Thinking, Autism and Artificial General Intelligence (AGI)

December 26, 2018, 4:30PM

138-415

 

Abstract:

Despite recent advances in deep learning, we do not know yet how we can combine these application-specific models to build an artificial general intelligence (AGI). Furthermore, the data is becoming the bottleneck to scale these approaches for the multiple tasks. In this talk, I propose a theory of the thinking and a neural algorithm that can bootstrap intelligence with limited computational resources and data. This neural algorithm approximates the O(n3) parameter space of the thinking theory into the O(1) parameters to make learning tractable for the biological intelligent agents. I will explain this proposal by cognitive phenomenon that are observed in a human, such as infant language acquisition, visual and verbal thinking, personality, creativity, exploit-exploration trade off, dreaming, one-shot learning, abstract language.