137 pages of very conceptual principals around Machine Learning, Object Detection, and Computer Vision. Here's some early concepts of pattern recognition, building descriptions, and enforcement:
RESEARCH
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MIT’s 1971 Artificial Intelligence Progress Report Revealed
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Insane find: MIT's Artificial Intelligence Progress Report from 1971 Here's a look inside
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Google’s PaLM benchmark results and open-source RLHF implementation
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Google's PaLM beats GPT-3 on 28 out of 29 Natural Language Processing tasks (!) and was just open-sourced for reinforcement training as of today: https://
github.com/lucidrains/PaL
M-rlhf-pytorch
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AI Plagiarism Detection Tool Addresses Model Overfitting Issues
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Y de eso va el vídeo. Si hablamos de plagio es plagio, y no a donde te lo has llevado sobre la inspiración y el aprendizaje humano. Si en determinados casos estos modelos pueden generar plagios a raíz del overfitting, con esta herramienta se podría comprobar. Ni más ni menos.
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Meta AI OPT-IML Benchmark Improves LLM Instruction Tuning
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Improving Instruction Tuning for LLMs: Meta AI Presents the OPT-IML Benchmark of 2000 NLP Tasks
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Experiment-Based Goals Over Outcome Targets in Tech
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A management lesson we learned early on in Dota. We'd initially set goals of the form "beating X player by Y date", but when we fell behind, wasn't clear to anyone how to speed it up since we didn't know what would work. Great results from instead "try X experiment by Y date".
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Prompt Engineering Remains, But Five New More Valuable Things Will Emerge
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prompt eng will always be "a thing" but this time next year we'll have found 5 more new "things" that are more valuable to build on
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AI Model Conditioning: Prompting vs. Fine-Tuning Techniques
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Generally I’d call that “conditioning the model”, which can take the form of either prompting or fine-tuning, but not sure if that’s exactly the right term. They’re two different techniques to make it respond how you want.
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Fine-tuning for AI Specialization and Performance
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Not sure I understand fully re: alignment, but fine-tuning is usually the most performant way to specialize when you have the data for it.
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Technical Debate on AI Example Among Research Community
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Here is a (very) long conversation involving @ccanonne_ @Aaroth @frankmcsherry and @matloff on this very example (warning that threading gets a bit weird at times), which didn't seem to get resolved in the end https://
mobile.twitter.com/frankmcsherry/
status/1396537641865289728
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