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Humans and Computers
Transcript of Humans and Computers
7)Grey Matter: Cell Bodies
8)White Matter: Myelin
Human Brains and Supercomputers
-Complex Cognitive Functions
-Behavioral Planning/Weighing Consequences of Behavior
-Integration of Sensory information
Gross Anatomical Organization
The Fundamentals of Neuroscience (Michael)
Supercomputers, Artificial Intelligence, and Brain-Like Computers (John)
Can a Computer Think Like A Brain? (John and Michael)
A Down and Dirty, Grossly Abbreviated, Highly Reductionistic Rundown of the Human Brain
2) Gross Anatomical Organization
3) Neurons and Action Potentials
The Nervous System: An Overview
Foundations of Artificial Intelligence
"Machines will be capable, within twenty years, of doing any work a man can do."
-Herbert A. Simon, 1965
"Artificial General Intelligence"
-Networks with one hidden layer: All continuous functions
-2 hidden layers: All functions
-Broad vs. Strong Priors: What algorithms excel at learning certain functions, and are less efficient with others?
-Do highly varying, complex functions need strong priors? (No.)
-Deep vs. Shallow
What are the goals of modern Artificial Intelligence?
-Reason, strategize, make judgements
-Communicate using language
-Integrate skills toward goals
-"Act like a person"
1) Mammalian Nervous System: An Overview
Deep Learning Networks
Stimulus-Response Network: Flipping a Page
Michael Murney and John Moody
"If you were a current computer science student what area would you start studying heavily?
If you feel like expanding on that, why do you think this area deserves the attention and how do you see it changing the technology game in the next 10 years?"
"The ultimate is computers that learn. So called deep learning which started at Microsoft and is now being used by many researchers looks like a real advance that may finally learn. It has already made a big difference in video and audio recognition - more progress in the last 3 years than ever before."
-Bill Gates, yesterday, reddit.com 'AMA'
-Deep Boltzmann Machine structure
-Recognized 15.8% of categories of object from an unlabeled dataset
-Performed better than humans at identifying some classes of objects
-Neural networks' learning is unintuitive
A General Purpose Algorithm For Learning?
Analog vs. Digital
Biological Matter vs. Silicon
-better information about brain -> better neural networks
-Human intelligence vs. carbon copy of brain
Human Nervous System
Central Nervous System (CNS)
Brain and Spinal Cord
Peripheral Nervous System (PNS)
(Sensory and Motor Neurons)
Put It All Together...
Resting Membrane Potential
Highly Parallel Processing: approx. 130 million rods/cones per eye, almost all of them carrying information at once