smart data webinar: machine learning update

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

[email protected]

mailto:[email protected]

Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.

FIRST, DEFINE TERMS

Artificial IntelligenceMachine LearningDeep LearningData Science


MachineLearning

Deep Learning

ArtificialIntelligence

DataScience

Each discipline has algorithms and models.

FIRST, DEFINE TERMS


#MODERN AI: ARTIFICIAL, AUTOMATED, AUGMENTED, AMPLIFIED…INTELLIGENCE

PERCEPTION

UNDERSTANDING

LEARNING

BigData

ClassicAI

DeepLearning


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


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


FUNDAMENTAL DESIGN CHOICE: SYMBOLS VS STATISTICS

Symbolic LogicRepresentations

ReasoningConcepts

Statistical Models

Mechanical Theorem Proving


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


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


RECOGNIZING CONCEPTS - DISCOVERY <> UNDERSTANDING

Courtesy ofLoopAI Labs.


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


MACHINE LEARNING - ARTIFICIAL NEURAL NETS

Input

OutputHighly ConnectedNeural Processors

A digital representation of the state of the input domain.Scalars, Vectors, Equations…


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


LIMITATIONS: HOW IMPORTANT IS IT TO BE ABLE TO EXPLAIN REASONING?


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


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.


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


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


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)


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


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

[email protected]

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

BOOK, VIDEOS, PROFESSIONAL SERVICES

WWW.AGEOFREASONING.COM

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http://AgeOfReasoning.com

CAPSULE REFERENCES


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

https://medium.com/ai

https://openreview.net/pdf?id=HJWLfGWRb

https://hackernoon.com/capsule-networks-are-shaking-up-ai-heres-how-to-use-them-c233a0971952