Experimental News Clipping Site

Tag: human feedback

  • 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…

  • Cloud Blog: Optimizing RAG retrieval: Test, tune, succeed

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    system automation
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    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/optimizing-rag-retrieval/ Source: Cloud Blog Title: Optimizing RAG retrieval: Test, tune, succeed Feedly Summary: Retrieval-augmented generation (RAG) supercharges large language models (LLMs) by connecting them to real-time, proprietary, and specialized data. This helps LLMs deliver more accurate, relevant, and contextually aware responses, minimizing hallucinations and building trust in AI applications. But RAG can be…

  • Hacker News: Task-Specific LLM Evals That Do and Don’t Work

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    system automation
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    Source URL: https://eugeneyan.com/writing/evals/ Source: Hacker News Title: Task-Specific LLM Evals That Do and Don’t Work Feedly Summary: Comments AI Summary and Description: Yes Summary: The text presents a comprehensive overview of evaluation metrics for machine learning tasks, specifically focusing on classification, summarization, and translation within the context of large language models (LLMs). It highlights the…

  • Hacker News: Launch HN: Human Layer (YC F24) – Human-in-the-Loop API for AI Systems

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    Source URL: https://news.ycombinator.com/item?id=42247368 Source: Hacker News Title: Launch HN: Human Layer (YC F24) – Human-in-the-Loop API for AI Systems Feedly Summary: Comments AI Summary and Description: Yes Summary: HumanLayer is an API that integrates human feedback and approval processes into AI systems to mitigate risks associated with deploying autonomous AI. This innovative approach allows organizations…

  • Hacker News: Batched reward model inference and Best-of-N sampling

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    system automation
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    Source URL: https://raw.sh/posts/easy_reward_model_inference Source: Hacker News Title: Batched reward model inference and Best-of-N sampling Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses advancements in reinforcement learning (RL) models applied to large language models (LLMs), focusing particularly on reward models utilized in techniques like Reinforcement Learning with Human Feedback (RLHF) and dynamic…