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Mind and Artificial Intelligence
Course Seminar CS 344
Aditya Somani
Prashant Pawar
Sanyam Goyal
Shashank
Introduction
An approach to simulate mind from AI
 Limitations in the path
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Step by step…
Symbolic System
 Neural Networks
 Neurons vs. Microtubules
 Quantum Physics
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Symbolic System
The philosophy behind is that human
intelligence is rational, and can be
represented by logical systems
incorporating truth maintenance.
 Formal system consisting of symbols.
 Used patterns and rules.
 Knowledge is represented in formal,
symbolic form.
 Eg . TheoremProver
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Symbolic System
Learning was lacking in symbol system.
 Model, based on the neuron-network in
the brain.
 Neural-networks
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Neural Networks
Model biological neural systems.
• Philosophy:
 Evolution and logical systems.
 Whatever works, works!
 Irrationality of mind.
• Make ever changing decisions about what rules to
follow.
•
Learning in Brain
Message passing.
• If the total input of neurotransmitters to a neuron
from other neuron exceeds some threshold, it fires an
action potential.
•
Synaptic terminals
Courtesy ::www.wikipedia.org
Learning in Brain
Synapses change size and strength with experience.
• When two connected neurons are firing at the same
time, the strength of the synapse between them
increases.
•
Modeling a Neuron
Can be modeled as a graph where cells are nodes and
synaptic connections are represented as weighted edges
between the nodes.
• Model net input to the jth cell as
1
•
net j   w jioi
w12
i
where oi is the output of each
neuron connected to j.
2
w16
w15
w13 w14
3
4
5
6
Courtesy ::www.wikipedia.org
Modeling a Neuron
• oi
is given by
0 if net j  T j
oj 
1 if neti  T j
where Tj is threshold for neuron j.
Neural Computation
Network is organized in layers made of nodes.
• Training examples are given in the form of an output
given a set of known input activations.
• Recognize cat by examples of cats.
•
Courtesy ::www.wikipedia.org
Learning in Backpropagational Neural
Networks
Supervised process with cycles of input examples.
• Occurs with forward activation flow of output and
backward error propagation.
• Gradient descent along the steepest vector of the
error surface towards a global minimum of error.
• Speed and momentum.
•
Neural Computation
Can be used to compute logical functions.
• Can simulate logical gates:
 AND: Let all wji be Tj /n, where n is the number of
inputs.
 OR: Let all wji be Tj
 NOT: Let threshold be 0, single input with a negative
weight.
• Can build any circuit and machines with such circuits.
•
Strengths & Weaknesses
Massive parallelism will allow computation efficiency.
• Behavior emerges from large number of simple units.
• Flexible long-term memory.
• Captures a variety of relations overcoming
assumptions of linearity, independence etc.
•
Require an adequate training dataset.
• Training can be quite slow.
• High error rate.
• Black box.
•
Neurons vs. Microtubules

New models for consciousness proposed in brain.

Can we achieve self aware computers (Singularity
)with neurons ?
Neurons vs. Microtubules

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The belief behind adopting neural networks was
that all the important action in the brain takes place
using neurons .
But what about consciousness , is It handled by
neurons ??
Studies of Paramecium
•
•
A number of studies have observed Paramecium
swimming and escaping from capillary tubes in
which they could turn around.
They take less and less time as we keep repeating
the experiment
Studies of Paramecium
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it is hard to explain how a one-celled animal like
paramecium with “NO neurons” can learn if we
say that neurons are responsible for learning in
multi-celled animals
The theory to explain this is that the nervous
system of the Paramecium (cytoskeleton ) is
responsible for doing all this computing .
Cytoskeleton


A collection of hollow fibers called microtubules
made out of a protein called tubulin.
The microtubules consist of molecules of tubulin
that can be in two different states depending on the
presence or absence of an electron, a nice digital
system.
Is Singularity Achievable
three reasons to say why singularity is not near :1)The mind is synchronized (But how??)
(i) how these ever-shifting, widely distributed groups of neurons in
sync?
Not answered yet!
this leads to doubts in taking neural-network
2)The brain is faster (so what ??)
In neural network, AI assumes that the neuron is analogous
to a single computer bit. But later it was found that each
neuron is supported by a additional circuitry., Which AI do
not take care.
3) Anesthesia (contradicts the assumed fact that
consciousness arises from firing neurons)
Microtubules to Quantum
Computing

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Penrose is among a number of researchers
proposing that – ”there is quantum computing
going on in the brain and quantum effects are
responsible for the flash of insight phenomenon.”
Penrose proposes that quantum computing is
happening in the microtubules of neurons , which is
responsible for consciousness
Mind and Quantum Physics
Penrose and Gödel's Theorem:
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Gödel's Incompleteness Theorem: with any set of mathematical
axioms, it is possible to produce a statement that is obviously true,
but could not be proved by means of the axiom.
Penrose's Argument(The Emperor’s New Mind ,1989):
The theorem showed that the brain had the ability to go beyond
what could be achieved by axioms or formal systems
Mind had some additional function that was not based on
algorithms
But, a computer is driven solely by algorithms
Brain could perform a function that no computer could perform
Called idea of non-computable functioning
Penrose: Brain and Quantum Physics
Not all human intelligence is algorithmic
 Physical laws are described by algorithm
 Not easy to come up with physical
properties or processes that are not
described by them
 How do then we explain the implied
superiority of human brain?
 Quantum Physics!

Quantum Theory: Coherence and
De-coherence
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Sufficiently isolated quanta : can be viewed as waves; waves of
probability(position, momentum).
Quanta subject to measurements, interaction with the
environment, wave characteristic lost, and a particle is found at a
specific point.(position waves).
Called collapse of the wave function
No cause-and-effect process
No system of algorithms can describe the choice (of position)for
the particle.
Seems to suit the search
But randomness
Not a promising basis for mathematical understanding.
Objective Reduction: The Idea
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Penrose's proposition of a new form of wave function collapse.
Relativity: mass causes curvature in space-time fabric
Space time fabric, continuous on relativistic scales but a network
on quantum scale
Reconciliation of relativity and quantum physics
Proposition each quantum superposition has it’s own curvature
Blisters on the spacetime fabric ~( 10 -35 meters, Planck scale)
Above Planck scale gravity comes into effect, system becomes
unstable
Collapse so as to choose just one of the possible values
Called Objective Reduction
Objective Reduction: The Time
Factor
Et = h/2pi; E = gravitational self-energy ,
t = time to collapse
 The greater the superposition the faster
is the OR
 For electron 10 million years, for a
kilogram object (10-37 seconds)
 For usual objects order relevant to neural
processing time.
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Objective reduction: the scope
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Choice of states neither random,
as are choices following measurement or
de-coherence, nor completely
algorithmically.
Orch OR model: Bringing Quantum
Physics to Brain
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Do we do Quantum Computing?
Microtubules may be supporting quantum processing: Shadows of the Mind (1994),
Penrose/ Hameroff
comprised of subunits of the protein, tubulins: contain hydrophobic (water repellent)
pockets
hydrophobic pockets from different tubulins within two nanometers of one another
close enough for the π electrons of the tubulins to become Quantum Entangled
Quantum Entanglement:
◦ “a state in which quantum particles can alter one another‘s properties instantaneously and at a
distance, in a way which would not be possible, if they were large scale objects obeying the laws of
classical as opposed to quantum physics”
◦ principle of non-locality
◦ the EPR experiment
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Hameroff's proposition: large numbers of the π electrons can become involved in a BoseEinstein condensate
Bose Einstein Condensate: These occur when large numbers of quantum particles
become locked in phase and exist as a single quantum object
happens usually at a very tiny scale but can be boosted to be a large scale influence in the
brain
Orch OR Model: making it big
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Gap junction:
◦ intercellular connection between cells
◦ allows various molecules and ions to pass freely between cells
◦ in addition to the synaptic connections
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proposition: condensates in microtubules in one neuron can link
with other neurons via gap junctions, using quantum tunneling
allows the Bose-Einstein Condensates to cross into other neurons
extend across a large area of the brain as a single quantum object
when condensates in the brain undergo an objective reduction of
their wave function, there is an instance of consciousness
brain gets access to a “non-computational process embedded in the
fundamental level of space time geometry”
The AHA moment!
Orch OR Model: Epilogue
proposition: Orch OR causes gamma
synchronization
 microtubules both influence and are influenced
by the conventional activity at the synapses
between neurons : Orchestrated OR
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Orch OR Model: Criticism and
Counter-Criticism
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Penrose's hypotheses: yet to be supported by experimental
evidence
Tegmark: microtubule quantum states would persist for only 10-34
seconds at brain temperatures
far too brief to be relevant to neural processing, rapid decoherence
Hameroff Retaliates:
◦ Tegmark’s model incorrect: 24 nanometers is too far
◦ Shielding by water molecules
◦ pumped into a coherent state by biochemical energy
◦ quantum error correction
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"Some people see that Penrose is obviously right. Some people see that
Penrose is obviously wrong.What's obvious then is that the issue is not
obvious" -- Donald R. Tveter
Consciousness and QP
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Earliest propositions: James Jeans(physicist), Alfred Lotka(biologist),
1920's
Two major schools of thought:
◦ Copenhagen Interpretation (Penrose et al.)
◦ Bohemian Interpretation (Bohm and party)
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Copenhagen Interpretation:
◦ The wave function is : "complete and literal description of the state of a
quantum system“
◦ Reality exits only when you measure it.
◦ Schrödinger's 'cat experiment‘
◦ Possible explanations:
 consciousness collapses the wave function and thereby creates reality
 whole universe must have existed originally as "potentia" in some transcendental
realm of quantum probabilities until self conscious beings evolved
Consciousness and QP
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Bohemian Interpretation:
◦ real existence of particles and field
◦ Implicate order: a vast ocean of energy on
which the physical, or explicate, world is just a
ripple
 already present in quantum physics: the quantum vacuum
or zero-point field
 perhaps something like the Addvait principle in the Indian
Philosophy
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Conclusion
- Mind offers a "model model" to pursue the goal for
human-like intelligence.
- However, the exact working of human mind is far
from trivial.
- Continuous research efforts should help us get closer
and closer to the knowledge of the actual principles
of the human brain
- We have already covered a long distance: Symbol
Systems -Quantum Physics
- long way to go!
References
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6.
http://www.wired.com/medtech/drugs/magazine/1604/ff_kurzweil_sb
Donald R. Tveter http://www.dontveter.com/caipfaq/systems.html
Consciousness, Causality, and Quantum Physics: David Pratt,
Journal of Scientific Exploration, 1998
http://www.en.wikipedia.org/wiki/Orch-OR
Orchestrated Objective Reduction of Quantum
Coherence in Brain Microtubules:The "Orch OR" Model
for Consciousness ,Robert Penrose and Stuart Hameroff 1996
http://www.cis.temple.edu/~vasilis/Courses/CS44/Handouts/neura
l.html and various other online resources.
Thanks!
Questions?