Ottawa Machine Learning Meetup slides for event March 20, 2017, A whirlwind tour of AI and Machine Learning: When to use which technique, by Robin Grosset, Chief Technology Officer, MindBridge.Ai.

1. We’ve built the world’s first commercial AI Auditor.
AI and Machine Learning:
The many different approaches

2. For Educational Purposes Only
Copyright 2017 MindBridge
certain content is copyright of the respective owners

3. About me
Robin Grosset
• Erstwhile Physicist from Scotland
• 20+ years in Business Analytics
• 16+ patents in the area of data processing, analytics and
security
• Founder, acquired twice: Cognos and IBM
• Appointed IBM Distinguished Engineer
• Watson Group in IBM, Chief Architect of Watson Analytics
• “InfoWorld's 2016 Technology of the Year” winner
Joined MindBridge as CTO in 2016, working on
revolutionizing financial anomaly detection with
Artificial Intelligence

4. Agenda, all about AI
• Fun background
• Promising Future
• Pitfalls and Hype
• Many Tribes of AI
• Breaking it down
• Discussion

5. Pre-”Artificial Intelligence” Era
• Talos, Greek Mythology a man
made of bronze, 400 BC
• “The Engine” in Gullivers Travels,
1726
• “The Brain” in Escape! by Isaac
Asimov, 1945
• Turing Test, 1950

6. “Artificial Intelligence”: 1956
A.I. term was coined during a conference at
Dartmouth College by 5 leading researchers
John McCarthy Marvin Minsky
Herbert Simon
Arthur Samuel
Allen Newell
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7. The Cold War, 1954-1966
• ALPAC (Automated Language Processing Advisory Committee)
• US Govt fund machine translation, $20M US
• Russian <-> English
• Much optimism
many predictions of imminent breakthroughs
• Damming report in 1966 ended the work.
• It half worked, it was very bad at
disambiguation
“out of sight, out of mind” = “blind idiot”
The first “AI Winter”

8. Perceptron: 1958
1st Artificial Neural Network, Computer Vision
New York Times July 8th 1958
NEW NAVY DEVICE LEARNS BY DOING;
Psychologist Shows Embryo of Computer
Designed to Read and Grow Wiser
Frank Rosenblatt created controversy around the
abilities of perceptron: space exploration with
machines
20x20 photocell
IEEE
Award
f(x)
x1
x2
x3
Perceptron = Single Layer Neural Net
Book by Minsky and Papert
1970. “Perceptron” = no good
2nd “AI Winter”

9. Symbolic AI: 1983
• represents knowledge in human-readable form
• the dominant form of AI research until late 1980s
• Lisp Machines
• Most successful form is called an ‘Expert System’ ,
comprising inference engine and knowledge base
• Ontology Classification
• Fuzzy Logic
• Hypothetical Reasoning
“Lisp Machine”
Symbolics 3640
Workstation
c. 1984
Problems:
• Maintenance, they became brittle
• Could not learn or improve itself, needs coders
• No reusability of expertise
• Slow progress -> investment moved away
3rd “AI Winter”

10. Meanwhile
Back Propagation :1986
• Geoffrey Hinton, University ofToronto
'Canadian Mafia’ in the Deep Learning Conspiracy
• “Smart scientists, go there to see their careers end.”
• Hinton’s lab was seen as a renegade project, more the
stuff of science fiction than vocation.
f(x)
x2
x3
Hidden Layer
x1
Error back propagation
Machine Learns…!!!!

11. 2012 : The new AI spring

12. Raw Pixels
Game State
Reward
(Score)
Game
Controls
2014: DeepMind Q Learner
Demis Hassabis
DeepMind Co-Founder

13. Playing Video Games
The Demo that got DeepMind bought by Google ($400M)
Link to YouTube:
https://www.youtube.com/watch?v=9jnVQDnGQfU

14. DIY! Atari Deep Q Learner by DeepMind
Requires
• Ubuntu Linux
• Nvidia GTX 9XX Series
4GB Video Memory
• Nvidia CUDA Install…
• Torch 7 etc..
https://github.com/kuz/DeepMind-Atari-Deep-Q-Learner

15. Google’s Cat Detector : Cost
2000 x 8 Core Machines for 1 week, $182K
Le at al Building high level features using large scale unsupervised learning ICML 2012
http://icml.cc/2012/papers/73.pdf
Best results
ever on
ImageNet
classification

16. Some of the Many ‘Tribes’ of AI
• Symbolists
• knowledge composition, rules, inverse deduction (inference), RDF, OWL.
• Connectionist
• multi-layer perceptrons with weights, backpropagation
• Analogizers
• Similarity, Kernel machines, Clustering
• Evolutionaries
• structure discovery, genetic algorithms
• Bayesians
• Uncertainty, Probabilistic inference, Monte-Carlo method (spam filter)
• Tree Huggers
• Decision Tree based models, Random Forest, + Gradient Boosting
And there are more…

17. The most common Machine Learning Algorithm
K-Means Clustering
Algorithms can learn to categorize by themselves
Algorithms tend to have an objective (cost function)
Iterates until done (cost is minimized)
K-Means Clustering
repeat {
for each point
find nearest centroid
assign to cluster
for each centroid
recalculate location
} until no point changes cluster

18. Genetic Algorithms
• NEAT (Neuro Evolution of Augmenting Topologies)
Professor Kenneth O Stanley
“Novelty Search” and how trying to move
straight towards the objective loses.
Why Greatness Cannot be Planned
“The myth of the objective”
http://picbreeder.org

19. Mar.io by Seth Bling
Requires
• Windows PC
• BizHawk emulator
• Mario World Rom
• 1 LUA file
https://github.com/pakoito/MarI-O

20. Mar.io by Seth Bling No special hardware, unlike DeepMind Q Learner
• YouTube link :
• https://www.youtube.com/watch?v=qv6UVOQ0F44

21. Tree Hugging: XGBoost
“Winningest” Algorithm on Kaggle
Good with structured data.
Why use it
• Execution Speed
• Model Performance
Random Forest + Boosting = Ensemble Learning

22. Deep Learning:
Downside: Lots of compute is required.
Tends to create Black Boxes and the outcomes are hard to
explain, and often not understood even by experts
Can be used as a ‘focusing function’ but not an explanation.
Deep Learning
Data
Insight!
Upside: No need to do feature extraction as
the Deep Learner will figure out for itself what
is important

23. Human Cognitive Augmentation
using Artificial Intelligence
• Human centric AI
• Help humans be more
effective, more focused.
• Free them from boring
tasks of looking through
millions of data points
looking for needles in
haystacks
• Allow focus on higher
value tasks that require
human intellect
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