Machine 'learns' like a human

Scientists have invented a machine that imitates the way the human brain learns new information, a step forward for artificial intelligence, researchers reported.
The system described in the journal Science is a computer model "that captures humans' unique ability to learn new concepts from a single example," the study said.
"Though the model is only capable of learning handwritten characters from alphabets, the approach underlying it could be broadened to have applications for other symbol-based systems, like gestures, dance moves, and the words of spoken and signed languages."

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Picture taken at the permanent exhibition "C3RV34U" at the "Cité des Sciences et de l'Industrie" in Paris, dedicated to the human brain

Joshua Tenenbaum, a professor at the Massachusetts Institute for Technology (MIT), said he wanted to build a machine that could mimic the mental abilities of young children.
"Before they get to kindergarten, children learn to recognize new concepts from just a single example, and can even imagine new examples they haven't seen," said Tenenbaum.
"We are still far from building machines as smart as a human child, but this is the first time we have had a machine able to learn and use a large class of real-world concepts -- even simple visual concepts such as handwritten characters -- in ways that are hard to tell apart from humans."
The system is a called a "Bayesian Program Learning" (BPL) framework, where concepts are represented as simple computer programs.
Researchers showed that the model could use "knowledge from previous concepts to speed learning on new concepts," such as building on knowledge of the Latin alphabet to learn letters in the Greek alphabet.
"The authors applied their model to over 1,600 types of handwritten characters in 50 of the world's writing systems, including Sanskrit, Tibetan, Gujarati, Glagolitic -- and even invented characters such as those from the television series Futurama," said the study.
Since humans require very little data to learn a new concept, the research could lead to new advances in artificial intelligence, the study authors said.
"It has been very difficult to build machines that require as little data as humans when learning a new concept," said Ruslan Salakhutdinov, an assistant professor of computer science at the University of Toronto.
"Replicating these abilities is an exciting area of research connecting machine learning, statistics, computer vision, and cognitive science."

The machine that learns like a CHILD: Algorithm recognises and scribbles symbols that look identical to those produced by humans

• Software has been called the Bayesian Program Learning framework
• It recognises a symbol by looking at it once and copying its general shape
• The framework can even draw symbols that are hard to spot by humans
• Machines usually take hundreds of attempts to memorise visual concepts

When children are shown a new object, such as letter in the alphabet, a picture or a real-world item, they generally only need a couple of instances to be able to identify it accurately.

Machines, by comparison, have to be trained hundreds and thousands of times to not only identify an object, but also to recognise it from different angles.

But researchers have designed an algorithm to solve this problem by allowing computers to learn visually in the same way as humans do.

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A computer algorithm that memorises general shapes of objects and can draw them out again has shown computers can learn visually in the same way as young children. The images above were drawn by a the computer and by a human. The machine generated symbols 1 and 2 in the top row and 2 and 1 in the second

This has allowed the machines to not only identify an object from its shape, but also draw it for themselves - much in the same way young children do when they are learning.

For example the machine can be shown a letter of the alphabet or a symbol and then draw it.

The resulting sketches were almost indistinguishable from those drawn by humans. 

Professor Joshua Tenenbaum, a cognitive scientist at the Massachusetts Institute of Technology, who was one of the researchers involved in the study, said: 'Before they get to kindergarten, children learn to recognise new concepts from just a single example, and can even imagine new examples they haven't seen'

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Humans need a couple of instances to be able to identify a symbol. It takes thousands of examples for a machine to learn one (artist's impression)

'We are still far from building machines as smart as a human child, but this is the first time we have had a machine able to learn and use a large class of real-world concepts - even simple visual concepts such as handwritten characters - in ways that are hard to tell apart from humans.'

The algorithm was created along with Dr Brenden Lake, a cognitive scientist from New York University.

When a child is shown the letter A, for example, they can identify the same shape even when it is written by different people in slightly different ways. 

Equally, showing someone a single picture of a kettle will likely be enough for that person to recognise other non-identical kettles, of different shapes and colours.

The team's program, called 'Bayesian Program Learning' (BPL) framework, was developed to work in a similar way.

When the computer is presented with a symbol - for instance, the letter A - it starts randomly generating different examples of that symbol, in various ways it could have been drawn.

Rather than looking at the symbol as a cluster of pixels, BPL memorises it as the result of a 'generative process'. 

This involves establishing which specific strokes were made to draw it. 

This approach allows the machine to recognise the letter in various guises, such as the differences between how two people draw the letter for example.

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In what they called a 'Visual Turing Test', the researchers showed both the handwritten and the machine-generated doodles to a group of people. Fewer than 25 per cent could spot the computer generated doodles. In the picture above the machine generated symbols are B and A in the top row and A and B in the bottom

The model was tested on more than 1,600 types of handwritten symbols in 50 different alphabets or codes. 

These included Sanskrit, Tibetan, Gujarati, Glagolitic, and even imaginary letters shown in the television series Futurama.

THE VISUAL TURING TEST 

Taking a leaf out of Alan Turing - the British computer scientist who devised a question-based test to tell the difference between a man and a machine-  the researchers asked their program and some human volunteers to invent new characters in the style of those they had been shown.

Then, in what they called a 'Visual Turing Test', both the handwritten and the machine-generated symbols were shown to another group of people, who were asked to identify which symbols had been created by the program.

 The researchers reported that fewer than 25 percent of the 'judges' managed to guess which symbols were computer-generated at a percentage significantly better than chance.

The program was consistently able to reproduce the characters after being shown only one example for each of them.

Taking it a step further, the researchers asked the machine as well as human volunteers to invent new characters in the style of those they had been shown. 

Then, in what the researchers called a 'visual Turing Test', both the handwritten and the machine-generated symbols were shown to another group of people, who were asked to identify which symbols had been created by the program. 

The researchers reported that fewer than 25 per cent of the 'judges' managed to guess which symbols were computer-generated at a percentage significantly better than chance.

'Our results show that by reverse engineering how people think about a problem, we can develop better algorithms,' explained Dr Lake, who was the lead author on the study, which is published in the journal Science.

'Moreover, this work points to promising methods to narrow the gap for other machine learning tasks.