Experimental News Clipping Site

Tag: human feedback

  • Hacker News: Ladder: Self-Improving LLMs Through Recursive Problem Decomposition

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    Kurt Seifried
    in

    Source URL: https://arxiv.org/abs/2503.00735 Source: Hacker News Title: Ladder: Self-Improving LLMs Through Recursive Problem Decomposition Feedly Summary: Comments AI Summary and Description: Yes Summary: The paper introduces LADDER, a novel framework for enhancing the problem-solving capabilities of Large Language Models (LLMs) through a self-guided learning approach. By recursively generating simpler problem variants, LADDER enables models to…

  • Hacker News: Simple Explanation of LLMs

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    Kurt Seifried
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    Source URL: https://blog.oedemis.io/understanding-llms-a-simple-guide-to-large-language-models Source: Hacker News Title: Simple Explanation of LLMs Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text provides a comprehensive overview of Large Language Models (LLMs), highlighting their rapid adoption in AI, the foundational concepts behind their architecture, such as attention mechanisms and tokenization, and their implications for various fields.…

  • Slashdot: Meet the Journalists Training AI Models for Meta and OpenAI

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    Kurt Seifried
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    Source URL: https://news.slashdot.org/story/25/02/23/2111201/meet-the-journalists-training-ai-models-for-meta-and-openai?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: Meet the Journalists Training AI Models for Meta and OpenAI Feedly Summary: AI Summary and Description: Yes Summary: The text discusses the evolving role of journalists in the AI landscape, particularly through platforms like Outlier, where they are engaged in training AI models. This shift highlights the intersection of…

  • Hacker News: RLHF Book

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    Kurt Seifried
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    Source URL: https://rlhfbook.com/ Source: Hacker News Title: RLHF Book Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses the concept of Reinforcement Learning from Human Feedback (RLHF), particularly its relevance in the development of machine learning systems, particularly within language models. It highlights the foundational aspects of RLHF while aiming to provide…

  • Hacker News: Mini-R1: Reproduce DeepSeek R1 "Aha Moment"

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    Kurt Seifried
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    Source URL: https://www.philschmid.de/mini-deepseek-r1 Source: Hacker News Title: Mini-R1: Reproduce DeepSeek R1 "Aha Moment" Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses the release of DeepSeek R1, an open model for complex reasoning tasks that utilizes reinforcement learning algorithms, specifically Group Relative Policy Optimization (GRPO). It offers insight into the model’s training…

  • The Register: AI revoir, Lucie: France’s answer to ChatGPT paused after faux pas overdrive

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    Kurt Seifried
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    Source URL: https://www.theregister.com/2025/01/29/french_ai_chatbot_lucie_suspended/ Source: The Register Title: AI revoir, Lucie: France’s answer to ChatGPT paused after faux pas overdrive Feedly Summary: Slew of embarrassing answers sends open source chatterbox back for more schooling As China demonstrates how competitive open source AI models can be via the latest DeepSeek release, France has shown the opposite.… AI…

  • Hacker News: Qwen2.5-Max: Exploring the Intelligence of Large-Scale Moe Model

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    Kurt Seifried
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    Source URL: https://qwenlm.github.io/blog/qwen2.5-max/ Source: Hacker News Title: Qwen2.5-Max: Exploring the Intelligence of Large-Scale Moe Model Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses the development and performance evaluation of Qwen2.5-Max, a large-scale Mixture-of-Expert (MoE) model pretrained on over 20 trillion tokens. It highlights significant advancements in model intelligence achieved through scaling…

  • Schneier on Security: AI Mistakes Are Very Different from Human Mistakes

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    Kurt Seifried
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    Source URL: https://www.schneier.com/blog/archives/2025/01/ai-mistakes-are-very-different-from-human-mistakes.html Source: Schneier on Security Title: AI Mistakes Are Very Different from Human Mistakes Feedly Summary: Humans make mistakes all the time. All of us do, every day, in tasks both new and routine. Some of our mistakes are minor and some are catastrophic. Mistakes can break trust with our friends, lose the…

  • Hacker News: Nvidia CEO says his AI chips are improving faster than Moore’s Law

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    Kurt Seifried
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    Source URL: https://techcrunch.com/2025/01/07/nvidia-ceo-says-his-ai-chips-are-improving-faster-than-moores-law/ Source: Hacker News Title: Nvidia CEO says his AI chips are improving faster than Moore’s Law Feedly Summary: Comments AI Summary and Description: Yes Summary: Jensen Huang, CEO of Nvidia, asserts that the performance of the company’s AI chips is advancing at a pace exceeding the historical benchmark of Moore’s Law. This…

  • Simon Willison’s Weblog: Building effective agents

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    system automation
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    Source URL: https://simonwillison.net/2024/Dec/20/building-effective-agents/#atom-everything Source: Simon Willison’s Weblog Title: Building effective agents Feedly Summary: Building effective agents My principal complaint about the term “agents" is that while it has many different potential definitions most of the people who use it seem to assume that everyone else shares and understands the definition that they have chosen to…