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Humans and Computers

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by

Michael Murney

on 6 May 2014

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Transcript of Humans and Computers

Spatial Summation
Temporal Summation
1)
Dendrites
2)
Cell Body/Soma

3)
Axon
4)
Myelin Sheath

5)
Synaptic Bouton

6)
Synaptic Cleft/Synapse

7)Grey Matter: Cell Bodies
8)White Matter: Myelin
Human Brains and Supercomputers
Cerebral Cortex
Frontal Lobes:

-Complex Cognitive Functions
-Behavioral Planning/Weighing Consequences of Behavior
Parietal Lobes:
-Integration of Sensory information
Occipital Lobes:
-Visual Processing
Temporal Lobes:
-Auditory/Visual Processing

Gross Anatomical Organization
Reptilian Brain

-->

Limbic System

-->

Cerebral Cortex
Overview
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
Neurons
Anatomy
Action Potentials
1)
Resting Phase
2)
Threshold
3)
Rising Phase
4)
Overshoot
5)
Falling Phase
6)
Hyperpolarization
7)
Resting Phase


(Boids!!!!)

Foundations of Artificial Intelligence
"Machines will be capable, within twenty years, of doing any work a man can do."
-Herbert A. Simon, 1965
Neural Networks
1960
1970
1980
1990
2000
"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
-Represent knowledge
-Plan
-Learn
-Communicate using language
-Integrate skills toward goals
-"Act like a person"
Consciousness?
Creativity?
Self-awareness?

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'
meow
-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?
Fundamental Problems
expert systems
Analog vs. Digital


Biological Matter vs. Silicon
The Future
-One algorithm??
-better information about brain -> better neural networks
-Computational power
-Human intelligence vs. carbon copy of brain
Human Nervous System
Central Nervous System (CNS)
Brain and Spinal Cord
(interneurons)
Peripheral Nervous System (PNS)
Everything Else
(Sensory and Motor Neurons)
backpropagation
Put It All Together...
Resting Potential
1)
Resting Membrane Potential

2)
Concentration Gradient

3)
Equilibrium Potential
Highly Parallel Processing: approx. 130 million rods/cones per eye, almost all of them carrying information at once
Full transcript