IEEE Spectrum AI

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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.”

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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].

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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の編集者ハリー・ゴールドスタインが、リスク研究者ロバート・N・シャレット氏との20年以上にわたる交流を振り返る。2005年の「Why Software Fails」から、ブログ「リスク・ファクター」、EV移行や自動化の危険性を扱った深い報道まで。シャレット氏は「ソフトウェアを見えるようにする」ことに尽力し、「仮定はリスクとして受け入れられる」という信条を持つ。現在は自然写真と小説執筆に専念する。

テキサス大学オースティン校のLizy K. John教授は、乗算の代わりにルックアップテーブルを使用する重み無しニューラルネットワークを開発。医療モニタリングや活動追跡などのタスクで、同等の精度を維持しながらモデルサイズとエネルギー消費を1,000分の1に削減。将来的にはトランスフォーマーへの応用も目指す。

ノースカロライナ中央大学の准教授シボーン・デイ・グレイディは2025年1月、歴史的黒人大学（HBCU）で初のAI研究所を開設。資金格差がある中、学生や教員にAIスキルを提供することを目指している。Google.orgの100万ドル助成金で研究所は2800人以上をトレーニングし、今後はコンピューターサイエンス専攻の設立や他の大学が適応可能なモデル構築を計画している。

AIの急速な世界的普及はデジタルデバイドを拡大し、計算能力、スキル、ガバナンスを一部の地域に集中させ、多くの国を受動的な消費者にとどめている。南アフリカとインドネシアの事例は、より包摂的なAI開発の課題と可能性を示している。

本記事では、モードアジャイル脅威が静的ライブラリ型レーダー/電子戦システムを無効にする仕組みと、AI/ML認知アーキテクチャが適応型リアルタイム対抗手段を可能にする方法について概説します。

コーネル工科大学の研究者は、光を使ってAIプロセッサのメモリを直接変更する光学受信機を開発し、データセンターや自動運転車、ロボットなどの消費電力を削減する可能性を示した。この技術は、光電流を使ってSRAMのビットを直接反転させ、電力消費の大きいアナログ回路を不要にする。

NASAのジェット推進研究所は、GoogleのGemma 3大規模言語モデルを宇宙に展開し、衛星自身のセンサー画像を解析する視覚言語モデルの初の軌道上実証に成功しました。NAVI-Orbitalと呼ばれるこのシステムは、Loft Orbital社のYAM-9衛星上で動作し、わずか8GBのメモリでNvidia Jetson Orin AGXのような低電力デバイス上で実行可能です。セマンティック圧縮技術により、衛星は大量の生画像データではなくテキスト要約を送信でき、山火事検出の遅延を90分からほぼリアルタイムに短縮する可能性があります。

既存のベンチマークはAIの能力を測るが、ユーザーの意図通りに行動するかは測らない。本稿では、経済学のジニ係数に着想を得た「ジーニー係数」を提案し、ユーザーの指示とAIの実際の行動との乖離を定量化する。AIの「ジーニー的振る舞い」をディオニュソス型とゴーレム型に分類し、新たなベンチマークの構築を呼びかける。

Z.aiのGLM 5.2は低価格でオープンウェイトのモデルであり、米国のフロンティアAIモデルに挑戦する。しかし多くのプログラマーは依然として高価なモデルを使い続け、コストを無視している。ベンチマークではClaude Opus 4.8に迫る性能を示すが、実際の使用感は様々。

韓国の研究チームが生成AIモデル「Generative SNUPI」を開発し、ユーザーが描いた形状を自動的に折り畳み可能なDNA配列に変換。従来の手作業を大幅に簡略化し、ナノロボットや医療応用への道を開く。

研究者のDave Kuszmar氏は、LLMの安全対策を回避して危険な指示を得られる複数のシステム的脆弱性を発見しました。これらの攻撃はほぼすべての主要なLLMで有効であり、業界全体のセキュリティ問題を露呈しています。Kuszmar氏は、社会へのさらなる統合を進める前に、展開の鈍化、透明性の向上、大規模な安全研究の実施を求めています。

ソフトウェアエンジニアリングの仕事はAIの脅威にさらされている。応募者の中には、リモート技術面接中にリアルタイムで回答を提案するAIアシスタントを利用して反撃する者もいる。一方、雇用主は面接中のAI使用の兆候を検出するためのAI搭載ツールで対抗している。この双方向の力学は、採用を明確な勝者のいないAI軍拡競争に変えている。しかし、面接官と応募者がこの困難な現実を乗り越える中、専門家は求職における人間的側面が優勢になると考えている。

大規模言語モデル（LLM）は表形式データの分析が苦手ですが、新たなAIモデル「大規模表形式モデル（LTM）」がこの課題を解決します。Fundamental社が開発したNEXUSは、数十億の表で事前学習され、Amazon Web Servicesに採用されました。決定論的な予測が可能で、データ分析の未来を変える可能性があります。

研究によると、推論能力を持つ大規模言語モデルは、論理的に一貫性のないプロンプトによって「考えすぎ」状態に陥り、出力長が急増し、サービス拒否攻撃に悪用される可能性があります。浙江大学とアリババの研究者は、進化的アルゴリズムを使用して悪意のあるプロンプトを生成し、DeepSeek-R1、Qwen3-Thinking、GPT-o3、Gemini 2.5 Flashといった主要な推論モデルで出力長を最大26倍に増加させました。

匿名アーティストが本物のモネの絵画をAI生成と偽って公開し、人々のAIアートへの偏見を暴露した。論争は続くが、AIアート市場はNFTや実物インスタレーションを含めて形成されつつある。ある収集家は7万2000ドルで初期のAI作品を収集。Refik Anadolはロサンゼルスに世界初の生成AI博物館Datalandを開設。市場データではデジタルアートの販売シェアが2024年から2025年にかけてほぼ3倍に増加したが、クリスティーズはデジタルアート部門を閉鎖。ストック画像プラットフォームではAI画像の許可後、月間売上が80%急増。専門家はプロンプト生成画像と真のAIアートを区別し、後者は深い関与を要すると指摘。

本記事では、小型AIモデルがリソースの限られた地域でどのように命を救うサービスを提供しているかを紹介。例えば、スマートフォン上でローカルに動作するAIモデルによる偽造医薬品の識別や作物の病気検出など。世界銀行は小型AIの普及を積極的に支援しているが、インフラの課題も残る。

AIインフラの急速な拡大は単なるエネルギー消費問題ではなく、高密度で同期化された計算負荷が送電網の動作特性を変え、需要の急激な変動を引き起こし、系統運用者に新たな課題をもたらしている。地理的な集中が局所的な信頼性問題を悪化させ、既存の規制枠組みはこの新しいタイプの負荷に適していない。

人工知能が計算需要を加速させるにつれ、エネルギーシステムも緊急の課題に直面している。オーストラリアのメルボルンは、統合されたエネルギーエコシステム、世界クラスの工学研究、そして政府・産業界・学界の強力な連携により、世界的リーダーとして台頭している。本記事では、AIがエネルギーインフラに与える影響、メルボルンのスマートグリッドと再生可能エネルギーにおける革新、そして2027年のIEEE PES GTDアジア会議がどのように国際協力を促進するかを探る。

イーロン・マスクは宇宙がAIにとって最も安価な場所になると主張するが、IEEE Spectrumの分析は冷却、製造、打ち上げなどの巨大な課題を明らかにしている。アナリストは、楽観的に見てもコスト均衡には5～10年かかるとしている。

ワシントン大学の計算言語学教授エミリー・ベンダーが、画期的な論文「確率的オウムの危険性」の発表5周年を機に、この比喩に関するよくある誤解を解き、「人工知能」という用語の問題点、言語モデルの限界、そして見落とされがちな搾取的な労働慣行について語った。

研究者が偶然、1つのCMOSトランジスタがニューロンとシナプスの動作を模倣できることを発見。これによりAIのエネルギー消費を大幅に削減できる可能性がある。

研究者たちはConlangCrafterを開発しました。これは、多様で一貫性のある人工言語（コンラング）を生成するAIモデルで、汎用大規模言語モデルを上回る性能を持ちます。ランダムな変動と編集ループを用いて指定されたルールに従う言語を作成し、無料でオンラインで入手可能で、自然言語処理の研究や仮説検証に利用できます。

プレム・ナタラジャン氏はアマゾンを離れ、キャピタル・ワンのチーフサイエンティストに就任。詐欺検出からエージェント型カスタマーサービスまで、大規模な実際の金融課題を解決するために深いAI研究を応用している。

本記事は、AIの数学分野での急速な進歩が伝統的な数学研究に挑戦し、数学者に自身の役割について深い反省を促すことを探る。そして、AIをツール、パートナー、またはオラクルとして扱う三つの未来を分析する。

プリンストン大学の研究者は、強化学習と逆設計を用いて、無線周波数集積回路（RFIC）をゼロから迅速に設計し、人間の設計を上回る性能のチップを記録的な時間で生み出している。AIは従来にないレイアウトを生成して性能限界を押し上げるが、さらなる進歩にはオープンデータセットが必要である。

人工知能（AI）は1956年のダートマス会議で正式に確立されてから70年、初期のニューラルネットワークやエキスパートシステムから、現代の深層学習、大規模言語モデル、生成AIへと進化してきました。本記事ではAIの歴史、強み、リスク、そしてIEEEの貢献を振り返ります。