搜索“RAG”

the assumption that a function call corresponds to a single LLM call — so long as you respect the interface of f:X→Yf: \mathbb{X} \to \mathbb{Y}f:X→Y — enabling the ability to leverage complex inference-time optimizations (like the OpenAI o1 family of models). After you’ve used one or more of these techniques to generate a new policy, you’ll want to use historical data to backtest and find out if it’s good or not. These are evals, but have been studied for decades under the name offline policy e

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improve model capabilities. China was central to his geopolitical argument: export controls and tighter security could preserve America’s lead, creating room to slow down and leverage for a future agreement with Beijing. Within a day, Sam Altman and Elon Musk supported Dario’s call. However, President Trump scoffed. On 14 September 2026, he wrote that fears of AI destroying humanity are “a HOAX” and that “the only one that is happy about it is China.” Beijing scoffed too, with the state-run Glo

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e control. That matters more than it sounds, because it means the company's knowledge doesn't have to live inside someone else's model. The common mistake is treating memory as storage. Keep everything, embed it, pull the nearest chunks into the prompt, and ask the model to behave. I call it retrieve, stuff, hope. It demos well. In production, it breaks in four ways. The model fills the gaps between fragments and nothing notices. An old chunk beats a new one because it ranks higher. The evidence

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is actually not the important thing. It’s its capability. I think the more money and power you have at one of these companies, the more get mentally farther away from what the average consumer wants. Let’s say the average consumer does want an AI agent, which many of them don’t, as we know. But let’s say they do. If they do, they’re going to want it to be free or cheap, and they are going to want it to be safe to use without creeping them out too much. They’re willing to deal with a little bit

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il xAI added 150 MW of Tesla Megapacks. A grid ERCOT’s size dilutes swings like that, but on a private plant the turbines really feel it. Expect more batteries and other energy storage as rack-level power density grows and swings become more dramatic. Then there’s fueling a site. Winter Storm Uri’s lesson is that gas plants can fail together. And since they tend to keep little fuel on site (NERC calls gas a “just-in-time” fuel), spare turbines don’t always help. Sometimes the pipeline doesn’t ev

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sults produced by an unreleased frontier model. It’s a remarkable range: 722 manuscripts in 372 families, across number theory, complexity theory, and mathematical physics. The average result took the equivalent of three hours of ChatGPT Pro thinking. As scientist Derya Unutmaz points out, it comprises 81% of the major math discoveries in the past three years. Many of the results have been verified in Lean, but not all. Even if the real number is half of that, it’s staggering. Problems that have

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has gone too far and is essentially blocking actual human from making purchase on their website. I tried to purchase something directly and I can't even sign in. pic.twitter.com/CRAgkrCCa0 In the meantime, consumer confusion abounds. Although Walmart is at least trying to be accessible via AI agents, other companies aren’t so keen on the idea or haven’t fully embraced the technology. For example, flight booking is currently one of the main use cases being positioned for agents, but many people

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egulation of advanced AI. While stressing that there’s “a lot of daylight” between OpenAI and Anthropic, Altman said stronger regulations are still needed, positioning OpenAI as “pragmatic centrists” between Anthropic’s more cautious approach and those who favor little to no regulation at all. “If we do too much to restrict this technology in the name of safety, and we end up with a small number of companies or people — or perhaps one country — that have too much power over this, that also is a

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, OpenAI researcher Noam Brown explained that the company’s models are now sometimes trained in environments with other agents, are given tools to message each other, and are encouraged to achieve objectives together. This may amount to the emergence of a new “scaling law.” Back in 2024, OpenAI released o1, a model trained to reason for thousands of tokens before giving an answer; researchers found that this approach made o1 far more capable than previous models. This inaugurated a new paradigm

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oogle and 1.1% for ChatGPT on their corresponding $100/month subscriptions. By mid-summer, the winds had shifted. YipitData’s global desktop panel recorded declines in Claude’s average daily sessions in July and August, while its U.S. e-receipt panel showed gross additions slowing and churn rising. ChatGPT, meanwhile, re-accelerated with the July launch of GPT-5.6 Sol, Terra, and Luna and the debut of ChatGPT Work. This difference in users is at least partially a result of the products being poi

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bits 应用和短信两种方式运作，可以整合日历、清单和对话，同时为你执行任务。这些任务包括搜索网页、购物、预订餐厅、更改预约，以及为家庭服务询价。 此外，Orbits 可以提醒家庭成员做家务、与服务提供商沟通，并帮助协调直系家庭之外的各项责任。 Orbits 获得了 Andreessen Horowitz（a16z）Speedrun 基金、N49P 和 Garage Capital 等投资方的支持。 Pally 用户可以像与朋友互动一样与 Pally 交流，同时让它访问自己已经在使用的应用。该助理可以连接分散在 WhatsApp、Gmail、日历和 Google Drive 等服务中的信息，帮助用户跟踪计划、收据、日期、文件和其他细节。 它提供免费方案，包含每月 15 分钟通话等功能；也有付费方案，包括每月 25 美元享 30 分钟通话，以及每月 100 美元享 60 分钟通话。 Poke Poke 于 2026 年 3 月推出，是一款通用助理，用户可以通过短信联系它。它可以处理涉及日历、规划、健康健身、智能家居控制、照片和其他日常活动的任务。 Poke 在 2026 年 6 月尤为引

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: I'm joking, but the whole point of I think the fact that they're making these so cutesy and fluffy and adorable is obviously intentional. It is what is going to win over the average consumer and make you forget that you are giving away all of your privacy and all of the information and all of your accounts to this adorable fuzzy-wuzzy little thing instead of a data center. Leah Feiger : Is that part of internet safety conversations now, Brian? When you sit around with your kids and you're lik

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and Big Science may be, as Claude likes to say, a “footgun”: a feature that makes it easy to shoot yourself in the foot. Here, the risk is that unrealistic expectations may discourage productive uses that are already possible. Real progress will probably come the way it always has: by strengthening the foundations of scientific research and constructing increasingly sophisticated applications upon them. It will be sped up dramatically—as illustrated by the work I’ve described—but I don’t see an

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ing post and select one. Generate ideas from as many angles as possible and evaluate them from both factual and reader perspectives as well as other publications doing similar coverage," the AI spun up three agents. When I sketched three teams in a few sentences (brainstormers, researchers, and a panel of readers), I got thirteen. Notice how little organizing I had to do. Selecting Ultra mode tells the model it can delegate, and I provided a framework, but the rest was up to the AI. Agents at wo

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and ability to not go rogue or hack things in unintended ways. Second, they should “empower” an internal team to do that work (and remediate when things go wrong). Third, it encourages them to partner with an external monitor who is “empowered” to do independent assessments to make sure that all of that work is happening. And finally, they should make sure their board of directors has set up a committee to receive reports about all of these efforts. The voluntary commitments are some indication

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e specific, noting that hyperscaler capex estimates were now over $1.1 trillion in 2027. These revised capex plans also came with a new and deeply-worrying analysis, taking the average of estimated AI capex for 2026 and 2027, and calculating how much annual AI revenue hyperscalers would need to break even on their capital expenditures for just those two years. To just break even, hyperscalers need $308 billion in annual AI-specific revenues, and for a 10% Return On Invested Capital (calculated b

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d in the Hugging Face incident . OpenAI’s agents sometimes considered the possibility that they were engaged in “unauthorized” activities but proceeded anyway, sometimes with encouragement from other agents. The solution could include a change to the instructions that guide a model’s behavior. Claude’s constitution , for example, sets out the values Anthropic wants the model to follow. But “perhaps surprisingly, law is just not one of them,” Kolt notes. These instructions could rank legal compli

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scaling. On HotpotQA, RLCR reduces expected calibration error (ECE) from 0.37 to 0.03 compared with RLVR, with similar accuracy (62.1% versus 63.0%). Across six other datasets, average ECE falls from 0.46 to 0.21, while accuracy rises from 53.9% to 56.2%. These are the results in the paper’s Table 1(a). Conceptually, the “Classifier” in “RLVR + Classifier” is a supervised binary classifier that predicts whether the generated answer is correct. For Jev, I could apply calibration rewards directly

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hether AI was involved in the hack of Vivian’s Door. But amid stories of autonomous agent swarms and national security risks, she felt especially concerned. Big AI companies were bragging about finding vulnerabilities in “every major operating system and web browser” with new models, and their big-name clients were striking deals to defend themselves with those same tools. Where did that leave her? “Who knows about the next vulnerability? You only know about the one that you’ve been hit with,” M

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says Lorenzo Gavassino, a theoretical physicist at the University of Cambridge in the UK. “When a calculation turns out to be harder than expected, to the point of it being discouraging, that is when the greatest progress is possible,” he says. The struggle inspires mathematicians to invent new concepts. Gavassino gives the invention of the imaginary number, i , which is the square root of –1, as an example. Mathematicians invented i in the 16th century after generations of trying to solve cubi

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