Judea Pearl
2018 · arXiv
Current machine learning systems operate, almost exclusively, in a statistical, or model-free mode, which entails severe theoretical limits on their power and performance. Such systems cannot reason about interventions and retrospection and, therefore, cannot serve as the basis for strong AI. To achieve human level intelligence, learning machines need the guidance of a model of reality, similar to the ones used in causal inference tasks. To demonstrate the essential role of such models, I will present a summary of seven tasks which are beyond reach of current machine learning systems and which have been accomplished using the tools of causal modeling.
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Great result. At my job I manage a machine learning team and so I am fairly much all-in for deep learning to solve practical problems. That said, I think the path to 'real' AGI lies in some combination of DL, probabilistic graph models, symbolic systems, and something we have not even imagined yet. BTW, a good paper just released on the limitations of DL by Judea Pearl https://arxiv.org/abs/1801.04016
Okay, read the abstract and Intro. Recently, in the paper "What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models" your thesis of Ai's lack of capacity to abstract or at least extract understanding from noisy data was largely experimentally confirmed. I am uncertain though about the exact mechanics b/c as they used LLM's, its not transparent what happened internally that lead to constant failure to abstract the concept despite ample predictive power. One interesting experiment was the introduction of the Oracle that literally enabled the LLM to solve the task that was previously impossible without the oracle, which means, at least its possible…
that LLM's can reconstruct known rules. They just can't find new ones. On a more fundamental level, I am not so sure why these experiments and mathematical proofs still are made since Judea Pearl already established about seven years ago in "Theoretical Impediments to Machine Learning " that all correlation based methods are doomed as they fail to understand anything. his point about causality is well placed, but will not solve the problem either. The question I have though, if we ignore all existing methods for one moment, then what makes you so sure that AGI is really Mathematically impossible? Suppose some advancement in quantum computing would allow to reconstruct incomplete information, does your assertion still holds true? https://arxiv.org/abs/2507.06952 https://arxiv.org/abs/1801.04016
Check out this paper by Turin-award winner Judea Pearl for fundamental limitations of current approaches to ML: https://arxiv.org/abs/1801.04016
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