TRANSFER LEARNING book [390 pages]: http://
amzn.to/3R4G0zm Amazon Summary:
"Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to
RESEARCH
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New Book Release on Transfer Learning for Machine Learning Systems
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Practical Guide to Building LLM and RAG Apps with LangChain
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"Generative AI and RAG for Beginners: A Practical Step-by-Step Guide to Building LLM and RAG Applications with LangChain and Python" Get your copy at http://
amzn.to/3MZZ9R5 Independently published: December 2025
Print length: 255 pages -
AI Agents and World Models: Research on Predictive Planning
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Can AI agents see into the future before acting? A team from UIUC, THU, JHU, and Columbia tested exactly that. They gave agents generative world models—external simulators that could predict outcomes before taking action. The result? Most agents refuse to simulate (under 1%
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Physics model explains AI learning mechanisms
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A simple physics-inspired model sheds light on how #AI learns
by @SissaMedialab @TechXplore_com Learn more: https://
bit.ly/4nufig1 #ArtificialIntelligence #MachineLearning #MI -
Popular AI writings from 2022-2023 still influence current models
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I suspect there was a moment, probably 2022-2023, where anything you wrote publicly about AI that was popular is likely to still have influence over current models. Since then, the open internet has become less key to training but the models remain very 2022-brained in many ways
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Essential AI Learning Resource for Engineers Discussed
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this is a big deal, on the order of Kelsey Hightower’s “Kubernetes The Hard Way” and probably all ai engineers should go thru this once mostly i advocate “just in time learning”, but this is one scenario you want “just in case”
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Agentic coding as machine learning
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Agentic coding is a form of machine learning. Generated code is best treated as a blackbox artifact whose behavior and generalization should be managed via empirical evaluation, like with any ML model.
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Gary Marcus on AGI and scaling limits
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So many people misremember (or never read) what I said in in 2022 in “Deep learning is hitting a wall”, which was neither about revenue or AI’s potential upper limits. Rather, it was an argument that the pure of scaling LLMs would not get us to AGI, and that we would need to
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Evolution of AI Models and Benchmark Shifts
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models will undoubtedly get to that point, and the METR benchmarks will undoubtedly shift to a frame above their current one—of which there are many
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Critiquing the practice of testing AI tools for failure
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“We got a tool to perform poorly” is the lowest form of science and journalism imo and is only relevant when the tool is, in fact, extremely useful