Artificial intelligence: The dark matter of computer vision
There is a range of efforts aimed at solving the shallowness and brittleness of deep learning, the main AI algorithm used in computer vision today. But sometimes, finding the right solution is predicated on asking the right questions and formulating the problem in the right way. And at present, there’s a lot of confusion surrounding what really needs to be done to fix computer vision algorithms.
In a paper published last month, scientists at Massachusetts Institute of Technology and University of California, Los Angeles, argue that the key to making AI systems that can reason about visual data like humans is to address the “dark matter” of computer vision, the things that are not visible in pixels.
Titled, “Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense,” the paper delves into five key elements that are missing from current approaches to computer vision. Adding these five components will enable us to move from “big data for small tasks” AI to “small data for big tasks,” the authors argue.
These dark components are functionality, intuitive physics, intent, causality, and utility (FPICU).
See the full story here: https://bdtechtalks.com/2020/06/01/artificial-intelligence-computer-vision-fpicu/