statistical mimicry. you claim to have read the literature but seem not aware of alternative positions.
@garymarcus
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Confusion with Stuart Russell on AI safety red lines
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have not read the book but either that’s AI-written or they confused me with Stuart Russell and made some leaps that are a bit off. Some of the first sentence is correct about me; some note. The red lines stuff is really Stuart’s. The fire alarms stuff is sloppy and not
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LLMs Cannot Lead to AGI: Seven Years of Warnings
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This place is toxic. For the last seven years I warned you that LLMs and similar approaches would not lead us to AGI. Almost nobody is willing to acknowledge that, even though so many of you gave me endless grief about it at the time. I also warned you -– first –- that Sam
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Machine Learning Community Unable to Handle Truth
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Clearly the machine learning community can’t handle the truth. Good to see that @MrEwanMorrison can.
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LLMs World Models Gap: Ongoing Critique and Arguments
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among other places on facebook immediately after my deep learning is hitting a wall paper where i laid out the arguments in question, and also for years here on twitter, eg in 2019 on X calling my critique that LLMs lacked world models a “rear-guard action” see my recent
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Scaling Limits Debate: LLMs Won’t Achieve AGI Claims
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Wow. Just wow. @ylecun taking credit for my March 2022 argument that scaling would hit a wall and that LLM would not bring us to AGI–after he initially attacked me for saying it and continued to promote them right up until ChatGPT ate has lunch–has to among the most
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Deep Learning Hitting A Wall: March 2022 Analysis
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3 years; March 2022. in the essay “Deep Learning is Hitting A Wall”
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AI Hallucinations and Reasoning Errors Remain Unresolved
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which? specific example have been patched, but the general problems i pointed out – like hallucinations and reasoning errors – have not.
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99% Performance Achievable with Smaller Language Models
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i bet you could get 99% of the performance with less powerful LLMs
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LLMs Delay Progress: We Need Novel Ideas, Not Incremental Improvements
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they (or more likely more efficient replacements) will play a role. but spending so much money, time, and energy on them has quite likely delayed us. we need new ideas, not slightly better LLMs.