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Scientists teach computers how to learn like humans

Human hand and computer hand drawing symbols
New research compares human and machine learning for a wide range of simple visual concepts such as drawing characters from alphabets(Danqing Wang)
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For artificial intelligence and smart machines to really take off, computers are going to have to think more like people, according to experts in the field.

Now, US scientists have created a computer model, or algorithm, that captures the unique human ability to grasp new concepts in a study that involved learning unfamiliar handwritten alphabet characters.

The algorithm enabled computers to recognise and draw simple symbols that were indistinguishable from those created by humans.

The study, reported in the journal Science, is a "significant advance" in the field of artificial intelligence, the scientists said.

This work, as well as research like it, will help scientists better understand human learning and develop new, more human-like learning algorithms, said the study's lead author Dr Brenden Lake, a cognitive and data scientist New York University.


The algorithm was designed to make a computer able to learn quickly from a single example in the way people do.

"You show even a young child a horse or a school bus or a skateboard and they get it from just one or a few examples," said study co-author Professor Joshua Tenenbaum, from the Massachusetts Institute of Technology.

Standard algorithms in machine-learning require tens, hundreds or even thousands of training examples to yield similar results, Professor Tenenbaum said.

Drawing alphabets from around the world

In the study, computers boasting the new algorithm and human subjects were presented with selected characters among a data set of about 1,600 handwritten characters from 50 alphabets from around the world.

They even included a fictional alien alphabet from the animated TV show Futurama.

Among other tasks, the human subjects and computers were directed to reproduce various characters after being given a lone example.

Human judges were then asked to identify which characters were reproduced by a computer.

The judges found the work produced by the computers to be virtually indistinguishable from that of human subjects.

Professor Ruslan Salakhutdinov of the University of Toronto said he hoped this new work would help guide progress in artificial intelligence by leading to next-generation intelligent machines "that hopefully will come close to displaying human-like intelligence".

"It has been very difficult to build machines that require as little data as humans when learning a new concept," Professor Salakhutdinov said.


The same approach used in the study might be applicable to machine learning for many other tasks like speech recognition and object recognition, Dr Lake said.