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SUMMARY:Artificial Life - Professor Chris Bishop\, University of Cambridge
DTSTART:20120302T173000Z
DTEND:20120302T183000Z
UID:TALK30613@talks.cam.ac.uk
CONTACT:Janet Gibson
DESCRIPTION:Abstract\n\nLiving organisms are extraordinary\, with capabili
 ties far exceeding any present-day technology. This talk will explore the 
 quest to create artificial systems which exhibit some of those capabilitie
 s\, and will thereby highlight the deep connections between two seemingly 
 disparate disciplines: biology and computer science. \n\nAlan Turing\, who
  laid many of the foundations for computer science\, was fascinated by the
  problem of pattern formation in natural systems\, and proposed specific m
 echanisms which have only recently been vindicated experimentally.\n\nOne 
 of the long-standing challenges in computer science is the creation of mac
 hines with human-like intelligence. Recent developments in the field of ma
 chine learning are starting to deliver rapid progress towards this goal.\n
 \nThe complex biochemical processes at work inside every living cell are s
 ometimes likened to the operation of a silicon microprocessor. This analog
 y turns out to be profound: the computational capabilities of DNA and its 
 associated molecular machinery are precisely the same as those of a modern
  digital computer. Our increasing ability to re-program living cells lies 
 at the heart of the new field of synthetic biology\, which offers the pote
 ntial to transform medicine\, energy generation\, agriculture\, and the en
 vironment. Already\, prototype organisms have been engineered to synthesis
 e diesel fuel\, to detect toxic pollutants\, and to manufacture anti-malar
 ial drugs. Over the coming years\, technological advances will allow cells
  to be programmed on an unprecedented scale\, far exceeding our ability to
  design the software by hand. Insights and tools from the field of compute
 r science will be essential in allowing the full potential of synthetic bi
 ology to be achieved. \n\nBiography\n\nChris Bishop is a Distinguished Sci
 entist at Microsoft Research\, and a Fellow of Darwin College\, Cambridge.
  He is also Vice President of the Royal Institution\, and Professor of Com
 puter Science at the University of Edinburgh. He is a Fellow of the Britis
 h Computer Society\, and a Fellow of the Royal Statistical Society\, and h
 as been awarded two Honorary Doctor of Science degrees. In 2004 he was ele
 cted Fellow of the Royal Academy of Engineering\, and in 2007 he was elect
 ed Fellow of the Royal Society of Edinburgh.\n\n \n\nHe is the author of t
 he leading textbook Neural Networks for Pattern Recognition (Oxford Univer
 sity Press\, 1995) which has over 16\,000 citations\, and which helped to 
 bring statistical concepts into the mainstream of the machine learning fie
 ld. His latest textbook Pattern Recognition and Machine Learning (Springer
 \, 2006) has over 5\,000 citations\, and has been widely adopted. His rese
 arch interests include probabilistic approaches to machine learning\, as w
 ell as their application in a broad range of scientific and technological 
 domains.\n\n \n\nIn 2008 he gave the Royal Institution Christmas Lectures\
 , broadcast on prime-time UK national television to an audience of close t
 o 5 million. In 2009\, he was awarded the Tam Dalyell Prize “for excelle
 nce in engaging the public with science”\, and in 2011 he was awarded th
 e prestigious Rooke Medal by the Royal Academy of Engineering\, “for his
  persistent drive in engaging members of the public in the vital work of e
 ngineers and their contribution to society”.
LOCATION:LMH\, Lady Mitchell Hall
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