What if robots could predict the world before acting? A consortium of researchers from NTU, UC Berkeley, Stanford, and other top institutions surveyed the rapid rise of world models in robot learning. These models act like mental simulators: they predict how the environment
RESEARCH
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Closed AI institutions risk public loss of operational freedom
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OF CRUCIAL IMPORTANCE – PLEASE READ If intelligence becomes something people can only rent from a few closed institutions, the public does not just lose software freedom. It loses operational freedom. AI is a civilizational infrastructure for work, education, science, software,
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SkillOpt: AI Paper and Open-Source Repository
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Paper: https://
arxiv.org/abs/2605.23904
Repo: https://
github.com/microsoft/Skil
lOpt
…
Website: https://
microsoft.github.io/SkillOpt/ -
New Scaling Law Approach Could Revolutionize AI Training
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New Approach to Scaling Laws Could Change How #AI Models Are Trained
by Andrew Myers @StanfordHAI Learn more: https://
bit.ly/49j8KLg #MachineLearning #ArtificialIntelligence #ML -
Gary Marcus criticizes neural networks and deep learning misnomers
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also “neural networks” that have almost nothing to do with actual brains, and “deep learning” that isn’t not that (conceptually) deep.
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Microsoft Paper on Self-Evolving Agent Skills
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New paper from Microsoft on Self-Evolving Agent Skills
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Gary Marcus contrasts past AI science with current greed
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Disagree. Certainly there has always been hype, but not this kind of unabashed greed or indifference to social consequence. People always courted funding to be sure, but people like Minsky and McCarthy (I met both) were primarily interested in science and ideas; money was
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AI Learning Order Revealed: Implicit Curriculum Hypothesis Uncovered
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Researchers cracked the hidden order behind how AI learns. Loss curves tell you a model is improving. They don't say which skills form, or in what order. A new paper proposes the Implicit Curriculum Hypothesis. Pretraining follows a hidden, predictable order across
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Benchmarks are gameable and 100% is hard due to glitches
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1. some other criteria, since benchmarks can often be gamed: https://
open.substack.com/pub/garymarcus
/p/where-will-ai-be-at-the-end-of-2027?r=8tdk6&utm_medium=ios
… 2. 100% is often hard because the benchmarks themselves have glitches (eg you cant really get 100% on mnist without cheating because some items have errors) -
Linear Algebra and Optimization for Machine Learning textbook announcement
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Linear Algebra and Optimization for Machine Learning [516-page textbook]: http://
amzn.to/39aWf8N
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#DataScience #DataScientist #ML #Mathematics #ORMS