Source: Business Insider, Dec 2015
a team of researchers has developed an AI that they say can learn handwritten characters from various alphabets after “seeing” just a single example, according to a study published Thursday in the journal Science.
The research had two goals: To better understand how people learn, and to build machines that learn in more humanlike ways.
“For the first time, we think we have a machine system that can learn a large class of visual concepts in a humanlike way,” study leader Joshua Tenenbaum, a cognitive scientist at MIT, said in a news briefing on Wednesday.
People, especially children, are remarkably good at induction, a concept that allows us to take a single example and generalize it to learn a broader concept.
oday’s AI algorithms — like those used by Facebook’s face recognition or Google’s translation service — often require huge datasets to learn even basic concepts, and while impressive, they still don’t have the rich understanding that humans have. The best-known of these is an approach known as “deep learning.”
Tenenbaum and his colleagues set out to build an AI that could do something most humans can do easily: See a handwritten alphabet character, recognize it, and draw it themselves.
To do this, they created a model that represents concepts as simple programs that explain examples in terms of their probability of being right, something they call “Bayesian program learning.” Their approach combines three important concepts:
- the idea that rich concepts are composed of simpler parts;
- these concepts are produced by cause-and-effect; and
- programs can “learn to learn” by relying on past experience.
the new AI model performed as well as humans at this task, and even better than deep learning algorithms. In the task where they had to identify the correct character, people had an average error rate of 4.5%. The new AI averaged 3.3% errors, whereas competitor programs varied between 8% and 34.7%.