Live from the @agihouse_org it’s the first ever Jamba hackathon! What long context use cases would you want to build with Jamba’s 256k context window?
LLMS
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Pygmalion Effect in Large Language Models: A Survey
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pygmalion effect in llms: a survey winterrose et al
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LLMs Reproducing Failed 1968 Planning Methods, Not True Planning
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@BobbyGRG It's reproduced a planning method from 1968; Nils Nilsson's book "Problem Solving Methods for AI", on 2nd most trivial possible planning problem. All thought it would scale–didn't for deep reasons. LLMs simulating 50+ year old failed planning methods is not planning. -
LLMs Lack Emergent Reasoning, Success Is Lucky
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Many people are trying to make excuses for why this trivial problem trips up LLMs. Occam's razor should remind you that there is no emergent reasoning in LLMs. Instead it gets lucky (and it is astonishing that it works so well–that says something deep about our language)
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Anthropic and Claude dominate the AI market landscape
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It really seems like Anthropic has scratched and Claude its way to the top.
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Mixture-of-Agents Architecture Enhances Language Model Capabilities
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Mixture-of-Agents Enhances Large Language Model Capabilities Wang et al.: https://
arxiv.org/abs/2406.04692 #ArtificialIntelligence #DeepLearning #MachineLearning -
Daily Content Sharing on Python, Data Science, and Machine Learning
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That's a wrap! If you are interested in any of these below topics: – Python – Data Science – Machine Learning – Data Analysis – LLMs – MLOps Find me → @Sumanth_077 I'm sharing daily content over here.
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Eastern Version of Noam Shazeer’s AI Architecture Innovation Arc
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this is the eastern version of the noam shazeer arc inventing a 10x better transformer architecture and not telling anyone until yesterday in the pursuit of providing ai gf
