nb OpenAI GPT 5.6 has also been delayed; it’s not just about Anthropic, anymore.
@garymarcus
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Dark June for US generative AI: policy, Chinese models, OpenAI IPO delay
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June has been a dark month for the US generative AI industry, between hamfisted US policy and the transition to cheaper Chinese AI models. And then there is the delay of the OpenAI IPO. Is that the consensus? Do people disagree?
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AGI requires innovative ideas, not just fast code writing
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no, innovative ideas rendered into code will create AGI. we still need the ideas. (but yes writing code faster is helpful; then again the code they write tends not to be innovative)
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Warning: US focus on winning AI race may cause global catastrophes
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“America’s preoccupation with “winning” the AI race with China could well lead to unprecedented catastrophes, even catastrophes on a global scale. Not all games are zero-sum, and if this fact doesn’t start playing a bigger role in American policy discourse, the AI revolution
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Symbol-manipulation in 2001 book defines neurosymbolic systems
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see the discussion of symbol-manipulation in my 2001 book. without question tools, harnesses etc depend on symbol-manipulation as i defined it there. and to be neurosymbolic is to use symbol-manipulation and neural networks jointly.
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Gary Marcus criticizes confusion between pure and neurosymbolic LLMs
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people with advanced degrees who can’t distinguish between pure LLMs (which is what I critiqued in 2022) and LLMs enhanced with neurosymbolic techniques (which is what I championed in 2022) disappoint me. i get that many tech bros don’t really get it, but scientists should take
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Neurosymbolic AI surpasses pure LLMs as predicted
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1. pure LLMs did in fact hit a wall and then as i predicted (2022) neurosymbolic AI provided a way past the LLMs; it’s very evident if you look at all the tools, harnesses, loops etc in Codex, Claude Code etc as a scientist i would have expected you to be more attuned to that
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Gary Marcus warns hyperscaling is a financial blunder for AI
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“Scale cannot solve AI’s fundamental problem with accuracy” If my argument @financialtimes
, excerpted below, is remotely correct, hyperscaling will prove to be among the biggest financial blunders in history. We must seek alternative foundations for AI. -
Gary Marcus: AI scaling is capital misallocation, but early days
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“Gary Marcus … says that investments into scaling AI is the “greatest capital misallocation in history” which leaves everyone “on the hook”. However, he also says that it is still early days for the AI industry and further exploration into different model architectures and the
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Scale cannot solve AI’s fundamental accuracy problem
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source: “How much compute does the world really need? Scale cannot solve AI’s fundamental problem with accuracy”