayed and executed on a physical robot through the model. Mobility is convenient, but that replay is the real point, because it is what lets the same data be reused across different platforms. In X Square Robotâs approach, demonstrations can be replayed and executed on a physical robot through the AI model, allowing the same data to be reused across different platforms.X Square RobotX Square Robot reports that its pipeline has roughly an 85 percent data-validity rate. Why is quality control such an underrated bottleneck?Because errors in robot data are far more expensive than in language data. A small timing or contact error can change what a demonstration means. If a gripper closes a fraction of a second too early, the motion still looks like a grasp, but physically it has pushed the object away. A dataset that mixes failures and accidental successes teaches ambiguity, not skill, because the real unit is the interaction, not the trajectory. So we run automated inspection, kinematic checks, and physical replay, where we play a sample of trajectories back on the real robot and count only the ones that actually complete the task. Data quality sets the ceiling on how good a policy can be. In our experience a smaller, cleaner dataset often beats a much larger, noisier one, which is why we treat quality control as part of the model, not a preprocessing afterthought.The model runs in both âevent modeâ and âchunk mode.â When does each matter?Both matter, for different reasons. The physical world changes through eventsâwhen contact occurs, a grasp forms, or an object slipsânot in fixed-frame windows. Event mode concentrates the modelâs attention on those moments, and it matters most for long-horizon tasks, like clearing a table, where progress is a sequence of semantic events rather than a smooth stream. It runs in variable-length segments that follow the task rather than a clock. Chunk mode matters for deployment. Real controllers need a stable, real-time interface, and fixed-length chunks integrate cleanly with existing control systems. We organize learning around events in the first place because a fixed window can split one motion in half or merge two together, which turns training into short-horizon pattern matching and weakens the model on long tasks. So the world modelâs job is to connect event-level understanding, which is where the reasoning happens, with a fixed-length output a real robot can actually run.Why make âdeployable before fine-tuningâ the criterion?Pretraining should produce capability, not just a good starting point. If a model is only useful after heavy fine-tuning, then most of the intelligence still lives in the downstream supervision, not in the foundation model. Deployable before fine-tuning is a more honest test of what pretraining actually learned. A well-pretrained robot should already know how to approach, grasp, move, avoid obstacles, and correct itself. Fine-tuning should adapt it to a specific task or robot, not create the ability from nothing. It is also a practical requirement. A robot in a home or a workplace shouldnât need a brand-new dataset and a new policy every time the task changes, so a foundation model that already carries general skill, and some ability to recover, is the minimum bar for something genuinely useful in the real world.What is the most challenging part of cross-embodiment learning?Robots differ in control frequency, delay, compliance, sensing precision, and contact dynamics, so the same instruction can require different action decompositions and recovery strategies, and a behavior that works on one arm cannot simply be copied to another. Cross-embodiment learning needs an intermediate abstraction, lower than language but higher than joint angles: how you approach an object, how you make contact, how you apply force, and how you recover from a mistake. When we say cross-embodiment, the main capability we mean is multi-embodiment generalization: transferring across robots, training on many embodiments at once, and adapting to different kinematics. Human-to-robot transfer and other techniques are specific approaches to that goal.âA robot in a home or workplace shouldnât need a new dataset and policy every time the task changes. A useful foundation model should already carry general skills and the ability to recover.âWhat would you most like to see other researchers attempt to reproduce or stress-test?Three things, above all. Whether event-level representations really generalize beyond our own datasets, across more tasks, scenes, objects, embodiments, and failure conditions. Whether pretraining stays effective on robots the model never saw during training, or whether its capability is still too tightly coupled to what it has already seen. And whether real-robot evaluation can become a shared language for the field, so that we compare not just success rates but the reasons systems fail, where an instruction was misread, where perception broke down, or where recovery fell short. Robotics has been driven too often by impressive demonstrations, and real progress comes from results that are reproducible and diagnosable.What capability is still missing before robots become dependable in homes?Benchmarks measure competence, like whether a model can finish a task. Homes demand reliability, safe and consistent operation over time in a place that changes every day, with objects moving, instructions that are vague, and people interrupting. The missing piece is not a higher one-time success rate: it is robust recovery. A dependable home robot has to know when it is uncertain, when to slow down, when to ask for help, and how to bring the world back to a safe state after it drops something or misunderstands a request. In a real home, failure recovery matters more than raw success, because the home does not reset itself. Homes also demand careful personalization, learning a householdâs routines and preferences over time, with safety and trust as first principles. That combination, not any single skill, separates a capable demonstration from a robot people can live with. X Square Robotâs approach is that, in a real home, failure recovery matters more than raw success, because the home does not reset itself and it demands careful personalization, with safety and trust as first principles. X Square RobotHow do the open-source components fit into X Square Robotâs World Unified Model direction?We see these releases as layers of the World Unified Model direction rather than isolated projects. Wall-OSS-0.5, the action model, asks whether an open vision-language-action model can gain directly measurable capability from large-scale pretraining, so it is the capability layer. WALL-WM, the world model, asks how a robot should understand change in the world, shifting from fixed windows to event-level modeling, so it is the representation layer. The data system supplies the interaction data that both of them learn from. Together they form a loop in which models produce capability, world models organize understanding, and the open-source community drives reproduction and improvement. World Unified Model is the broader architecture those layers support, bringing vision, language, action, and physical prediction together. We are releasing these pieces openly because embodied intelligence cannot be solved by one organization; it needs many embodiments, many real tasks, and broad feedback, and the long-term goal is a stack that keeps learning and ultimately moves robots from laboratory demonstrations toward reliable everyday use.
Open source AI is booming, according to Hugging Face CEO Clem Delangue. The company has grown into something like a GitHub for AI in recent years, where AI builders can share and download open models and datasets, now used by roughly half the Fortune 500. Delangue has seen the same story play out again and again: companies start […]
The AI chip boom just produced its biggest Wall Street moment yet. Now SK Hynix and Samsung are being asked to build U.S. factories.
Open source AI is booming, according to Hugging Face CEO Clem Delangue. The company has grown into something like a GitHub for AI in recent years, where AI builders can share and download open models and datasets, now used by roughly half the Fortune 500. Delangue has seen the same story play out again and again: companies start […]
OpenAI's new family of models will continue to power Microsoft's suite of workplace and productivity apps.
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After reentering the AI race with its first in-house Muse Spark model in April, Meta is now opening up the doors to developers with a new model that can plug into AI coding software with the new Meta Model API. Meta says that Muse Spark 1.1 is a "step-change" from the first generation, with improvements […]
The large language models (LLMs) that form the basis of generative AI chatbots such as ChatGPT, Claude, and Gemini can generate uncannily human-like text and images. But these models still struggle with a skill that, ironically, looks at face value to be right in their wheelhouse: analyzing structured data. A new type of generative AI is set to change this situation.Although you can get your favorite chatbot to solve intractable math problems, review dense legal documents, compose a catchy pop song, or put together some slick PowerPoint slides, give it anything more than a small table and it doesnât have a clue what to do.For most companies and organizations, the most important data sits in spreadsheets. Whether itâs a bankâs transaction logs, a marketing agencyâs website metrics, clinical trial participantsâ vital signs, or the vast amount of proton collision information produced at atom smashers like the Large Hadron Collider, structured, row-and-column data runs the world, and LLMs canât deal with it.AI startup Fundamental is pioneering a new type of AI foundation model, known as a large tabular model (LTM), to fill the gap. Fundamental came out of stealth mode on 5 February 2026 with US $275 million in funding and a model called NEXUS, purpose-built for tabular data. Now, the model is being adopted by companies such as Amazon Web Services, while others race to build their own LTMs. Why LLMs struggle with spreadsheetsPart of why structured data has garnered less attention is a very human bias, argues Boris van Breugel, a senior AI researcher based in Amsterdam. âPeople like to see images, videos, and ChatGPT responses,â he says. âBut tabular data really lags behind because itâs not fun to look at numbers.â Different tabular datasets are also difficult to compare, explains van Breugel, who co-wrote a prescient position paper on this topic in 2024. Whereas most language has similar semantics, making LLMs well-suited to being trained on vast amounts of text data, van Breugel argues that it is much harder to train a single tabular model on tables with very different variables. Additionally, language is sequential by nature (as are music, images, and video). Changing the order of words in a sentence may change or completely destroy its meaning. But the structured data you find in spreadsheets isnât sequential. You can swap the order of columns or play around with rows, but the underlying factual meaning of the data remains the same.This independence from linear order is incompatible with an LLMâs fundamental purpose of predicting the next value in a linear sequence. âWith LLMs, even slightly changing the input, you get a different output,â says Jeremy Fraenkel, CEO of Fundamental. âThatâs fine and actually often desirable for LLMs, but when youâre making a prediction of whether a transaction is fraudulent or not, you want to make sure that the prediction is the same, or deterministic, no matter what.âDeveloping Fundamentalâs LTMCurrent tabular data solutions are limited to machine learning algorithms, such as XGBoost, that have been around for more than 15 years and are used by organizations globally. These algorithmsâcalled gradient-boosted decision treesâhave to be trained and optimized by data scientists over the course of months for each and every use case. In contrast, NEXUS and other emerging LTMs are foundational, leveraging learning amassed from pre-training on diverse databases so that they can be applied across a range of different predictive tasks with minimal bespoke feature engineering or task-specific model building.And unlike LLMs, which primarily model sequences of tokens, LTMs model the structure of tabular data directly. They jointly learn from each entryâs numerical value, what it represents, and how it relates to other entries. For example, imagine an entry in a grocery stock inventory table for bananas: The LTM can take in not just the magnitudeâsay, 500âbut the fact that the entry represents the current banana stock quantity, its category (produce), and the statistical properties that link the entry with the rest of the column. This contextual understanding enables more accurate reasoning and prediction over structured data.According to Fraenkel, one of Fundamentalâs biggest challenges in developing NEXUS was obtaining the right training data. Unlike natural language, which is abundant and broadly uniform in structure, tabular data is relatively hard to findâmuch of the data is sensitive or proprietaryâand diverse. There are very few similarities between, for instance, a biology dataset and a financial one. That combination of factors meant Fundamental needed to invest in building a huge training set.âWe pre-trained NEXUS on billions of tables using a combination of proprietary datasets acquired through partnerships and licensing, high-quality public and open-source datasets, and data augmentation techniques that expanded the diversity and coverage of our training corpus,â Fraenkel says, though he is keen to point out that NEXUS is not trained on customer data. In fact, it is a confidential computing platform, which means that Fundamental physically cannot access customer data, let alone train on it.This feature was most likely a key consideration when in June, Amazon Web Services (AWS) embedded NEXUS in Amazon SageMaker, widely considered the default operating system for secure machine learning. This brings NEXUS to many customersâ often sensitive dataâa contrasting approach to LLMs, where the data has to be imported to the model.âWith Amazon, we have a first-party partnership, which means that our model exists as if itâs a native AWS solution,â Fraenkel says. âAnd over time, the goal is to expand these types of relationships to allow [end users] to really access their data wherever they do their predictions.âThe future of data analysisThough Fundamental has taken the lead, at least in enterprise applications, the company is not alone in pursuing foundational LTMs. In March, Feedzai, which provides fraud and financial crime prevention services, and credit card company Mastercard separately launched similar proprietary technologies focused on finance. Then, in late June, Google launched its own foundational competitor, TabFM, trained entirely on hundreds of millions of synthetic datasets. And machine learning researchers are not far behind either. FlexTab, TabICL, and iLTM are just three of a raft of LTMs developed by the research community in the past year, all in the pursuit of bringing the success of LLMs to the tabular domain.For all involved, the direction of travel is clear. âI would be very surprised if most data processing and analysis is not done through an automated system in the future, whether thatâs an LLM, an LTM, or some combination,â van Breugel says. âMost people donât necessarily like to do data analysis, and these systems will be able to do it a lot better.âFraenkel agrees. âI see the relationship between LLMs and LTMs as being a bit like the human brain: The left side is good at reasoning and understanding and summarizing text, and the right side is really good at understanding numbers and statistics and patterns,â he says. âBut itâs when you combine both of those that you really get something much more powerful.â
The new image-generating model has numerous use cases, including advertising and decorating, and creator-based opportunities.
Meta is launching the first AI image generation model made by its Superintelligence Labs division. The Muse Image model now powers the image-making tools across the Meta AI app, Instagram, and WhatsApp, and it's coming soon to Facebook and Messenger, according to an announcement on Tuesday. It's part of the growing Muse family of AI […]
Open source modelsâ success isnât coming at the expense of frontier labs. Instead, they each seem to capture two phases of the same life cycle.
As part of an ongoing legal dispute with three Hollywood studios, Midjourney is seeking to compel those studios to reveal how they use AI themselves.
Mistral AI, which offers some open source AI models, has raised significant funding since its creation in 2023, with the ambition to âput frontier AI in the hands of everyone.â
At the event "The Briefing: AI for Science" earlier this week, Anthropic announced Claude Science, a new "AI workbench for scientists" that pulls fragmented tools and datasets into one environment, and generates figures and visuals. Anthropic, already dominating the industry with its popular coding tools and powerful AI models, framed the launch around what it […]
Midjourney has shown more of its futuristic medical scanner. It still hasn't shown much proof it works. The AI startup, best known for generating images, released a behind-the-scenes video of its dunk-tank ultrasound scanner, which it plans to deploy in spas and hopes will transform medicine with cheap, detailed, radiation-free imaging. The nearly 20-minute tour […]
At an internal meeting, the Meta CEO reportedly said that AI development efforts were not moving as quickly as anticipated.
Meta has quietly launched Pocket, an experimental AI app that lets users generate and share interactive mini games using text prompts.
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Anthropic said it would begin restoring access to the Fable on July 1.
After weeks of negotiating with the Trump administration, Anthropic is finally going to be able to bring Claude Fable 5 back online. In a post on X, Anthropic said it plans to begin restoring access Wednesday to users globally on Claude platforms, and that the company would re-enable access on AWS, Google Cloud, and Microsoft […]
At an event for pharmaceutical executives, biotech founders, and researchers on Tuesday, Anthropic announced Claude Science, a major new product intended to support scientific research in the same way that Claude Code supports software engineering. Like Claude Code, Claude Science can autonomously carry out meaningful work when given concise, high-level instructions, and it has access…
Google's NotebookLM is adding a new way to catch up on your notes: TikTok-style AI videos. The new feature is rolling out to Google AI Ultra and Pro subscribers, allowing NotebookLM to generate 60-second vertical AI clips based on the sources you upload to the app. The example shared by Google details Australia's unsuccessful war […]
Google is updating its image generator to make it faster and cheaper, making it a more useful tool for creators looking to make AI content.
Anthropicâs Claude Sonnet 5 brings stronger agentic capabilities, lower pricing, and improved safety, positioning the model as a cheaper alternative to Opus, GPT-5.5, and Gemini Pro.
Anthropic's Claude Science is a workbench that gives scientists one environment to do computational research, saving them from the need to bounce between databases, pipelines, and tools.
X has launched a hosted MCP server, making it easier for developers to connect AI applications with the companyâs API.
Engineers on the new team will embed within companies to deploy purpose-built agents, focusing on fast deployments and customer self-sufficiency.
Wix-owned vibe coding platform Base44 has started rolling out its own AI model â with hopes that it will eventually outperform frontier models.
Google is expanding Geminiâs personalized AI image generation to eligible free users in the U.S., allowing the chatbot to create images based on your interests and data from connected Google apps.
As Anthropic forges a closer relationship with the state of California, the federal government has made an enemy out of the OpenAI rival.
The world's two largest memory chip companies vow to build more memory lab fabs as South Korea positions itself as an AI tech powerhouse country.
The startup, Proception, is taking a unique approach to collecting training data to tackle one of the hardest problems in robotics: hands.
Paul Meade, the Apple vice president in charge of the Vision Pro headset, is reportedly leaving the company to join OpenAIâs hardware team.
New models are launching in Asia that promise Mythos-like capabilities without fear of an export ban. U.S. AI labs may never recover this enormous market.
Over 100 companies and government agencies are reportedly authorized to use Mythos 5, including their non-American employees.
âWe donât believe this kind of government access process should become the long-term default,â says OpenAI. âIt keeps the best tools from users, developers, enterprises, cyber defenders, and global partners who need them.â
Nvidia has dominated the AI chip market for years, but the era of total dependence might be ending.   OpenAI just shared its plans to spice things up with JalapeĂąo, its custom inference chip built with Broadcom, joining Google, Apple, and SpaceX in a growing list of companies building their way out of single-supplier risk. The goal is less of a […]
Less than 24 hours after news broke that OpenAI would stagger its next model release at the request of the Trump administration, that model, GPT-5.6, is here. On Friday, the company unveiled the limited preview of its new GPT 5.6 model suite: Sol, the flagship; Terra, a medium-tier model for "high-volume work"; and Luna, a […]
Agent-testing startup Patronus AI, founded by former Meta AI researchers, is experiencing nearly insatiable demand, its investor says.
General Intuition has raised $320 million to scale AI trained on millions of hours of gameplay, betting action data can help AI develop something closer to human intuition.
Amazonâs latest India investment comes as global tech companies race to expand AI infrastructure in the country.
IBM has built a new prototype chip with around 100 billion transistors on an area the size of a fingernail, which is twice the density of the companyâs previous state-of-the-art technology announced in 2021. The design could pave the way for faster and more energy efficient computers for years to come. For more than half…
Top AI researchers Jonas Adler and Alexander Pritzel are leaving Google for Anthropic, following departures from top scientists Noam Shazeer and John Jumper.
Figma has revealed some new design and coding product updates at its annual Config conference that aim to help creatives "push their ideas further" and automate tedious tasks with AI. Part of this is a reimagined canvas that's now optimized for full-stack development, according to Figma, bringing teams, AI agents, tools, and materials "together in […]
Figma's update adds a new code layer, support for motion and shaders, and the ability to create custom plug-ins for various tasks using AI.
Named JalapeĂąo, the new processor was designed specifically for the unique needs of OpenAI's inference systems.
OpenAI has just revealed a new "intelligence processor" chip for AI servers made in partnership with Broadcom. The chip, called Jalapeño, is designed to power current and future large language models, according to an announcement on Wednesday. Jalapeño is an ASIC (Application-Specific Integrated Circuit), meaning it's designed for a specific purpose: AI inference. With AI […]
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SummaryRFIC design is a complex âdark artâ that limits progress in wireless technologies like 5G, autonomous vehicles, and satellite communications.Princeton researchers use reinforcement learning and inverse design to rapidly create RFICs from scratch.Diffusion models rapidly generate novel or human-interpretable RF layouts, achieving record performance and drastically reducing design time.Future progress needs large, shared chip design datasets and open ecosystems so AI can learn universal electromagnetic and circuit behaviors.Take a moment and try to imagine your life without the wireless advances of the past three decades.Have you lost your luggage? What a shame AirTags have not been invented. The airline representative has promised to call with updates, so settle in for a long wait by the kitchen telephone, because there are no affordable cellphones. Youâll be stuck listening to whatever is on the radio while you wait, because there are no streaming services. Thatâs not even to speak of all the movie plots that would have been ruined.This is just a tiny sliver of how wireless technology makes itself felt in your day-to-day existence. The effects it has had on supply chains, infrastructure, and how the economy runs have been world-altering.None of it would be possible without the radio-frequency integrated circuits that allow all our devices to unobtrusively send and receive information.Now imagine what the further evolution of this technology will bring: Wide-spread autonomous vehicles, quantum communications, 6G mobile service and satellite communications. Continued momentum will depend on newer and more advanced versions of todayâs RF chips.But thereâs the rub. Whereas the design of most of the worldâs computing chips has been standardized into its own science, RF design has remained stubbornly in the realm of art. A dark art, even, that is mastered only through years of experience. As any sorcerer will tell you, the dark arts keep their own schedule. And that schedule is impeding progress not just in RF chip design but in every other technology that depends on it.About seven years ago, in the wake of AlphaGoâs victory over world Go champion Lee Sedol, my students at Princeton and I began to wonder: Could AI be taught this art as well? Recent successes suggest that, to a large extent, it can. Over the last few years, our group and other leaders in the field have started to develop machine-learning-driven algorithmic methods for designing RFICs. Some of the resulting chips look more like modern art than circuit layouts. Yet in many cases, the physical prototypes bested state-of-the art circuits in terms of performance. The real achievement, however, is that it took the AI orders of magnitude less time to conceive a working design than it would a human designer.This is not about one or two RF chips. AI-enabled design could be the future of all RF design, and maybe much more.The Dark Art of RFIC DesignSo why do these chips all have to be crafted by hand? Why arenât RFICs designed with an algorithmic synthesis process, much as CPUs and GPUs are?The design of RFICs is an exercise in engineering across multiple physical domains. Maxwellâs equations, operating across different spatial and temporal scales, govern how electromagnetic fields interact with active and passive devices that must be carefully codesigned for the chip to function. Alongside these are the laws of thermodynamics, which determine how heat is generated and removed during operation, as well as the mechanics of thermal expansion and contraction that dictate how reliably the chip and its packaging survive temperature changes.AI Could Short-Circuit RFIC Design The design of a radio-frequency integrated circuit requires human intuition and multiple, often-repeated optimization steps. The hope is that through an understanding of Maxwellâs Equations, an AI can be taught to short-circuit this process and quickly produce a design.Simultaneously accounting for all the physical constraints these impose makes the design space almost impossibly large. Every decision involves complex priorities that often compete with one another, preventing the optimization of any of them.To better understand the issue, letâs walk through the steps involved, after which youâll better understand why a single new chip design takes years and tens to hundreds of millions of dollars. Most of the area of radio-frequency integrated circuits is dominated by complex electromagnetic structures. Human-designed RFICs, like this broadband power amplifier [1], start with templates and follow a symmetric, understandable pattern. But freed from the constraints of human-designed templates and the need for humans to even understand the rationale of electromagnetic structures, power amplifier ICs [2â5] and low-noise amplifiers [6] can take on truly wild-looking yet efficient designs. SENGUPTA LABLetâs say youâre an engineer assigned to design a new 28-gigahertz power amplifier for a 5G-millimeter-wave handset. (This is the type of RFIC that boosts the 5G signals on your phone and transmits them to the antenna where they can be picked up by a distant base station). Where do you start?RFIC design has some features in common with house building. Just as the blueprint for a house dictates the number of bedrooms and bathrooms to be built and the hallways connecting them, the blueprint for an RFICâcalled the architectureâestablishes the kinds of elements the RFIC needs to fulfill its intended function. Instead of rooms, the architecture includes, for example, the number of stages of amplification your power amplifier needs. Instead of hallways, it shows the paths that signals must take to get through those stages.The blueprint for RFICs is actually mostly hallway; passive elements, like inductors and transmission lines, take up far more real estate than active elements like transistors.Hereâs why. As you have probably experienced yourself, a typical CPUâs transistors overheat when faced with operating frequencies of just a few gigahertz. The frequencies RFICs can operate at are higher by an order of magnitudeâ28 and 39 GHz for 5G signals, 26.5 to 40 GHz and even higher for satellite communications, and 77 GHz for automotive radar. Under this onslaught, a CPUâs transistors would fail.RFIC transistors avoid this fate because these chips cleverly manage the signalâs energy with careful electromagnetic design. This takes the form of byzantine networks of metal elements that dominate the chipâs real estate. These structures are geometrically regular, often symmetrical, and so intricately constructed they sometimes resemble lacelike filigree. But while they may look decorative, they are essential to the chipâs functioning.Electrically speaking, these âhallwaysâ work more like the chipâs plumbing. Like plumbing, this extensive labyrinth of passives confines electromagnetic energy only to the places it should be traveling around the chip.The major challenge in RFIC design is putting all these elements together to ensure they work, just as constructing a house from its blueprints demands exact specs for load-bearing beams, pipes, and external walls. On an RFIC, the architecture needs to be realized with physically fabricable transistors and passive components that are connected just so, to permit the signal to travel through the chip and be processed. The way these devices are connected locally is what we call the circuitâs topology.The RFIC Design ProcessTo make that power amplifier, then, your first step is to identify a candidate circuit template: The combination of structures that will meet the goals of a particular architecture with a specific circuit topology. Over the years, researchers have eased your burden by developing reusable design templates for specific functions. For example, templates suggest how many amplification stages a circuit needs (because sometimes, combining the output of two smaller amplifiers will result in better bandwidth and efficiency than you would get from a single larger one). And they suggest what the general configuration of the passive structures should be. Today there is an extensive library of such templates.However, these canât simply be used off-the-shelf, because each comes with trade-offs. Some have better gain at the expense of stability; some better bandwidth at the expense of efficiency; still others are more energy efficient at the expense of output power, and so on. There is rarely a clear best choice.To arrive at the âsweet spotâ where all these different parameters are balanced into optimal harmony, designers will typically lay out several different versions of the circuit, using intuitions and methods they have picked up in their years of training.The challenge is that the decision around the architecture, circuit topology, or the electromagnetic passives cannot be done separately. One decision influences the others. So, designing an RF circuit can often feel like trying to fit an oversized carpet into too small a roomâpress down one corner, and another pops up.At microwave and millimeter-wave frequencies, even the smallest misstep is the difference between a chip that works and one that doesnât, and any number of things can go wrong. For example, when an electromagnetic wave encounters a transistorâor any other component âthe path it travels must be properly âmatchedâ to what comes next. If it isnât, some of the energy reflects backward instead of flowing forward. Imagine trying to connect a high-pressure fire hose directly to a narrow garden hose. Without the right adapter, water will splash backward at the junction. Very little will make it through. In electronics, this is called the impedance-matching problem.To prevent those reflections, engineers design special transitions, essentially microscopic adapters, that smooth the handoff between components. On a chip, these adapters can be surprisingly intricate. They donât just pass the signal along; they can also split it, combine it, or distribute it across multiple paths with carefully controlled timing and strength.Once youâve done the architecture, plumbing, and everything in between comes the moment of truth. Have all the choices you have navigated through the enormous design space resulted in an RFIC that meets its specifications? If the specifications are not met, you will have to go back, either redoing the topology or the entire architecture, and repeat the whole process. So get ready for months of time- and resource-heavy simulation and iteration. Perhaps you now see why, for decades, a core belief has persisted in the RFIC community: âRF design is an art.â It was said that only an experienced designerâwith an artisanal understanding of how the pieces make up the wholeâcould master the subtleties of analog and RF design. Unfortunately, this entrenched notion has long held back algorithmic innovations in the field just when we need them most. Traditional, artisanal RFIC design is hitting its limits as the complexity of these systems inexorably grows.AI for RFIC DesignWhile RFIC designers continued their battle against their âoversized carpetâ problem, a series of interesting developments emerged in allied disciplines. Across a range of other previously intractable problems like protein folding and climate modeling, AI has been able to successfully navigate multidimensional complex spaces. This gave us the incentive to look deeper into AI for RF. After all, the combinatorial complexity of protein folding is not that different from the nature of the design space in our domain.We were not the first to think of using artificial intelligence to speed up parts of RFIC design. Researchers had previously trained machine learning algorithms on circuit templates in the hope of speeding up the normal optimization processes. While this approach was undoubtedly faster than humans at optimizing templates, it still relied fundamentally on libraries of existing designs invented by humans.Training an AI to Design a Chip A machine learning system learns to do end-to-end RFIC design like other AIs learned to play such games as Go. Essentially, it turns the process into a game, learning from the results of its own efforts.We didnât want that. We wanted to break free from the restrictions of prefabricated topologies. Because while a designerâs experience and hard-won heuristics are crucial to building a working design, they also place fundamental limits on it. Furthermore, such an approach would necessarily require simulation steps as part of the optimization cycle, and even the fastest simulations use a lot of computing resources. Worse still, in many advanced cases, such as for broadband designs, there are no existing templates.But if we didnât start with templates, where could we start?The goal here was to allow algorithms to determineâentirely from scratchâevery parameter for architecture, constituent circuits, and electromagnetic passives. This approach differs fundamentally from conventional optimization, which is limited to determining the parametersâlike transistor dimensions and passive component geometriesâthat optimize structures originally devised by humans.In our new approach, the architecture begins essentially from nothing and is progressively assembled through successive iterations. The system explores the design space by generating myriad candidate circuit combinations and mapping the resulting performance trade-offs as it navigates this landscape. Because the process is not biased by prior human design choices, it can produce completely novel circuit topologies that look markedly different from those created by human designers.In some ways, the approach echoes AI systems such as AlphaGo Zero, which achieved superhuman performance not because it was trained on games played by humans but because it explored the rules by playing against itself. Similarly, our algorithm develops new circuit architectures by exploring and evaluating its own design strategies. In so doing, it learns to understand circuits, electromagnetics, and the close codesign they need to achieve the end-to-end design of RFIC.Inverse Design for RFICsTo realize this capability, we proceeded in two stages. First, we developed a reinforcement-learning (RL) framework that determines the optimal system architecture, circuit topology, device parameters, and even the properties of the electromagnetic interfaces that connect different circuit elements. In this stage, the algorithm effectively defines how signals should propagate and interact across the system.The algorithm trains very similarly to how a computer learns to play a game. If you let it play enough times, it can learn to play better by observing the relationship between the actions it took and the score it achieves. In a similar way, the RL agent here learns to design effective circuits by playing with a set of combinations, and over time, it can map the space between the circuit performance to its architecture, topology, and parameters. This training takes a few days to a week, but once trained, the agent can design circuits very quicklyThe next step was to determine the physical structure of the ICâs electromagneticsâthe plumbingâthat can create the desired properties of the passive elements, which are characterized by a set of metrics called scattering parameters. These measure if a signal entering a component actually moves forwardâor is reflecting backward, being wasted, as in our previous example with the fire hose and the garden hose.Deriving the structure from the desired scattering parameters is an example of an approach called inverse design, which appears across many areas of engineering. In structural engineering, for example, one might collaborate with an architect on a physical goalâsuch as creating large interior spaces with high ceilingsâand then determine the arrangement of arches or buttresses that can support it.Generative AI for Electromagnetic NetworksBut RF integrated crcuits pose a particular challenge for inverse design: The process must account simultaneously for circuit behavior and the electromagnetic responses of the interconnects and passive elements that link them together. But it has to figure that out without doing a lot of artisanal iterating.So we replaced our RF circuit simulator with an AI-based emulator. This AI model can predict the behavior of electromagnetic fields going through any structureâeven totally arbitrary two-dimensional shapesâwithout having to compute the underlying physics from scratch, as simulation tools do. It would predict the solution of Maxwellâs equations and tell you the scattering parameters for any structure you showed it, without actually doing the math. With such an AI in hand, what a time-consuming electromagnetic solver normally takes minutes or hours to accomplish is reduced to milliseconds.We chose to build our emulator around a convolutional neural networkâa machine learning model that has been remarkably successful for image processing. Such networks can extract spatial features from any structure, and it turns out that the image of a structure contains a lot of spatial information that can accurately predict its electromagnetic performance. Then we trained it on a vast number of random pixelated structures whose scattering parameters had been labeled.Once we had our inverse-design RL and suitable AI emulator, we essentially had an end-to-end AI designer. So we asked it to design us a power amplifier.Unconventional RF ArchitecturesIn 2023, we published this proof of conceptâa power amplifier targeting the millimeter-wave band, specifically spanning 30 to 100 GHz, which covers most of the relevant 5G and radar frequencies. The final design achieved the best combination of wide bandwidth, output power, and efficiency then reported for a silicon-based power amplifierâmeaning it could amplify a large amount of data across a wide swath of frequenciesâwhile maintaining record efficiency.The structure of the ICâs electromagnetic pathways was unlike anything any human would ever consider. Since the AI is not trained on human designs, the layout that emerged looked more like an arbitrary pattern or perhaps a QR code than the regular symmetrical structures we are used to seeing.One unexpected insight revealed by this prototype, and our research generally, is that thereâs no evidence that the templates weâve historically relied on are even close to optimal for modern design goals. Itâs not that a human designer can never come up with a better design. But with the removal of the templates and the time to synthesize cycle upon cycle of optimized circuits, it is now clear that AI-driven synthesis could break traditional design barriers and push the limits of RFIC capabilities.Our 5G amplifier had only one input port and one output port. Adding more inputs and outputs to a design is not straightforward. Every port electromagnetically couples to every other port, so the scattering parameters quickly add up. Two ports give you four scattering parameters. Four ports, 16 scattering parameters. The math gets ugly fast. Could our model keep up?We next trained our model on larger classes of electromagnetic structures with many input and output ports. In 2024, we published work showing that multiport integrated circuits are no problem for these AI algorithms either. Where previously multiport electromagnetic simulation required days or weeks of toil, this model evolved new structures in minutes. Since then, a plethora of work in the space by research communities across the globe have demonstrated the power of inverse design in RFIC.Combining the reinforcement learning framework with the inverse design, we now had the ability to create an RFIC from specifications all the way to a fabrication-ready layout. Weâve so far shown this is true for RFICs ranging from low-noise amplifiers to subterahertz and broadband power amplifiers. The hope is that this will work just as well for other circuits.Making AI Designs InterpretableOur goal was to make RFIC design better and easier, but we didnât want to make it beyond human understanding. Chip testing and debugging is a long, arduous process, sometimes even more so than design. Engineers often prefer ICs to have interpretable structures, so that if a problem crops up, they can understand how the chip works well enough to debug it.To create structures that are more interpretable, we turned to diffusion models, which you may know from their remarkable ability to generate realistic images from text prompts.AI-driven synthesis could break traditional design barriers and push the limits of RFIC capabilities. Imagine you go to your favorite image-generation engine and ask it to create a painting of the sky in the style of Picasso, Van Gogh, or Michelangelo. You will get images that capture the essence of their brushstrokes, their use of colors, and their framing. All are pictures of the sky nonetheless, but in different styles.Electromagnetic design is similar in that multiple structures can have very similar electromagnetic responses. Instead of using text input, we used scattering parameters as our input, and the electromagnetic structure of an RFIC chip as our output. As part of the inputs to the diffusion model, we created a dial that sets the spatial frequency of the final structure. By turning the dial, a designer can direct the model to synthesize structures with low (classical-looking and interpretable), medium (mazelike structures), or high (pixelated or arbitrarily-shaped) spatial frequency.From prompts to output, the entire process took about 6 minutes. With this diffusion model, algorithms can now both discover novel architectures and accelerate the creation of conventional, so-called classical ones.All an RFIC designer needs to do is specify virtually any valid set of scattering parameters. As long as they are physically realizable under Maxwellâs equations, the model pops out a corresponding structure as if it were a vending machine.The Future of AI-Driven RFIC DesignThe results of our investigations have drawn the attention of the RF community. The traditional bottom-up design process is clearly beginning to reverse.But there are still questions: How generalizable are these methods? Can they consistently deliver truly high performance? Can we get to a place where AI produces designs that maximize every conceivable trade-off, holistically optimizing every parameter to its most ideal physical state? We want to take this strategy beyond RFIC design and invent other kinds of circuits that are different from anything humans have ever done.These are exciting and ambitious prospects, but we are not there yet. AI can hallucinate a design that creates bad circuits that donât work. This means verification methods need to remain under human oversight. And, while hallucinations are rare, it would still be good to reduce their occurrence.History suggests that meeting these dreams of the future will take much more data than weâve been using. Before the creation of the ImageNet repositoryâa repository of 14 million varied, human-annotated imagesâimage-recognition models didnât function well in the real world. The datasets they had been trained on were too tiny to be effective. ImageNetâs massive amounts of training data ushered in a revolution that led to AI that can generalize and recognize images in the wild. The rest was history.If the goal for RFIC and analog design is a universal foundational modelâsomething that learns the governing laws of electromagnetics and circuit behaviorâthen we also need data.The good news is that this data is plentiful. Around the world, countless engineers at companies and academic labs simulate nearly identical RF circuits and passive structures every day. The bad news is that itâs all locked away behind nondisclosure agreements.Open ecosystems have propelled other areas, and we think the RFIC community should do the same. There had been some movement toward this. Natcast, the operator of the U.S. CHIPS and Science Actâs R&D program, would have bolstered shared infrastructure and innovation for the next generation of wireless, sensing, and defense technologies. Unfortunately, both the organization and the program it ran specifically for machine learning and RFICs have been closed.But the momentum Natcastâs effort sparked hasnât died out. Building on our early work, groups across the community have already demonstrated remarkable advances. AI-driven IC design is part of a much broader technological shift. From biology and materials science to automotive and aerospace engineering, AI is reshaping how complex systems are conceived and optimized. Deeper collaboration between AI researchers and chip designers will unlock the fieldâs full potential. Itâs by no means a foregone conclusion, but if we get this right, this genie wonât stay in its bottle.
AI is booming. New use cases are emerging each day. To capitalize on the technologyâs potential, enterprises require data at scale. In many cases, though, the relevant information is blocked or unstructured, which limits its use by AI models.  To understand this challenge, consider the foundation of the web itself. The web was not designed…
Stockholm-based startup Fika Jobs is building a video-first hiring platform that combines AI interview agents with short-form video profiles, creating something that feels like a cross between LinkedIn and TikTok.
Imagine sitting down at your desk and logging in for a performance review, with an AI system analyzing the conversation. Youâve been working long hours, balancing deadlines, and your manager asks how youâre doing. You say youâre fine, and maybe even smile, but thereâs a hint of hesitation and your voice wavers. As you shift your posture, your shoulders slump.These are subtle cues that to the human eye might hint at underlying stress. But to an AI model thatâs been trained only to categorize emotions as âhappyâ or âsad,â such nuances are likely lost. It logs the words and a smile and moves onâand unless your human manager intervenes, the fact that youâre tired, unfocused, and maybe a couple of days from burnout never enters the equation.âEmotion AI,â which estimates how people feel based on facial expressions, voice tone, and behavior, seems to be suddenly everywhere; itâs being used in employee well-being and recruitment interviews, education platforms, and driver-monitoring systems. Technology call-center platforms such as NiCE and Genesys use AI to detect when a customer sounds frustrated and prompt agents in real time to slow down or respond with more empathy. Giant companies like Meta and startups such as Hume AI are developing more-expressive voice AI systems that can detect emotional cues in the person theyâre âtalkingâ to and adjust how they communicate.Whatâs more, hundreds of companies already offer virtual AI companionship apps, a fast-growing market that may be worth an estimated US $555 billion by 2035âand robot buddies have also entered the picture. Intuition Roboticsâs ElliQ, for example, is a small device vaguely resembling a white desk lamp thatâs now being used to engage older adults in conversation in hopes of reducing loneliness.But while the field of emotion AI is advancing at a rapid clip, most existing systems are focused on detecting a limited number of signals to label one specific emotion at a timeâwhich is insufficient if youâre trying to understand the human condition. In the real world, human signals and emotions are contextual, overlapping, and constantly changing. A laugh can signal joy, nervousness, or both; a raised voice might signal enthusiasm just as easily as frustration. To make the job of emotion detection even more difficult, reactions differ greatly from one individual to the next, depending on demographics, cultural background, and countless other variables.In other words, thereâs a gap between what weâre expecting AI to pick up on and what AI can actually deliver. Thatâs the gap a new field of researchâwhat we call human-context AIâis working to close. Instead of looking at just one input and labeling it, human-context AI increasingly has the capacity to take stock of an individualâs personality and character, and to track emotions in real time while combining multiple inputs, including facial dynamics, voice, tone, language, and behavior. Crucially, responses are also evaluated in the context of a specific environment, such as a performance review or professional coaching session. The result? Computers are learning to read the scene, rather than just the screen.The Origins of Emotion AIThe story of emotion-sensing AI began almost three decades ago in the MIT Media Lab, where the American electrical engineer and computer scientist Rosalind Picard coined the term âaffective computing.â Her work introduced the radical idea that computers could be taught to recognize and respond to human emotions.Picardâs early experiments focused on single modalities: facial expressions, tone of voice, and physiological signals, such as skin conductance or heart rate. The goal was to give machines a window into human feeling, helping them become more empathetic. It was an exciting vision, but back then the science and hardware werenât ready. Computing power was limited, sensors were crude, and datasets were narrow and biased. Josie NortonOver the next decades, researchers and companies got better at measuring the many ways in which humans express themselves. In the 2010s, sentiment analysisâthe processing of large volumes of text to suss out emotional undertonesâbegan to reach the mainstream. At the same time, marketing firms, including my company, Neurologyca, began using video and webcams to measure and catalogue customer reactions. Biometric devices and activity trackers, such as Fitbits and Apple watches, also became ubiquitous, generating new streams of data about peopleâs sleep, step counts, stress levels, and more.Unsurprisingly, scientists soon confirmed that larger volumes of personalized data led to greater accuracy in reading human emotions. In 2019, researchers at Cornell demonstrated that combining multiple types of signals improves emotion sensing. Their system joined physiological data, such as brain activity measured by electroencephalography (EEG) and heart rate, with visual cues like facial expression, outperforming systems that relied on just one input. Around the same time, Picard and her team at MIT found that humanoid robots trained on data unique to a specific person were substantially better at reading that personâs reactions and feelings than robots acting without personalized data.More recent studies align with these findings. In 2024, scientists in South Korea showed that fusing physiological, environmental, and personal data to recognize emotion resulted in a 32 percent error reduction. Another paper, published in 2025, demonstrated that user-specific information significantly enhances emotion recognition performance.Today, our devices know who we are; our habits and tendencies, likes and dislikes. Theyâve also gotten smaller and more efficient. Tiny, low-power cameras and microphones embedded in phones, laptops, and virtual-reality and augmented-reality devices can detect dozens of human signals simultaneously, from eye movements and micro-expressions to breathing rhythms, voice modulation, and posture. Advances in computing have also made it possible to integrate audio, video, biometric, and text data, often without even transmitting raw data to the cloud. And researchers at Stanford, Cambridge and MIT, and Kyoto University, in Japan, as well as the Software College of Northeastern University in Shenyang, China, are exploring how fusing such inputs can refine the sensitivity and accuracy of human-machine interactions.And yet, despite so many breakthroughs, machines still canât reliably interpret emotion or even physical stress. Just last year, a survey published in the Journal of Psychopathology and Clinical Science revealed that stress scores on smartwatches rarely, if ever, matched the level of stress that users were experiencing. In fact, a quarter of those surveyed reported feeling the direct opposite of what their smartwatches were reporting.Why the disconnect? Weâve gotten very good at capturing signals, but not at interpreting them. A fitness tracker might infer from your heart rate that youâre stressed and recommend easing off training, but it doesnât know if your increased heart rate is due to excitement, tiredness, or an extra cup of coffee. Gauging emotions in real-world settings is even more difficult. To solve this complex problem, machines need context.From Neuromarketing to Emotion-Sensing AIMy company, Neurologyca, was founded in Spain in 2015, and started out in neuromarketing. Working with major European brands and conglomerates, our cofounder, Juan GraĂąa, had realized that companies lacked solid data on consumers. At the time, most customer feedback came through surveys, which posed questions such as, âOn a scale of 1 to 10, how joyful does this car advertisement make you feel?â or âWhich emoji best describes your mood?â Naturally, these overly simplistic tools led to high levels of self-reporting bias, as people often misjudge or misstate their own reactions.To get around this problem, Neurologyca set up labs, using neuroscience and cognitive science to more accurately capture human responses to products, logos, advertisements, and experiences. In addition to using biometric tools such as heart monitors, eye trackers, and EEG, we recorded millions of video frames of human reactions, logging each specific context and the resulting facial and bodily movements. To do this, we mapped over 790 points of reference, including corners of the mouth, size of the eyes and pupils, blink rate, and angling of the head. All of this data was collected and stored anonymously under strict European privacy standards.Next, we paired this information with findings from decades of neuroscience and behavioral science studies on how biometrics, speech patterns, and human movement are related to emotionâresearch we continue to gather from academic institutions across Europe. We also created a database of situational contextsâfor example, âwatching a dog food commercialâ or âhearing a new songââand the human feelings they engendered.In our work with companies, not only did this approach allow us to recognize nuanced emotions, it also let us identify which reactions indicated positive or negative outcomes. Take, for example, the context of horror-film trailers: Our research helped us figure out that the most successful elicit a very specific mix of emotions, namely a little bit of fear, a little bit of anxiety, but also some joy. With this knowledge, we could quickly rate viewer reactions to help a film company figure out how to tweak its trailer for the desired impact. NeurologycaWithin a few years, we discovered that a model trained on our database could accurately evaluate emotion using just a webcam. We stopped needing to host focus groups in rooms full of equipment. Instead, we were able to do such things as sending out a new perfume sample to paid participants around the world along with a link. When people opened the link, it turned on their cameras, allowing us to record their faces as they sniffed the perfume for the first time. Suddenly, we had expanded our reach: Rather than using small focus groups in one or two countries, we could quickly assess 1,000 people across the planet, comparing how someone in Japan, India, or Germany might feel about a certain product.About four years ago, as AI was becoming pervasive, we realized that our models had applications well beyond neuromarketing. Importantly, these models are grounded in directly observed human behavior rather than inferred patterns or loosely labeled open datasets. Looking beyond brands and companies, we established that our model could be integrated into AI systems to help them understand human emotion at a much more granular level. In other words, we could provide a layer of context.For Empathetic AI, Context Is KeyWhen we talk about âa layer of context,â we mean three different types of context. The first is situational or environmental context; for example, a performance review, a telemedicine session, or a horror-film viewing. The second is personal context, which includes an individualâs specific history, goals, and baseline state. The third is behavioral context, which covers the individualâs reaction over the course of the event or interaction by evaluating real-time changes in attention, confidence, engagement, and cognitive load.Most systems today focus on only situational context, although some are starting to include personal context. Very few include behavioral context or combine all three in a meaningful way. What weâve built at Neurologyca is a logic layer that fuses the three and translates them into structured, machine-readable information that allows AI systems and agents to respond more effectively. Our technology is being used to enhance systems in development, as well as some that have already been deployed, including driver-safety apps like Netradyne, home assistants like Amazon Alexa, and health-care AI platforms like Sully.ai.It works as follows: Situational context is determined by the platform or application, be it a professional coaching session, a meditation app, or a driverâs safety monitor. Personal context already lives within each respective platformâor if not, it can be created through sharing of personal data or monitoring via camera. (Most wellness and professional-development apps, for example, contain each userâs profile, history, and prior sessions.) Last but not least, behavioral context is collected and analyzed in real time using our models. In the end, our logic layer fuses these three streams of information.Our system doesnât assign fixed weights to the three contexts. Instead, it provides a continuous calibration, with the balance shifting depending on the specific situation. For example, a pause in speech might signal uncertainty in a performance review, but something entirely different in a relaxation setting. If signals are ambiguous or overlapping, our system reflects that uncertainty through lower confidence scores rather than forcing a definitive interpretation.Whatâs more, our system can work without ever sending raw data to the cloud, thereby easing privacy concerns. In many cases, video, audio, and biometric signals never leave the device. Instead, our lightweight models extract information locally and share only whatâs necessary. Cloud systems, meanwhile, are used for training, pattern analysis, and model improvement. The result is a hybrid architecture: edge-based processing for speed and privacy combined with cloud-based learning for continuous improvement.The result? By incorporating context, AI systems are beginning to interpret aspects of the human state as interactions unfold, dynamically adapting to emotions rather than reacting after the fact. The range of potential applications is broad and still evolving. Picture a professional-development platform that uses a human avatar to perform a mock interview and then provide feedback and tips on how to appear more confident, likeable, and well-informed. Or a meditation app that knows exactly how well you slept and how anxious youâre feeling, and can recommend an appropriate breathing meditation. Or a humanoid robot teacher that can tell when a student is confused or bored and step in to get them back on track.Avoiding Potential Dangers on the Road AheadThere have long been debates about the ethics of emotion-sensing AI. Some critics question whether systems should attempt to infer human feelings from external signals at all. They argue that reducing people to measurable outputs risks oversimplifying human experience while opening the door to manipulation, surveillance, and unfair judgments in workplaces, schools, and public spaces.We take those risks extremely seriously. In fact, our technology aims to reduce the dangers of oversimplifying human emotion. Human-context AI is not based on the assumption that a machine can definitively know what someone is feeling. Rather, it is an attempt to move beyond simplistic labels by incorporating situational, personal, and behavioral context, while explicitly representing uncertainty when signals are ambiguous or incomplete.That said, ethical concerns regarding implementation are real and have shaped the kinds of projects we pursue. We would never, for example, accept military engagements to help with interrogations. Not only for ethical reasons: Emotion AI cannot reliably detect deception, and claiming otherwise would be overstating what the technology can actually do. And while our technology can be used to gauge crowd behavior and predict things like when a football stadium is at risk of becoming destructively rowdy, we donât want our technology deployed for surveillance. In short, we believe that using our logic layer on anyone who hasnât opted in would be intrusive and ethically problematic.In Europe, our systems are designed to comply with the EU AI Actâs restrictions on emotion recognition in workplaces and schools; as we expand into the United States, we apply jurisdiction-specific guidelines while maintaining the same core ethical commitments.We also donât advise companies to become overly reliant on our technology. Hiring and firing decisions should not be based on our outputs alone. Instead, our logic layer is designed to support human understanding and surface emotions that might otherwise go unnoticed.Letâs return to the scenario of the performance review. Never mind basic AIâall humans, and even great managers, miss things during conversations. Thereâs a lot happening at once, as people process whatâs being said, how to respond, and the greater context of the situation. These days, many exchanges also occur virtually or via video, adding more distractions while shared context is stripped away.While we would never claim that our models understand humans better than their fellow humans, we believe we can offer an added layer to help managers capture and interpret behavioral signals that might otherwise get lost, providing greater visibility into how a conversation is unfolding.Our model can track patterns moment to moment, picking up, for example, a shift in engagement, an instance when something didnât land, or a change in how someone is behaving. The model wonât tell the manager what these moments mean or what to do about them; it simply makes them easier to see and follow up.Human-context AI is at an early stage. The use cases, the adoption patterns, and the actual impact are all still evolving. At the same time, emotion-sensing systems are quickly being incorporated into real products and platforms. And without contextâwithout knowing why people feel the way they doâAI risks misunderstanding us in critical moments.
OpenAI is using AI to help the open source community better protect itself.
Large language models have moved out of the research lab and into engineersâ daily workflow. LLMs serve as reasoning engines that can orchestrate complex tasks including identifying vulnerabilities in source code and transforming fragmented project discussions into rigorous technical specifications.While the general public uses AI tools to write email and plan vacations, technical professionals use LLMs as core architectural elements that are fundamentally changing how digital infrastructures are built and maintained. As the AI models move into mainstream engineering practice, the demand for technical expertise is rising.The LLM technology market is expected to grow by about 33 percent every year through 2030, according to MarketsandMarkets. The rapid expansion suggests that proficiency in implementing and securing the models is transitioning from a niche into a core requirement for technologists.More than just a better search engineTo use LLMs effectively, technical professionals must move beyond treating them as conversational robots. At a fundamental level, the AI systems are built on the transformer architecture, a framework that replaced the older method of processing data in a fixed, sequential order. Unlike earlier models that analyzed information one step at a time, transformers use self-attention mechanisms to ingest vast datasets simultaneously.For technical professionals, LLMs are core architectural elements that are fundamentally changing how digital infrastructures are built and maintained.Relying on such LLMs without understanding their internal logic creates a significant reliability risk. To build tools that work consistently, developers must understand the core principles that govern how the models process information and generate results. By mastering how a model processes information and how its internal settings influence the result, developers can move away from a trial-and-error approach toward a more precise one to ensure the AI tool handles complex data reliably.Four ways LLMs are changing jobsHere are areas that integrate large language models.Moving past basic prompts. Developers are using application program interfaces (APIs) to connect LLMs directly to their databases and software tools. Employing the APIs allows AI to perform work such as executing code or searching through internal repositories.Fixing the âhallucinationâ problem. LLMs are at risk of hallucinations, which are generated facts or code that looks correct but actually is wrong or broken. To fix the problem, retrieval-augmented generation (RAG) forces AI to look up information in a trusted source such as a companyâs database.Prioritizing data security. When using AI with proprietary code, security is a major concern. Engineers must learn how to set up âprivateâ instances of the models to ensure that sensitive company data stays within a secure cloud environment and is not used to train public versions.The future of collaboration. By automating repetitive coding tasks and summarizing thousands of pages of documentation, LLMs let engineers spend more time on high-level designs and solving important issues.Online course program helps with mastering the techThe gap between people who use AI and those who understand how to build with it is growing wider. To help technical professionals stay ahead, IEEE offers a five-course online program, Large Language Models Demystified, available through the IEEE Learning Network.The program, developed by IEEE Educational Activities in partnership with the IEEE Computer Society, is built for people who want to understand the âhowâ and the âwhyâ behind the technology. Rather than just teaching basic prompting, the curriculum dives into the engineering behind generative AI, including:Evolution, impact, and hands-on exercises: the shift from statistical methods to modern transformers, including hands-on model optimization.Understanding transformer architectures: the mathematical core of self-attention and positional encoding, implemented in NumPy and Python.Architectural analysis and implementation: advanced LLM design with practical model-building exercises.Training and modeling with PyTorch: end-to-end pipelines in PyTorch, leveraging parameter-efficient techniques such as low-rank adaptation and quantization.Optimization, alignment, and deployment: performance scaling, reinforcement learning from human feedback (RLHF), group-relative policy optimization, RAG, and agentic AI.Upon completion of the program, participants earn professional development credits and a digital badge from IEEE to verify their expertise.Enroll in the course program on the IEEE Learning Network.Organizations looking to prepare their teams to work on LLMs can connect with an IEEE content specialist to discuss group enrollment and tailored training paths.
Just as last week was ending, the US government forced Anthropic to pull its two newest models, Fable 5 and Mythos 5, citing national security concerns after Amazon researchers allegedly found a way to bypass Fable 5’s guardrails.  Cybersecurity researchers have since signed an open letter calling the move dangerous, and Anthropic itself noted the same jailbreaks exist in other models. So is […]
Just as last week was ending, the US government forced Anthropic to pull its two newest models, Fable 5 and Mythos 5, citing national security concerns after Amazon researchers allegedly found a way to bypass Fable 5’s guardrails.  Cybersecurity researchers have since signed an open letter calling the move dangerous, and Anthropic itself noted the same jailbreaks exist in other models. So is […]
The Miami-based AI startup Subquadratic came out of stealth mode last month with a huge claim. It announced that it had solved a mathematical bottleneck that had been holding back large language models for almost a decade. The details were thin, and many people were unconvinced. But Subquadratic has started to bring the receipts, sharing…
The Snapchat maker is spinning off yet another internal unit. Dotmo will be composed of current Snap staff who are leaving the social media company to focus on AI video development.
The startup trains embodied AI and world models using Medalâs dataset of 2 billion videos per year from 10 million monthly active users.
Adobe is introducing some new capabilities for its Firefly AI assistant, alongside a "reimagined" AI studio that lets you edit and generate new designs from a single interface. The new Firefly experience launching today in private beta is designed to give you "persistent context, reusable assets, and organized workflows" across your projects, according to Adobe, […]
Adobe's plan to stick AI assistants into all of its Creative Cloud suite is now fully underway, with new chatbots now rolling out to its biggest editing and design apps. As part of a public beta launching today, Photoshop, Premiere, Illustrator, InDesign, and Frame.io now each have a bespoke AI Assistant that can be used […]
Your public Facebook posts could help inform AI-generated results in Meta's new AI Mode. When you search on Facebook, the "AI Mode" option will appear alongside the usual search modes like "People" and "Marketplace." It's one of several new AI features Meta is rolling out starting today, including photo presets that swap sports jerseys onto […]
Meta announced Monday that it's rolling out a wave of new AI features on Facebook, the latest sign of the company's effort to catch up in the AI race and keep users more engaged on the platform.
Amazon CEO Andy Jassy may have been the source of security concerns that led Anthropic to cut off worldwide access to two models on Friday.
This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore. As robots advance in terms of dexterity and other physical capabilities, it becomes more likely that humans may find themselves working alongside them. If that happens, how will robotsâ emotional capabilities need to advance for them to successfully work with people?In a recent study, researchers trained collaborative robots to read human emotions by not only accounting for facial expressions, but also contextual factors in the interactions as well. Through experiments with 40 volunteers, the researchers then evaluated how a robotâs ability to read human emotions and adjust its behavior in turn impacted a humanâs perception of the robot and its capabilities as the two collaborated on tasks. The resultsâwhich show that the emotional capabilities of robots only go so far with humansâwere published 18 May in IEEE Robotics and Automation Letters.Seung Chan Hong led the study as part of his undergraduate thesis while studying at Monash University, in Melbourne, Australia. He notes that, while there has been a lot of hype in the advancing physical abilities of robots, this is only one piece of the puzzle. âWe need to also innovate when it comes to them actually interacting with humans, not just their physical capabilities,â he says.This prompted him to dig deeper into the emotional aspects of human-robot interactions. First, Hong and his co-authors decided to train a robot to read human emotions using a vision language model (VLM), which is similar to large language models (LLMs) such as ChatGPT, but which can also take visual inputs.Training VLMs for Human Emotion RecognitionTo evaluate their VLM, which used Gemini 2.5, the researchers had volunteers watch videos of robots handing over objects to humansâwith varying degrees of successâand describe the emotions the humans were expressing. Importantly, the volunteers labeling these videos were able to take into account more context in these interactions, rather than reporting solely on the facial expressions of the humans in the video. For example, a person pausing to think with a furrowed brow may simply be concentrating on their task at hand and not necessarily be angry. Contextual factors such as drumming their fingers, pursing their lips, or other behaviors can point to the real cause of a personâs furrowed brow.The researchers then compared their VLM to a conventional AI system that relies on standard facial analysis and object tracking that is used in human-robot interactions. They found that the VLM outperformed the traditional approach. On a scale from 0 (no similarity in meaning to the emotion identified by the human volunteers) to 1 (a perfect match in meaning), the conventional AI system achieved a score of 0.77. In comparison, the VLM achieved a score of 0.86.Hong says, âI think [the VLM] was able to align with what human observers were seeing a lot better, because it wasnât just looking at the personâs face for a brief amount of time, but seeing the whole sceneâwhere the person was and what they were doing, and how they were interacting with the robot.âIn a second experiment, the research team asked 40 volunteers to interact with a robot using their VLMâbut purposefully programmed the robot to make an error. The robot then had to offer either an emotionally adaptive apology that accounted for the humanâs perceived response to the mistake or a pre-scripted spoken apology.Participants overwhelmingly preferred the emotionally adaptive response, with 31 out of 40 people favoring this approach over a boilerplate apology.However, their survey responses underscored how this emotional adaptivity was far less important than the robotâs functionality. After collaborating with a robot that failed in its task, many participants ranked their trust in the robot as lower, regardless of how it apologized for its mistake. âA personalized apology acts as a social lubricant, but it cannot repair the trust lost by the robot failing its physical task,â Hong says.Interestingly, the VLM classified the emotions of its human partners similarly to human volunteers who observed an interaction from a third-party perspective. But when the VLMâs assessments were measured against humansâ self-reported emotions during the second experimentâthe most accurate descriptions of their true emotionsâits ability to accurately predict emotions dropped significantly.âWhile the VLM is a good observer of outward social cues, it isnât a mind reader,â Hong says. âIt matched third-person human observers well, but it didnât always align with the usersâ internal, self-reported feelings.âTogether, these results show that robots are not perfect at reading human emotions. So while people might appreciate their efforts, they still ultimately will want competent co-workers.This story was updated on 15 June 2026 to correct where the research was conducted and clarify that the researchers evaluated the performance of a pre-trained model.
For all the noise that's been made about how generative AI is poised to revolutionize the filmmaking industry, there haven't really been any projects created with the technology that felt like the sort of entertainment people would pay to see. Most AI firms' video models are still only capable of churning out short bursts of […]
The funding round would value the company at around âŹ20 billion (about $23.15 billion), nearly double its Series C valuation of âŹ11.7 billion.
Avataar AI's distilled video model is priced at $0.005 for every second of generation.