As we contemplate learning today, we need to include “machine learning” somewhere in the overall process. For a number of different reasons. One being that knowing how machines can learn, and developing new ways for them to learn, sheds light on how humans learn, and could learn.
Secondly, we are to a degree melding human minds with the cloud, where machine minds may dwell. Learning that depends on machines understanding who the learner is, and what activity or process is best for them in real time, is one of the more important components of that learning meld. An advanced capability to direct adaptive learning is preferable to yesterday’s “best available” technology.
One of the new approaches to machine learning discussed below is called Bayesian learning which is distinguished from Neural Network learning.
Although such networks are modeled after the behavior of biological neurons, they do not yet learn the way humans do — acquiring new concepts quickly. By contrast, the new Bayesian software program described in the Science article is able to learn to recognize handwritten characters after “seeing” only a few or even a single example.
There’s also this:
[gview file=”https://publicservicesalliance.org/wp-content/uploads/2015/12/A-Learning-Advance-in-Artificial-Intelligence-Rivals-Human-Abilities-The-New-York-Times.pdf”]“With all the progress in machine learning, it’s amazing what you can do with lots of data and faster computers,” said Joshua B. Tenenbaum, a professor of cognitive science and computation at M.I.T. and one of the authors of the Science paper. “But when you look at children, it’s amazing what they can learn from very little data. Some comes from prior knowledge and some is built into our brain.”