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@ylecun believes that LLMs are a dead end on the path to human-level #AI. “If you are a Ph.D. student in AI, you should absolutely not work on LLMs.” https://
wsj.com/tech/ai/yann-l
ecun-ai-meta-0058b13c?st=mYuka9
… via @WSJ
MACHINE LEARNING
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Yann LeCun: LLMs are dead end for human-level AI
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AI Mobile Banking Truly Stole the Show with Conversational Service
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AI mobile banking truly stole the show. It introduces a new “conversation-as-a-service” model, where customers simply use natural language to request services, and the AI assistant responds in real time to complete multiple transactions. This marks a major upgrade in overall
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AI Transforms Real-World Data into Drug Development Insights
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Turning Real-World Data into Actionable Drug Development Insights With AI
#AI #AIio #AIInnovation #ML #DataScience #Futureofwork @demishassabis @Ronald_vanLoon @TamaraMcCleary @geoffreyhinton @goodfellow_ian @jeffdean @erikbryn -
Stanford AI Model Recognizes Object Parts and Transfers Skills
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To be useful in the real world, computer vision systems need to recognize the parts of an object, know their function & be able to transfer a skill from one object to another. Stanford scholars introduce a new AI model that brings us closer to this goal.
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GPT-5.1 now available on Pertplexity – to reason or not?
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GPT-5.1 is now already available on Pertplexity To reason or not to reason?
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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