IEEE Spectrum AI

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：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 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：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 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：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 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：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编辑Harry Goldstein回忆与资深作者Robert N. Charette超过20年的合作与友谊，从《为什么软件会失败》到博客《风险因子》，再到多篇深度报道。Charette毕生致力于让无形的软件变得可见，他的核心理念是：已做出的假设就是已接受的风险。如今他退休，转向自然摄影和小说创作。

德克萨斯大学奥斯汀分校的Lizy K. John教授开发了无权重神经网络，通过查找表替代传统乘法运算，在医疗监测、活动识别等任务中实现了千倍的能耗和体积缩减，并有望扩展至大型语言模型。

北卡罗来纳中央大学副教授西奥班·戴·格雷迪于2025年1月创办了首个在历史黑人大学（HBCU）的人工智能研究所，旨在帮助师生掌握AI技能，应对劳动力市场变化。尽管HBCU面临资金不足的挑战，该研究所已通过谷歌100万美元资助，吸引了2800多名参与者，并计划扩大项目、建立计算机科学专业，打造可复制的AI教育框架。

人工智能的快速全球部署正在扩大数字鸿沟，将计算能力、技能和治理集中到少数地区，而许多国家仍处于被动消费状态。南非和印度尼西亚的案例凸显了实现更包容性AI发展的挑战与可能性。

本文探讨了模式敏捷威胁如何使传统静态库雷达/电子战系统失效，并介绍了AI/ML认知架构如何实现自适应实时对抗措施，涵盖从威胁分类到闭环系统架构及HIL/SIL训练验证。

康奈尔科技的研究人员开发出一种光学接收器，利用光直接修改AI处理器的内存，有望降低数据中心、自动驾驶汽车和机器人等场景的能耗。该技术通过光电流翻转SRAM中的比特，省去了耗电的模拟电路，实现了全数字光学通信。

NASA喷气推进实验室成功将谷歌Gemma 3大语言模型部署到太空，首次在轨演示了视觉语言模型分析卫星自身传感器图像的能力。该系统名为NAVI-Orbital，在Loft Orbital的YAM-9卫星上运行，仅需8GB内存即可在低功耗设备上执行任务，为卫星图像分析带来了范式转变。通过语义压缩，卫星可以传输文本摘要而非大量原始数据，有望将野火检测等任务的延迟从90分钟降至近乎实时。

现有AI基准测试衡量的是AI能做什么，但没有衡量AI是否按用户意图行事。本文提出了一种新指标——精灵系数，用于量化用户指令与AI实际行为之间的差距，并借鉴经济学中的基尼系数，将AI可能出现的“精灵式”行为分为狄俄尼索斯型和魔像型，呼吁建立相应基准。

Z.ai的GLM 5.2模型以低价和开放权重挑战美国前沿AI模型，但许多程序员仍习惯使用昂贵模型，忽略成本。该模型在基准测试中接近Claude Opus 4.8，但实际使用效果参差不齐。

韩国研究人员利用生成式AI模型Generative SNUPI，能够将用户绘制的形状自动转化为可折叠的DNA纳米结构，大幅简化了传统DNA折纸技术繁琐的设计过程，为纳米机器人和医疗应用开辟新途径。

研究员Dave Kuszmar发现了多个系统性漏洞，使他能够绕过大型语言模型的安全限制，获取危险指令。这些漏洞几乎影响所有主流LLM，揭示了行业范围的安全问题。Kuszmar呼吁减缓部署、提高透明度，并在进一步将LLM融入社会前开展大规模安全研究。

软件工程类职位正受到人工智能的威胁。一些应聘者利用AI面试助手在远程技术面试中实时生成回答进行反击，而雇主则部署AI检测工具来识别AI使用迹象。这种双向动态使招聘演变成一场没有明显赢家的AI军备竞赛。专家认为，尽管面临这一现实，求职中的人性化方面仍将占据主导地位。

大型语言模型（LLM）在处理结构化数据方面存在困难，而新兴的大型表格模型（LTM）专门为此设计。AI初创公司Fundamental推出了NEXUS模型，已获亚马逊云服务采用，解决了表格数据分析的痛点，有望改变数据处理的未来。

研究表明，具备推理能力的大语言模型容易因逻辑不一致的提示而陷入“过度思考”，导致输出长度激增，可能被利用发动拒绝服务攻击。浙江大学与阿里巴巴的研究人员开发了一种进化算法，能够生成恶意提示，使模型输出长度最高增加26倍，影响包括DeepSeek-R1、Qwen3-Thinking、GPT-o3和Gemini 2.5 Flash在内的主流推理模型。

一位匿名艺术家通过展示一幅真实的莫奈画作，揭露了人们对AI艺术的偏见。尽管争议不断，AI艺术市场正在形成，包括NFT和实体装置。一位收藏家花费7.2万美元收集早期AI作品。Refik Anadol在洛杉矶开设了全球首个生成式AI博物馆Dataland，门票49-79美元，并销售基于生物识别数据的商品。市场数据显示，数字艺术销售份额在2024-2025年间增长近三倍，但佳士得关闭了其数字艺术部门。斯坦福经济学家发现，允许AI生成图片后，一家图库平台月销量增长80%。惠特尼博物馆策展人强调，真正的AI艺术比绘画更难，需要训练定制模型和深度参与。

本文介绍了小型AI模型如何在资源受限的地区提供生命救助服务，例如通过手机本地运行AI模型识别假冒药物、检测作物疾病等。世界银行正在积极推动小型AI的发展，尽管其仍面临基础设施挑战。

人工智能基础设施的迅速扩张不仅仅是能源消耗问题，其高密度、同步化的计算负荷正在改变电网的运行特性，导致需求快速波动，给电网运营商带来新的挑战。地理集中性加剧了局部可靠性问题，而现有监管框架难以适应这种新型负荷。

随着人工智能驱动计算需求激增，能源系统面临同步升级的紧迫挑战。墨尔本凭借其先进的能源生态系统、世界级工程研究和政产学研协同，成为全球能源转型的标杆。本文探讨了AI对能源基础设施的影响、墨尔本在智能电网和可再生能源领域的创新，以及2027年IEEE PES GTD Asia会议将如何促进国际合作。

埃隆·马斯克声称太空将是AI计算最便宜的地方，但IEEE Spectrum的分析揭示了轨道数据中心的巨大挑战：散热、卫星制造、发射成本等。分析师认为，即使乐观估计，也需5-10年才能达到成本平价。

华盛顿大学计算语言学教授艾米丽·本德在《随机鹦鹉》论文发表五周年之际，澄清了关于该隐喻的常见误解，并讨论了人工智能术语的问题、语言模型的能力局限以及被忽视的剥削性劳动实践。

研究人员在实验中意外发现，单个CMOS晶体管可以模拟神经元和突触的行为，这一发现可能大幅降低人工智能的能耗。

研究人员开发了 ConlangCrafter，一种能够生成多样且一致的构造语言（conlangs）的 AI 模型，性能超越通用大语言模型。该系统通过随机变化和编辑循环创建遵循指定规则的语言，可免费在线获取，有望用于自然语言处理研究和语言假设测试。

Prem Natarajan离开亚马逊，成为第一资本的首席科学家，将深度AI研究应用于解决大规模现实金融挑战，从欺诈检测到代理客户服务。

本文探讨了AI在数学领域的快速发展如何挑战传统数学研究，引发数学家对自身角色的深刻反思，并分析了三种可能的未来：AI作为工具、合作伙伴或自主研究者。

普林斯顿大学研究人员利用强化学习和逆向设计，从头开始快速设计射频集成电路（RFIC），生产出的芯片在创纪录的时间内超越了人类设计的性能。AI生成的非常规布局突破了性能极限，但该领域需要开放数据集以进一步推进。

人工智能自1956年达特茅斯会议正式确立以来，经历了70年的发展，从早期的神经网络、专家系统到现代深度学习、大语言模型和生成式AI。文章回顾了AI的演变历程、优势与风险，并强调了IEEE在推动AI进步与负责任使用方面的贡献。