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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. Step by step…. Symbolic System Neural Networks Neurons vs. Microtubules Quantum Physics.

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mind and artificial intelligence
Mind and Artificial Intelligence

Course Seminar CS 344

AdityaSomani

PrashantPawar

SanyamGoyal

Shashank

introduction
Introduction
  • An approach to simulate mind from AI
  • Limitations in the path
step by step
Step by step…
  • Symbolic System
  • Neural Networks
  • Neurons vs. Microtubules
  • Quantum Physics
symbolic system
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
symbolic system1
Symbolic System
  • Learning was lacking in symbol system.
  • Model, based on the neuron-network in the brain.
  • Neural-networks
neural networks

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

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 brain1

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

1

w12

w16

w15

w14

w13

2

3

4

5

6

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

where oiis the output of each

neuron connected to j.

Courtesy ::www.wikipedia.org

modeling a neuron1

Modeling a Neuron

oi is given by

where Tjis threshold for neuron j.

neural computation

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

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 computation1

Neural Computation

Can be used to compute logical functions.

Can simulate logical gates:

AND: Let all wjibe Tj/n, where n is the number of inputs.

OR: Let all wjibe Tj

NOT: Let threshold be 0, single input with a negative weight.

Can build any circuit and machines with such circuits.

strengths weaknesses

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
Neurons vs. Microtubules
  • New models for consciousness proposed in brain.
  • Can we achieve self aware computers (Singularity )with neurons ?
neurons vs microtubules1
Neurons vs. Microtubules
  • 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
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 paramecium1
Studies of Paramecium
  • 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
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
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
Microtubules to Quantum Computing
  • 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
penrose and g del s theorem
Penrose and Gödel's Theorem:
  • 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
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
Quantum Theory: Coherence and De-coherence
  • 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
Objective Reduction: The Idea
  • 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
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.
objective reduction the scope
Objective reduction: the scope
  • Choice of states neither random, as are choices following measurement or de-coherence, nor completely algorithmically.
orch or model bringing quantum physics to brain
Orch OR model: Bringing Quantum Physics to Brain
  • 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
  • Hameroff's proposition: large numbers of the π electrons can become involved in a Bose-Einstein 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
Orch OR Model: making it big
  • Gap junction:
    • intercellular connection between cells
    • allows various molecules and ions to pass freely between cells
    • in addition to the synaptic connections
  • 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
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
orch or model criticism and counter criticism
Orch OR Model: Criticism and Counter-Criticism
  • 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
  • "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
Consciousness and QP
  • 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)
  • 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 qp1
Consciousness and QP
  • 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     -       
conclusion
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
References
  • http://www.wired.com/medtech/drugs/magazine/16-04/ff_kurzweil_sb
  • Donald R. Tveterhttp://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/neural.html and various other online resources.