IBM Watson Brand Overview
•
7 likes•1,681 views
What is IBM Watson, What is Cognitive Computing, What organizations benefit from IBM Watson, and get an exclusive look into IBM Watson in an in-depth demo exploration. For more information about Watson, email Cresco at info@crescointl.com or visit http://www.crescointl.com.
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (20)
Similar to IBM Watson Brand Overview
Similar to IBM Watson Brand Overview (20)
Recently uploaded
Recently uploaded (20)
IBM Watson Brand Overview
- 2. 2 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
- 3. 3 Examples of unstructured text data • Equipment operating manuals • Maintenance documentation • Regulatory requirements • Enterprise policies • Doctor, nurse, and lab notes • Etc. • ……. • …….
- 4. 4 Observe Interpret DecideEvaluate 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
- 5. 5 Observe Interpret DecideEvaluate 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
- 6. 6 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
- 7. 7 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 Interpret DecideEvaluate
- 8. 8 The Association of Information and Image Management recognizes the opportunity, along with many other thought leaders
- 9. 9 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)
- 10. 10 Enhances Watson enhances the cognitive process of professionals to strengthen decision making in the moment Observe InterpretDecide Evaluate Observe InterpretDecide 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
- 11. 11 • 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? RDRUNER31@AOL.COM). 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!
- 12. 12 Watson can identify key information in a policy document Medical Policy Document
- 13. 13 Watson can read medical information “disease or syndrome” CUI = C0011849 “sign or symptom” CUI = C0014743
- 14. 14 Patents also have (Manually Created) Chemical Complex Work Units (CWU’s) As text Chemical names found in the text of documents As bitmap images Pictures of chemicals found in the document Images Watson can read deep research studies and chemical diagrams Chemical nomenclature can be daun1ng
- 15. 15 Watson can identify objects and people in visual data, and derive speech from audio data
- 16. 16 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:
- 17. 17 When Watson is given this: …it can understand this: © 2014 International Business Machines Corporation
- 18. 18 Hidden relationships related to fraud can be detected
- 20. 20 Watson cognitive technology has broadened dramatically beyond the initial Jeopardy evidence-based Q&A capability © 2016 International Business Machines Corporation Watson Capability What It Does Usage Example Dialog and Discovering Concepts and Answers NLP-Driven SoPware Mul1-Criteria Recommenda1on Rela1onship Discovery and 360 Degree View Personality, Emo1on, and Tone Crea1ve Compu1ng Obtain answers + evidence for complex quesLons Control sobware with natural language Recommend opLons that meet a set of criteria Discover relaLonships between enLLes Derive portraits of personaliLes from text Create new ways to combine enLLes Customer service agent asks for product informaLon to help a customer Sales manager calls up sophisLcated BI tables and charts by asking in natural language Doctor receives recommended treatment opLons based on the paLents diagnosis Drug researcher learns new relaLonships between proteins that will impact new product development Market research analyst develops new personality-based market segmentaLon Food brand manager creates new recipe ideas for use in adverLsing Watson Engagement Advisor, Watson Discovery Advisor (and WDA for Life Sciences, Natural Language Classifier, Dialog, Retrieve and Rank Watson AnalyLcs, Watson Explorer (soon) Oncology Advisor, Clinical Trial Matching, Policy Services Watson Discovery Advisor (and WDA for Life Sciences), Watson Explorer, RelaLonship ExtracLon, Concept Insights Concept Expansion Personality Insights, Tone Analyzer, EmoLon Analyzer Chef Watson Offering Names Image, Audio, and Sensor Recogni1on IdenLfy objects and/or characterisLcs from visual and audio data Security manager uses facial recogniLon to idenLfy people entering a facility Visual RecogniLon, Speech to Text, Text to Speech, Language TranslaLon, AlchemyAPI
- 21. 21 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
- 22. 22 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
- 23. 23 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
- 24. 24 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
- 25. 25 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
- 26. 26 Watson Developer Cloud (including Watson Bluemix Services)
- 27. 27 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
- 28. 28 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
- 29. 29 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
- 30. 30 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
- 31. 31 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 Data Insights • 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.)
- 32. 32 Many new Watson API’s are due within the next year
- 33. 33