Artificial Intelligence is being supplanted by "Artificial Brain," i.e. neuromorphic technologies. Yet there still a whopping gap that neuromorphic systems need to close before they will become a match for successful AI applications.
...but what is AI?
John McCarthy (1955, roots in Lithuania) suggested:
AI is a science and engineering aimed at creating
But what is an “intelligent machine”?
Turing test (1950): a machine can reason if:
It can imitate humans so that an average person
would assume he/she is dealing with another person
Jeff Hawkins: an intelligent system can correctly predict what’s
coming. Created hierarchical temporal memory (Numenta, Grok;
Dr. Kestas Kveraga, Harvard University: visual brain systems use
predictive feedback loops to speed up object recognition
Penti Haikonen (Nokia): intelligent systems can predict outcomes of
Imagination is required in order to simulate consequences of
choosing alternative actions. Igor Aleksander: computers with
Achievements of AI
IBM’s Deep Blue computer defeated the world champion
Garry Kasparov in 1997.
Used simple search algorithm
Triumph of pure AI: no inspiration from ther brain
Apple popularized it 25 years ago with Macintosh.
computer algorithms have trouble pronouncing irregular words.
Terrence Sejnowski, Salk professor, in 1987 created a neural net Nettalk that learned to pronounce
them in a way like human baby.
Showed advantages of brain-like algorithms.
Face detection algorithm is ubiquitous in photo cameras.
Based on the algorithm invented by MIT professor Paul Viola.
Used inspiration from visaul system of primate brain. Had many conversations with him
A harder problem, but several companies solve it successfully, including Dr. Algimantas Malickas’
company in Vilnius: Neurotechnology.
Relies on AI methods (machine learning, such as SVMs)
Achievements of AI, cnt’d
Nuance Dragon used in speech transcription, e.g. medicine.
Google Voice – suitable for transcribing 70-80% voice messages.
Weakness: break down in presence of accent, background noise.
IBM Watson won “Jeopardy” game (2011)
Largest project – Stanford Research Institute’s “personal assistant” (DARPA).
SRI system was commercialized and Apple bought it in 2011: SIRI app for iPhone.
Maintaining dialogue: No machine has passed the Turing test
Recognition of human actions and intentions:
Simple algorithms for human action recognition in security tapes
Numenta (Vicarious ?): uses brain- inspired principles for human action detection,
and other applications.
Intentiva: recognizes human intentions
Recognizing emotional and social states:
San Diego company Emotient recognizes emotions from facial expressions.
AI vs. Artificial Brain
- Principle of operation: logical rules created
- Works with standard digital computers
- Software is separated from hardware
- Memory is separate from CPU
- Advantage: the logics of operation is
straightforward to understand
- Limited tolerance for errors
- Needs a lot of energy for each
- Principle of operation: adaptive processes,
statistical learning rules
- Requires neuromorphic electronics
- Software is merged with hardware
- Each processor has its own memory
- Tolerance to errors and malfunctions,
ability to compensate them
- Asynchronous parallel operation needs
- Disadvantage: unexpected emergent
phenomena can arise
- e.g.: artificial “epilepsy”
When computers and AI surpass
algorithms in computers
UCSD robot child
Recognizes and reacts
to human emotions
All modern robots run on
EB project “Artificial curiosity”:
robots with internal motivation for
Robot training imitates development of a baby
(e.g., vision, audition, speech,
Global behavioral systems
Integration of information and
(amplification, WTA, sparse
Support a function
Processes info for supporting
elements of behavior
Choreography of behaviors,
Churchland & Sejnowski
most complex cells
Neuron’s job: integrate spikes coming from
other neurons via synapses and decide
whether to generate a spike
- Spikes are on or off
- Spike influence is determined by synaptic
weights and synchrony
- Excitation and inhibition are balanced
Buračas et al.
Neuronal circuits –
info processing units
Most famous neuronal circuit: macrocolumn of cortex,
Minicolumn: ~80 neurons
Macrocolumn = 100 minicolumns (0.5mm)
Blue Brain project: Dr. Henry Markram
EU project >1 bln Euros, Lausanne &
Heidelberg univ. (86 institutions, 10 years)
Evolved Machines: Dr. Paul Rhodes
Cortical “maps” & functional systems
Kveraga et al.
2 mln macrocolumns in human cortex
Brain and behavior
Universal neurochemistry of love: dopamine->testosterone->occytocin
Cartography of human brain (fMRI)
Speech generation Speech
The same parts of the brain are
activated when observing, dreaming
and imagining the same scene/object
Network for Introspection (yellow) Brain/mind reading:
SyNAPSE – the most ambitious program for building neuromorphic systems
(Systems of Neuromorphic Adaptive Plastic Scalable Electronics)
DARPA, HP, HRL, IBM and 5 universities (2009-now: ~$100M)
Neurosynaptic chips Neurosynaptic processors
Brain-like chips and systems
- 10 bln neurons
- 100 trln synapses
- 2 liters of volume
- 2009 cat brain-sized system
simulated on Blue Gene
- 2011 a chip with 256 nrn, 250K
- Not finished: 2013 multi-chip
neuroprocessor system (1M nrn on
systems are coming?
The brain is more effective energetically than computers by a factor of 1mln – 1bln
SyNAPSE DARPA chip:
256 neurons (digital)
262K programmable synapses
65K trainable synapses
NeuroGrid, Stenford Uv, 2009:
1 mln. Neurons
6 bln. Synapses
Real time(10 spikes/sek)
But not trainable...
Neuroinformatics Institute, ETH, Zurich,
neuromorphic trainable chip
Silicone physics used to simulate
Implements “liquid states” just like the
Qualcomm and The Brain Corporation
Secretive, however, Qualcomm is building
Schematics of the digital
Applications of neuromorphic systems
Applications for vision:
Several neuromorphic chips connected serially by analogy to human visual
system: can attend to interesting objects in the visual field.
Neuroscience is moving towards
neuroindustry: spiking camera
Machine learning/neural network research has been
pushing the limits of computer vision and AI, but to
date it has been mostly based on traditional von
Neumann type computer architectures
As the amount of data and complexity of the systems
grows, the need for neuromorphic solutions
becomes more apparent
Since neuromorphic systems as of 2014 are still very far
from being of practical value, interim solutions take
the shape of ASICs dedicated for machine learning
tasks (e.g. Nervana Systems, San Diego)
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