Of course the second. Over-hiring when everything is going well is so easy a temptation
@thom_wolf
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Tech Icons Crisis: Elon’s Leadership Under Fire at Twitter
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however twitter will end –and I hope it survives– Elon doesn’t look like an amazing team leader in this mess Elon, Zuck, SBF…. it’s the week of falling tech icons
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Team structure and ethical considerations in AI dataset creation
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I would say: smaller teams of full time people in a single org, simpler performance goals, more paid compute, no specific goals on ethical sourcing or social impact of the dataset creation process
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Parallel Experiments Challenge in Multilingual Machine Learning Project
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I also think there was a will to experiments on too many aspects at the same time and in parallel in BS: try a new organisation, a multilingual model with low-ressources languages, a new way of building a dataset (crowd sourcing of sources), attempts to train in fp16, etc etc
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Large AI Teams: BigScience Lessons on Project Organization
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yes, agree on the exhaustion feeling. BigScience was an experiment in trying to involve as many/diverse people as possible but a take-away is that too large teams can be counter-productive. Smaller projects like T0/ethics/RAIL were more successful inside BS than larger Bloom imo
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LLMs Generate Convincing but Wrong Completions Often
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this is an issue I also have with copilot. it often very convincingly pushes for long and beautiful but wrong completions LLM as “internet influencers”: surface form trumps content https://
x.com/shortstein/sta
/shortstein/status/1587857803678748672
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NeRF Zero to SOTA Tutorial at Hugging Face Paris
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I'm in Paris, FR this Thursday 11/3, ping me if you want to chat. I'll do an internal tutorial zero->SOTA on NeRF at Hugging Face, might extract a 5min video to share online from it if its exciting to people.