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IEEE Santa Clara Valley
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Past Event |
Abstract
Regarding the workings of the human mind, memory and
pattern recognition seem to be intertwined. You generally do not have
one without the other. Taking inspiration from life experience, a new
form of computer memory has been devised. Certain conjectures about
human memory are key to the central idea. The design of a practical and
useful “cognitive” memory system is contemplated, a
memory system that may also serve as a model for many aspects of human
memory. The new memory does not function like a computer memory
where specific data is stored in specific numbered registers and
retrieval is done by reading the contents of the specified memory
register, or done by matching key words as with a document search.
Incoming sensory data would be stored at the next available empty
memory location, and indeed could be stored redundantly at several
empty locations. The stored sensory data would neither have key words
nor would it be located in known or specified memory locations. Sensory inputs concerning a single object or subject are
stored together as vectors in a single “file
folder” or “memory folder.” When the
contents of the folder are retrieved, sights, sounds, tactile feel,
smell, etc., are obtained all at the same time. Sensor fusion is a
memory phenomenon. The sensory signals are not fused, but they are
simply recorded together in the same folder and retrieved together. Retrieval would be initiated by a prompt signal from a
current set of sensory inputs or patterns. A search through the memory
would be made to locate stored data that correlates with or relates to
the present real-time sensory inputs. The search would be done by a
retrieval system that makes use of autoassociative artificial neural
networks. Applications of cognitive memory systems have been made
to visual aircraft identification, aircraft navigation, and human
facial recognition. Other applications to speech recognition and
control systems are being explored. Bernard Widrow received the S.B., S.M., and Sc.D.
degrees in Electrical Engineering from the Massachusetts Institute of
Technology in 1951, 1953, and 1956, respectively. He joined the MIT
faculty and taught there from 1956 to 1959. In 1959, he joined the
faculty of Stanford University, where he is currently Professor of
Electrical Engineering. He began research on adaptive filters, learning
processes, and artificial neural models in 1957. Together with M.E.
Hoff, Jr., his first doctoral student at Stanford, he invented the LMS
algorithm in Autumn of 1959. Today, this is the world’s most
widely used learning algorithm. He has continued working on adaptive
signal processing, adaptive controls, and neural networks since that
time. Dr. Widrow is a Life Fellow of the IEEE and a Fellow of
AAAS. He received the IEEE Centennial Medal in 1984, the IEEE Alexander
Graham Bell Medal in 1986, the IEEE Signal Processing Society Medal in
1986, the IEEE Neural Networks Pioneer Medal in 1991, the IEEE
Millennium Medal in 2000, and the Benjamin Franklin Medal for
Engineering from the Franklin Institute of Philadelphia in 2001. He was
inducted into the National Academy of Engineering in 1995, and into the
Silicon Valley Engineering Council Hall of Fame in 1999. Dr. Widrow is a past president and currently a member of
the Governing Board of the International Neural Network Society. He is
a member of the AdCom of the IEEE Computational Intelligence Society.
He is associate editor of several journals and is the author of over
100 technical papers and 18 patents. He is co-author of
“Adaptive Signal Processing” and
“Adaptive Inverse Control,” both Prentice-Hall
books. A new book, “Quantization Noise,” is in
preparation. |
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