Despite the recent progress in AI powered by deep learning in solving narrow tasks, we are not close to human intelligence in its flexibility, versatility, and efficiency. Efficient learning and effective generalization come from inductive biases, and building Artificial General Intelligence (AGI) is an exercise in finding the right set of inductive biases that make fast learning possible while being general enough to be widely applicable in tasks that humans excel at. To make progress in AGI, we argue that we can look at the human brain for such inductive biases and principles of generalization. To that effect, we propose a strategy to gain insights from the brain by simultaneously looking at the world it acts upon and the computational framework to support efficient learning and generalization. We present a neuroscience-inspired generative model of vision as a case study for such approach and discuss some open problems about the path to AGI.

中文翻译：

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从验证码到常识：大脑如何教我们人工智能


尽管最近在深度学习的推动下，人工智能在解决狭窄任务方面取得了进展，但我们在灵活性、多功能性和效率方面与人类智能还相差甚远。高效的学习和有效的泛化来自于归纳偏差，而构建通用人工智能 (AGI) 就是寻找一组正确的归纳偏差的练习，这些偏差使快速学习成为可能，同时足够通用，可以广泛应用于人类擅长的任务。为了在通用人工智能方面取得进展，我们认为我们可以观察人脑的这种归纳偏差和泛化原则。为此，我们提出了一种策略，通过同时观察大脑作用的世界和支持高效学习和泛化的计算框架来获得大脑的洞察力。我们提出了一个受神经科学启发的视觉生成模型作为这种方法的案例研究，并讨论了有关 AGI 之路的一些悬而未决的问题。