We spotlight ML researchers & practitioners. High (S) fact: ~50% code contributors to ML paper implementations are practitioners collaborating with researchers

Joined November 2021
Inspirational talk by Andrej to a young audience on the importance of building AI agents. An interesting line snuck in between- “and I got distracted a bit with self-driving…” he later adds self-driving belongs to a class of problems that appears easy but is very hard to actually make into a product - perhaps will take a decade. That observation is the most honest and damning statement on self-driving @Tesla to date. As a Tesla user for 7 years now, while I am uncomfortable driving any car other than a Tesla given how much it reduces the cognitive overload of driving, the mass in the long tail of boundary conditions seems too hard to solve by just more sampling.
Inspired by @karpathy’s words on why you - yes YOU - should work on AI Agents
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First #ChatGPT answer which made me pause.
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TasksWithCode retweeted
That is an existential threat to any company involved in NLP tasks. Perhaps it will not be too long when generative models for images solve discriminative tasks too - text/image in - text out - a chimera of image captioning and text conditioned generative models
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TasksWithCode retweeted
nference CTO, Ajit Rajasekharan, describes how #ChatGPT is the culmination of a major shift that has been quietly taking place in the NLP ecosystem. "We now have an agent who understands our intent like never before, thanks to @OpenAI." @ajitvraj @medium ajitrajasekharan.medium.com/…
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TasksWithCode retweeted
If you are @NeurIPSConf and interested in vision and language, @antoine77340 and I will present “Zero-Shot Video Question Answering via Frozen Bidirectional Language Models” in Poster Session 2 (Tuesday 2-4pm, New Orleans)! Feel free to come or DM me to chat🙂 #NeurIPS2022
Delighted to share that our paper « Zero-shot Video Question Answering via Frozen Bidirectional Language Models » is accepted at #NeurIPS2022 @NeurIPSConf 😀 w/ @antoine77340, J. Sivic, I. Laptev & @CordeliaSchmid Paper: arxiv.org/abs/2206.08155 Code: github.com/antoyang/FrozenBi… 1/2
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Excited to share results on fitting protein structures to density maps with diffusion models! Our setup uses priors from language models and simultaneously predicts the structure and fits the atomic model to densities, with zero pre- or post-processing. (1/3)
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