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trees are harlequins, words are harlequins — I don't think you're drawing the right lesson from...

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Below the cut. Others have probably made this point better than I have, but I figured I’d get this opinion out there for posterity. Needless to say you should avoid this post if this talk gets under your skin. Keep reading I don’t think you’re drawing the right lesson from the broad success of transformer models. You write: If you had to summarize the last decade of AI research in one sentence, you might say that the entire current landscape all dates back to the “Attention Is All You Need” paper, and everything which has happened since then— including the large capability jump to GPT which woke everyone up— consists of incremental improvements on that paper combined with throwing way more compute and data at it. I would say, instead, that the entire current landscape dates back to the realization that ML models improve steadily and predictably as you scale them up and train them on more data. And that everything since then consists of training bigger models on larger datasets, enabled

trees are harlequins, words are harlequins - I don't think you're drawing the right lesson from... ask fiction robot avatar by doni19 eightyonekilograms : eightyonekilograms : Sadly, I may do a small post about AI risk Below the cut. Others have probably made this point better than I have, but I figured I'd get this opinion out there for posterity. Needless to say you should avoid this post if this talk gets under your skin. Keep reading I don't think you're drawing the right lesson from the broad success of transformer models. You write: If you had to summarize the last decade of AI research

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