Thankfully, artificial intelligence is not at the point where it can learn as fast as the human brain. However, researchers from Harvard University are working hard to change that.
An AI system will have to look at thousands of photos or videos to recognize an object, but humans can do so by seeing something just once. Now, Intelligence Advanced Research Projects Activity (IARPA) granted three Harvard departments a cool $28 million to figure out why our brains are able to learn the way they do.
Researchers from the John A. Paulson School of Engineering and Applied Sciences (SEAS), Center for Brain Science (CBS) and the Department of Molecular and Cellular Biology are going to be paying close attention to what happens inside the brain’s visual cortex to try and figure this out. What they’ll be looking at are the connections of neurons. The data they get from this will, in theory, help them find a way to build better a artificial intelligence.
Harvard assistant professor David Cox said:
“This is a moonshot challenge, akin to the Human Genome Project in scope. The scientific value of recording the activity of so many neurons and mapping their connections alone is enormous, but that is only the first half of the project. As we figure out the fundamental principles governing how the brain learns, it’s not hard to imagine that we’ll eventually be able to design computer systems that can match, or even outperform, humans.”
This is one of this projects that I’d be cool with if it weren’t being used to improve artificial intelligence. How many times do we have to reference Skynet for it to sink it? Anyway, this is in no way an easy undertaking and the teams “have no illusions that this will be easy.” The whole process is expected to generate a whopping petabyte (1.6 million CDs) of data and will likely lead to advances in computing as they’ll have so much data to process that they’ll need to conjure up new ways to manage data and speed up processing.