AI researchers at MIT and UCLA discuss the need to address the "dark matter" of computer vision, the things that aren't visible in pixels. This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. What makes us humans so good at making sense of visual data? That’s a question that has preoccupied artificial intelligence and computer vision scientists for decades.
This article is part of our [reviews of AI research papers_](https://bdtechtalks.com/tag/ai-research-papers/), a series of posts that explore the latest findings in artificial intelligence._
What makes us humans so good at making sense of visual data? That’s a question that has preoccupied artificial intelligence and computer vision scientists for decades. Efforts at reproducing the capabilities of human vision have so far yielded results that are commendable but still leave much to be desired.
Our current artificial intelligence algorithms can detect objects in images with remarkable accuracy, but only after they’ve seen many (thousands or maybe millions) examples and only if the new images are not too different from what they’ve seen before.
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.
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Every month we decipher three research papers from the fields of machine learning, deep learning and AI, which left an impact on us in the previous month. In general, we try to present papers that are going to leave a big impact on the future of machine learning and deep learning. We believe that these proposals are going to change the way we do our jobs and push the whole field forward. Have fun!