Computers using your brains
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Computers - Using your Brains. Jim Austin Professor of Neural Computation. Pentium III. So how complex is it ? 10 12 neurons … 1,000,000,000,000 1000 connections between neurons. One brain can hold ... 1,000,000,000,000,000 numbers !. What do 10 12 neurons look like ?.

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Computers - Using your Brains

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Computers using your brains

Computers - Using your Brains

Jim Austin

Professor of Neural Computation


Pentium iii

Pentium III


Computers using your brains

So how complex is it ?

  • 1012 neurons … 1,000,000,000,000

  • 1000 connections between neurons.

  • One brain can hold ...

    1,000,000,000,000,000 numbers!


What do 10 12 neurons look like

What do 1012 neurons look like ?

  • 1600 times Population of the world (6,100,000,000)

  • 78,125 times the complexity of the Pentium III

  • Equal to the number of stars in our galaxy

4 Meters

4 Meters

4 Meters

Sand


The good and the bad

  • What are computers good at ?

    • Adding up fast

    • Storing data - numbers and facts

    • Pushing data around

  • What are computers bad at ?

    • Being reliable

    • Finding information - knowledge

    • Doing very complex things - recognizing images

    • Learning to do the job them selves!

The good and the bad


Why are computers so restricted

Why are computers so restricted ?

ACE


Leo for stock control

Leo - for stock control


Colossus for breaking codes

Colossus - for breaking codes


Pegusus for scientific work

Pegusus - for scientific work


Neurons verses gates

Neurons verses Gates

Input 1

Output

Input 2

NAND Gate

Boolean Logic - both inputs OK, output not OK


Gates nand

=

=

Gates - NAND

ALL inputs to be OK for output to be NOT OK

Output

Input 1

Input 2

=


Evolution

Evolution ?

Should have picked a NAND gate for the brain...


Neuron

Neuron

Output = threshold (input A x weight A + input B x weight B)

A

+

Inputs

Output

B

“Weights”

Threshold logic - threshold 1 - one or more inputs OK  output OK


Neuron1

=

=

Neuron

At least one OK for output to be OK

=

At least three OK’s for output to be OK


Weights

Can also alter connections/importance of inputs

using the weights on the inputs

Weights

1

0

1

1

+

3.5

0.5

1

1


Why did this difference develop

Why did this difference develop ?

  • “The analysis of the operation of a machine using two indication elements and signals can be conveniently be expressed in terms of a diagrammatic notation introduced, in this context, by Von Neuman and extended by Turing. This was adopted from a notation used by Pits and McCulloch as a possible way of analyzing the operation of the nervous system,…” Calculating Instruments & Machines, D Hartree, 1950, Cambridge University Press.

  • Probably dropped due to the development of the silicon chip

    • simpler to build Boolean logic gates rather than neuron units.


Functional elements

n

z

k inputs

Functional elements.

Threshold n gate

k n

1

z

Excitation, “OR”

2

z

Excitation, “AND”


Ict orion computer

ICT Orion Computer

  • Used ‘Neuron’ logic - 1962


Learning

Learning !

Learning at neuron level =

Adjustment of which inputs are important

Conventional computers have no implicit learning ability


Computers using your brains

Spot the difference


Computers using your brains

+

Happy

+

Hungry

Threshold = 2


Computers using your brains

+

Happy

+

Hungry


Computers using your brains

+

Happy

+

hungry


Can we build useful systems with neurons

Can we build useful systems with neurons ?

Better tolerance to failure

Parallelism/use of threshold logic/distributed memory

Faster operation

Massive parallelism

Better access to uncertain information

Threshold logic/neurons

Where the inputs are uncertain

Threshold logic/neurons.

Where we want low power

Asynchronous systems

Adaptability

Use of weights and learning methods.


So what have we done with these

So what have we done with these ?

Cortex-1

28 Processor cards, each holding 128 hardware neurons.

Each with 1,000,000,000 weights.

16MHz.

PCI based card.


Computers using your brains

Complete Machine:

400,000,000 neuron evaluations per second

28,000 inputs

30 bits set on input

1,000,000 neurons.


Cortex 1 node

Cortex-1 node

5,120,000,000 neuron weights, 640 neurons.


Recognising addresses for the post office

Recognising Addresses for the Post Office


Recognising trademarks

Recognising trademarks


Text search engines

Text search engines

  • Tolerant to spelling errors.

  • Finds similar words to those supplied, for example chair, seat, bench.

  • Learns these similarities automatically from text.

  • Uses neural engine for document storage.

  • Estimated 400,000,000 documents searched per second.


Molecular databases

Molecular Databases

  • One of few systems that deal with the full 3D molecule


Computers using your brains

Query

Good matches

Bad Match


Computers using your brains

Thanks...

Aaron Turner

Mick Turner

Vicky Hodge

Julian Young

Anthony Moulds

Zyg Ulanowski

Ken Lees

Michael Weeks

Sujeewa Alwis

John Kennedy

David Lomas

and many others ….

(It’s Brains from Thunderbirds !)


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