Embracing Uncertainty – a Microsoft Research exhibit at the 2010 Royal Society Summer Science Exhibition, held in London from June 25 to July 4 2010
Scientists have worked for decades to try to create intelligence in computers. Traditional approaches relied on hand-crafted solutions and had limited applicability. New techniques being developed at Microsoft Research Cambridge and other institutions around the world, are based on computers which can learn for themselves by analyzing large sets of data. This capability is known as machine learning.
The key to learning from examples is to recognize that real-world data is full of complexity, ambiguity, and uncertainty. Computers can be programmed to handle these challenges by using a branch of mathematics called probability theory. The Embracing Uncertainty exhibit included a “Galton machine” which illustrates how probability theory allows random events to be described in a precise way. In the case of the Galton machine, the random events correspond to beads bouncing randomly off the pins inside the machine.
Visitors to the Embracing Uncertainty exhibit were able to explore the science of uncertainty for themselves through hands-on interactions with all of these demonstrations:
The Clinical Drug Trial demonstration is a simple example which illustrates the use of probabilities to model the results of a trial for a new drug. We see how the probabilities change as we show more data to the computer. Such changes in the probabilities correspond to a reduction in uncertainty as the computer learns from the data.
The Movie Recommender demonstration provides a practical example of how probabilities and machine learning can be applied to the problem of recommending movies to people based on ratings they have given to other movies which they have already seen. This data is combined with the recommendations made by lots of other people, and allows the computer to work out, for any new movie, the probability that the user will like it.
LiveObject illustrates the use of machine learning for the visual recognition of everyday objects such as pens and mobile phones. The system can be trained on a new category using a few example objects and the accuracy of the system improves as it sees more examples of that object.
Organ Navigator is a practical application of recognition technology, which uses probabilistic methods to determine the position and extent of anatomical structures in conventional 3-dimensional medical scans.
Finally, Background Removal shows how machine learning can be used to simplify tasks in image editing. Using just a few mouse clicks, the system removes the background from an object (such as an animal or a person) within an image, allowing the object to be pasted into a new image.
We gave away sets of non-transitive dice during the event. The dice have a surprising property that helps to demonstrate that mathematics of uncertainty. To find out more about the dice, visit aka.ms/ntdice.