AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence¶
Perhaps the most ambitious scientific quest in human history is the creation of general artificial intelligence, which roughly means AI that is as smart or smarter than humans. The dominant approach in the machine learning community is to attempt to discover each of the pieces required for intelligence, with the implicit assumption that some future group will complete the Herculean task of figuring out how to combine all of those pieces into a complex thinking machine. I call this the “manual AI approach”. This paper describes another exciting path that ultimately may be more successful at producing general AI. It is based on the clear trend in machine learning that hand-designed solutions eventually are replaced by more effective, learned solutions. The idea is to create an AI-generating algorithm (AI-GA), which automatically learns how to produce general AI. Three Pillars are essential for the approach: (1) meta-learning architectures, (2) meta-learning the learning algorithms themselves, and (3) generating effective learning environments. I argue that either approach could produce general AI first, and both are scientifically worthwhile irrespective of which is the fastest path. Because both are promising, yet the ML community is currently committed to the manual approach, I argue that our community should increase its research investment in the AI-GA approach. To encourage such research, I describe promising work in each of the Three Pillars. I also discuss AI-GA-specific safety and ethical considerations. Because it it may be the fastest path to general AI and because it is inherently scientifically interesting to understand the conditions in which a simple algorithm can produce general AI (as happened on Earth where Darwinian evolution produced human intelligence), I argue that the pursuit of AI-GAs should be considered a new grand challenge of computer science research.
也许人类历史上最雄心勃勃的科学探索是创造通用人工智能，这大致意味着人工智能与人类一样聪明或更聪明。机器学习社区的主流方法是尝试发现智能所需的每个部分，其隐含的假设是，未来的某个团队将完成一项艰巨的任务，即弄清楚如何将所有这些部分组合成一个复杂的思考机器。我称之为“手动人工智能方法”。本文描述了另一条令人兴奋的路径，最终可能会在产生通用人工智能方面取得更大的成功。正是基于机器学习的明显趋势，手工设计的解决方案最终会被更有效的学习解决方案所取代。这个想法是创建一个 AI 生成算法 （AI-GA），它会自动学习如何生成通用 AI。该方法有三个支柱是必不可少的：（1）元学习架构，（2）元学习学习算法本身，以及（3）生成有效的学习环境。我认为，任何一种方法都可以首先产生通用的人工智能，无论哪种是最快的路径，这两种方法在科学上都是有价值的。因为两者都很有前途，但 ML 社区目前致力于手动方法，我认为我们的社区应该增加对 AI-GA 方法的研究投入。为了鼓励这种研究，我描述了三大支柱中每一项有前途的工作。我还讨论了特定于 AI-GA 的安全和道德考虑。 因为它可能是通向通用人工智能的最快途径，而且因为它在科学上理解一个简单的算法可以产生通用人工智能的条件是有趣的（就像在地球上发生的那样，达尔文进化论产生了人类智能），我认为追求AI-GAs应该被视为计算机科学研究的一个新的重大挑战。