Why general artificial intelligence will not be realized

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Conflicts of Interest

Identified Weaknesses

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Paper Summary

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Are Computers Philosophers Yet? (Spoiler: Nope)

The author argues against the possibility of achieving Artificial General Intelligence (AGI) because computers lack embodiment, lived experience, and the ability to interact with the world like humans. The paper claims that recent advancements in AI, while impressive, are limited to narrow applications (ANI) and do not represent progress towards true general intelligence.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Philosophical rather than scientific argument

The author's argument against AGI hinges on the idea that computers are "not in the world," lacking embodiment and lived experience. This is a philosophical argument, not a scientific one, and doesn't address the practical progress and potential of AI research.

Disregarding ANI advancements

The author dismisses advancements in ANI as irrelevant to AGI, but progress in narrow AI can contribute to the development of more general AI capabilities.

Misrepresentation of opposing views

The author oversimplifies the arguments of AI proponents, misrepresenting their views on causality and the potential of AI. This creates a straw man argument, weakening the overall analysis.

Selective use of evidence

The examples provided are cherry-picked to support the author's argument, focusing on early Turing test failures and overlooking advancements in areas like natural language processing and computer vision.

Overly strong claim without sufficient evidence

The author claims that the goal of AGI is impossible in principle, a very strong claim that requires more substantial justification.

Rating Explanation

The paper presents a philosophical critique of AGI, but lacks scientific rigor and misrepresents opposing views. The author makes overly strong claims without sufficient evidence and dismisses the relevance of practical advancements in AI. The core arguments rely on anecdotal examples and philosophical assumptions, rather than a systematic and data-driven analysis of AI research and its potential.

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