Technology Review lists technologies that will change our world:
– Universal Translation
– Synthetic Biology
– Nanowires
– Bayesian Machine Learning
– T-Rays
– Distributed Storage
– RNA Interference
– Power Grid Control
– Microfluidic Optical Fibers
– Personal Genomics
An excerpt about Bayesian Learning:
Daphne Kollers research using a once obscure branch of probability theory called Bayesian statistics is generating more excitement than skepticism. The Stanford University associate professor is creating programs that, while tackling questions such as how genes function, are also illuminating deeper truths about the long-standing computer science conundrum of uncertaintylearning patterns, finding causal relationships, and making predictions based on inevitably incomplete knowledge of the real world. Such methods promise to advance the fields of foreign-language translation, microchip manufacturing, and drug discovery, among others, sparking a surge of interest from Intel, Microsoft, Google, and other leading companies and universities.
How does an idea conceived by an 18th-century minister (Thomas Bayes) help modern computer science? Unlike older approaches to machine reasoning, in which each causal connection (rain makes grass wet) had to be explicitly taught, programs based on probabilistic approaches like Bayesian math can take a large body of data (its raining, the grass is wet) and deduce likely relationships, or dependencies, on their own. Thats crucial because many decisions programmers would like to automatesay, personalizing search engine results according to a users past queriescant be planned in advance; they require machines to weigh unforeseen combinations of evidence and make their best guesses. Says Intel research director David Tennenhouse, These techniques are going to impact everything we do with computersfrom user interfaces to sensor data processing to data mining.
Koller unleashed her own Bayesian algorithms on the problem of gene regulationa good fit, since the rate at which each gene in a cell is translated into its corresponding protein depends on signals from a myriad of proteins encoded by other genes. New biomedical technologies are providing so much data that researchers are, paradoxically, having trouble untangling all these interactions, which is slowing the search for new drugs to fight diseases from cancer to diabetes. Kollers program combs through data on thousands of genes, testing the probability that changes in the activity of certain genes can be explained by changes in the activity of others. The program not only independently detected well-known interactions identified through years of research but also uncovered the functions of several previously mysterious regulators. People are limited in their ability to integrate many different pieces of evidence, says Koller. Computers have no such limitation.
