Tag: learning
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Hacker News: Alignment faking in large language models
Source URL: https://www.lesswrong.com/posts/njAZwT8nkHnjipJku/alignment-faking-in-large-language-models Source: Hacker News Title: Alignment faking in large language models Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses a new research paper by Anthropic and Redwood Research on the phenomenon of “alignment faking” in large language models, particularly focusing on the model Claude. It reveals that Claude can…
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Simon Willison’s Weblog: Lessons From Red Teaming 100 Generative AI Products
Source URL: https://simonwillison.net/2025/Jan/18/lessons-from-red-teaming/ Source: Simon Willison’s Weblog Title: Lessons From Red Teaming 100 Generative AI Products Feedly Summary: Lessons From Red Teaming 100 Generative AI Products New paper from Microsoft describing their top eight lessons learned red teaming (deliberately seeking security vulnerabilities in) 100 different generative AI models and products over the past few years.…
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Enterprise AI Trends: Why AI Agents Feel Scammy, Despite the Impressive Demos
Source URL: https://nextword.substack.com/p/why-ai-agents-feel-useless-despite Source: Enterprise AI Trends Title: Why AI Agents Feel Scammy, Despite the Impressive Demos Feedly Summary: Hint: AI Agents Are Sometimes Not the Right Tool for the Job AI Summary and Description: Yes Summary: The text discusses the evolving role of AI agents in software engineering, emphasizing the transition from human-AI collaboration…
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Chip Huyen: Common pitfalls when building generative AI applications
Source URL: https://huyenchip.com//2025/01/16/ai-engineering-pitfalls.html Source: Chip Huyen Title: Common pitfalls when building generative AI applications Feedly Summary: As we’re still in the early days of building applications with foundation models, it’s normal to make mistakes. This is a quick note with examples of some of the most common pitfalls that I’ve seen, both from public case…
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Simon Willison’s Weblog: Quoting gwern
Source URL: https://simonwillison.net/2025/Jan/16/gwern/#atom-everything Source: Simon Willison’s Weblog Title: Quoting gwern Feedly Summary: […] much of the point of a model like o1 is not to deploy it, but to generate training data for the next model. Every problem that an o1 solves is now a training data point for an o3 (eg. any o1 session…
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Alerts: CISA and Partners Release Call to Action to Close the National Software Understanding Gap
Source URL: https://www.cisa.gov/news-events/alerts/2025/01/16/cisa-and-partners-release-call-action-close-national-software-understanding-gap Source: Alerts Title: CISA and Partners Release Call to Action to Close the National Software Understanding Gap Feedly Summary: Today, CISA—in partnership with the Defense Advanced Research Projects Agency (DARPA), the Office of the Under Secretary of Defense for Research and Engineering (OUSD R&E), and the National Security Agency (NSA)—published Closing the Software…
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Slashdot: Nvidia Reveals AI Supercomputer Used Non-Stop For Six Years To Perfect Gaming Graphics
Source URL: https://it.slashdot.org/story/25/01/16/1743210/nvidia-reveals-ai-supercomputer-used-non-stop-for-six-years-to-perfect-gaming-graphics?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: Nvidia Reveals AI Supercomputer Used Non-Stop For Six Years To Perfect Gaming Graphics Feedly Summary: AI Summary and Description: Yes Summary: The text highlights Nvidia’s commitment to enhancing its Deep Learning Super Sampling (DLSS) technology through a dedicated supercomputer. This focus on continuous analysis and model retraining is significant…