Project Aristo: Towards Machines that Capture and Reason with Science Knowledge

International Conference on Knowledge Capture 2019 3 cites
2019:02020:02021:02022:02023:02024:02025:02026:3
2019 2026
3

Peter Clark

Abstract

AI2’s Project Aristo seeks to build a system that has a deep understanding of science, using knowledge captured mainly from large-scale text. Recently, Aristo achieved surprising success on the Grade 8 New York Regents Science Exams, scoring over 90% on the exam’s non-diagram, multiple choice (NDMC) questions, where even 3 years ago the best systems scored less than 60%. In this talk, I will describe the journey of Aristo through various knowledge capture technologies that have helped it, including acquiring if/then rules, tables, knowledge graphs, and latent neural representations. I will also discuss the growing tension between capturing structured knowledge vs. capturing knowledge latently using neural models, the latter proving highly effective but hard to interpret. Finally I will speculate on the larger quest towards knowledgable machines that can reason, explain, and discuss, and how structured and latent knowledge can interact to help reach this goal.

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