Title: Computers - Using your Brains
1Computers - Using your Brains
- Jim Austin
- Professor of Neural Computation
2(No Transcript)
3(No Transcript)
4Pentium III
5- 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!
6What 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
7The good and the bad
8Why are computers so restricted ?
ACE
9Leo - for stock control
10Colossus - for breaking codes
11Pegusus - for scientific work
12Neurons verses Gates
Input 1
Output
Input 2
NAND Gate
Boolean Logic - both inputs OK, output not OK
13Gates - NAND
ALL inputs to be OK for output to be NOT OK
Output
Input 1
Input 2
14(No Transcript)
15Evolution ?
Should have picked a NAND gate for the brain...
16Neuron
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
17Neuron
At least one OK for output to be OK
At least three OKs for output to be OK
18Weights
- Can also alter connections/importance of inputs
- using the weights on the inputs
1
0
1
1
3.5
0.5
1
1
19Why 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.
20Functional elements.
Threshold n gate k ?n
1
z
Excitation, OR
2
z
Excitation, AND
21ICT Orion Computer
22(No Transcript)
23Learning !
- Learning at neuron level
- Adjustment of which inputs are important
- Conventional computers have no implicit learning
ability -
24Spot the difference
25(No Transcript)
26(No Transcript)
27(No Transcript)
28(No Transcript)
29Can 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.
30So 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.
31Complete Machine 400,000,000 neuron
evaluations per second 28,000 inputs 30 bits set
on input 1,000,000 neurons.
32Cortex-1 node
5,120,000,000 neuron weights, 640 neurons.
33Recognising Addresses for the Post Office
34Recognising trademarks
35Text 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.
36Molecular Databases
- One of few systems that deal with the full 3D
molecule
37(No Transcript)
38Query
Good matches
Bad Match
39Thanks...
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 .
(Its Brains from Thunderbirds !)