smartdata webinar: commercial cognitive computing -- how to choose and build your first cognitive...
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Commercial Cognitive ComputingHow to choose and build your first cognitive computing application
Adrian Bowles, PhDFounder, STORM Insights, Inc.
Commercial Cognitive ComputingHow to choose and build your first cognitive computing application
Webinar topics Fundamentals of cognitive computing The cognitive computing ecosystem First steps
I. Fundamentals of Cognitive Computing
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Cognitive computing is a problem-solving approach that uses hardware or software to approximate the form or function of natural cognitive processes.
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Natural Cognitive Processes
LearningPerception
Motivation
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Start with nature…
Learning is the sine qua non, or essential condition of cognitive computing.
Natural Cognitive Processes
PerceptionMotivation
reflectioninferencededuction
Learning
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reasoning
Three key approaches to machine learning...
Natural Cognitive Processes
PerceptionMotivation
reinforcement
Learning
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unsupervisedsupervised
Three key approaches to
Natural Cognitive Processes
reinforcement
MachineLearning
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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.
MachineLearning
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deep learning
Deep learning refers to a biologically-inspired approach to machine learning that leverages a collection of simple processing units - analogous to neurosynaptic elements - that collaborate to solve complex problems at multiple levels of abstraction. These 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.
LearningPerception
Motivation
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see tastesmellhear touch
Perception: how we sense/recognize data in the outside world
Perception: how we recognize data in the outside world
Perception
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see tastesmellhear touch
Text,Images,Surface structured records…
SpeechMusicCuesNoise
Sensors: Temperature Tactile Texture
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Perception: obvious structure is easy to process… but most of the interesting stuff isn’t obvious to a computer.
Motivation: why we act.
Natural Cognitive Processes
LearningPerception
Motivation
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Problem Solving & Learning
Simple: deterministic,
retrieve/calculate
Response Types Visible Reports Invisible Memory updates
Input Types well-structured output from other systems
Basic Cognitive Computing
Memory
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Perception/ Language
Problem Solving & Learning
Complex: probabalistic hypothesize, test, rank, select Creative: discover, generate
Input Class/Type Visual Text Image Aural Speech Music Cues Noise Informative Touch Temperature Tactile Texture Taste Smell
Response Types Visible (to the environment) Reports Verbal/NL Text Behavioral (system changes) Haptics Invisible Memory updates
Advanced Cognitive Computing
Memory
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Cognitive computing is a problem-solving approach that uses hardware or software to approximate the form or function of natural cognitive processes.
Two major approaches to cognitive computing:
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1. Neuromorphic architectures: computer or device-level systems modeled after biological systems or components, such as neurons and synapses. These may be implemented in analog, digital or hybrid hardware.
Cognitive computing is a problem-solving approach that uses hardware or software to approximate the form or function of natural cognitive processes.
Copyright (c) 2015 by STORM Insights Inc. All Rights reserved.
An IBM - SyNAPSE boardSource: Qualcomm
Neuromorphic Architectures
Copyright (c) 2015 by STORM Insights Inc. All Rights reserved.
Cognitive computing is a problem-solving approach that uses hardware or software to approximate the form or function of natural cognitive processes.
LearningPerception
Motivation
reflectioninferencededuction
Two major approaches to cognitive computing:2. Functional equivalence:
Model the behavior of biological systems, not their structure.
Copyright (c) 2015 by STORM Insights Inc. All Rights reserved.
reasoning
Cognitive computing is a problem-solving approach that uses hardware or software to approximate the form or function of natural cognitive processes.
Copyright (c) 2015 by STORM Insights Inc. All Rights reserved.
Cognitive Computing
HW ArchitectureNeuromorphicvon
Neumann-based
Cognitive WorkloadsNatural
Language ProcessingHypothesis Gen/&
TestingConfidence-
Weighted Reporting
Supporting Workloads
Experience-Based
Learning
Shallow-structured
Data
Descriptive Analytics
Deep-structured
Data Predictive/Prescriptive
The Cognitive Computing Landscape
Natural Cognitive Processes
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LearningPerception
Motivation
II. The Cognitive Computing Technology Ecosystem
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Machine Learning
Human
Sensors/Systems
Infrastructure
Input Output
Visualization
Narrative Generation
Voice/NLP
Video/Images
Reports
Gestures
Emotions
Text/NLP
Surface Structured DataSurface Structured Data
The Cognitive Computing Technology Ecosystem
DataManagement
Alt/NeuromorphicHardware
ProfessionalServices
Analytics
Reports
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Human
Sensors/Systems
Infrastructure
Input Output
Visualization
Narrative Generation
Voice/NLP
Video/Images
Reports
Gestures
Emotions
Text/NLP
Surface Structured DataSurface Structured Data
The Cognitive Computing Technology Ecosystem
DataManagement
Alt/NeuromorphicHardware
ProfessionalServices
Analytics
Reports
Machine Learning
Metamind
IBM
Ersatz Labs
Scaled Inference
Microsoft
Saffron
IP Soft
Numenta
ai-oneDigital Reasoning
Nervana Systems
BigML
Sentient Technologies Vicarious
Skymind
Lumiata
wise.io
Dato
Kimera SystemsH2O
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Human
Sensors/Systems
Infrastructure
Input Output
Visualization
Narrative Generation
Voice/NLP
Video/Images
Reports
Gestures
Emotions
Text/NLP
Surface Structured DataSurface Structured Data
The Cognitive Computing Technology Ecosystem
DataManagement
Alt/NeuromorphicHardware
ProfessionalServices
Analytics
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Reports
Solution Providers
IBMWatson
AccentureIP Soft
Cognitive Scale
AltiliaH2O
Visualization*Narrative GenerationVoice/NLP
Video/Images
Gestures
Emotions
Text/NLP
The Cognitive Computing Technology Ecosystem: I/O Technologies
• Affectiva• BeyondVerbal• Emotient• Limbic• Nuance• Nviso
• Expect Labs• Gridspace• IBM• Maluuba• Nuance• PopupArchive• Skymind• SpaceTimeInsight• Viv Labs• Wit.ai
• ABBYY• Altilia• Cortical.io• IBM• idibon• Kaypok• Luminoso • Maluuba• Wit.ai
• BRS Labs• Clarifai• Dextro• Madbits (twitter)• Mindops• Skymind• Teradeep• Visenze
• Narrative Science• OnlyBoth
• APX Labs• EyeSight• GestureTek• LeapMotion• Nod• Intel
*Visualization functionality is often bundled with analytics software. We currently combine these in a single category.
Machine Learning
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Data Management
Alt/Neuromorphic Hardware
Professional Services
Analytics/Visualization
The Cognitive Computing Technology Ecosystem: Infrastructure
• Accenture• Cognitive Scale• Deloitte• Hitachi• IBM• Xerox
• Artificial Learning• DWave• HRL Laboratories• IBM• Nervana Systems• Qualcomm• Teradeep
• 1010data• Actuate • Adatao• Alpine Data Labs• Alteryx • Altilia• Angoss• Attivio • Birst• Civis Analytics• ClearStoryData• Connotate• Context Relevant• Dataiku• Datameer• Emerald Logic • First Rain• ForeSee• Fractal Analytics • Guavus• IBM• indico• Inkiru (Wamart)• KNIME• KXEN (SAP)• LiftIgniter• Loop AI Labs• MathWorks (Matlab)• Microsoft• Mu Sigma
• Actian • Aerospike • Alation• Altilia• Basho• Caspio • Cognitive Scale• Cognizant Technology• Couchbase• CrowdFlower• CumuLogic• Data Bricks• DataRPM • DataStax• DataWeb, Inc.• DDN • diffbot• GigaSpaces• Giraph• Grappa • GridGain• Hortonworks• HP Vertica• IBM• import io• kimono• MapR
• Nara Logics• NuTonian• Opera Solutions• Oracle• Palantir• Pentaho • Prediction IO• Predixion• Qliktech • Quid • Rapid Miner• Revolution Analytics(MSFT)• Salford Systems• SAP• SAS Institute• SiSense • Spark Beyond• Spotfire (Tibco)• StatSoft (Dell)• Synthos Technologies• Teradeep• ThinkBig Analytics (Teradata)• Versium• Wolfram Mathematica• Yhat
• MarkLogic • MongoDB• NeoTechnology• Oracle• Paxata• RainStor• Reactor • SAP• Space Curve• Viv Labs
Machine Learning
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Copyright (c) 2015 by STORM Insights Inc. All Rights reserved.
Early adopters…healthcare (payers, providers, patients), diagnoses, intern training, self service… legal - from due diligence to compliance security - from police to HSA to DoD… call centers, supporting complex products retail - recommendations from outdoor/adventure goods… travel/hospitality - taking the burden off the customer to recommend solutions telecomm - managing operations
The ideal app…Performs a function that is already being done by skilled professionals who can’t keep up with the data, or are too expensive, or that involves high risk.
Document-centric
Data-centric
Behavior/ event-centric
Knowledge- centric
Data Model Source
Manual &
Systems
Systems &
Sensors
Systems Sensors
Machine-learning Cognitive Computing
Municipal Knowledge
Figure 12-1. Foundations of Cognitive Computing for Smarter Cities from Cognitive Computing and Big Data Analytics, Hurwitz, Kaufman & Bowles, 2015
Analysis
Descriptive/ Predictive/ Learning
Descriptive/ Predictive
Descriptive
Mat
urity
Silos
Shared
Integrated
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Can a cognitive computing application disrupt your industry?
Copyright (c) 2015 by STORM Insights Inc. All Rights reserved.
Start with the hard questions!
Do you have the skills?
Do you have the data?
Are your customers ready for probabilistic or non-deterministic answers? (can they deal with uncertainty and multiple possible answers?)
Does anybody else have the data?
Will NLP add value in the eyes of your customers?
How important is it to be able to explain how the system got an answer or made a recommendation…? (medical diagnosis - HIGH, recommending a sweater, not so much)
How important is it for the system to improve its performance over time? (vs consistent answers)
0. Foundation
Experience-Based
Learning1. Learn
2. Interact
3. ExpandIntegrate
Augmented/VirtualReality
Confidence-weightedReporting
Motivation
reflection
inference
Natural Cognitive Processes
deduction
Hypothesis Generation& Testing
reasoning
Natural Language Processing
Cloud
…Analytic
s
Data Management
Neu
rom
orph
icAr
chite
ctur
es Learning
Perception
Which technologies are most important to serving your customers?Copyright (c) 2015 by STORM Insights Inc. All Rights reserved.
Identify the domain Choose a primary machine learning model
for general supervised learning, identify the attributes and sources of training data for reinforcement learning, identify events/states that need to be reinforced (positive or negative) for unsupervised learning, identify discovery parameters
Identify the data sources Build/buy decision time…
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Ready?
Identify Data Sources
Generate Hypothesis
Ingest Data
RefineTrain System
Operate/ Run System
Identify Anomalies & New Patterns
Baseline Knowledge
Before you start, can you build a virtuous cycle for your domain?
Refine/ Update Corpus
CorpusData
Ontologies
Taxonomies
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Human
Sensors/Systems
Voice/NLP
Video/Images
GesturesEmotions
Text/NLP
Surface Structured Data
For more information:
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