{"id":217045,"date":"2025-09-11T02:18:09","date_gmt":"2025-09-11T02:18:09","guid":{"rendered":"https:\/\/www.europesays.com\/us\/217045\/"},"modified":"2025-09-11T02:18:09","modified_gmt":"2025-09-11T02:18:09","slug":"thinking-machines-lab-wants-to-make-ai-models-more-consistent","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/us\/217045\/","title":{"rendered":"Thinking Machines Lab wants to make AI models more consistent"},"content":{"rendered":"

There\u2019s been great interest in what Mira Murati\u2019s Thinking Machines Lab is building with its $2 billion in seed funding<\/a> and the all-star team of former OpenAI researchers who have joined the lab. In a blog post<\/a> published on Wednesday, Murati\u2019s research lab gave the world its first look into one of its projects: creating AI models with reproducible responses.<\/p>\n

The research blog post, titled \u201cDefeating Nondeterminism in LLM Inference,\u201d tries to unpack the root cause of what introduces randomness in AI model responses. For example, ask ChatGPT the same question a few times over, and you\u2019re likely to get a wide range of answers. This has largely been accepted in the AI community as a fact \u2014 today\u2019s AI models are considered to be non-deterministic systems\u2014 but Thinking Machines Lab sees this as a solvable problem.<\/p>\n

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Today Thinking Machines Lab is launching our research blog, Connectionism. Our first blog post is \u201cDefeating Nondeterminism in LLM Inference\u201d<\/p>\n

We believe that science is better when shared. Connectionism will cover topics as varied as our research is: from kernel numerics to\u2026 pic.twitter.com\/jMFL3xt67C<\/a><\/p>\n

\u2014 Thinking Machines (@thinkymachines) September 10, 2025<\/a><\/p><\/blockquote>\n

The post, authored by Thinking Machines Lab researcher Horace He, argues that the root cause of AI models\u2019 randomness is the way GPU kernels \u2014 the small programs that run inside of Nvidia\u2019s computer chips \u2014 are stitched together in inference processing (everything that happens after you press enter in ChatGPT). He suggests that by carefully controlling this layer of orchestration, it\u2019s possible to make AI models more deterministic.<\/p>\n

Beyond creating more reliable responses for enterprises and scientists, He notes that getting AI models to generate reproducible responses could also improve reinforcement learning (RL) training. RL is the process of rewarding AI models for correct answers, but if the answers are all slightly different, then the data gets a bit noisy. Creating more consistent AI model responses could make the whole RL process \u201csmoother,\u201d according to He. Thinking Machines Lab has told investors that it plans to use RL to customize AI models for businesses<\/a>, The Information previously reported.<\/p>\n

Murati, OpenAI\u2019s former chief technology officer, said in July that Thinking Machines Lab\u2019s first product will be unveiled in the coming months<\/a>, and that it will be \u201cuseful for researchers and startups developing custom models.\u201d It\u2019s still unclear what that product is, or whether it will use techniques from this research to generate more reproducible responses.<\/p>\n

Thinking Machines Lab has also said that it plans to frequently publish blog posts<\/a>, code, and other information about its research in an effort to \u201cbenefit the public, but also improve our own research culture.\u201d This post, the first in the company\u2019s new blog series called \u201cConnectionism,\u201d seems to be part of that effort. OpenAI also made a commitment to open research when it was founded, but the company has become more closed off as it\u2019s become larger. We\u2019ll see if Murati\u2019s research lab stays true to this claim.<\/p>\n

The research blog offers a rare glimpse inside one of Silicon Valley\u2019s most secretive AI startups. While it doesn\u2019t exactly reveal where the technology is going, it indicates that Thinking Machines Lab is tackling some of the largest question on the frontier of AI research. The real test is whether Thinking Machines Lab can solve these problems, and make products around its research to justify its $12 billion valuation.<\/p>\n

Techcrunch event<\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\tOctober 27-29, 2025\n\t\t\t\t\t\t\t<\/p>\n