Generation Language Learning Theory 
Google Research AI blog
APRIL 30, 2023
If you’re registered for ICLR 2023, we hope you’ll visit the Google booth to learn more about the exciting work we’re doing across topics spanning representation and reinforcement learning, theory and optimization, social impact, safety and privacy, and applications from generative AI to speech and robotics.
Stanford AI Lab Blog
DECEMBER 6, 2021
Linderman, David Sussillo Contact : jsmith14@stanford.edu Links: Paper | Website Keywords : recurrent neural networks, switching linear dynamical systems, interpretability, fixed points Compositional Transformers for Scene Generation Authors : Drew A. Smith, Scott W.
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Stanford AI Lab Blog
APRIL 25, 2022
Manning, Jure Leskovec Contact : xikunz2@cs.stanford.edu Award nominations: Spotlight Links: Paper | Website Keywords : knowledge graph, question answering, language model, commonsense reasoning, graph neural networks, biomedical qa Fast Model Editing at Scale Authors : Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, Christopher D.
The AI Alignment Forum
FEBRUARY 20, 2025
Published on February 20, 2025 11:54 PM GMT TLDR: We made substantial progress in 2024: We published a series of papers that verify key predictions of Singular Learning Theory (SLT) [ 1 , 2 , 3 , 4 , 5 , 6 ]. The S4 correspondence in small language models. Alignment).
The AI Alignment Forum
FEBRUARY 7, 2025
In either of these settings, theres a chance that the LLMs will write messages that encode meaning beyond the natural language definitions of the words used. Externalizing reasoning: It could be safer to have much smaller language models which put more reasoning into natural language. and Mathew et al. Roger and Greenblatt ).
Google Research AI blog
NOVEMBER 28, 2022
Platt , Fernando Pereira , Dale Schuurmans Keynote Speakers The Data-Centric Era: How ML is Becoming an Experimental Science Isabelle Guyon The Forward-Forward Algorithm for Training Deep Neural Networks Geoffrey Hinton Outstanding Paper Award Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding Chitwan Saharia , William Chan (..)
The AI Alignment Forum
MARCH 28, 2025
Daniel Filan (00:08:54): Right, and so, as you go across that line, the proof length, aka time to generate this confidence, linearly grows, and also the accuracy bound linearly grows. Whereas in the crosscoder paper, language modeling doesnt seem like the kind of thing that is going to be very symmetric. Daniel Filan (00:51:28): Yeah.
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