AI has gone through a number of mini-boom-bust periods. The current one may be short lived as well but I have reasons to think AI is finally making some sustained progress that will see its way into mainstream technology.

1. History of AI and
future possibilities
AI is hot again
John Mathon
MV Speaker Series 2015-03-03

2. 80s - Genesis
 When I started my career at MIT AI was thought to be on the verge of discovering
the secret of human intelligence.
 My MIT programming contest victory, mini-max rethought
 The initial successes were things like blocks world, some initial neural network
work, Mathematica
 The idea was that if you combined enough smart things like mathematica and
blocks world you would eventually get intelligence
 Neural networks didn’t go very far. Lots of problems getting the networks to
converge.
 I saw through this and abandoned the study quickly. I called it the chicken
problem. The problem of deeplearning had not really been attempted
 Marvin Minsky called the bluff publicly and the industry collapsed quickly after that

3. 90s – Rule based systems
 In the late 80s a revitalization of AI occurred and a new
heyday based on rule based systems that allowed you to
operate on knowledge systems. Primary among these was
KEE (Knowledge Engineering Environment) from
Intellicorp
 This ultimately failed again and seemed to put AI into a
deep funk

4. 00s - Statistics
 Machine Learning - Brute force statistical approach
 This is a statistical approach to learning
 Find an “algorithm” or “model” or “formula” which mimics
the data close enough to get paid, >10 different statistical
techniques in use including neural nets
 Pass lots of “labeled” data through the system
 Watson
 Like Mathematica a hard-core machine learning approach

5. 2010 Convoluted Neural Nets
Neural Net called Convoluted Neural Network(CNN) behind the
scenes advancements
 Invariance built-in (Convolution)
 Back propagation and various gradient descent algorithms
 2 layers at a time “learn” abstraction and filter – filter layer reduces
computation
 Brain is composed of layers and vision system seems to be similar
in learning abstractions in layers
 First 2 layers learn lip, ear, nose, brow, tire, window, …
 Next 2 layers combinations make face, truck
 Next 2 Layers – Female, Male, Mustang, ..
 Large data sets of “labeled data” is same problem as Machine
Learning

6. Deep Belief Networks
CNN +
 The holy grail of learning is not having to use “labeled” datasets. DBN
gets around this by using a Markov probabilistic approach to neuron
evolution. No “labeled” data initially makes it much easier to use.
 After initial training we pass “labeled” data through to reinforce
learned pathways and do better selection of best abstractions.
 Training individual layers is much faster and results in better
abstractions
 Recurrent (Feed lowest layer back into top)
 Unlimited layers
 Addition of Memory (New Neuron type) – secret sauce
 Made cursive much better

7. Achievements of DeepLearning
 The best at cursive recognition (DBN have beaten all others)
 The best at text recognition
 The best at object recognition (54% to 64%, google+)
 The best at facial recognition (facebook 97% accurate better
than FBI, 9-layer DBN)
 The best at voice recognition (100% penetration DBN)
 Used in Watson now
 Possibly other purposes at Google
 DeepMind acquisition gives Google 2 of the 4 geniuses in the
field, facebook 1 and only 1 left in academia

8. Why is the AI problem hard?
 Brain is remarkable at invariance: translation, scale, distortion,
blocking, color, quality, background
 Very hard to replicate anything close to flexibility, uses many clues
 Brain applies abstractions across different disciplines
 There is almost never an attempt to learn more than one “thing”
because different input tend to create instability and less precise
recognition
 Brain creates abstractions upon abstractions in a stable way
across dozens/hundreds of layers or levels
 With little problems with back propagation problems or unlearning
 Have not been able to go beyond a few layers

9. Fears of DeepLearning
 Elon Musk, Stephan Hawking, Bill Gates
 All fear DeepLearning from DeepMind
 Elon says what he knows/has seen of DeepLearning gives him
fear
 Google did establish a “ethics panel” to make sure to use AI
safely and was key to DeepMind agreeing to be bought by
Google
 It is a love/hate relationship. We fear AI being smarter and
malevolent taking our jobs away or killing like we fear aliens
instinctively. We fear losing our “special” status and place. We
also want to like and be fair to any sentient creatures. There is a
natural curiosity that seems inevitably will get us there.
 Fear of accidentally creating intelligence, some simple thing
replicates like a virus in a computer and takes over. In Hyperion
(Dan Simmons) an 88-byte code fragment that replicates and
evolves eventually becomes AI.
 Isaac Asimov established the 3 rules of Robot AI… Cannot harm a
human … How would you put such a rule in a CNN based AI?

10. Why is AI finding interest again?
 Social is very much about pictures, voice, video, etc… which is
greatly enhanced by image and facial recognition
 Making things intelligent or at least a little intelligent, for
instance recognizing voice better – siri, skyvi makes things
easier to use, provide much greater value.
 Self-driving cars recognizing signs, people, etc…
 Smarter is better even a little bit smarter … see google
 BIG DATA
 SkyNetworks, H2O.ai, 16z, Azure Machine Learning Studio,
Google…

11. Can DeepLearning become “really
intelligent?”
Required
1) Plasticity
2) Many levels of abstraction
3) Planning
4) Memory in general
5) Self-perception
6) Creativity
Unsure but associated
7) Common sense
8) Emotions
9) Self-preservation
10) Qualia
11) ExperientialTagged Memory
12) Consciousness
CNN lack critical features of “intelligence”, “sentience” or “consciousness”

12. Quantum Computers could be a
pathway to AI
 Performance could be log the performance of traditional
computers, i.e instead of a million computers 6 would be
enough, instead of a trillion computers 12 would be enough.
For some problems simply x ^ (n/2) improvement so instead of
10^60 operations, 10^30 operations.
 Many of the things we think a brain does seem to be the kinds
of things quantum computers would be good at. Pattern
recognition, searching databases, optimization, i.e. neural
weighting optimization
 The applications of Quantum computers would be in
optimization, recognition problems and security applications.

13. Quantum Mechanics
Richard Feynman: If you think you understand quantum mechanics, you don't understand quantum mechanics.
 Wave/Particle Duality and the Measurement Problem:
 A particle acts like a self-interfering wave which when you look at it collapses from being
anywhere in space to one location breaking the speed of light.
 12 current theories of “why” collapse seems to happen including the newest one called quantum
darwinism in which space itself has memory and evolution mimicking genetic evolution. Many
Worlds, Copenhagen,QuantumGravity
 Quantum foam : virtual particles the infinite possibilities, Higgs Particle, non-zero vacuum state
 Quantum Superposition: Particles when not being observed seem to occupy ALL possible
states and take all possible paths simultaneously. Yet when “measured” they choose with
probability varying by the energy consumed in the whole process. All paths are possible and
appear but the least energy path is predominant. This seems simple but how does NATURE
figure this out? It’s nontrivial.
 Calculate the solution to a 3 body problem in quantum mechanics is nearly impossible nearly an
infinite number of possible loops a task that takes a year of supercomputer time … yet nature
does this 10^40 times a second for 10^75 particles Many worlds is popular because nature
doesn’t compute anything, it just splits for every possible choice and the highest probability is
the universe we happen to statistically find ourselves in. Also eliminates collapse problems but
introduces the problem of an infinite number of universes created every second.

14. Quantum Computers are way
different than regular computers
 Basic Quantum Operations:
 Hadamard Operation: Put a qubit into multiple states with
equal probability
 Result a true random number generator
 Do Hadamard operation twice get a NOT operation
 Schor Algorithm – optimization, factorization
 A particle traverses a puzzle we set up
 Grovers algorithm – database search
 QCL – Quantum computing language (D-Wave)
In a quantum computer we set up the experiment, then run it,
the answer is whatever nature does.

15. How practical are Quantum
Computers?
 D-Wave-2 512 qubit used by Google to demonstrate
superior performance to any existing computer (up to 5x
or more) … However not suitable to all problems
 D-Wave-3 releases 1152 qubit computer in March,
doubling capacity every year and improving
entanglement.
 2 ^ 1152 patterns of 1152 bits in superposition
simultaneously.
 NOT doubling computing SQUARING computing capability!
 D-Wave 6 might only by 8000 qubit computer but more
powerful than all computers on the earth existent today

16. Brain is a quantum computer?
 Roger Penrose believes this. If he believes it then it is worth looking at.
 Evidence that nature “Uses” quantum mechanics to solve difficult problems
 Taking a single photon from the sun to build plants
 For Birds to sense direction
 In the eyes, ears, nose creating ultra-sensitive senses
 If nature uses it for these functions it could also use it for many other functions
 We see that quantum mechanics is good at solving problems a brain has to do: pattern
recognition, efficient operation, searching for information, neuron weighting calculations
 Brain science has not discovered how the brain stores all the experiential information we
consume, nor the process of pattern recognition, reasoning or incredibly complex weighting
optimization characteristic of learning
 Recent evidence of quantum effects in microtubules of dendrites of nerves. Recent evidence
of molecular process of phosphorylating microtubules to encode memories.

17. If Brain uses QM we are far away
from “intelligence”
 The human brain could be composed of up to 1 trillion
quantum computers or a trillion trillion qubit quantum
computer or some combination
 Compared to a 1152 qubit D-wave at $10 million quite a deal
 We know that Elephants with the largest neural structure
compared to humans (1/2 the size) are not as intelligent as
humans.
 The largest CNN we have built is 650,000 virtual neurons,
which is between 1/10,000 -> 1/1,000,000 of the brain

18. AI still probably a distance away
but extremely useful
 AI is extremely useful and getting better
 They can and will be able to do some things better than humans
 They will be able to make our lives easier
 They will in some cases remove people’s jobs
 They make programs more intuitive, helpful, efficient
 Fears of AI overtaking humans are overblown
 They make stupid errors, they have no common sense
 They will not be gaining consciousness soon
 They show no creativity
 They show no planning ability
 They show no ability to learn multiple disciplines

19. How Can WSO2 benefit
 Machine Learning Adapter and Connectivity
 Provide easy interfacing to machine learning systems
 Wizard-like simplicity to setup bigdata systems
 More and more in bigdata usage
 D-wave adapters to funnel data and programming to and
from d-waves to solve problems
 WSO2 Deep Learning Server
 Configurable Layers and parameters
 Autoscaling

20. Roger Penrose and Consciousness
 Roger Penrose invented Spinors andTwistor Space.
 He was slow learner. His high school teacher had to give him
two classes to finish tests. He worked everything out from
basics. He was extremely visual.

21. He did what physicists told him he
shouldn’t
Twistor Space is discrete NOT continuous.
When you calculate from one vertex to another you get a result with
different space-time coordinates. Intermediate space-time coordinates
don’t exist.
What seems like “fuzzy spooky foam probabilistic action at a distance ” in
space-time appears as simply non-existant points in aTwistor lattice.
Collapse is not a problem (Measurement problem) because the motion of
particles is simply moving from vertex to vertex with different probabilities.
 All Physicists say: “You can’t possibly imagine the
quantum world so give up. Just study the math, forget
trying to visualize it.”
 Roger invented Spinors to visualize spinning particles
involved “making real” imaginary numbers.
 Imaginary numbers are central to quantum physics. They
appear everywhere. So, Roger createdTwistor space: 5
dimensions, 2 complex

22. Quantum Consciousness
Orchestrated Objective Reduction
 Strong Evidence of Quantum processes in nature and in microtubules of dendrites
in brains
 Penrose calculates decoherence time at approx 40 times/second which
corresponds to brain waves – otherwise completely unmotivated
 UsingTuring machines and some Godel theorems Penrose shows the human brain
does things no computer can EVER DO.
 He says the human brain and consciousness is not only a quantum computer but
that there are capabilities of this quantum computer that exceed even what we
know about quantum mechanics. I.e. New physics.
 TheTheory says that human consciousness is in the quantum fuzz similar to
Quantum Darwinism and the brain is a transducer. Evidence that something
makes decisions before the human brain is even aware of it.
 Which means that our consciousness may transcend our bodies