Symbolic AI Debate
The cluster discusses the resurgence and merits of symbolic AI (GOFAI) versus modern deep learning, debates on combining them into neurosymbolic approaches, and criticisms from figures like Gary Marcus and Chomsky.
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How might symbolic AI become fashionable?
My favorite "paper" on AI pretty accurately describes this line of thinkinghttps://ai.vixra.org/pdf/2506.0065v1.pdf
Marcus has been on HN many times before, always criticizing other people's work as "machines that manipulate data but aren't really intelligent, because they have no understanding of the world:" https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que... . For a good summary of th
Why is there a pendulum? Aren't both necessary and two sides of the same coin? I know little of modern AI, but I've seen work where both low level raw power for NNs is combined with a symbolic organization of many NNs.
Did he say that about GOFAI or Deep Learning?
Chomsky and Gary Marcus predict that the current approach will not be sufficient; is and will increasingly be detrimental to society because of that “almost right at best” reason. Chomsky only has hopes in a combined approach that integrates “old AI” with the engineering / data / GPU driven one. See also discussion here for anyone interested: https://news.ycombinator.com/item?id=3385754
What if we mix GOFAI with machine learning? Wouldn't the result surpass the capacity of either?
I think they meant rule engines/symbolic AI loses to LLMs/machine learning again.
The author, Gary Marcus, makes a cogent argument for why we need both deep learning and symbol manipulation to build "true" AI. what I found convincing was the argument that deep learning models currently can't even generalize to novel instances outside the training space in the field of object recognition, which is their strong suit. Why would any rational person believe that they have the capability to generalize to novel instances for other types of problems outside of p
This paper drives a stake through the heart of claims that explicit symbolic reasoning is needed for intelligent systems. It builds extra-symbolic (deep learning) systems that substantially outperform symbolic reasoning on tests specifically designed to require symbolic reasoning.Since today's programming is 1000% explicitly symbolic, the potential shift to better methods that are extra-symbolic will be pretty disruptive!