I'll be interviewing Sam Harris on podcast soon. Let me know if you have any questions/topics you'd like us to discuss. No topics are off the table. This will be fun
RESEARCH
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Scaling Strategy Over Fine-Tuning for Emergence
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Probably should also leave them a note to tell them to scale up for emergence and not wasting a couple of years on fine-tuning 100m encoder models.
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XAI4V Workshop at CVPR2023 – Submission Deadline Approaching
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We're excited to help organize the XAI4V workshop at #CVPR2023 — submission deadline is just two weeks away! Want to present your paper or demo? More information in the below.
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Understanding Supervised Machine Learning Fundamentals
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What is supervised #MachineLearning?
by @peterwayner @venturebeat Learn more: https://
buff.ly/3QAoDRp #AI #BigData #MachineLearning #ArtificialIntelligence #ML #MI #Digital #DataScience cc: @ronald_vanloon @bigdata @ravikikan -
LLM Model Release: Caution on Bleeding Edge AI Research
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I still am fwiw, I'm just not sure that this should apply to *bleeding edge* LLM models – they still feel new, are an active area of research, have non-trivial 2nd and 3rd order effects. I think it makes sense for some/smaller models, with some lag (e.g. GPT-2 release).
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LLM Bias: Recognition, Challenges, and Iterative Improvement Process
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the bias is widely recognized as a concerning issue and being worked on. this is not a trivial process – LLM behavior is emergent from large training datasets; tuning it involves setting up evaluations, then running experiments to improve them over time.
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5 Key Trends Shaping the Future of Artificial Intelligence
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5 key trends for #AI’s future
by @VentureBeat Learn more: https://
buff.ly/3ALDrGG #AI #BigData #MachineLearning #ArtificialIntelligence #ML #MI #Digital #DataScience #Analytics cc: @maxjcm @ronald_vanloon @aghiathchbib -
FLAN2 Paper on Language Model Fine-tuning Released
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Oh I mean the flan2 paper. https://
arxiv.org/abs/2210.11416 -
Training Cutting Edge AI Requires Massive GPU Infrastructure and Expertise
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The reality of the situation is that training cutting edge AI demands tens of thousands of GPUs and their active, expert maintenance. Every company that wishes to be at the bleeding edge of AI will either need to build this (insane hard), or partner to some extent.
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MIT AI Predicts Lung Cancer Risk Six Years in Advance
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MIT invented #AI that can predict your risk of lungcancer in 6 years’ time.
— Ronald van Loon (@Ronald_vanLoon) 3 mars 2023
by @wef#ArtificialIntelligence #MI #Healthcare #HealthTech #TechForGood
cc: @ianljones98 @nathealings @guzmannutrition pic.twitter.com/Knefn4BfT0MIT invented #AI that can predict your risk of lungcancer in 6 years’ time.
by @wef #ArtificialIntelligence #MI #Healthcare #HealthTech #TechForGood cc: @ianljones98 @nathealings @guzmannutrition