New approaches to machine learning have moved away from trying to program complex tasks that humans take for granted, opening up the field to a huge range of applications, according to Christopher Bishop, assistant director of Microsoft Research Cambridge.
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Presenting the 2005 BCS Lovelace Lecture, Bishop said, "Computers are great at doing many big calculations a second, but getting them to read handwriting, for example, is a big challenge. It is impossible to write an algorithm for recognising handwriting; many have tried but all have failed. There are too many differences even in the writing of one person to be able to write a set of rules or algorithm.
"This is one of a whole range of problems in pattern recognition, which people are very good at, but which has been very difficult to get computers to solve. This is one aim of machine learning.
"With handwriting recognition, for example, we do not try to program a solution; we give the computer lots of examples of handwriting, along with the text, and the machine learns to match the electronic image to the text.
"A key issue in machine learning is uncertainty. We are dealing with situations which are very variable or where there is a lot of noise in the signal. We are used to dealing with uncertainty in everyday life and we have to deal with it in computing too."
Recent approaches to machine learning focused on three key developments, Bishop said.
The first is the development of work of an 18th century clergyman and mathematician, Thomas Bayes, who published a theory that led to a mathematical method of calculating the probability of future occurrences of an event from past occurrence data.
The second development has been the appearance of graphical tools that simplify and speed up work on probability modelling.
Finally, dealing with uncertainty has been eased by new techniques for making approximations as accurate as possible.
Bishop gave some examples of the new approaches, demonstrating an image editing tool being developed by Microsoft, called Grabcuts, which accurately cut a flowerhead from a photo and pasted it into a new file.
The user highlights the object by roughly tracing a box around it. The system partly uses the colours of the highlighted object to separate it from the background, even working in individual pixels at the edge of the main object to cut out the main colour from the background. This gives a much sharper result than could be achieved by a human tracing the outline - and in seconds rather than many minutes.
The Forza car racing game, recently released for the Microsoft XBox games console, was a totally different example of the use of machine learning presented by Bishop.
"In previous games the artificial intelligence that drives the other cars is handcrafted scripts," he said. "People test the game and then tweak the script and try again. This is very time consuming. With Forza, instead of handscripting the cars we get people to use the game and drive them - and capture the data and try to mimic their style. If you buy Forza you can train the computer-controlled drivers in your driving style."
Bishop's other examples ranged from image retrieval to tracing HIV as it mutates in the body, with the long-term aim of developing a vaccine.
"The range of applications of machine learning has mushroomed in the last 10 years, from a handful of niche applications to virtually every area," he said.