Science and engineering are complementary disciplines, and in their distinctions, we see promise for an expanded role for machine learning (ML). The goal of science is discovery - identifying patterns of evidence that point to fundamental truths. Engineering uses this knowledge to build systems and solutions to problems. Science discovers the truth, engineering uses the understanding of truth to create. Within the #ModernAI landscape, machine learning has become the gold-standard for pattern discovery. Applications ranging from the identification of cat images on YouTube to autonomous vehicle control have captured the imagination. Less heralded are opportunities to apply ML to systems that understand what they have discovered. That’s the next frontier. This webinar will present an overview of ML fundamentals and then show examples and a framework to identify opportunities for ML-enabled understanding.

1. MACHINE LEARNING – FROM DISCOVERY TO UNDERSTANDING
Adrian Bowles, PhD
Founder, STORM Insights, Inc.
Lead Analyst, Aragon Research
info@storminsights.com
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
APRIL 13, 2017

2. Basic Concepts
Learning, Reasoning, UnderstandingRecognition vs Understanding
Discovery vs Search
Contrasting AI Approaches
ML & DL Basics
Trends
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
AGENDA

3. Learn
Plan Reason
Understand
Model
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
BASIC CONCEPTS
Plan (v)
Identify a goal/desired state
and a set of steps/activities
to reach that state.
Reason (v)
An evidence-based process for
determining the truth or
probability of a conclusion.
Deductive - Top down reduction,
Results are Certain
Inductive - Bottom up generalizations,
creating hypotheses with confidence
levels/probability
Abductive - Bottom up, probabalistic
development
of theories from observations
Understand
Awareness of the
meaning of data.
Learn
To acquire understanding
of data.

4. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
RECOGNIZING CONCEPTS - DISCOVERY <> UNDERSTANDING
Courtesy of LoopAI Labs.

5. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
Hearing (audioception)
~12,000 outer hair cells/ear
~3,500 inner hair cells
Vision (ophthalmoception)
Photoreceptors - Per Eye
~120,000,000 rod cells
(triggered by single photon)
~6,000,000 cone cells
(require more photons to trigger)
~ 60,000 photosensitive
ganglion cells
Touch (tactioception)
Thermoreceptors, mechanoreceptors,
chemoreceptors and nociceptors for touch, pressure, pain,
temperature, vibration
Smell (olfacoception)
Chemoreception
Taste (gustaoception)
Chemoreception
Human Cognition
~100,000,000,000 (100B) Neurons
~100-500,000,000,000,000 (100-500T) Synapses
NEUROSYNAPTIC PROBLEM SOLVING SCOPE: PERCEPTION VS COGNITION
Learn
ModelReason
Understand
Plan

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ASSOCIATION IS NOT UNDERSTANDING
It is possible, if not likely, that we will soon build a deep learning
system that knows everything but understands nothing.

7. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
CONTRASTING AI APPROACHES
Knowledge-Centric Data-Centric/
Deep Learning
Representation Learning
Use ML to discover the representation
Lots of Up-Front Effort
Developing the Algorithms
or Rules
Should have
Complete Transparency
Identify the Categories
Let the Data Drive the Process
Can Become a Black Box
ATTRIBUTES
APPROACH Use ML to discover the mappingUse experts to create the
representation and mapping

8. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
TAXONOMIES REPRESENT HIERARCHICAL CONCEPTS - YOUR REPRESENTATION MATTERS
Nature
Animal
Mineral Vegetable
Aves
Amphibians
Fish
Insects
Mammals
Primates
Brute Ferae
Haplorhini
Hominini
Humans(Homo) Chimps(Pan)
Bob
LOWABSTRACTIONHIGH
You Are Here!

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PROXIMITY/DISTANCE ALGORITHMS
Mapped with vectors,
proximity algorithm
based on purpose.
Mapping for autocorrect/complete vs Mapping for meaning
Boy
Bay
Map
Mop
Man
Nay May
Mope
Buy
Hop Hope
Boy
Bay
Map
Mop
Man
Nay
May
Mope
BuyHop
HopeSimilar structure ->
similar meaning in
vision, not always
in language.
Memory-Based
Reasoning

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AI LEARNING APPROACHES - HEAVY LIFTING FOCUS SHIFTS OVER TIME
ALGORITHMS
&
RULES
DATA

11. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
MACHINE LEARNING FUNDAMENTALS
Natural learning approaches vary. Some can be simulated with code, for
example mechanical theorem proving in formal logic.
However, a true machine learning system must improve its performance
based on experience with data, not by reprogramming.
REFLECTIONINFERENCEDEDUCTION
Learning
REASONING

12. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
KEY APPROACHES TO MACHINE LEARNING
REINFORCEMENT
UNSUPERVISED
SUPERVISED
The system is taught to detect or match patterns
based on training data. Learning by example.
The system learns/develops strategies based on
performance feedback.
An unsupervised learning system discovers patterns
based on experience.

13. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
MACHINE LEARNING FUNDAMENTALS
SUPERVISED
The system is taught to detect or match patterns
based on training data. Learning by example.
Good for: Applications in which there is a large body of
experience/evidence that can be codified into a training
data set with question-answer pairs.
Example: Medical diagnostics, matching symptoms to
conditions.

14. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
MACHINE LEARNING FUNDAMENTALS
REINFORCEMENT
The system learns/develops strategies based on
performance feedback.
Good for: Applications in which there are too many
variables to code, but where one can recognize good/
bad behavior and reinforce/extinguish it.
Example: A guidance system for an autonomous
helicopter.

15. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
MACHINE LEARNING FUNDAMENTALS
UNSUPERVISED An unsupervised learning system discovers patterns
based on experience.
Good for: Applications where detecting a change in
behavior may be meaningful.
Example: Network intrusion detection.

16. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
MACHINE LEARNING FUNDAMENTALS
DEEP
LEARNING
Deep learning refers to a biologically-inspired approach to machine
learning that leverages multiple layers or collections of simple
processing units - analogous to neurosynaptic elements - that
collaborate to solve complex problems at multiple levels of
abstraction.
Modern neural networks can support supervised, reinforcement, or
unsupervised learning systems.
In general, deep learning solutions require a high degree of parallelism,
which may be implemented in hardware and/or software.

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MACHINE LEARNING - ARTIFICIAL NEURAL NETS
Input
Output
Highly Connected
Neural Processors
A digital representation of the state
of the input domain.
Scalars, Vectors, Equations…

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MACHINE LEARNING - ARTIFICIAL NEURAL NETS
Input
Output
Preserved State

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SEQUENTIAL PROCESSING
Concept 1
Concept 2
Concept 3
Concept 4
Aggregate

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DEEP LEARNING
Visible Layer
Hidden Layer
Hidden Layer
Output Layer
Hidden Layer
Input: Observable Variables
HIGHABSTRACTIONLOW
Output
Pixels
Depth
of the
Model
Edges
Object
Shapes/Parts
Object Class
Brightness/
Contrast
Geometry
Rules
Features
to
Extract
Methods

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DEEP LEARNING
Visible Layer
Hidden Layer
Hidden Layer
Output Layer
Hidden Layer
Input: Observable Variables
HIGHABSTRACTIONLOW
Output
Features
to
Extract
Gender
Regional Origin
Emotional State

22. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
LOOKING FOR FEATURES: WHICH ONE IS NOT LIKE THE OTHERS?
Edges are easy
Objects are easy
What are the
distinguishing features?

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LOOKING FOR FEATURES: WHICH ONE IS NOT LIKE THE OTHERS?
Edges are easy
Objects are easy
What are the
distinguishing features?
Context is King for Discovery

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WHAT CAN A DL SYSTEM “LEARN” FROM THIS PICTURE?

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TRUST & TRANSPARENCY
The Dark Secret at the Heart of AI
Will Knight, MIT Technology Review, April 11, 2017

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HOW IMPORTANT IS IT TO BE ABLE TO EXPLAIN REASONING?

27. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
AI LEARNING TRENDS
DATA
More Data + Faster HW make
Deep Learning Practical
Deep Learning Success With Recognition
Spurs Investment
ALGORITHMS
&
RULES
Caution for Applications Where
Transparency is Critical
Investment Leads to Investigation
Broaden the Scope of Applications
New “Explainability” Research Emerges
Hybrid Solutions to Augment Intelligence
Will Thrive for Critical Applications

28. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
RESOURCES
The Dark Secret at the Heart of AI
Will Knight, MIT Technology Review, April 11, 2017
Deep Learning
Goodfellow, Bengio, and Courville, MIT Press, 2016.

29. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
KEEP IN TOUCH
adrian@storminsights.com
Twitter @ajbowles
Skype ajbowles
Upcoming 2017 Webinar Dates & Topics
May 11 Streaming Analytics for IoT-Oriented Applications
June 8 Machine Learning Case Studies
Insurance, Healthcare, Pharma
July 13 Advances in Natural Language Processing I: Understanding