How artificial neural networks build their own learning machinery

MIT News reports that in a paper published Oct. 9 in the journal PNAS[1], researchers at MIT's McGovern Institute and the K. Lisa Yang ICoN Center argue that a key test of whether a network learns in a brain-like way is "the network's ability to organize itself, without the guiding hand of a developer."[2]According to MIT News, with some simple instructions about how and when connections should change, a group of randomly connected units can reshape itself into an effective information-processing network[1].Co-first author Qianli Liao told MIT News that "Current AI technology is working very well, but it doesn't necessarily use a learning algorithm that is used by the brain."[1]MIT News reports that most modern AI implements gradient descent through some form of the back-propagation algorithm, which neuroscientists say is unlikely to be used by the brain.[1] According to the outlet, the team compared its self-organizing network to networks designed to learn in this way and found that it performed at a similar level.[1]Professor Tomaso Poggio told MIT News that if the brain does use the proposed motifs, "we will have a strong connection at the level of fundamental mechanisms between artificial and natural networks"[3].According to Tech Xplore, which republished the MIT story, Liao points to the development of brain-computer interfaces[4] as one possible use. Mirage News reports that Liao said such a network does not necessarily learn the same way the brain does - but it's a critical starting point for biological plausibility.[3]