#AI system automates scientific software design, outperforming human-written code in key benchmarks
by Anne J. Manning @TechXplore_com Learn more: https://
bit.ly/4dqI91k #GenerativeAI #ArtificialIntelligence #MachineLearning #DeepLearning
MACHINE LEARNING
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AI system outperforms human-written code in scientific software design
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Apple uses custom 1.2T-parameter Google model for Siri
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Apple isn’t just adding Gemini to Siri. It is reportedly using a custom 1.2T-parameter Google model as the brain behind parts of the next Siri overhaul (Reuters). That is no small size. The question, therefore, is how Apple’s Gemini will perform and how fast it runs. In
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World models as mental simulators for robot learning survey
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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
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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
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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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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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Non-human identity sprawl is the real risk of Agentic AI
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Non-human identity sprawl is #AgenticAI's real risk
by Nick Nikols @InformationWeek Learn more: https://
bit.ly/4dyypRm #LLM #GenerativeAI #ArtificialIntelligence #MachineLearning -
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) -
Ultimate ML Job Interview Questions Workbook with Crash Courses
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Ultimate Machine Learning Job Interview Questions Workbook: Brief Crash Courses and Real Interview Questions taking you from Beginner to Offers Available at http://
amzn.to/4s21D0j