Building Machines That Learn and Think Like People
Associated Papers
Other Meetings in this Series
Experimental Investigation of Ant Traffic Under Crowded Conditions
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This week will be a short break from our NLP/CogSci papers. Ants are one of the few creatures on the planet that engage in two-way traffic just like us. By looking at how ants navigate their self-organized traffic systems, we can learn how to better organize our own homologous systems (such as intersections, roadways, etc.). This paper experimentally investigates the efficiency of ants navigating paths involving bidirectional movement, and found that ants are capable of a level of efficiency that is twice as high as humans' in equivalent scenarios. What makes ants so much better than humans at traffic organization? What can we learn from ants' organizational paradigms? Should ants be driving our cars instead of humans? These are some of the questions investigated in this week's paper.
Building Machines That Learn and Think for Themselves
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We agree with Lake and colleagues on their list of key ingredients for building humanlike intelligence, including the idea that model-based reasoning is essential. However, we favor an approach that centers on one additional ingredient: autonomy. In particular, we aim toward agents that can both build and exploit their own internal models, with minimal human hand-engineering. We believe an approach centered on autonomous learning has the greatest chance of success as we scale toward real-world complexity, tackling domains for which ready-made formal models are not available. Here we survey several important examples of the progress that has been made toward building autonomous agents with humanlike abilities, and highlight some outstanding challenges.
Contributing Authors
John Muchovej
Founder of AI@UCF. Researcher in cognitive science and machine learning. Focusing on intuitive physics and intuitive psychology.
