The Pragmatic AGI Era: How Transformers Grounded a New Form of Functional General Intelligence

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Identified Weaknesses

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Paper Summary

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Is AGI Here? Maybe, If You Define it Loosely (and Ignore a Few Things)

This paper argues that a form of Artificial General Intelligence (AGI) is already emerging, driven by the Transformer architecture and exemplified by large language models (LLMs). It proposes a pragmatic definition of AGI focused on functional capabilities comparable to average human intelligence, while acknowledging limitations such as a lack of physical grounding and robust causal reasoning. The paper claims these limitations reflect the current stage of development rather than an inability to achieve AGI.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Overly broad definition of AGI

The author's definition of AGI is quite broad and arguably sets a low bar. It focuses on functional capabilities and comparing AI to *average* humans rather than peak human performance, potentially overselling the current state of AI. This perspective could lead to premature declarations of AGI achievement.

Downplaying crucial limitations

While the paper acknowledges limitations like the lack of physical grounding and causal reasoning, it downplays their significance in achieving true general intelligence. Dismissing these as merely "current stage and artificial nature" overlooks the profound difference between manipulating symbols and understanding the real world.

Over-reliance on LLM performance in limited domains

The core argument relies heavily on LLMs' performance on informational tasks like language and code. While impressive, this neglects other crucial aspects of general intelligence, like creativity, social intelligence, and physical embodiment, giving a skewed picture of AGI's capabilities.

Narrow focus on transformers

The paper makes sweeping generalizations about AGI based primarily on the Transformer architecture and LLMs. This neglects other promising AI approaches and potentially limits the scope of the discussion about achieving true general intelligence. It presents a single narrative and ignores the diversity in the field of AI.

Lack of quantitative/experimental support

The paper makes a rather philosophical discussion and lacks quantitative or experimental data.

Rating Explanation

This paper makes an interesting argument but suffers from a broad definition of AGI, downplaying crucial limitations like grounding and causal reasoning, and focusing too narrowly on LLMs. While it highlights important progress in AI, the conclusions about AGI feel premature. Therefore, the paper deserves a 3, as an average study with notable limitations.

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