smart data webinar: machine learning update
TRANSCRIPT
MARCH 8, 2018
Machine Learning UpdateAn Overview of Technology Maturity and Product Vendors
Adrian J Bowles, PhDFounder, STORM Insights, Inc.
Lead Analyst, AI, Aragon Research
Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
FIRST, DEFINE TERMS
Artificial IntelligenceMachine LearningDeep LearningData Science
Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
MachineLearning
Deep Learning
ArtificialIntelligence
DataScience
Each discipline has algorithms and models.
FIRST, DEFINE TERMS
Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
#MODERN AI: ARTIFICIAL, AUTOMATED, AUGMENTED, AMPLIFIED…INTELLIGENCE
PERCEPTION
UNDERSTANDING
LEARNING
BigData
ClassicAI
DeepLearning
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Systems
Controls
LearnReason
Understand
Model
Data Mgmt
Human
Machine
Input OutputGestures
Emotions
Language
Narrative Generation
Visualization
Reports
Haptics
Sensors(IOT)
SystemsControls
ML IN THE MODERN AI LANDSCAPE
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Human Input
Gestures
Language
Context
LearnReason
Understand
Model
Data Mgmt
Detected byHuman Senses
Derived
ImagesSee
Hear
Touch
Smell
Taste
Sounds
ObjectsEmotions
Meaning
Concepts
Intent
Emotions Meaning
Concepts IntentContext
ML IN THE MODERN AI LANDSCAPE
Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
FUNDAMENTAL DESIGN CHOICE: SYMBOLS VS STATISTICS
Symbolic LogicRepresentations
ReasoningConcepts
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
MopeBuy
Hop Hope
BoyBay
Map
Mop
Man
Nay
May
Mope
BuyHop
HopeSimilar structure ->similar meaning in vision, not always in language.
Memory-BasedReasoning
MACHINE LEARNING FOCUS CONTINUES TO EVOLVE
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DATA
More Data + Faster HW make Deep Learning Practical
Deep Learning Success With RecognitionSpurs Investment
ALGORITHMS&
RULES
Caution for Applications Where Transparency is Critical
Investment Leads to InvestigationBroaden the Scope of Applications
New “Explainability” Research Emerges
Hybrid Solutions to Augment IntelligenceWill Thrive for Critical Applications
Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
RECOGNIZING CONCEPTS - DISCOVERY <> UNDERSTANDING
Courtesy ofLoopAI Labs.
Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
Supervised Unsupervised
Deep
GeneralReinforcement
Learning by example,using training data. Strategies based
on performancefeedback.
Discovers patterns basedon 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
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MACHINE LEARNING - ARTIFICIAL NEURAL NETS
Input
OutputHighly ConnectedNeural Processors
A digital representation of the state of the input domain.Scalars, Vectors, Equations…
Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
DEEP LEARNING
Visible Layer
Hidden Layer
Hidden Layer
Output Layer
Hidden Layer
Input: Observable Variables
HIG
HAB
STRA
CTIO
NLO
W
Output
Pixels
Depthof the Model
Edges
Object
Shapes/Parts
Object Class
Brightness/Contrast
GeometryRules
Featuresto
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
Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
WHAT CAN A DL SYSTEM “LEARN” FROM THIS PICTURE?
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 detectorsthat 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 neuralnetworks can be used to learn features that output a whole vector of instantiation parameters and we argue that thisis a much more promising way of dealing with variations in position, orientation, scale and lighting than the methods currentlyemployed in the neural networks community. It is also more promising than the hand- engineered features currently used in computervision 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 forviewpoint invariance in the activities of “neurons” that use a single scalar output to summarize the activities of alocal pool of replicated feature detectors, artificial neural networks should use local “capsules” that performsome quite complicated internal computations on their inputs and then encapsulate the results of thesecomputations into a small vector of highly informative outputs.
Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
Maturity/Refinement
InitialNeural
NetworksRules CapsulesAd Hoc
ML TECHNOLOGIES MATURITY OVERVIEW
Utility: Demonstratedreliability & validity
ML Technologies/Approaches:Arrow Width Indicates Estimated Future Development/Potential
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 EngineManaged service for ML models
Microsoft Azure Machine Learning Studio
Ease of UseBreadth of ServicesDepth of Services
LinkedIn Data
Weather Data
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)
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
Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
THE MACHINE LEARNING MARKET: NOTEWORTHY ALTERNATIVE MODELS
Developer of the Hierarchical Temporal Memorymodel based on the human neocortex.
Intel Saffron - Bio-inspired Associative memory model
Twitter @ajbowlesSkype 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
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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