<ul><li>A research paper by Apple has debunked the idea that large language models are reliable for reasoning tasks.</li><li>The paper revealed that leading models like ChatGPT struggle with complexity and collapse when faced with new challenges.</li><li>Neural networks can generalize within their data distribution but fail when they encounter novel scenarios.</li><li>Scaling these models to be bigger does not solve the reasoning limitations, as shown in the Apple paper.</li><li>Even classic puzzles like the Tower of Hanoi pose challenges for leading generative models.</li><li>The paper highlighted that LLMs lack logical and intelligent problem-solving processes.</li><li>AGI should combine human adaptiveness with computational reliability, not just replicate human limits.</li><li>LLMs cannot be a reliable substitute for well-specified conventional algorithms in solving complex problems.</li><li>Despite their uses in coding and writing, LLMs are not a direct path to transformative AGI.</li><li>The new paper underscores the limitations of generative AI and the need for caution in trusting its outputs.</li></ul>

When billion-dollar AIs break down over puzzles a child can do, it's time to rethink the hype | Gary Marcus

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