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Tag: clark

  • Simon Willison’s Weblog: Politico: 5 Questions for Jack Clark

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    Kurt Seifried
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    Source URL: https://simonwillison.net/2025/Mar/8/questions-for-jack-clark/ Source: Simon Willison’s Weblog Title: Politico: 5 Questions for Jack Clark Feedly Summary: Politico: 5 Questions for Jack Clark I tend to ignore statements with this much future-facing hype, especially when they come from AI labs who are both raising money and trying to influence US technical policy. Anthropic’s Jack Clark has…

  • The Register: DeepMind working on distributed training of large AI models

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    Kurt Seifried
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    Source URL: https://www.theregister.com/2025/02/11/deepmind_distributed_model_training_research/ Source: The Register Title: DeepMind working on distributed training of large AI models Feedly Summary: Alternate process could be a game changer if they can make it practicable Is distributed training the future of AI? As the shock of the DeepSeek release fades, its legacy may be an awareness that alternative approaches…

  • Simon Willison’s Weblog: Quoting Jack Clark

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    Kurt Seifried
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    Source URL: https://simonwillison.net/2025/Jan/28/jack-clark-r1/#atom-everything Source: Simon Willison’s Weblog Title: Quoting Jack Clark Feedly Summary: The most surprising part of DeepSeek-R1 is that it only takes ~800k samples of ‘good’ RL reasoning to convert other models into RL-reasoners. Now that DeepSeek-R1 is available people will be able to refine samples out of it to convert any other…

  • The Register: DeepSeek’s R1 curiously tells El Reg reader: ‘My guidelines are set by OpenAI’

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    Kurt Seifried
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    Source URL: https://www.theregister.com/2025/01/27/deepseek_r1_identity/ Source: The Register Title: DeepSeek’s R1 curiously tells El Reg reader: ‘My guidelines are set by OpenAI’ Feedly Summary: Despite impressive benchmarks, the Chinese-made LLM is not without some interesting issues DeepSeek’s open source reasoning-capable R1 LLM family boasts impressive benchmark scores – but its erratic responses raise more questions about how…

  • Simon Willison’s Weblog: Quoting Jack Clark

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    Kurt Seifried
    in

    Source URL: https://simonwillison.net/2025/Jan/20/jack-clark/ Source: Simon Willison’s Weblog Title: Quoting Jack Clark Feedly Summary: [Microsoft] said it plans in 2025 “to invest approximately $80 billion to build out AI-enabled datacenters to train AI models and deploy AI and cloud-based applications around the world.” For comparison, the James Webb telescope cost $10bn, so Microsoft is spending eight…

  • Simon Willison’s Weblog: Quoting Jack Clark

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    system automation
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    Source URL: https://simonwillison.net/2024/Dec/23/jack-clark/#atom-everything Source: Simon Willison’s Weblog Title: Quoting Jack Clark Feedly Summary: There’s been a lot of strange reporting recently about how ‘scaling is hitting a wall’ – in a very narrow sense this is true in that larger models were getting less score improvement on challenging benchmarks than their predecessors, but in a…

  • Simon Willison’s Weblog: Quoting Jack Clark

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    system automation
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    Source URL: https://simonwillison.net/2024/Nov/18/jack-clark/ Source: Simon Willison’s Weblog Title: Quoting Jack Clark Feedly Summary: The main innovation here is just using more data. Specifically, Qwen2.5 Coder is a continuation of an earlier Qwen 2.5 model. The original Qwen 2.5 model was trained on 18 trillion tokens spread across a variety of languages and tasks (e.g, writing,…