The AI constructed a grid in a high-dimensional space and then projected this more complex structure into two dimensions. And instead of using a whole-number grid with points like (1,3) or (-3,6), the AI construction used something called algebraic integers to build this more complicated grid.
大多数人认为解决数学难题需要全新的理论突破或创新方法，但作者认为AI通过巧妙应用现有数学知识（高维空间投影和代数整数）就能解决长期悬而未决的问题。这挑战了人们对数学创新必须依赖全新方法的常识认知。
What if instead of building one giant AI, we evolved a coordinator to orchestrate a diverse team of specialized AIs?
大多数人认为AI发展的方向是构建越来越大的单一模型，但作者提出了一种反直觉的观点：通过进化一个协调者来管理多个专业化AI可能更有效。这挑战了当前AI行业普遍追求模型规模扩大的共识。
Forward Deployed Engineers tune Relvy for your stack
令人惊讶的是：Relvy采用了一种混合AI模式，结合了自动化系统和人类工程师的调优，这种'人机协作'的方式可能代表了AI在专业领域应用的新范式，既利用了AI的效率，又保留了人类的专业判断。
Yang, Scott Cheng-Hsin, Chirag Rank, Jake Alden Whritner, Olfa Nasraoui, and Patrick Shafto. ‘Unifying Recommendation and Active Learning for Information Filtering and Recommender Systems’. Preprint. PsyArXiv, 25 August 2020. https://doi.org/10.31234/osf.io/jqa83.
In 2001, AI founder Marvin Minsky asked "So the question is why didn't we get HAL in 2001?"[167] Minsky believed that the answer is that the central problems, like commonsense reasoning, were being neglected, while most researchers pursued things like commercial applications of neural nets or genetic algorithms. John McCarthy, on the other hand, still blamed the qualification problem.[168] For Ray Kurzweil, the issue is computer power and, using Moore's Law, he predicted that machines with human-level intelligence will appear by 2029.[169] Jeff Hawkins argued that neural net research ignores the essential properties of the human cortex, preferring simple models that have been successful at solving simple problems.[170] There were many other explanations and for each there was a corresponding research program underway.
Eventually the earliest successful expert systems, such as XCON, proved too expensive to maintain. They were difficult to update, they could not learn, they were "brittle" (i.e., they could make grotesque mistakes when given unusual inputs), and they fell prey to problems (such as the qualification problem) that had been identified years earlier. Expert systems proved useful, but only in a few special contexts