GPT-5.1 is now already available on Pertplexity To reason or not to reason?
MACHINE LEARNING
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Sparse Neural Networks Improve AI Model Interpretability Research
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Most neural networks today are dense and highly entangled, making it difficult to understand what each part is doing. In our new research, we train “sparse” models—with fewer, simpler connections between neurons—to see whether their computations become easier to understand.
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Sparse Models Reveal Interpretable Task-Specific Components
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Unlike with normal models, we often find that we can pull out simple, understandable parts of our sparse models that perform specific tasks, such as ending strings correctly in code or tracking variable types. We also show promising early signs that our method could potentially
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New Interpretable Training Method for Small Language Models
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We’ve developed a new way to train small AI models with internal mechanisms that are easier for humans to understand. Language models like the ones behind ChatGPT have complex, sometimes surprising structures, and we don’t yet fully understand how they work. This approach
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Stanford AI Lab Postdoc Fellowship Connects Research with Impact
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What research questions drive AI forward? At @StanfordAILab
, postdoc fellows work at the forefront of AI and ML – connecting vision with language, theory with application, and innovation with impact. Apply as a SAIL Postdoc Fellow by Dec. 15: https://
bit.ly/43mRPEE -
GPT 5.1 launches on ChatGPT and surprises, but not as expected
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GPT 5.1 just released on #ChatGPT and it shocked me… But not as you imagine → https://youtu.be/MPWedHYy9dw #GPT5 #GPT5_1
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MIT Professor Patrick Winston’s Simplest Neural Network Explained
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A breakdown of the simplest neural network from the late MIT Prof. Patrick Winston during his 6.034 (Artificial Intelligence) course.
— MIT CSAIL (@MIT_CSAIL) 13 novembre 2025
Clip v/@7etsuo & @MITOCW pic.twitter.com/hWZvAcHUv4A breakdown of the simplest neural network from the late MIT Prof. Patrick Winston during his 6.034 (Artificial Intelligence) course. Clip v/
@7etsuo & @MITOCW -
Baidu Open-Sources ERNIE-4.5-VL-28B Multimodal Model
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Big news from @Baidu_Inc
! ERNIE-4.5-VL-28B-A3B-Thinking — a breakthrough lightweight multimodal reasoning model — is now officially open-sourced. It’s already trending #1 on Hugging Face’s Image-Text-to-Text models list. -
3B Parameter Model Matches GPT-5 and Gemini Performance
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With just 3B active parameters, it matches Gemini-2.5-Pro and GPT-5-High, and even surpasses them on ChartQA and DocVQAval What can it achieve with only 3B active parameters?
For example, its “Thinking with Images” feature lets it zoom in, analyze details, and reason through -
Stanford Uses Generative AI to Simulate Brain MRI
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Imagine using generative AI to simulate your brain in the future based on your behavior today. Researchers at Stanford are creating synthetic brain MRIs to supercharge computational neuroscience and accelerate our understanding of brain disorders.