Latest AI Algorithms Presented at NeurIPS

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Advances will help machines identify objects and figure out how to work together, improve their ability to understand language, and improve their ability to learn.

eetasia.com, Dec. 18, 2019 – 

A human can look at a bird and understand its shape in 3-D space. What is simple intuition for humans is an extraordinary task for a machine, but AI researchers keep devising innovative new ways for machines to do things humans do naturally. Several intriguing advances were revealed at NeurIPS, the annual mass convergence of AI researchers, held last week in Vancouver, Canada.

Here are the details on a selection of interesting papers from the hundreds presented, in no particular order.

Nvidia: 3D from 2D

Nvidia presented an inverse rendering framework which predicts 3D information about objects based on 2D photographs and videos.

EETimes spoke to the paper's co-author, Professor Sanja Fidler, director of AI at Nvidia (she is also a professor at the University of Toronto).

"3D is super important for robotics, to grasp objects and navigate through scenes," she said. "For that you need to have some understanding of the world, how the geometry of the world actually looks."

This is a hot research topic today. The main issue, Fidler said, is that while imagery created for use in robotics often has 3D information associated with it, perhaps from complementary sensors, most of the imagery available today from other sources does not have this 3D information available. Nvidia is working towards a way to infer 3D data from this imagery so it can be used for robotics purposes.

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