AI模型易被修改协助恐怖主义和极端主义活动
推荐理由:报告揭示了AI模型被用于恐怖主义和极端主义活动的风险,读者可以了解AI安全领域的最新挑战和趋势。
推荐理由:报告揭示了AI模型被用于恐怖主义和极端主义活动的风险,读者可以了解AI安全领域的最新挑战和趋势。
推荐理由:Google DeepMind 和 Google Cloud 为欧洲科学家提供前沿模型和计算资源,支持欧洲科学创新和竞争力。
推荐理由:报告揭示了 Google 在欧洲经济中的影响,以及 AI 技术对欧洲企业增长的贡献,读者可以了解 AI 技术在欧洲的应用和发展趋势。
推荐理由:NVIDIA 在英国投资了多个初创公司,涉及生物分子模型、AI代理、基础设施预测、AI发现引擎、生物基础模型、量子计算等领域,展示了其在推动英国创新方面的努力。
推荐理由:NVIDIA 对英国人工智能安全与安全研究的投资,有助于推动该领域的发展,并为英国在人工智能领域的领导地位提供支持。
华为在内部会议中成功识别并处理了一起潜在的窃听器事件,展示了其严格的安全措施和AI技术在应对复杂安全威胁方面的能力。公司强调了对技术、数据和人员保护的重视,并展示了其全面的安全程序。
We discovered a sophisticated recording device that was hidden inside one of our offices. We used our models to understand the device and where it had come from, identifying that a specific employee had planted it a few days earlier. We referred the matter to the FBI, and we’re grateful to the agents and prosecutors who moved quickly to make an arrest. The FBI’s investigation is ongoing and we will continue to support their work. At this time, we have no indications of this individual inappropriately accessing or exfiltrating model weights, source code, or customer data. The employee was terminated. We take protecting our technology, data, and people seriously, and as a matter of national importance. Our security program is designed to protect against a wide range of threat actors, from insider risks to cybercriminals to nation states, and this case is an example of how AI can help address these sophisticated security threats.
推荐理由:原文介绍了 Google DeepMind 与新加坡政府合作开发的隐私保护型诈骗检测模型,读者可以了解该模型如何帮助打击犯罪并保护用户隐私。
DeepSeek 刘胜宇认为LLM产业化中,机器成本超过人力成本,工程经验比原始智能更重要。财务自由不能带来创造自由,进入新领域需深入学习、取得成果或具备整合资源的能力。
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.