369 条资讯
<a href="https://news.google.com/rss/articles/CBMiTEFVX3lxTE9scF82VEM2X2JFZ3hnWTBaMEhKbEQ4Q1V0YUI3cWxWcHg0MG9OLVVwY2llZXl4X210dzFTeDJqaXJjX3pUUWo1LU1uODk?oc=5" target="_blank">传新iPhone 最高涨价1350 元；阿里发Qwen3.8，2.4 万亿参数；DuckDuckGo 推「反科技」太阳镜</a> <font color="#6f6f6f">极客公园</font>
<a href="https://news.google.com/rss/articles/CBMiTEFVX3lxTE9scF82VEM2X2JFZ3hnWTBaMEhKbEQ4Q1V0YUI3cWxWcHg0MG9OLVVwY2llZXl4X210dzFTeDJqaXJjX3pUUWo1LU1uODk?oc=5" target="_blank">传新iPhone 最高涨价1350 元；阿里发Qwen3.8，2.4 万亿参数；DuckDuckGo 推「反科技」太阳镜</a> <font color="#6f6f6f">极客公园</font>
And why no rules is the riskiest rule of all
And why no rules is the riskiest rule of all
Your weekly listens from How I AI, part of the Lenny's Podcast Network
Your weekly listens from How I AI, part of the Lenny's Podcast Network
On AI, human biology, and what it will actually take to discover transformative new medicines
On AI, human biology, and what it will actually take to discover transformative new medicines
Watch now | ️ChatGPT Voice now manages multiple threads, reads your screen, and runs background tasks, and Nick Baumann walks me through the whole workflow live
Watch now | ️ChatGPT Voice now manages multiple threads, reads your screen, and runs background tasks, and Nick Baumann walks me through the whole workflow live
Meta's earnings were a bit disappointing; future promises about AI products were more disconcerting.
Meta's earnings were a bit disappointing; future promises about AI products were more disconcerting.
<a href="https://news.google.com/rss/articles/CBMiTEFVX3lxTE5VNWlvc3g3dUZfcnNoUzBmUlIwZUJfRW1Nc21BS3ZLcS16UFppMWJwQmo2YVlBek9QOTJQaG1FRHkybk96dC16WkZ6RzI?oc=5" target="_blank">内存奇缺，Macbook Air 或缺货；智元母公司推《魔兽世界》机器人；微信地震预警上线新功能</a> <font color="#6f6f6f">极客公园</font>
<a href="https://news.google.com/rss/articles/CBMiTEFVX3lxTE5VNWlvc3g3dUZfcnNoUzBmUlIwZUJfRW1Nc21BS3ZLcS16UFppMWJwQmo2YVlBek9QOTJQaG1FRHkybk96dC16WkZ6RzI?oc=5" target="_blank">内存奇缺，Macbook Air 或缺货；智元母公司推《魔兽世界》机器人；微信地震预警上线新功能</a> <font color="#6f6f6f">极客公园</font>
Listen now | Tom Verrilli explains why fewer, more senior PMs doing real IC work outperform any ratio-driven org structure
Listen now | Tom Verrilli explains why fewer, more senior PMs doing real IC work outperform any ratio-driven org structure
<a href="https://news.google.com/rss/articles/CBMiTEFVX3lxTE5zWjBvUDB1WERGTnlaMGxwS1h5VkZaRXJuYkxIdUd1d2Z1UDBqeXV3Z3pkbnFndWlMVF83RXI3b2JkeGJUQ21Xck5pRnI?oc=5" target="_blank">马斯克和Altman 达成共识：人类已迈入AI 奇点时代；小米手机多款涨价300 元起；苹果警告AI 算力短缺或导致产品延期发布</a> <font color="#6f6f6f">极客公园</font>
This new visual project in partnership with Airwars breaks down how AI is transforming every aspect of war, and how militaries are offloading life and death decisions to brittle and flawed technologies. The post Anatomy of an AI Kill Chain with Airwars appeared first on AI Now Institute .
This new visual project in partnership with Airwars breaks down how AI is transforming every aspect of war, and how militaries are offloading life and death decisions to brittle and flawed technologies. The post Anatomy of an AI Kill Chain with Airwars appeared first on AI Now Institute .
My keynote at ICML 2026
My keynote at ICML 2026
<p>The concept of <strong>recursive self-improvement (RSI)</strong> dates back to <a href="https://philpapers.org/rec/GOOSCT">I. J. Good (1965)</a>, where he defined an “ultraintelligent machine” as a system that can surpass humans in all intellectual activities and design better machines to improve itself. <a href="https://www.lesswrong.com/posts/JBadX7rwdcRFzGuju/recursive-self-impro
<p>The concept of <strong>recursive self-improvement (RSI)</strong> dates back to <a href="https://philpapers.org/rec/GOOSCT">I. J. Good (1965)</a>, where he defined an “ultraintelligent machine” as a system that can surpass humans in all intellectual activities and design better machines to improve itself. <a href="https://www.lesswrong.com/posts/JBadX7rwdcRFzGuju/recursive-self-impro
<p>Scaling laws are one of the most critical empirical findings in deep learning. The observation is simple in form: the training loss $L$ decreases predictably as we scale up model size $N$, dataset size $D$, and compute $C$, following a power-law curve, which appears as a straight line on a log-log plot. We can view scaling laws as a framework for describing the relationship between compute, los
<p>Scaling laws are one of the most critical empirical findings in deep learning. The observation is simple in form: the training loss $L$ decreases predictably as we scale up model size $N$, dataset size $D$, and compute $C$, following a power-law curve, which appears as a straight line on a log-log plot. We can view scaling laws as a framework for describing the relationship between compute, los