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74 recent industry stories relevant to the field â releases, launches, and announcements beyond the papers.
As Meta pours billions into AI infrastructure and agents, Zuckerberg is working to convince investors that the payoff will be worth the price.
On the companyâs second-quarter earnings call Wednesday, CEO Mark Zuckerberg said Meta sees a âlarge enterprise opportunityâ spanning AI agents, APIs, compute, and internal software.
Microsoft is working on an AI "super app" that combines Copilot's chat, coding, and agentic capabilities. During an earnings call on Wednesday, Microsoft CEO Satya Nadella said the app will span "both consumer and commercial experiences" when it launches this year. "Copilot is evolving rapidly from chat to Cowork to Autopilots," Nadella said. "This quarter, […]
Meta is all-in on AI, and sometime soon, the company is going to make a big push into personal AI agents that can do things on your behalf. On Wednesday's Q2 2026 earnings call, CEO Mark Zuckerberg previewed a high-level vision of how the company is thinking about personal agents and what it will do […]
The startup analyzes calls, messages, and CRM data to identify effective sales techniques and turn them into playbooks for AI agents.
Pangram has raised $9 million to scale its AI detection software. The startup has also released a new AI text detection model, Pangram 4, and an AI image detection model in research preview.
Imagine a healthcare system made up of multiple AI agents: one that manages symptom assessment, another scheduling, a third insurance, and a fourth pharmacy. Each is an expert in its domain. But they all have their own distinct knowledge and objectives. Today they can exchange data, but they are not yet able to actually coordinate…
Drug discovery is a high-cost, high-risk endeavor that is under growing pressure from a market increasingly defined by first-mover advantage. Since the 1950s, the cost of developing new pharmaceuticals has roughly doubled every nine yearsâa phenomenon known as Eroomâs Law. Today, bringing a new drug to market takes an average of 10-15 years and costs…
Forget YouTube videosâfrontier physical AI models need multiple camera angles, dense annotation, and soon, brain wave readings.
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.
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 […]
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.
Etched, founded by three Harvard dropouts, has created new chips and memory components that speed up inference on any AI model -- no GPUs required, it says.
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 languageâŠ. 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…
ServiceNow's investment gives BusinessNext a strategic partner to expand its AI-powered banking software globally.
Uber is also investing in Travis Kalanick's company Atoms, which has made gauzy claims about using industrial AI to modernize the world.
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 […]
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.
The conversation about AI often centers on algorithms, computing power, or huge investments in new semiconductor fabrication plants and hyperscale data centers. But beneath each of these advances is another layer of innovation that makes them possible: advanced materials. Every new generation of AI technology demands more processing power, more memory, greater energy efficiency, and…
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.
Roblox's new "Build" feature lets users generate basic games using a single text prompt.
Applied Computing has raised a $20M Series A to build a foundation AI model for the oil, gas and petrochemical industry.
It's the company's first public proof point after a year and a half spent building AI infrastructure largely out of public view.
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…
Anthropic-backed Ode launches as AI labs bet that embedding forward-deployed engineers inside enterprises is the key to accelerating enterprise AI adoption.
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 […]
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 latest family of models promises improvements across a range of areas, including cybersecurity.
The AI firm Anthropic has developed a technique that has given it the clearest glimpse yet at whatâs really going on inside large language models as they answer questions or carry out tasks. What they found ranges from the mundane to the unnerving. Researchers at the company built a tool called the Jacobian lens (or…
Meta's pitch to users is Spark's ability to handle large agentic workloads, fix bugs, and help with large code migrations â the kind of automation that enterprises are increasingly turning to AI companies to provide.
The company is taking a modular approach to designing these chips, anticipating that their needs will change as AI evolves rapidly by the time the chips are in production.
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.
The news comes about a week after OpenAI announced its own custom AI chip in a partnership with Broadcom.
OpenAI CEO Sam Altman has reportedly proposed giving 5% of the companyâs equity to a U.S. sovereign wealth fund, reviving discussions about letting the public share in the financial gains from the AI boom.
Sound built its reputation on concentrated, high-conviction bets in category-leading AI labs, while Kutcher's new fund appears to be chasing the layer underneath those companies â the infrastructure and energy that power them.
Meta is developing plans for a cloud infrastructure business, selling access to AI compute power and models. The move would pit it against the big cloud providers like Amazon Web Services, Google Cloud, and Microsoft Azure.
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…
EquiLibre Technologies, a Prague-based AI lab founded by three ex-DeepMind researchers, is now valued at more than $500 million.
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.
Engineers on the new team will embed within companies to deploy purpose-built agents, focusing on fast deployments and customer self-sufficiency.
Artificial intelligence is transforming what is possible in agriculture, but industry leaders should be wary of investing in AI without first laying the groundwork.  The use cases are promising, especially for an industry navigating volatile fertilizer costs, unpredictable weather, and margins that leave little room for error. Research shows AI-enabled predictive models can improve crop…
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.
Top AI researchers Jonas Adler and Alexander Pritzel are leaving Google for Anthropic, following departures from top scientists Noam Shazeer and John Jumper.
Named Jalapeño, 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…
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 […]