Machine Learning (ML) approaches and their supporting technologies can generally be classified as Supervised vs Unsupervised, and within those categories as General or Deep Learning (with Reinforcement Learning as a special case within Supervised Learning). The approaches may be based on biological models or statistical models, or hybrids. As demand for machine learning functionality in consumer and enterprise applications increases, it becomes important to have a framework for comparing ML products and services. This webinar will present an overview of the machine learning landscape, from platform providers to point solutions in each major ML category, and help participants understand their options for experimentation and deployment of ML-based applications.

1. MARCH 8, 2018
Machine Learning Update
An Overview of Technology Maturity and Product Vendors
Adrian J Bowles, PhD
Founder, STORM Insights, Inc.
Lead Analyst, AI, Aragon Research
info@storminsights.com

2. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
FIRST, DEFINE TERMS
Artificial Intelligence
Machine Learning
Deep Learning
Data Science

3. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
Machine
Learning
Deep
Learning
Artificial
Intelligence
Data
Science
Each discipline has algorithms and models.
FIRST, DEFINE TERMS

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#MODERN AI: ARTIFICIAL, AUTOMATED, AUGMENTED, AMPLIFIED…INTELLIGENCE
PERCEPTION
UNDERSTANDING
LEARNING
Big
Data
Classic
AI
Deep
Learning

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Systems
Controls
LearnReason
Understand
Model
Data Mgmt
Human
Machine
Input Output
Gestures
Emotions
Language
Narrative Generation
Visualization
Reports
Haptics
Sensors
(IOT)
Systems
Controls
ML IN THE MODERN AI LANDSCAPE

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Human Input
Gestures
Language
Context
Learn
Reason
Understand
Model
Data Mgmt
Detected by
Human Senses
Derived
ImagesSee
Hear
Touch
Smell
Taste
Sounds
Objects
Emotions
Meaning
Concepts
Intent
Emotions Meaning
Concepts Intent
Context
ML IN THE MODERN AI LANDSCAPE

7. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
FUNDAMENTAL DESIGN CHOICE: SYMBOLS VS STATISTICS
Symbolic Logic
Representations
Reasoning
Concepts
Statistical Models
Mechanical Theorem Proving

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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

9. MACHINE LEARNING FOCUS CONTINUES TO EVOLVE
Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
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

10. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
RECOGNIZING CONCEPTS - DISCOVERY <> UNDERSTANDING
Courtesy of
LoopAI Labs.

11. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
Supervised Unsupervised
Deep
General
Reinforcement
Learning by example,
using training data. Strategies based
on performance
feedback.
Discovers patterns based
on experience with data.
Biologically-inspired ML approach.
Leverages simple processing units - analogous to neurosynaptic elements organized in
layers that collaborate to solve complex problems.
ML MATURING RAPIDLY - ALREADY WELL OVER THE USABILITY THRESHOLD

12. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
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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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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LIMITATIONS: HOW IMPORTANT IS IT TO BE ABLE TO EXPLAIN REASONING?

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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

16. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
WHAT CAN A DL SYSTEM “LEARN” FROM THIS PICTURE?

17. THE MACHINE LEARNING LANDSCAPE: CAPSULES
Transforming Auto-encoders
G. E. Hinton, A. Krizhevsky & S. D. Wang Department of Computer Science, University of Toronto
Abstract. The artificial neural networks that are used to recognize shapes typically use one or more layers of learned feature detectors
that produce scalar outputs. By contrast, the computer vision community uses complicated, hand-engineered features, like SIFT [6],
that produce a whole vector of outputs including an explicit representation of the pose of the feature. We show how neural
networks can be used to learn features that output a whole vector of instantiation parameters and we argue that this
is a much more promising way of dealing with variations in position, orientation, scale and lighting than the methods currently
employed in the neural networks community. It is also more promising than the hand- engineered features currently used in computer
vision because it provides an efficient way of adapting the features to the domain.
This paper argues that convolutional neural networks are misguided in what they are trying to achieve. Instead of aiming for
viewpoint invariance in the activities of “neurons” that use a single scalar output to summarize the activities of a
local pool of replicated feature detectors, artificial neural networks should use local “capsules” that perform
some quite complicated internal computations on their inputs and then encapsulate the results of these
computations into a small vector of highly informative outputs.

18. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
Maturity/
Refinement
Initial
Neural
Networks
Rules CapsulesAd Hoc
ML TECHNOLOGIES MATURITY OVERVIEW
Utility: Demonstrated
reliability & validity
ML Technologies/Approaches:
Arrow Width Indicates Estimated Future Development/Potential

19. THE MACHINE LEARNING MARKET BIG 4 CLOUD-NATIVE, SCALABLE
Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
Amazon AWS - Model, Vision, Language services…
IBM Watson. Watson Machine Learning
Google Cloud Machine Learning Engine
Managed service for ML models
Microsoft Azure Machine Learning Studio
Ease of Use
Breadth of Services
Depth of Services
LinkedIn Data
Weather Data

20. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
THE MACHINE LEARNING MARKET: NOTEWORTHY
ML platform supports business users and “citizen data scientists”
Private deployment & subscription models (virtual private cloud on AWS, Azure, Google)
H2O Compute Engine - Open Source Platform
Cognitive Scale: Augmented Intelligence Platform with industry-optimized
“CortexAI Systems” (IBM Watson & Microsoft Partners)

21. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
Develops custom DL solutions
THE MACHINE LEARNING MARKET: NOTEWORTHY
Skymind - Skymind Intelligence Layer (SKIL) Leverages Spark to help users “productionize”
TensorFlow, Keras, DL4J
Skytree - ML platform, MLaaS for data scientists
LoopAILabs Loop Q Platform, Natural language-independent reasoning

22. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
THE MACHINE LEARNING MARKET: NOTEWORTHY ALTERNATIVE MODELS
Developer of the Hierarchical Temporal Memory
model based on the human neocortex.
Intel Saffron - Bio-inspired Associative memory model

23. adrian@storminsights.com
Twitter @ajbowles
Skype ajbowles
KEEP IN TOUCH
Upcoming SmartData Webinar Dates & Topics
April 12 Knowledge as a Service:
An Introduction to the Emerging Pre-Built Knowledge Market
May 10 Case Studies: Transforming Industries with AI
(Manufacturing & Retail)
June 14 Natural Language Processing:
From Chatbots to Artificial Understanding with Affective I/O
COMING SOON…
AGEOFREASONING.COM
BOOK, VIDEOS, PROFESSIONAL SERVICES
WWW.AGEOFREASONING.COM

24. CAPSULE REFERENCES
Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
https://medium.com/ai³-theory-practice-business/understanding-hintons-capsule-networks-
part-i-intuition-b4b559d1159b
https://openreview.net/pdf?id=HJWLfGWRb
https://hackernoon.com/capsule-networks-are-shaking-up-ai-heres-how-to-use-them-
c233a0971952