https://arxiv.org/abs/2505.03754

#math #calculus #integrals #integration #benchmarks #euler #unification #generalization

Ilya Sutskever argues that we’re shifting from the age of scaling to the age of research: today’s models excel on benchmarks but still generalize far worse than humans. The interview highlights why future progress will depend on new learning principles, continual learning, and a deeper understanding of generalization — not just more compute.
https://www.dwarkesh.com/p/ilya-sutskever-2
#AIResearch #Generalization #FutureOfAI

Day 11 of the #30DayMapChallenge: minimal map.

For this one, I used the R package rmapshaper to generalise the German states using the Douglas-Peucker algorithm.

#gis #cartography #rstats #generalization

Researchers isolate memorization from reasoning in AI neural networks https://arstechni.ca/k2rK #mechanisticinterpretability #computationalneuroscience #AllenInstituteforAI #transformermodels #gradientdescent #machinelearning #AIarchitecture #AImemorization #generalization #neuralnetworks #weightmatrices #losscurvature #modelediting #AIalignment #overfitting #AIbehavior #AIresearch #copyright #AIsafety #Goodfire #Biz&IT #K-FAC #OLMo #AI

How does the #brain transfer #MotorSkills between hands? This study reveals that transfer relies on re-expressing the neural patterns established during initial learning in distributed higher-order brain areas, offering new insights into learning #generalization @PLOSBiology https://plos.io/41LOAWf

Just Ask for Generalization

https://evjang.com/2021/10/23/generalization.html

#HackerNews #Just #Ask #for #Generalization #AI #Generalization #MachineLearning #DataScience #HackerNews

Pipeline release! nf-core/drugresponseeval v1.1.0 - Drugresponseeval 1.1.0 - Humongous Zapdos!

Please see the changelog: https://github.com/nf-core/drugresponseeval/releases/tag/1.1.0

#celllines #crossvalidation #deeplearning #drugresponse #drugresponseprediction #drugs #fairprinciples #generalization #hyperparametertuning #machinelearning #randomizationtests #robustnessassessment #training #nfcore #openscience #nextflow #bioinformatics

'Random Pruning Over-parameterized Neural Networks Can Improve Generalization: A Training Dynamics Analysis', by Hongru Yang, Yingbin Liang, Xiaojie Guo, Lingfei Wu, Zhangyang Wang.

http://jmlr.org/papers/v26/23-0832.html

#pruning #pruned #generalization

Humans can apply solutions of past problems to new problems. @gershbrain @nicoschuck &co reveal the neural correlates of #generalization and show that humans apply past policies in a reward-sensitive manner that leads to high performance @PLOSBiology https://plos.io/3SJPMof

AI Learns by Watching - Sholto & Trenton on Dwarkesh

#generalization #ai #reinforcementlearning

π0.5: A VLA with open-world generalization

https://pi.website/blog/pi05

#HackerNews #π0.5 #VLA #openworld #generalization #machinelearning #AI

e509 — Maverick and Marbles

e509 with Michael and Michael – stories and discussion all around #AI, #LLMs, #llamas, generated #Quake, #grokking, #generalization and much more.

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https://gamesatwork.biz/2025/04/14/e509-maverick-and-marbles/

e509 — Maverick and Marbles

e509 with Michael and Michael - stories and discussion all around #AI, #LLMs, #llamas, generated #Quake, #grokking, #generalization and much more.

https://gamesatwork.biz/2025/04/14/e509-maverick-and-marbles/

e509 — Maverick and Marbles

e509 with Michael and Michael - stories and discussion all around #AI, #LLMs, #llamas, generated #Quake, #grokking, #generalization and much more.

https://gamesatwork.biz/2025/04/14/e509-maverick-and-marbles/

Pipeline release! nf-core/drugresponseeval v1.0.0 - 1.0.0!

Please see the changelog: https://github.com/nf-core/drugresponseeval/releases/tag/1.0.0

#celllines #crossvalidation #deeplearning #drugresponse #drugresponseprediction #drugs #fairprinciples #generalization #hyperparametertuning #machinelearning #randomizationtests #robustnessassessment #training #nfcore #openscience #nextflow #bioinformatics

People value us for the value (they believe) we (might) add to them.

Generalizing of course, but it's all transactional. There's no (longer) valuing people for just who they are.

#society #people #life #generalization

Grokking at Edge of Numerical Stability
https://arxiv.org/abs/2501.04697
https://old.reddit.com/r/MachineLearning/comments/1i34keg/grokking_at_the_edge_of_numerical_stability
https://en.wikipedia.org/wiki/Grokking_(machine_learning)

* sudden generalization after prolonged overfitting
* massively overtrained NN can acq. "emergent"/supra performance/unexpected abilities
* unexp./accid. finding
* mechanisms starting to unravel

Grokked Transformers are Implicit Reasoners: Mechanistic Journey to Edge of Generalization
https://arxiv.org/abs/2405.15071
https://news.ycombinator.com/item?id=40495149

#LLM #ML #grokking #NN #emergence #generalization

A post from August 2024 by @grimalkina, boosted by someone on another instance, about why to report demographics in research even when you're not studying those groups. This seems like a great primer for people who have little background in basic #sampling and #generalization (for some reason I can't link/boost from here, so):

https://mastodon.social/@grimalkina/112966685297897685

My 2 cents (already at least partially covered by Dr. Hicks):

1. Your study is never just about your study. Good science is #open and reusable. e.g., maybe your study on tech-enabled healthcare access isn't specifically about LGBTQ+ or Hispanic people, but what are you doing to help a researcher who comes along in 10 years? That information will change what they find and report.

2. Marginalized groups are often minorities, meaning representative probability samples (or --uncomfortable gesture-- convenience samples) for bread-and-butter research frequently have subpopulations too small for reasonable power in correlations, group differences, etc. That's just reality. It's also a big problem for our understanding of #marginalized + #minority groups. Oversampling or targeted studies of those groups are important. It's also important to have a large number of less-targeted studies with relevant information that can be synthesized later (see #1): one study with 1.3% trans participants doesn't tell us much about the trans population, but 20 studies, each of which has 1.3% trans participants, could tell us meaningful things.

3. Representation is important. My belief is that #marginalized+minoritized people need their identities and existence public and constant. In #science, both they and other people consuming the research will benefit from being reminded that they are there, almost always, in our #research.

'Generalization on the Unseen, Logic Reasoning and Degree Curriculum', by Emmanuel Abbe, Samy Bengio, Aryo Lotfi, Kevin Rizk.

http://jmlr.org/papers/v25/24-0220.html

#sparse #learns #generalization

'Mentored Learning: Improving Generalization and Convergence of Student Learner', by Xiaofeng Cao, Yaming Guo, Heng Tao Shen, Ivor W. Tsang, James T. Kwok.

http://jmlr.org/papers/v25/23-1213.html

#learners #learner #generalization