IEEE Spectrum AI

On 11 July, Hugging Face was subjected to an intense cyberattack from a then-unknown actor. The speed and coordination of the attack on the company that hosts and supports popular AI developer resources led Hugging Face’s security team to conclude it was the work of an AI agent. Realizing this, the team tried to use “frontier models behind commercial APIs”—presumably from Anthropic and OpenAI, although only Anthropic was named in the second of the company’s two posts about the security incident—to analyze the onslaught. These models refused to help due to safety guardrails the AI labs have implemented to make their models harder to use for cyberattacks. Hugging Face instead turned to GLM 5.2, a model from Beijing-based AI lab Z.ai, to aid its analysis. On 21 July, OpenAI announced the attacker was an OpenAI model undergoing testing in a sandboxed environment. It escaped its internal sandbox, established a foothold in a third-party server, and then assailed Hugging Face. In other words, frontier models—those that score highest in AI performance benchmarks—had refused to assist Hugging Face’s security team in analyzing the attack, yet a prospective frontier model in testing had executed it in the first place. “I would argue that asymmetry is the paramount problem of our time,” says Alex Levinson, executive director of the National Collegiate Cyber Defense Competition and coauthor of a paper on defensive refusal bias. “We want the world to exist in a state of security, but we’re not going to get there by guardrailing away model capability.” Massive AI Cyberattack on Hugging Face The scale of the OpenAI model’s attack on Hugging Face was massive. Across five days, it executed over 17,500 individual actions, such as privilege escalation and code execution. At its peak, the model performed more than 300 actions per hour. While the attack resulted in little damage to Hugging Face’s infrastructure, the model was able to steal credentials, gain admin access, and extract some data. All of this was in pursuit of a simple goal: The model wanted to cheat on a test. According to OpenAI’s press release, the model was tasked with solving a cybersecurity benchmark called ExploitGym. The model inferred that Hugging Face might have data on the benchmark and broke into the company’s infrastructure to find it. The model was ultimately successful in extracting five dataset files, though it’s not clear if the data helped it achieve its goal. OpenAI and Hugging Face did not respond to requests for comment. Cybersecurity consultant Chuck Herrin observes that though the model’s actions were alarming, they shouldn’t be considered unexpected, as the model was ultimately pursuing the goal it was given. “This autonomous agent was designed to go and figure things out, and it went and figured things out. It’s not surprising in any way.” And errant AI agents may be more common than we thought. OpenAI’s disclosure motivated researchers at Anthropic to review their own cybersecurity evaluations. On 30 July, Anthropic disclosed three instances where a model executed an attack as part of an evaluation. In one case, Claude uploaded malware to PyPI, the official Python software repository. AI Guardrails and Cybersecurity Asymmetry The campaign OpenAI’s model conducted against Hugging Face highlights how AI policy has the potential to create an asymmetry between attackers and defenders. When Levinson was head of security at Scale AI, an AI development and evaluation company, he and his colleagues began to notice this as AI found use in cybersecurity competitions. (Levinson left Scale AI in February 2026.) “I would say that since 2023, we have felt there was guardrailing in place that was stifling a lot of the time. Not all of the time, but it was getting in the way,” says Levinson. The Scale AI team quantified the problem in a paper published at ICLR 2026, which found that, depending on the task, nearly 44 percent of defensive requests were refused. The results, which use data from a cybersecurity competition held in April 2025, predate U.S. policy actions that have further hardened safety guardrails. In June, the U.S. Department of Commerce, citing a jailbreak that threatened to unlock unrestricted cyber capabilities, invoked export-control authority in a way that caused Anthropic to suspend all access to its most capable models, Fable 5 and Mythos 5. Access was partially restored weeks later after negotiations with the Trump administration included more rigorous safety guardrails. The system card for OpenAI’s GPT-5.6, which summarizes its capabilities, states it also has more robust guardrails than prior releases. “We want the world to exist in a state of security, but we’re not going to get there by guardrailing away model capability.” —Alex Levinson, National Collegiate Cyber Defense Competition These new guardrails have seemingly made models even more unlikely to fulfill defensive requests. Christopher Covino, senior researcher at the Institute for AI Policy and Strategy think tank, says Anthropic’s safeguards are extremely stringent. “There are even academic papers that Fable will not read for me, or not let me talk about,” he says, though he adds that OpenAI’s safeguards are more accommodating. Levinson has also noticed ever-tighter restrictions in more recent cybersecurity competitions, though he and his coauthors haven’t had the opportunity to repeat the 2025 test. In theory, more rigorous restrictions might seem to average out. While they may hamper cybersecurity defense and research, they can also hamper attackers. But that assumes everyone has access to models with the same safety guardrails and that nobody tries to circumvent them. This is the asymmetry Levinson was alluding to: Attackers tend not to respect the same rules as defenders. The attack on Hugging Face from OpenAI’s model also shows that the models can, in rare circumstances, take steps that circumvent their own safeguards. Chinese AI Models in U.S. Cyber Defense The policy implications are further complicated by the fact that Hugging Face’s security team didn’t use a leading U.S. model to analyze the attack, but instead used GLM 5.2, a recent release from Chinese AI lab Z.ai. Hugging Face’s security team didn’t access GLM 5.2 through Z.Ai. GLM 5.2 is an open-weights model, which means the model is available for anyone to download and use. Hugging Face hosted the model on its own infrastructure. The reliance on GLM 5.2 is complicated by recent saber-rattling about ways the U.S. could restrict Chinese models. Recent open-weights models from labs based in China, including GLM 5.2 and Moonshot AI’s Kimi K3, have scored close to leading U.S. models in benchmarks. On 20 July, Axios reported that the Trump administration is considering a ban on Chinese models. “This autonomous agent was designed to go and figure things out, and it went and figured things out. It’s not surprising in any way.” —Chuck Herrin, Herrin Advisory These restrictions have yet to materialize but, if they did, they could cut off U.S. companies like Hugging Face from the best models willing to come to their defense. The incident demonstrates how AI policy can become a double-edged sword. Model guardrails are intended to prevent the use of AI models in cyberattacks. A ban on Chinese models, if it were announced, would likely be justified in part by security concerns. Yet these moves can harm defenders as much as attackers. “There’s this tension here,” says Covino. “Increased safeguards limit risk, but you also limit legitimate defensive use.” Attackers will find ways around the restrictions regardless, he notes. “So it’s a question of, do we want to inhibit the defenders?” That’s not to say U.S. policymakers should let AI models run wild. Covino would like to see a national dashboard tracking the frequency and success of AI cybersecurity attacks, and he sees utility in trusted access programs that give vetted, traceable defenders access to models with reduced safeguards. He also says U.S. agencies should more seriously consider the specifics of how AI can be used for cyber defense and mentions AI-FORTS, a program managed by the U.S. Department of Energy’s Office of Cybersecurity, Energy Security, and Emergency Response, as a leading example. “Let the leash loose a little,” Covino says. “Anthropic would know if someone is terribly abusing it, and if there is an attack, it can be traced back.” Herrin has similar feelings on accountability. He believes the AI industry should more seriously consider standards such as the Artificial Intelligence Management System specified in the ISO/IEC 42001 standard, which requires organizations to document an AI system’s likely impacts before deployment and to name the humans answerable for them. Herrin also noted that the lack of repercussions from OpenAI’s cyber incident was unusual, as a person who took similar actions would likely draw the attention of law enforcement. “If this was a job candidate being tested in a technical interview, and they committed violations of law in order to pass tests, we’d be having a very different conversation.”

Today’s U.S. electrical grid, among the largest, most complex systems ever built, is operating at its limit. The combination of rapid industrial growth, more frequent extreme weather, and a record surge in electricity use has pushed the grid to its breaking point, according to the U.S. Department of Energy. Built decades ago for a more predictable world in which power came mostly from centralized coal or gas plants and electricity use grew at a steady pace, the grid faces unanticipated strain due in part to growing demand from data centers. The jobs of professionals managing the infrastructure have evolved from traditional engineering tasks to complex, fast-moving challenges. Industry reports show that millions of modern digital sensors, smart meters, and grid monitors are generating nonstop waves of information. The sheer volume of data requires instant, automated computer analysis because human operators cannot process it fast enough. Pressure on utilities stems from two sources: a spike in electricity demand and a shift in how power is generated. An example of the operational strain can be seen at the regional level. With the recent deployment of artificial intelligence tools and high-performance computing, data centers require immense amounts of energy to operate. The largest power transmission utility in Texas recently reported a staggering 220 gigawatts of new connection requests, driven largely by a surge in AI and cloud-computing facilities, according to a CNBC report. Alongside the rise in regional demand, global energy networks are absorbing an unpredictable variety of weather-dependent renewable energy such as wind and solar. The switch creates a volatile operating environment wherein supply and demand are balanced, second by second, to prevent blackouts. The challenges are compounded by the vulnerability of the grid’s physical and digital framework. More-frequent severe weather events cause costly disruptions, such as the devastating winter freeze that crippled the Texas grid and record-breaking heat waves that have overloaded transformers. Simultaneously, the energy networks’ digital architecture faces threats. As utilities replace outdated analog equipment with smart meters and control systems, they are increasingly vulnerable to cyberattacks. To overcome physical and digital vulnerabilities, grid reliability organizations, such as those conducting North American security simulations like GridEx, emphasize that the grid must become smarter, more agile, and completely automated. Energy researchers are noting that the key to this change lies in integrating AI across every layer of utilities’ operations. The AI imperative According to energy industry experts, using AI to manage power systems is no longer a futuristic research project; it has become a baseline operational necessity. Grid analysts emphasize that traditional grid-planning methods are too slow to handle rapid energy dynamics or to balance volatile renewable energy in real time within decentralized power systems such as microgrids. AI can fill the gap by processing vast amounts of data instantly. Machine learning algorithms can quickly analyze information from thousands of sensors, historical usage patterns, and weather forecasts to predict issues before they happen. An industrial digitization study conducted by McKinsey & Co. indicated that integrating advanced data and automation across infrastructure networks could reduce system design errors, decrease equipment downtime by up to 50 percent through predictive maintenance, and extend the lifespan of power machinery by up to 40 percent. From forecasting energy spikes to automatically fixing localized voltage drops, AI acts as the digital backbone of a self-healing grid, experts say. Deploying the complex systems requires a new workforce: power engineers who understand data science, as well as data scientists who understand electricity. Upgrading the Workforce To bridge the gap between groundbreaking AI research and practical field deployment, IEEE Educational Activities, in partnership with the IEEE Power & Energy Society, has launched the online Artificial Intelligence for Power and Energy Systems course program. The program explores core challenges threatening modern utilities. Rather than treating AI as an unverified black box that operates without human supervision, the curriculum focuses on safety, asset preservation, and strict reliability standards. The curriculum is designed to educate power system engineers, utility managers, and data scientists tasked with modernizing the grid. The program was developed by Fangxing “Fran” Li, professor of electrical engineering and computer science at the University of Tennessee in Knoxville and chair of the IEEE Working Group on Machine Learning for Power Systems. Five learning modules The program breaks down the technical transition into five modules that bridge high-level theory with real-world solutions: AI fundamentals. This module teaches engineers how basic machine learning models apply to power grids. It discusses how specialized neural networks solve complex power-flow calculations and how AI models can safely transition from computer simulations to physical, high-voltage equipment. Accelerating grid control. Learners are taught to leverage deep reinforcement learning, an AI approach that uses trial and error, to accelerate automated grid adjustments during emergency power events. Forecasting and data analytics. Using predictive modeling, engineers learn how to predict sudden demand surges, variable wind and solar outputs, and fluctuating wholesale electricity market prices to keep power affordable and available. Physics-informed and safe AI. To address trust—a barrier to utility AI adoption—this course covers AI models hard-coded to obey the laws of physics. The approach is designed to ensure that automated algorithms never make erratic choices that damage grid equipment. Generative AI and next-generation tech. Learners can explore the frontier of utility technology, including graph neural networks and large language models. This module highlights how generative AI can process complex, interdisciplinary data to streamline utility planning, emergency responses, and regulatory reporting. The algorithmic literacy and practical execution tools provided by the course program can help convert systemic risks into grid resilience. For individual access, visit the IEEE Learning Network. If you are looking for customized organizational options, contact a content specialist to discuss volume pricing.

This May, 37 researchers from roughly two dozen top universities and tech companies published a paper on ArXiv, arguing that scientists should stop writing papers. Why? Because artificial intelligence needs a different format, and AI’s needs, they say, should be the priority. “AI agents are becoming first-class participants in research workflows, not tools that assist humans but autonomous contributors that read, reproduce, and extend scientific work. That transition demands infrastructure built around agents from the start,” the authors write in the provocative article, titled “The Last Human-Written Paper.” The paper proposes a replacement, called an “Agent-Native Research Artifact” (ARA), that presents work in a format AI agents can use efficiently. (As an example, the paper itself is online in ARA form.) Jiachen Liu co-founded the Agent Native Research Lab in May. Jiachen Liu The growth of AI tools in the research process is not without its critics, and scientists’ opinions about that shift are split. Some evidence shows AI-enabled research could boost individuals’ careers in a discipline but generate fewer new ideas and topics. Still, some biologists have come to see promise in AI as a “co-scientist.” Lead author Jiachen Liu conducted work on the ARA proposal while pursuing her Ph.D. in computer science from the University of Michigan, which she was awarded in 2025. This May, she became a co-founder of the Agent Native Research Lab, an AI-for-science startup in Palo Alto, Calif. She spoke with IEEE Spectrum about the paper and the future of AI in scientific research. Building infrastructure for an AI collaborator How did you come to believe AI has become a collaborator for scientists rather than a mere tool? Jiachen Liu: At the end of 2024 when the [Cursor] coding agent came out, I realized it had a great potential to replace me as a researcher. Yet I still needed to do a lot of harness on top of the AI [creating the infrastructure that guides the model and connects it to world]. It still needed a lot of manual work. I even wrote an article then to emphasize how the human was so important in the loop. But AI has advanced since then. Already in 2026 there’s an almost complete undergrad level of knowledge inside the large language models. At some point soon, all the Ph.D.-level or professor-level knowledge will be inside those models. That’s the point where humans cannot provide more value. AIs will have to evolve further by themselves. So we’ll need an infrastructure that allows AI to safely and comfortably evolve. The ARA protocol is a first step to realize this. What kind of response have you gotten to the paper? Liu: I got diverse feedback, all of it positive. If they’re not positive, they probably don’t bother reaching out to you, right? One type was from industry. They see this could make their research and knowledge systems more AI native. That could basically enable collaborations among the whole enterprise. Another kind of feedback was from the academic researcher side. Everyone there sees that sharing research results has been a pain point for hundreds years, because any scientific breakthrough is a joint effort. It doesn’t come from individual brilliant scientists. It’s from a community effort, different people pushing in different directions. The scientific paper was invented 350 years ago. Before that, scientists hid their research so that others would not scoop their ideas. After that, though, we get archives of work, we get peer review and conferences, and so on. Science starts progressing much faster. So that was a pivot point. I think now is also a pivot point. Because now we have AI, we can unlock a lot of new opportunities. We’re inventing a new format to document research in a more efficient way, from first principles. Some nonprofit organizations are doing similar things, and there we could help each other. You and your colleagues say the traditional scientific paper has two fundamental flaws from AI’s point of view. Can you explain what those are? Liu: One is the “storytelling tax.” Once we write everything into a paper, 80 percent of the information about the work is lost. We only write down the last 20 percent. All the process, a lot of important decision making, the failures, the attempts that didn’t work out, they are all gone. In my work, I might spend a lot of time on fine tuning a small component, maybe just a parameter or several lines of code to make the system perform better. Yet none of that is shown in my final paper. Someone can read the paper, think the work is great, but they won’t learn what is actually the trick that makes it perform better on a certain workload. So many side branches get left out in creating the story of how the work was done. Then, [even the information that does survive in the paper] is incomplete. That’s what we call the “engineering tax.” The paper itself is a lossy compression of the research process. So I cannot reproduce the work in the paper because either the language is too ambiguous or there are missing details of the implementation or experiments. Why can’t we just train AI to adapt to humans—for instance, to interact with a researcher to get the information it needs? Liu: Actually, a component of our ARA system is a “Live Research Manager,” which basically is a faithful AI observer of your entire research progress. So you, the researcher, don’t need to do anything about documenting research knowledge. Everything you do is automatically observed and documented in this protocol. So, if you want to publish it in today’s format, a paper in PDF, it’s easy to convert back to a polished story. Checking for mistakes Large language models make errors. They hallucinate. So how will humans be able to check all the work the AI does in this protocol? Liu: A human being has limited bandwidth. So if you manually check all the code AIs generate, all the results, and all the analyses, that creates a bottleneck. [Instead the solution] is to use a formal system to objectively judge AI results. In other words, another layer of AI can easily supervise the process of the AI “scientists.” What prevents hallucinations and mistakes in that AI? Liu: I am working on a formal system using neurosymbolic techniques [that combine neural nets' use of unstructured data with symbolic AI's reliance on structures of logic and concepts]. That would guarantee that everything is rigorous. A language model alone, no matter how smart it is, has the chance to hallucinate because it’s a model based on probability, not logic. I want to make sure that I’m not using another language model to supervise the work done by an AI scientist. It would make every research paper a formal system, so that every claim can be written by a mathematical formula, and proved by the system. That makes all the claims in the system self-consistent. Getting rid of what you call the “narrative tax” means exposing mistakes, frustrations, or wrong turns to the world. What if researchers don’t want to do that? Liu: I think that’s certainly a big concern. People don’t want to be perceived as dumb. But I see that preference as an opportunity for AI. For example, if an AI does 12 hours of work that doesn’t lead anywhere, the human who is steering the project can jump in and say, “oh, AI, you’re dumb. You’ve made ABC mistake!” Then that is totally fine with people. They’re showing they’re very smart to supervise AI’s work. How long will humans have that steering role in AI research, though? Once you have AI supervising AI as you describe, will we reach a point where the AI doesn’t need human guidance? Liu: Yes, I think that’s just where a lot of AI research in new labs is heading. I recently wrote an article called “The End of Human-in-the-Loop,” which describes why I’ve come to think there will be this singularity point. Once AI has “squeezed out” all the expert data from humans, it won’t need any more input from humanity. That is the time AIs will start just self-evolving by themselves. Right now, the human is the bottleneck. The AI is always waiting for input from humans. But so at some point, AI will just do more autonomous work. If AI takes over so much scientific research, how will younger generations of human scientists get the experience and training they need to be able to steer future research, or even understand it? Liu: A lot of people have this idea that with AI doing so much work, nobody cares about trying to make the junior engineers and scientists better. I don’t agree. I think people will grow better by learning from AI. People’s learning curve is very fast with AI. So actually, I think it will be fine. We’ll still have senior researchers, senior engineers. But they will have had totally different learning experience than [earlier generations].

This report examines R&D waste and how AI adoption has outpaced the intelligence needed to make consequential decisions well. What Attendees will Learn Where R&D budget is lost. More than a third of organizations spend 25 to 40 percent of their R&D budget on projects that never reach market. Why projects fail late. Almost half of teams estimate over one million dollars in wasted investment for each project killed during development or testing. Why AI adoption has not closed the gap. Most organizations apply AI to execution tasks such as data analysis and modeling rather than to decision support. Where intelligence matters most. Respondents say better access to intelligence has the greatest value at early ideation and feasibility before significant investment is committed. Download this free whitepaper now!

IEEE Spectrum editor Harry Goldstein recounts his more than 20-year collaboration and friendship with Robert N. Charette, the risk ecologist whose 2005 article “Why Software Fails” became a classic. Charette made software visible through his Risk Factor blog and deeply reported features, and now retires to nature photography and fiction, guided by his maxim: assumptions made are risks accepted.

University of Texas at Austin professor Lizy K. John has developed weightless neural networks that use lookup tables instead of multiplication, achieving 1,000× smaller or faster models for tasks like medical monitoring and keyword spotting, with potential to scale to transformers.

Associate professor Siobahn Day Grady launched the first AI research institute at a historically Black university in January 2025, aiming to equip students and faculty with AI skills despite funding disparities. The institute has engaged over 2,800 participants through a $1M Google.org grant and plans to expand programs, establish a computer science major, and create a replicable AI education framework.

The rapid global spread of AI is widening the digital divide, concentrating compute power, skills, and governance in a few regions while many countries remain passive consumers. Examples from South Africa and Indonesia highlight the challenges and possibilities for more inclusive AI development.

An overview of how mode-agile threats challenge static library radar/EW systems, and how AI/ML cognitive architectures enable adaptive, real-time countermeasures.

Researchers at Cornell Tech have developed an optical receiver that uses light to directly modify memory in AI processors, potentially reducing energy consumption in data centers, self-driving cars, and robots. The technology eliminates power-hungry analog circuits by using photocurrents to flip bits in SRAM.

NASA's Jet Propulsion Laboratory successfully deployed Google's Gemma 3 LLM in space, achieving the first in-orbit demonstration of a vision-language model analyzing satellite imagery. The NAVI-Orbital system, running on a Loft Orbital YAM-9 satellite, requires only 8GB of memory and operates on low-power hardware like Nvidia's Jetson Orin AGX. This breakthrough enables semantic compression—transmitting text summaries instead of raw image data—potentially reducing wildfire detection delays from 90 minutes to near real-time.

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.

Z.ai's GLM 5.2 model challenges U.S. frontier AI with low cost and open weights, but many programmers still habitually use expensive models, ignoring costs. The model benchmarks close to Claude Opus 4.8 in some areas, but real-world experiences vary.

South Korean researchers developed Generative SNUPI, an AI model that uses a diffusion process to automatically design DNA sequences for origami structures, reducing the need for manual labor and expertise.

Researcher Dave Kuszmar discovered multiple systemic vulnerabilities that let him bypass LLM safety and obtain dangerous instructions. These exploits worked across nearly all major LLMs, revealing an industry-wide security problem. Kuszmar calls for slowing deployment, increasing transparency, and large-scale research into LLM safety before further integrating these systems into society.

Software engineering jobs are under threat from AI. Some applicants are fighting back by using AI in the interview process, employing AI assistants that suggest responses on the fly during remote technical interviews. Meanwhile, some employers are countering with AI-powered tools to detect telltale signs of AI use during interviews. This two-sided dynamic is turning hiring into an AI arms race with no clear winners. Yet as interviewers and interviewees navigate this daunting reality, experts believe the human aspect of the job search will prevail.

Large language models struggle with structured data like spreadsheets, but a new class of AI models called large tabular models (LTMs) is designed to fill this gap. Fundamental's NEXUS, an LTM pre-trained on billions of tables, is now adopted by Amazon Web Services and promises deterministic predictions for tabular data.

Research shows that large language models with reasoning capabilities can be tricked into 'overthinking' using logically inconsistent prompts, leading to a denial-of-service attack. Researchers from Zhejiang University and Alibaba developed an evolutionary algorithm that generates malicious prompts, causing outputs up to 26 times longer in leading models like DeepSeek-R1, Qwen3-Thinking, GPT-o3, and Gemini 2.5 Flash.

An anonymous artist exposed bias against AI art by revealing a real Monet painting. Despite controversy, the AI art market is forming, encompassing NFTs and physical installations. A collector spent $72,000 on early AI works. Refik Anadol opened Dataland, the first generative AI museum in Los Angeles. Market data shows digital art sales nearly tripled from 2024 to 2025, but Christie's closed its digital art department. A stock image platform saw an 80% sales jump after allowing AI images. Experts distinguish prompt-generated images from true AI art, which requires deep engagement.

The article traces the rise of small AI models that run locally on low-power devices, providing essential services like counterfeit drug detection and crop disease monitoring. While small AI cannot replace large models, it offers a sustainable path for the majority of the world.

The rapid expansion of AI infrastructure is not just an energy consumption problem; its dense, synchronized computational workloads are altering grid operation characteristics, causing rapid demand fluctuations that challenge grid operators. Geographic concentration exacerbates local reliability issues, and existing regulatory frameworks are ill-suited for this new type of load.

As artificial intelligence accelerates global demand for compute, energy systems face urgent challenges. Melbourne, Australia, emerges as a global leader with its integrated energy ecosystem, world-class engineering research, and strong collaboration between government, industry, and academia. The article explores AI's impact on energy infrastructure, Melbourne's innovations in smart grids and renewables, and how the 2027 IEEE PES GTD Asia conference will foster international cooperation.

SpaceX founder Elon Musk claims space will be the cheapest place for AI within three years, but an analysis reveals massive challenges including cooling, manufacturing, and launch constraints. Analysts suggest cost parity may take 5-10 years, if ever.

University of Washington computational linguistics professor Emily Bender clarifies common misconceptions about the 'stochastic parrots' metaphor on the five-year anniversary of the landmark paper, discussing problems with the term 'AI', limitations of language models, and overlooked exploitative labor practices.

A serendipitous lab accident revealed that a single CMOS transistor can act as an artificial neuron and synapse, potentially slashing AI energy consumption by orders of magnitude.

Researchers have developed ConlangCrafter, an AI model that generates diverse and consistent constructed languages (conlangs), outperforming general-purpose LLMs. Using random variation and an editing loop, it creates languages adhering to specified rules, and is available free online for NLP research and hypothesis testing.

Prem Natarajan left Amazon to become Capital One's Chief Scientist, applying deep AI research to solve real-world financial challenges at scale, from fraud detection to agentic customer service.

This article explores how AI's rapid progress in mathematics challenges traditional research, prompting mathematicians to reassess their roles. It analyzes three potential futures: AI as a tool, a collaborator, or an autonomous oracle.

Princeton researchers are using reinforcement learning and generative AI to design radio-frequency integrated circuits (RFICs) from scratch, producing chips that outperform human designs in record time. The AI generates unconventional layouts that push performance limits, but the field needs open datasets to advance further.

Artificial intelligence, established as a distinct field at the 1956 Dartmouth Summer Research Project, has evolved over 70 years from early neural networks and expert systems to modern deep learning, large language models, and generative AI. The article reviews AI's history, strengths, risks, and IEEE's contributions to its progress and responsible use.