[seminar] Toward Automatic Math Word Problem Solving

  • 연사: Dr.Chin-Yew Lin (Microsoft)
  • 방식: 비대면 (webex)
  • 주소: https://dongguk.webex.com/dongguk/j.php?MTID=mf0fe51d4943a75e0335e8b64b1ec3b37
  • 비밀번호: aixx
  • 초록: Computer programs can complete many tasks much more effectively and efficiently than human beings, such as calculating the product of two large numbers, or finding all occurrences of a string in a long text. However, the performance of computers on many intelligent tasks is still low. For example, in a chatting scenario, computers often generate irrelevant or incorrect responses; we can easily find amusing results in automatic machine translations; and it is still a very challenging task for state-of-the-art computer programs to solve even primary-school-level math word problems. As an exploration project in grounded and executable semantic parsing and an effort to push toward real world knowledge computing, the SigmaDolphin project at MSRA aims to build an intelligent computer system that can automatically solve math word problems. In this talk, I will summarize our findings in addressing the three major challenges of math word problem solving: dataset creation, math word problem understanding and math equation generation.

[seminar] Responsible Data Use in the context of Explainable AI

  • 초록: Responsible Data Use is becoming increasingly important for all businesses that manage data and for LinkedIn where trust is key, we are taking Responsible Data Use very seriously, from protecting members, to privacy, to data governance, and explainable AI. I will in this talk first provide an overview of LinkedIn and Data Science at LinkedIn. I will then briefly cover what responsible data use is and focus on the area of explainable AI and describe our end-to-end solution that is now being used in production for some of our internal products. The emphasis on this deep dive is to communicate what it takes to get such work into production beyond the main algorithms and theory.

[seminar] Commonsense Knowledge in AI

  • 연사: Prof. Henry Liberman
  • 소속: MIT
  • 초록: Despite all the recent successes of AI, computers still struggle to capture simple knowledge about people and everyday life — what we call “commonsense” knowledge. Commonsense knowledge underlies our ability to understand language and perform problem solving. Commonsense knowledge is different from “factual” knowledge, as you might find in Wikipedia or encyclopedias. Commonsense reasoning is also different from probabilistic reasoning, as humans (as far as we know) perform commonsense reasoning without the counting operations inherent in probability. Commonsense reasoning is about plausibility rather than truth per se, and is best performed by analogical reasoning. I will describe efforts to collect commonsense knowledge, to reason with it, and to synthesize both commonsense and probabilistic approaches. Commonsense knowledge is important in user interfaces for intelligent agents, for sensible default behavior for interfaces, and for explanation and debugging.