Viewing 3 videos from 2017 in Distinguished Lecture Series See AI2’s full collection of videos on our YouTube channel.
    • November 6, 2017

      Gary Marcus

      All purpose, all-powerful AI systems, capable of catering to our every intellectual need, have been promised for six decades, but thus far still not arrived. What will it take to bring AI to something like human-level intelligence? And why haven't we gotten there already? Scientist, author, and entrepreneur Gary Marcus (Founder and CEO of Geometric Intelligence, recently acquired by Uber) explains why deep learning is overrated, and what we need to do next to achieve genuine artificial intelligence.

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    • September 15, 2017

      Horacio Saggion

      In the current online Open Science context, scientific data-sets and tools for deep text analysis, visualization and exploitation play a major role. I will present a system developed over the past three years for “deep” analysis and annotation of scientific text collections. After a brief overview of the system and its main components, I will present our current work on the development of a bi-lingual (Spanish and English) fully annotated text resource in the field of natural language processing that we have created with our system. Moreover, a faceted-search and visualization system to explore the created resource will be also discussed.

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    • May 22, 2017

      Abhinav Gupta

      In 2013, we proposed NEIL (Never Ending Image Learner), a computer program to learn visual models and commonsense knowledge from the web. In its first version, NEIL ran for 2.5 years learning 8K concepts, labeling 4.5M images and learning 20K common-sense facts. But it also helped us discover the shortcomings of the current paradigm of learning and reasoning with knowledge. In this talk, I am going to describe our subsequent efforts to overcome these drawbacks.

      On the learning side, I will talk about how we scale up learning visual models to rare and compositional categories (“wet possum”). Note the web-search data for compositional categories are noisy and cannot be used “as is” for learning. The core problem in compositional categories is respecting contextuality. The meaning of primitive categories change based on concepts being composed with (red in red wine is different from red in red car). I will talk about how we can respect contextuality while composing categories.

      On the reasoning side, I will talk about how we can incorporate the learned knowledge graphs in end-to-end learning. Specifically, we will show how these “noisy” knowledge graphs can not only improve classification performance but also provide “explainability” which is crucial for AI systems. I will also show some of our recent work on using knowledge graphs for zero-shot learning (again in an end-to-end manner).

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