They use "optimal" in a relative sense. I read it in a reply to my post, and then subsequently looked it up in LLM search engine—which happily told me why "Chinchilla is inference optimal because […]". But that's only relatively to undertrained models.
RESEARCH
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Embracing Innovation in Engineering and Technology
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How to Embrace Innovation https://
mckinsey.com/featured-insig
hts/mckinsey-guide-to-managing-yourself/how-to-embrace-innovation
… @ASMEdotorg @3DSNorthAmerica @cyngn @MargaretSiegien @3DSdelmia @3DStherese @Cindybolt61 @fogoros @DrFerdowsi @CRudinschi @PawlowskiMario @IIoT_World @MEngineeringMag #Science #Engineering #Technology #SET -
Training AI Models with Extended 30k Context Windows
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Worth mentioning that we can also train these models with very large context windows — up to 30k instead of 1k like the original.
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Fast Personalization Encoder for Text-to-Image Models
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Designing an Encoder for Fast Personalization of Text-to-Image Models Gal et al.: https://
arxiv.org/abs/2302.12228 #ArtificialIntelligence #DeepLearning #MachineLearning -
Large Language Models Generate Cumulative Ambiguities Not Certainties
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Humans have to generate a lot of words to try and explain the potential & challenges of large language #AI models https://
wsj.com/articles/chatg
pt-heralds-an-intellectual-revolution-enlightenment-artificial-intelligence-homo-technicus-technology-cognition-morality-philosophy-774331c6?mod=opinion_lead_pos5
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"Enlightenment science accumulated certainties; the new AI generates cumulative ambiguities" -
Avoiding Data Fallacies: Strategies for Successful Data Science
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Stop making assumptions about data and unlock its full potential! Avoid common #DataFallacies and use the right strategies for successful #DataScience projects. By @ingliguori #AI #BigData #CloudComputing #Fintech #Python #Cybersecurity #100DaysOfCode #JavaScript #IoT #innovation
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LLMs Generating Training Data for Smaller Production Models
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I was referring to NLP tasks that already exist pre LLM hype era where LLMs are typically used to few shot or zero shot generate a lot of data for smaller production models.
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Production Performance vs Academic Benchmarks Eval Correlation
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What production performance are you referring to then? Or are you referring to academic benchmarks being bad in general? Because you would always need some eval benchmark ideally correlated to the prod use case.
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LLM Production Use Cases Beyond ChatGPT Benchmarking
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It really depends on what you mean by production settings though. LLMs are used for many things other than being a ChatGPT. Overall I agree with your thread and benchmark results should be interpreted with caution. (But can be still useful some times)
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Validating Tech Solutions Through Lived Experience and Expertise
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"The surest cure for BS is exposing it to the scrutiny of people who truly, deeply understand a problem through years of lived experience." +100! Plus "the problem" isn't crypto or #ai but what those techs are trying to solve in that use case. Thx @EthanZ for a def recommend read