Patrick Henry Winston, “The Next 50 years: A Personal View”

Winston, Patrick Henry. “The Next 50 years: A Personal View.” Biologically Inspired Cognitive Architectures 1 (July 2012): 92–99. © 2012 Elsevier B.V.

Abstract: I review history, starting with Turing’s seminal paper, reaching back ultimately to when our species started to outperform other primates, searching for the questions that will help us develop a computational account of human intelligence. I answer that the right questions are: What’s different between us and the other primates and what’s the same. I answer the what’s different question by saying that we became symbolic in a way that enabled story understanding, directed perception, and easy communication, and other species did not. I argue against Turing’s reasoning-centered suggestions, offering that reasoning is just a special case of story understanding. I answer the what’s the same question by noting that our brains are largely engineered in the same exotic way, with information flowing in all directions at once. By way of example, I illustrate how these answers can influence a research program, describing the Genesis system, a system that works with short summaries of stories, provided in English, together with low-level common-sense rules and higher-level concept patterns, likewise expressed in English. Genesis answers questions, notes abstract concepts such as revenge, tells stories in a listener-aware way, and fills in story gaps using precedents. I conclude by suggesting, optimistically, that a genuine computational theory of human intelligence will emerge in the next 50 years if we stick to the right, biologically inspired questions, and work toward biologically informed models.

Winston is "old school" symbolic AI and, as such, is dismissive of neural nets. His view of the matter was reasonable until 2012, the year the deep learning took off when AlexNet won the ImageNet Challenge. Note that that's the year Winston published this article. However, if it turns out that symbolic techniques will be necessary to reach AGI (whatever that it), then this article won't appear quite so wrong-headed in its dismissal of neural nets. OTOH, note that he views his approach as being biologically inspired. Nor do I think his emphasis narrative or concept patterns is mistaken. Arguably, though, it's not until GPT-2 or GPT-3 that neural nets could deal with those things.