IBM Watson Brand Overview
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Transcript of IBM Watson Brand Overview
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VinceDaukasWatsonSolu1onArchitectvdaukas@us.ibm.comWithBusinessPartner:[email protected]
Watson Cognitive Computing and Brand Overview
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2010 2020
We are here Sensors
Social
VoIP
Enterprise
Volume of Data (Exabytes)
© 2016 International Business Machines Corporation
There is an enormous amount of undiscovered insight contained within unstructured data (text, images, video, audio, etc.) The 4 V’s (volume, variety, velocity, and veracity) related to this data makes it challenging to find the information within the data.
2.5B gigabytes of new data are generated every day.
Approximately 80% of data collected is unstructured.
Oncologist Wealth Manager
Digital Marketing
Expert
Contact Center Manager
Master Chef
Etc…
Most progress is driven by innovative and deep expertise contained within human brains.
Innovative expertise tends to stay in relatively few heads (low levels of transference) This expertise is not captured well by traditional computer systems – traditional rules-oriented programming techniques are challenged
There is lots of information within systems
There is lots of information within human brains
Enterprises continue to struggle to quickly find and apply the right insights from the available data
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Examples of unstructured text data
• Equipment operating manuals • Maintenance documentation • Regulatory requirements • Enterprise policies • Doctor, nurse, and lab notes • Etc. • ……. • …….
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Observe Interpre t Decide Evaluate
Most enterprises today are not effectively leveraging the data that is not in traditional structured form
Traditionally Structured Data (numbers, or small chunks of text)
• Collected by structured automated methods
• Enters as structured inputs
• Stored in relational systems
• The structure defines the rules and meaning
• Accessing and processing are very fast
• Numerical slicing and dicing
• Statistical and other advance techniques are easy to apply
• Decision rules are easy to assign
• Predictive analytics
• Decisions are clear from strong evidence
• Decisions support business experts
• Analyses are fast and accurate
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Observe Interpre t Decide Evaluate
Most enterprises today are not effectively leveraging the data that is not in traditional structured form
Traditionally Structured Data (numbers, or small chunks of text)
• Collected by structured automated methods
• Enters as structured inputs
• Stored in relational systems
• The structure defines the rules and meaning
• Accessing and processing are very fast
• Numerical slicing and dicing
• Statistical and other advance techniques are easy to apply
• Decision rules are easy to assign
• Predictive analytics
• Decisions are clear from strong evidence
• Decisions support business experts
• Analyses are fast and accurate
Data with Other Structures (blocks of text, images, video, audio, sensory, etc.)
• Collected in large batches with many different formats
• Enters systems with little structure
• Stored in massive file repositories or data lakes
• Machines might derive very general descriptions, but to get to deeper meaning, humans are required
• Accessing and processing the data are challenges and require expert programmers
• Often nothing is done
• Unless humans first do the difficult task of structuring the data, machines can not do much with it, so this is usually done by humans
• Analysis techniques are not familiar and requires expert analysts
• Often, nothing is done
• Decisions are often not clear as the supporting evidence is often not well defined
• Support is needed for non-experts, but is not human-friendly
• Interpretation and evaluation can be very slow and inaccurate
• Often nothing is done
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New techniques bring the ability to analyze ambiguously-structured data, and to help enterprises to develop, leverage, and transfer innovative expertise.
Final Score: $ 24,000 $ 21,600 $ 77,147
IBM enters a Q&A computer called Watson in the Jeopardy! exhibition, and it successfully beats the best human contestants
Extensive research has developed better technology
Grand Challenge: Automatic Open-Domain Question Answering
~2008 2011
IBM Research generates many additional potential offerings based on new technologies
The Watson Brand group is established (SaaS solutions and PaaS API)
2012-2013 2014
IBM Research tackles a long-standing Artificial Intelligence challenge
Watson Brand
Additional
potential
offerings based
on new
technologies
Watson
Offerings
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New capabilities have started a different phase in the history of computing: Cognitive Computing
Data with Other Structures (blocks of text, images, video, audio, sensory, etc.)
• Understands very sophisticated contexts
• Finds new insights that were not possible from only structured data
• Can make sense of massive volumes of data
• Automatically interprets and evaluates quickly and accurately
• Provides for evidence-based decisions
• Supports non-experts
• Can be tuned by subject matter experts instead of programmers
• Adds rich context and derives deep insights, with new capabilities (some examples below):
• Identify Features, Cluster
• Add Semantic and Advanced Context, Interpret, Convert
• Create Structure (index), Classify, Categorize
• Summarize
• Enable Federated Access, Find, Filter, Rank with Evidence
• Match Complex Criteria, Fit, Analyze Trade-offs
• Correlate, Show Relationships, Expand
• Orchestrate Dialog
• Create New Combinations
• Contribute to Predictive Analysis and Next Best Action
• Simplifies the processing of mass amounts of data
• Leverages machine learning to reduce the need for programming
• Makes access, processing and interaction human-friendly
Observe Interpre t Decide Evaluate
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The Association of Information and Image Management recognizes the opportunity, along with many other thought leaders
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Cognitive Computing - Clarifications • It is inspired by human cognition, and not attempting to replicate it
– Brains: bio-chemical and largely analog – Computers: other materials, ones and zeros, and usually Von Neumann designs – The objective is to automate tasks that previously required humans
• There is no specific technical definition – the key themes are “unstructured data” and “relating in a human-like way”
• Understanding natural language is only part of it – the goal is to reach much richer and deeper contexts
• It is not just about deep Artificial Neural Networks (ANN) – Leverages the best choice between ANN, statistical algorithms, rules techniques, heuristic
approaches, etc. – Often, a combination of techniques is used
• Machine learning is just one aspect – Tuning algorithms without programming – For most situations, supervised machine learning is the best fit
• It is more about Recognition than about Prediction (Forecasting)
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Enhances Watson enhances the cognitive process of professionals to strengthen decision making in the moment
Observe
Interpret Decide
Evaluate
Observe
Interpret Decide
Evaluate
Watson: a brand cover many cognitive solutions that can offer tremendous benefits
Watson scales expertise by elevating the consistency and objectivity of decision making across an organization.
Scales
Accelerates Watson captures the expertise of top performers and accelerates the development of that expertise in others.
Master
Practice
Apprentice
Study
Traditional Learning Curve
Learning Curve with Watson
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• 198082, Jan 1, 2000, 7:00:00 AM, WHILE TRAVELING DOWN THE HIGHWAY AT APPOX. 65 MPH I BEGAN TO APPLY THE BRAKES, THE
ENTIRE VEHICLE AND STEERING WHEEL SHOOK VIOLENTLY. THIS CAME WITHOUT WARNING, AFTER A FEW GENTLE PUSHES ON THE BRAKE IN THE NEXT FEW MILES THE SHUDDER WAS LESS VIOLENT. IT NOW FEELS AS IF THE ROTORS ARE WARPED, I HAD THE ROTORS TURNED AT 6500 MILES AND NOW IT SEEMS AS THEY ARE WARPED AGAIN. IN RECENT DAYS I HAVE BEEN CAREFUL TO NOTE THE VEHICLES BEHAVIOR, IT SEEMS THAT AFTER 5-6 STOPLIGHTS OR BRAKING PERIODS IN QUICK SUCCESSION THAT THE NEXT FEW STOPS ARE ESPECIALLY VIOLENT, AND THEN A FEW STOPS LATER IT GOES AWAY. THE VIOLENT SHAKING IS STRONG ENOUGH TO CAUSE PROBLEMS AT HIGHWAY SPEEDS, I AM NOW CONFINED TO <45 MPH UNTIL THE VEHICLE IS INSPECTED NEXT WEEK
• 198283, Jan 3, 2000, 7:00:00 AM, GOING FROM REVERSE TO DRIVE, CAR ACCELERATED AND SMASHED INTO A MOBILE HOME.(DETAILS? [email protected]). I WAS ATTEMPTING TO BACK OUT OF SOMEONE'S DIRT DRIVEWAY. I WAS POSITIONED AT AN ANGLE. WHEN I BACKED OUT (GOING ABOUT 3-5 MPH), I BUMPED INTO A TREE. I THEN PUT MY FOOT ON THE BRAKES SO THAT I COULD PUT THE CAR IN DRIVE. WHEN I PUT THE CAR IN DRIVE, THE CAR ACCELERATED AT AN EXTREME RATE AND MADE A TERRIBLY LOUD NOISE. THE CAR THEN THRUSTED FORWARD (THE ENTIRE TIME MY FOOT WAS HEAVY ON THE BRAKE - I AM 100% SURE OF THIS! ). THE BRAKE DID NOT STOP THE CAR. AFTER TRAVELING ABOUT 60 FEET, THE CAR THEN CAREAMED INTO A MOBILE HOME. I TURNED THE IGNITION OFF. I BELIEVE THOUGH THAT THE CAR WAS STOPPED BY THE METAL BEAMS FROM THE MOBILE HOME. THE CAR IS NOW AT THE DEALERSHIP
• 198488, Jan 4, 2000, 7:00:00 AM, MY WIFE REGINA WAS DRIVING THE CAR SHE HIT A CAR BROADSIDE THAT HAD RUN A RED LIGHT. SHE WAS GOING ABOUT 20 MILES PER HOUR, AND THE OTHER CAR WAS GOING ABOUT 25 MILES PER HOUR. THE FAILURE WAS THAT THE AIR BAGS DID NOT DEPLOY. OFFICER PETERSON FROM THE MEADVILLE CITY POLICE FILED THE ACCIDENT REPORT. THE ACCIDENT HAPPENED AT THE INTERSECTION OF PARK AVENUE AND NORTH STREET IN THE CITY OF MEADVILLE, PA 16335.
• 198518, Jan 4, 2000, 7:00:00 AM, WHILE DRIVING CONSUMER STEPPED ON THE BRAKE PEDAL TO STOP VEHICLE, BUT BRAKES DID NOT RESPOND. CONSUMER TRIED TO AVOID REAR ENDING ANOTHER VEHICLE BY DRIVING VEHICLE OFF THE ROAD. BUT WAS INVOLVED IN A ROLLOVER. UPON IMPACT, AIR BAGS DID NOT DEPLOY.
• 198612, Jan 5, 2000, 7:00:00 AM, ON THE 21ST OF DEC 99, MY WIFE WAS INVOLVED IN AN ACCIDENT IN OUR WINDSTAR. THIS ACCIDENT COULD HAVE BEEN AVOIDED IF THE HORN WERE USEABLE. THE WAY THE HORN BUTTON IS NOW YOU WILL HAVE A DIFFICULT TIME TRYING TO FIND THE EXACT SPOT TO PUSH TO GET THE HORN TO SOUND. WHEN TIME IS CRITICAL IN THE OUTCOME, SEARCHING FOR THAT EXACT SPOT ISN'T A PLAYER. THIS WAS THE CASE ON THE 21ST. WHEN AN INDIVIDUAL TRIED TO CROSS THE HIGHWAY HE HIT OUR VAN ON THE RIGHT SIDE CAUSING $6500 IN DAMAGE. PRIOR TO THE IMPACT SHE TRIED TO HIT THE HORN BUT COULD NOT FIND IT WHEN IT WAS NEEDED MOST. WE ARE ALMOST 100% POSITIVE THAT IF SHE COULD HAVE FOUND THE HORN SHE COULD HAVE SOUNDED IT, AND THE OTHER DRIVER WOULD HAVE SEEN HER COMING. LUCKILY MY 18 MONTH OLD AND 5 YR OLD DAUGHTERS WERE NOT WITH HER AT THE TIME.
Examples of unstructured records in the NHTSA database – Watson can make sense out of millions of these!
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Watson can identify key information in a policy document
MedicalPolicyDocument
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Watson can read medical information
“diseaseorsyndrome”CUI=C0011849
“signorsymptom”CUI=C0014743
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Patentsalsohave(ManuallyCreated)ChemicalComplexWorkUnits(CWU’s)
Astext
Chemicalnamesfoundinthetextof
documents
Asbitmapimages
Picturesofchemicalsfoundinthedocument
Images
Watson can read deep research studies and chemical diagrams
Chemicalnomenclaturecan
bedaun1ng
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Watson can identify objects and people in visual data, and derive speech from audio data
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Personality portraits cover a large number of aspects
Watson can recognize personality characteristics, emotions, and tone
Watson helps people to detect communication styles: • Social • Emotional • Writing
Tone Analyzer recognizes a spectrum of emotional tones:
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When Watson is given this: …it can understand this:
© 2014 International Business Machines Corporation
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Hidden relationships related to fraud can be detected
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Customer
SearchEngine
FindsDocumentsContainingKeywords
DeliversDocumentsBasedonPopularity
DecisionMakerDisLllsQuesLonto
2-3Keywords
ReadsDocuments,FindsAnswers
DecidesEvidence&Analyzes
WatsonQ&A
DerivestheQuesLon’sIntent
RetrievesPossibleResults
DeliversResponse,Evidence&Confidence
AppliesSophisLcatedRanking,w/Confidence
AsksNaturalLanguageQuesLon
ConsidersAnswer&Evidence
Customer“Myhusbandhardlyeveruseshisphone,whichplanisrightforhim?
“Ineedawindowcoveringformydiningroom.Iwantthenaturallight,butinthemorningthesunshinesinlowandIhavetoclosethecurtains.”
Watson’s Q&A capability is very different from ordinary search
Customer
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Watson cognitive technology has broadened dramatically beyond the initial Jeopardy evidence-based Q&A capability
© 2016 International Business Machines Corporation
WatsonCapability WhatItDoes UsageExample
DialogandDiscoveringConceptsandAnswers
NLP-DrivenSoPware
Mul1-CriteriaRecommenda1on
Rela1onshipDiscoveryand360DegreeView
Personality,Emo1on,andTone
Crea1veCompu1ng
Obtainanswers+evidenceforcomplexquesLons
Controlsobwarewithnaturallanguage
RecommendopLonsthatmeetasetofcriteria
DiscoverrelaLonshipsbetweenenLLes
DeriveportraitsofpersonaliLesfromtext
CreatenewwaystocombineenLLes
CustomerserviceagentasksforproductinformaLontohelpacustomer
SalesmanagercallsupsophisLcatedBItablesandchartsbyaskinginnaturallanguage
DoctorreceivesrecommendedtreatmentopLonsbasedonthepaLentsdiagnosis
DrugresearcherlearnsnewrelaLonshipsbetweenproteinsthatwillimpactnewproductdevelopmentMarketresearchanalystdevelopsnewpersonality-basedmarketsegmentaLon
FoodbrandmanagercreatesnewrecipeideasforuseinadverLsing
WatsonEngagementAdvisor,WatsonDiscoveryAdvisor(andWDAforLifeSciences,NaturalLanguageClassifier,Dialog,RetrieveandRank
WatsonAnalyLcs,WatsonExplorer(soon)
OncologyAdvisor,ClinicalTrialMatching,PolicyServices
WatsonDiscoveryAdvisor(andWDAforLifeSciences),WatsonExplorer,RelaLonshipExtracLon,ConceptInsightsConceptExpansion
PersonalityInsights,ToneAnalyzer,EmoLonAnalyzer
ChefWatson
OfferingNames
Image,Audio,andSensorRecogni1on
IdenLfyobjectsand/orcharacterisLcsfromvisualandaudiodata
SecuritymanagerusesfacialrecogniLontoidenLfypeopleenteringafacility
VisualRecogniLon,SpeechtoText,TexttoSpeech,LanguageTranslaLon,AlchemyAPI
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Bluemix Services (REST API’s as PaaS) • Natural Language Classifier
• Dialog services
• Retrieve and Rank
• Alchemy (Language, Vision, News)
• Document Conversion
• Personality Insights
• Tone Analyzer
• Relationship Extraction
• Concept Expansion
Watson Solutions and Watson Developer Cloud Services (via Bluemix) Watson Solutions On-Premise
• Watson Explorer – Advanced Edition
SaaS • Watson Analytics
• Watson Engagement Advisor
• Watson Discovery Advisor for Life Sciences
• Watson Discovery Advisor (coming soon)
• Watson for Wealth Management
• Watson Company Advisor
• Watson Oncology Advisor
• Watson for Clinical Trials Management
• Chef Watson
Can be combined to create compound solutions
• Concept Insights
• Cognitive Commerce
• Cognitive Graph
• Speech to Text
• Text to Speech
• Language Identification
• Language Translation
• Tradeoff Analytics
• Visualization Rendering
• Visual Recognition
These are typical starting points
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Watson’s points of differentiation
Creates knowledge graph, indexing, faceting, metadata, etc.
Specialized analytics or processing
Sophisticated human-friendly inbound and outbound interaction
Offers easy and fast tooling
Builds for enterprise size, strength, and security
Facilitates extension
Deploys as SaaS
Extraordinarily Deep Context
Enables integration
Accessible via many types of user devices
Inbound and Outbound Interaction
Analytics and Processing
Contextualizing
Knowledge Graph, Indexing, Faceting, Metadata, etc
Data Sources
Context Platform
Core Capabilities
Connects to and crawls the data sources intelligently Curated Data
Parses, evaluates, and adds context
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IBM offerings that easily integrate with Watson
Watson also easily integrates with many solutions: • Intelligence (i2) • Advanced Care Insights
(Smarter Healthcare) • Epic (healthcare EMR) • Curam (healthcare case mgmt) • Emptoris (purchasing) • Genesys (contact center) …and more solutions
…and more products
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Key challenges for Cognitive Computing • Understanding what the technology can and can not do, and how to apply it
• Defining the high value use cases
• Accounting for the benefits and ROI
• Changing the enterprise for adoption of the technology and solutions
• Finding data sources with superior value
• Accessing and converting data
• Training routines
• Addressing perceived risks • Data privacy risks • Cloud risks • Artificial Intelligence risks
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Because this domain’s technologies and techniques are changing so rapidly, the maturing of offerings is not always smooth
Watson’s offering development routine
Long-term roadmap – v1
Beta
Offering Development
Research
Long-term roadmap – v2
Happy Path
Withdrawn at Beta
Withdrawn at early version
Extraordinary new version
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Watson Developer Cloud (including Watson Bluemix Services)
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Watson Developer Cloud is a platform that provides developers easy access to expertise via a collection of REST APIs & SDKs
WDC services are accessed via Bluemix, an open-standards, cloud-based platform for building, running, and managing applications https://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/services-catalog.html
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Example: Agent Assisted Insurance claims CRM
Q&A Direct responses to user inquiries fueled by primary document sources
Relationship Extraction
Intelligently finds relationships
between sentence components Concept Insights
Explores information based on the ideas, rather than traditional text matching
Personality Insights Deeper understanding of people's personality characteristics, and values
Watson Explorer Build a 360 view of all your information
AlchemyVision Imagine recognition
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Watson APIs
• Natural Language Classifier – determines the essential “intent” of questions or statements, according to
classifications for which it can be trained
• Dialog Service – orchestrates a natural language dialog interaction
• Retrieve and Rank – performs an indexed search, and has a trainable ranking function to determine the best
evidence-based responses
• Concept Insights – automatically tags content in relation to a concept graph that is based on content ingested
from the English language Wikipedia (can ingest test and/or a collection)
• Language Translation – provides translation for a number of languages, and language identification for a large
number of languages
• Speech to Text – converts speech into text
• Text to Speech – synthesizes speech audio from text with either male or female voices
• Document Conversion - converts a single HTML, PDF, or Microsoft Word™ document into a normalized formats
(e.g. HTML, plain text, or JSON)
• Personality Insights – recognizes 52 personality characteristics from human text compositions
• Tone Analyzer (Beta) – classifies text as to emotional state (e.g. anger, fear, joy, sadness, and disgust. )
• Relation Extraction (Beta) - identifies Subject-Action-Object relations within text according to predefined rules
• Concept Expansion (Withdrawn) – identifies contextually related words: “The Big Apple” refers to NY City
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Watson APIs (cont.)
AlchemyLanguage (various pre-trained text analytics functions)
• Entity Extraction –extracts entities like people, locations and organizations for 23 languages
• Sentiment Analysis - analyzes words and phrases to categorize as to sentiment
• Keyword Extraction – analyzes text data to extract keywords that can be used to index content, generate tag clouds, and
more
• Concept Tagging – analyzes text to tagging according to desired class or type ("My favorite brands are BMW, Ferrari, and
Porsche." = "Automotive Industry")
• Taxonomy Classification - analyzes text to classify by topic (baseball, mobile phones, etc.)
• Author Extraction - If a news article or blog post specifies an author, AlchemyAPI will attempt to extract it automatically
• Language Detection - identifies more languages (95+) than any other text analysis service, at extremely high rates of
accuracy
• Text Extraction - extracts only important text and title information from any web page
• Feed Detection - automatically discover syndicated content feeds associated with specific web sites or individual web
pages
• Relationship Extraction - enables you to extract useful information from input text, such as entities and the relationships
that exist among them
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Vision
• AlchemyVision – analyzes images with a pre-trained classifier to create metadata about the features found within
(focuses on people, faces, gender, age, celebrity ID, and text)
• Visual Insights – analyzes an images, or collections of images, with a pre-trained classifier to create metadata about the
features found within (focuses on general activities, places, interests and people)
• Visual Recognition (Beta) – analyzes images to classify features, with a sophisticated trainable classifier
DataInsights• Tradeoff Analysis – enables decisions for situations with multiple variables or requirements by allowing the selection of
specific weights to be applied to the different variables or requirements
• AlchemyData News – provides searching for news articles according to key topic for 60 days of history across 75,000
unique news sources (250,000 new articles each day) that have been analyzed via a pre-trained news-oriented classifier
Watson APIs (cont.)
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Many new Watson API’s are due within the next year
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