Opinion says AI progress mostly comes from diligence rather than superhuman intelligence
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An opinion holds that many of the impressive feats of large language models (LLMs) come from speed, diligence, and basic competence rather than superhuman intelligence. The opinion illustrates this with three cases: GPT Astra's ability to understand and modify a neural network from scratch is said to rely on off-the-shelf interpretability techniques and shortcuts humans would also use; Claude helping someone recover $400,000 worth of Bitcoin relied on traversing files, combining passwords and keys, and fixing a configuration bug; and OpenAI once orchestrated 10,000 agents collaborating for 88 hours to solve a Millennium Prize problem, which the author sees as closer to "artificial diligence" than special optimization capability. All of the above judgments come from the author of this opinion; the report provides no independent verification.
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- Hacker News AIA lot of AI progress is just persistence
The author argues that much of the hype around LLMs stems not from superhuman intelligence but from their willingness to tirelessly attempt tedious tasks, citing examples like Claude recovering $400,000 in Bitcoin through brute-force search and optimizing ML architectures through sheer volume of attempts. While acknowledging genuinely impressive recent results like OpenAI's Millennium problem work, the piece contends there is no proof AI has special ability to optimize neural networks or reverse engineer programs, and that artificial diligence explains a large fraction of perceived capability.
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