If we had done everything I suggested in my 2020 arXiv article “The Next Decade in AI”, we might act...
TL;DR · AI 摘要
Gary Marcus认为，如果在2020年提出的AI十年规划中所有建议都得到实施，我们可能已经达到了AGI。然而，过度依赖纯扩展导致其他关键目标进展缓慢。
核心要点
- Gary Marcus在2020年的arXiv文章中提出了AI十年规划。
- 目前，AI在神经符号AI方面取得了进展，但在知识数据库、可靠推理系统和世界模型表示方面进展缓慢。
- 过度依赖纯扩展延迟了AI的全面发展，未来应专注于其他三个关键目标以实现AGI。
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- AI十年规划
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如果在2020年提出的AI十年规划中所有建议都得到实施，我们可能已经达到了AGI。
目前，AI在神经符号AI方面取得了进展，但在知识数据库、可靠推理系统和世界模型表示方面进展缓慢。
过度依赖纯扩展延迟了AI的全面发展，未来应专注于其他三个关键目标以实现AGI。
In the last three years, after a detour driven by the false promise of pure scaling, we have gone a long way to the first of the four goals I laid" / X
Gary Marcus, MIT PhD and NYU Professor Emeritus on X: "If we had done everything I suggested in my 2020 arXiv article “The Next Decade in AI”, we might actually have reached AGI by now. In the last three years, after a detour driven by the false promise of pure scaling, we have gone a long way to the first of the four goals I laid" / X
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Gary Marcus, MIT PhD and NYU Professor Emeritus
If we had done everything I suggested in my 2020 arXiv article “The Next Decade in AI”, we might actually have reached AGI by now. In the last three years, after a detour driven by the false promise of pure scaling, we have gone a long way to the first of the four goals I laid out: neurosymbolic AI. (Claude Code; DeepMind’s new math system that solved 9 Erdos problems, etc). Harnesses and tools, for example. But the field has made little progress on the other three: - databases of explicit, systematic, machine interpretable knowledge [LLMs are a partial but inadequate/unreliably substitute] - reliable reasoning systems that can work with partial and incomplete information - the ability to represent and induce explicit world models (which I called cognitive models in 2020) that AI systems can reason over. An excess commitment to pure scaling delayed all of these. If we focus on the other three AGI may be achievable, possibly in the next decade.
Last edited Opens edit history 4:04 PM · May 27, 2026
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