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MrPlow writes:

Submitted via IRC for SoyCow1984

Researchers from UCLA Samueli School of Engineering and Stanford have demonstrated a computer system that can discover and identify the real-world objects it "sees" based on the same method of visual learning that humans use.

The system is an advance in a type of technology called "computer vision," which enables computers to read and identify visual images. It is an important step toward general artificial intelligence systems -- computers that learn on their own, are intuitive, make decisions based on reasoning and interact with humans in a more human-like way. Although current AI computer vision systems are increasingly powerful and capable, they are task-specific, meaning their ability to identify what they see is limited by how much they have been trained and programmed by humans.

[...] The engineers' new method, described in the Proceedings of the National Academy of Sciences, shows a way around these shortcomings.

The approach is made up of three broad steps. First, the system breaks up an image into small chunks, which the researchers call "viewlets." Second, the computer learns how these viewlets fit together to form the object in question. And finally, it looks at what other objects are in the surrounding area, and whether or not information about those objects is relevant to describing and identifying the primary object.

To help the new system "learn" more like humans, the engineers decided to immerse it in an internet replica of the environment humans live in.

Lichao Chen, Sudhir Singh, Thomas Kailath, Vwani Roychowdhury. Brain-inspired automated visual object discovery and detection. Proceedings of the National Academy of Sciences, 2018; 201802103 DOI: 10.1073/pnas.1802103115

Source: https://www.sciencedaily.com/releases/2018/12/181220163210.htm

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