Manoj Saxena, GM IBM Watson -- Keynote at Innotech 2011


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  • Hello Manoj Saxena, GM IBM Watson
    We would find a place for Watson in Sustainable Development in the Western Africa and Sub Saharan Africa regions. Could we link Watson in a Cloud interface?

    Sidney Clouston
    International Director for RSECE
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  • Key Message: Speaking Points: Additional Background Information:
  • As the world becomes instrumented, interconnected and intelligent, we have the opportunity to think and act in new ways—economically, socially and technically.
  • Computer programs are natively explicit and exacting in their calculations over numbers and symbols. But Natural Language - -the words and phrases we humans use to communicate with one another -- is implicit -- the exact meaning is not completely and exactly indicated -- but instead is highly dependent on the context -- what has been said before, the topic, how it is being discussed -- factually, figuratively, fictionally etc. Moreover, natural language is often imprecise – it does not have to treat a subject with numerical precision…humans naturally interact and operate all the time with different degrees of uncertainty and fuzzy associations between words and concepts. We use huge amounts of background knowledge to reconcile and interpret what we read. Consider these examples….it is one thing to build a database table to exactly answer the question “ Where is someone born?”. The computer looks up the name in one column and is programmed to know that the other column contains the birth place. STRUCUTRED information, like this database table, is designed for computers to make simple comparisons and to be exactly as accurate as the data entered into the database. Natural language is created and used by humans for humans. A reason we call natural language “ Unstructured ” is because it lacks the exact structure and meaning that computer programs typically use to answer questions. Understanding what is being represented is a whole other challenge for computer programs . Consider this sentence <read> It implies that Albert Einstein was born in Ulm – but there is a whole lot the computer has to do to figure that out any degree of certainty - it has to understand sentence structure, parts of speech, the possible meaning of words and phrases and how they related to the words and phrases in the question. What does a remembrance , a water color and an Otto have to do with where someone was born. Consider another question in the Jeopardy Style … X ran this? And this potentially answer-bearing sentence. Read the Sentence… Does this sentence answer the question for Jack Welch - -what does “ ran ” have to do with leadership or painting . How would a computer confidently infer from this sentence that Jack Welch ran GE – might be easer to deduce that he was at least a painter there.
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  • Manoj Saxena, GM IBM Watson -- Keynote at Innotech 2011

    1. 1. 8:00 AM Opening Keynote: Putting IBM Watson to Work Manoj Saxena General Manager Watson Solutions IBM Software Group Mr. Saxena will discuss how IBM is scaling the Watson computer system for business solutions. Find out how Watson ’s ability to quickly and accurately understand natural language will impact industries such as healthcare, finance, and many others.
    2. 2. Putting IBM Watson to Work Manoj Saxena General Manager, IBM Watson Solutions
    3. 3. <ul><li>Watson Wins! </li></ul><ul><li>Largest Jeopardy! in 5 years </li></ul><ul><ul><li>34.5M Jeopardy! Viewers </li></ul></ul><ul><ul><li>1.3B+ Impressions </li></ul></ul><ul><li>Over 10,000 Media Stories </li></ul><ul><li>11,000 attend watch events </li></ul><ul><li>2.5M+ Videos Views (top 10 only) </li></ul><ul><li>10,897 Twitter </li></ul><ul><li>23,647 Facebook Fans </li></ul>On February 14, 2011, IBM Watson changed history introducing a system that rivaled a human ’s ability to answer questions posed in natural language with speed, accuracy and confidence. Click graph when in display mode to play 30 sec intro video
    4. 4. IBM Watson a look behind the scenes 2880 Processing Cores 16 Terabytes Memory (RAM) – 20TB Disk System Specifications 90 IBM P750 Servers 80 Teraflops Computing Power = 200m books in 3 sec Workload Optimized Systems In the past 5 years IBM has spent over $14B in acquisitions and $6B in R&D annually Big Data Content Analytics IBM Technology Depth Business Analytics Databases / Data Warehouses
    5. 5. Agenda What is IBM Watson and why is it important? How is IBM putting Watson to work? What can we expect in the future?
    6. 6. + + An opportunity to think and act in new ways— economically, socially and technically. The World is Getting Smarter Intelligent Instrumented Interconnected =
    7. 7. In 2005 there were 1.3 billion RFID tags in circulation… … by 2010 there will be 33 billion. An estimated 2 billion people will be on the Web by 2011 ... … and a trillion connected objects – cars, appliances, cameras, roadways, pipelines Unstructured data is proliferating. . . … 249 B e-mails (2.8M/sec) and 200M Tweets daily … 220+ B pieces of user generated content on web
    8. 8. Businesses on a Smarter Planet are “dying of thirst in an ocean of data” 1 in 2 Business leaders don ’t have access to data they need 83% of CIO ’s cited BI and analytics as part of their visionary plan 5.4X more likely that top per formers use Business analytics <ul><ul><li>80% of the world ’ s data today is unstructured </li></ul></ul><ul><ul><li>90% of the world ’ s data was created in the last two years </li></ul></ul><ul><ul><li>Traditional technology solutions only leverage 20% of the available information </li></ul></ul>
    9. 9. <ul><li>Traditional IT </li></ul><ul><li>Structured data, local scope </li></ul><ul><li>Deterministic Applications </li></ul><ul><li>Search Oriented </li></ul><ul><li>Small Data </li></ul><ul><li>Machine Language </li></ul><ul><li>Systems of records </li></ul><ul><li>Emerging IT </li></ul><ul><li>Global Structured & unstructured </li></ul><ul><li>Probabilistic Applications </li></ul><ul><li>Discovery Oriented </li></ul><ul><li>Small and Big Data </li></ul><ul><li>Natural Language </li></ul><ul><li>Systems of engagement </li></ul>Today ’s business challenges are causing organizations to rethink what it will take to get ahead tomorrow
    10. 10. <ul><ul><li>Medical information is doubling every 5 years, much of which is unstructured </li></ul></ul><ul><ul><li>81% of physicians report spending 5 hours or less per month reading medical journals </li></ul></ul>“ Medicine has become too complex (and only) about 20 percent of the knowledge clinicians use today is evidence-based.” Steven Shapiro, Chief Medical and Scientific Officer, UPMC Healthcare Industry is beset with some of the most complex information challenges we collectively face
    11. 11. Why is it so hard for computers to understand humans Where was Einstein born? Source: Excel File, Database, etc. Source: Jack Welch and the GE Way, Robert Slater Source: Excel File, Database, etc. Source: Structured Data Unstructured Data Welch ran this? “ One day, from among his city views of Ulm, Otto chose a water color to send to Albert Einstein as a remembrance of Einstein´s birthplace” “ If leadership is an art then surely Jack Welch has proved himself a master painter during his tenure at GE” Source: IBM Research Physicist Birth Place A. Einstein Ulm N. Bohr Copenhagen M. Curie Warsaw Person Organization L. Gerstner IBM J. Welch GE W. Gates Microsoft
    12. 12. What if an enterprise had all the answers it needs to succeed? Can we design a computing system that rivals a human ’s ability to retrieve, analyze and interpret vast amounts of information?
    13. 13. A Brief History of IBM Watson IBM Research Project (2006 --) Jeopardy! Grand Challenge (Feb 2011) Watson for Healthcare (Aug 2011 --) Watson Industry Solutions (2012 --) R&D Demonstration Commercialization Cross-industry Scale up New class of industry specific business analytics
    14. 14. IBM Watson brings together a set of transformational technologies to drive optimized outcomes … built on a massively parallel probabilistic evidence-based architecture Understands natural language and human speech Adapts and Learns from user selections and responses Generates and evaluates hypothesis for better outcomes Renal Failure UTI Diabetes
    15. 15. IBM Smarter Healthcare Capture accurate, real-time information from devices & systems Enable seamless information sharing across groups Use advanced analytics to improve research, diagnosis and treatment A smarter health system improves visibility and collaboration across all health system participants making best use of resources to prevent and treat diseases, reduce overall healthcare costs, and keep people healthy. Intelligent Instrumented Interconnected + + Click turquoise background when in display mode to play 30 sec TV Healthcare spot
    16. 16. Understands natural language questions Analyzes large volumes of unstructured data Generates and evaluates hypothesis Presents responses with confidence Supports iterative dialogue to refine results Learns from results over time      What condition has red eye, pain, inflammation, blurred vision, floating spots and sensitivity to light? Physician Notes, Medical Journals, Clinical Trials, Pathology Results, Blogs, Wikipedia Possible Diagnosis Confidence Uveitis 91% Iritis 48% Keratitis 29% Family History, Patient Interview, Physical Exam, Current Medications What actions were taken? What treatments were prescribed? What was the outcome? Why is Watson Technology ideal for Healthcare? 
    17. 17. IBM and WellPoint are working together to put Watson to work in healthcare &quot;Imagine having the ability within three seconds to look through all of that (medical) information….at the moment you're caring for that patient.&quot; Dr. Sam Nussbaum, WellPoint's Chief Medical Officer, WellPoint WellPoint Serving 1 in 9 insured Americans + IBM Watson IBM Watson = Leverage medical records TO diagnose and identify treatment options TO enhance the quality of medical care delivered
    18. 18. Putting the pieces together at point of impact can be life changing <ul><li>Extract Symptoms from record </li></ul><ul><li>Use paraphrasings mined from text to handle alternate phrasings and variants </li></ul><ul><li>Perform broad search for possible diagnoses </li></ul><ul><li>Score Confidence in each diagnosis based on evidence so far </li></ul><ul><li>Identify negative Symptoms </li></ul><ul><li>Reason with mined relations to explain away symptoms (thirst is consistent w/ UTI) </li></ul><ul><li>Extract Family History </li></ul><ul><li>Use Medical Taxonomies to generalize medical conditions to the granularity used by the models </li></ul><ul><li>Extract Patient History </li></ul><ul><li>Extract Medications </li></ul><ul><li>Use database of drug side-effects </li></ul><ul><li>Together, multiple diagnoses may best explain symptoms </li></ul><ul><li>Extract Findings : Confirms that UTI was present </li></ul>Most Confident Diagnosis : Esophagitis Most Confident Diagnosis: Diabetes Symptoms UTI Diabetes Influenza hypokalemia Renal failure (Thyroid Autoimmune) Esophagitis Diagnosis Models Symptoms Fam. History Pat. History Medications Findings Confidence Most Confident Diagnosis: Influenza difficulty swallowing Most Confident Diagnosis: UTI Family History Patient History Medications A 58-year-old woman presented to her primary care physician after several days of dizziness , anorexia , dry mouth , increased thirst , and frequent urination . She had also had a fever and reported that food would “get stuck” when she was swallowing . She reported no pain in her abdomen, back, or flank and no cough , shortness of breath , diarrhea , or dysuria . Her family history included oral and bladder cancer in her mother, Graves' disease in two sisters, hemochromatosis in one sister, and idiopathic thrombocytopenic purpura in one sister. Her history was notable for cutaneous lupus , hyperlipidemia , osteoporosis , frequent urinary tract infections , three uncomplicated cesarean sections, a left oophorectomy for a benign cyst, and primary hypothyroidism , which had been diagnosed a year earlier. Her medications were levothyroxine , hydroxychloroquine , pravastatin , and alendronate . A urine dipstick was positive for leukocyte esterase and nitrites . The patient was given a prescription for ciprofloxacin for a urinary tract infection and was advised to drink plenty of fluids. On a follow-up visit with her physician 3 days later, her fever had resolved, but she reported continued weakness and dizziness despite drinking a lot of fluids. Her supine blood pressure was 120/80 mm Hg , and her pulse was 88 beats per minute ; on standing, her systolic blood pressure was 84 mm Hg , and her pulse was 92 beats per minute . A urine specimen obtained at her initial presentation had been cultured and grew more than 100,000 colonies of Escherichia coli , which is sensitive to ciprofloxacin. Findings Click large blue bar to play 3;21 sec Herb Chase video / demo no abdominal pain no back pain no cough no diarrhea pravastatin Alendronate levothyroxine hydroxychloroquine frequent UTI cutaneous lupus hyperlipidemia osteoporosis hypothyroidism dizziness anorexia fever dry mouth thirst frequent urination Graves ’ Disease Oral cancer Bladder cancer Hemochromatosis Purpura supine 120/80 mm HG urine dipstick: leukocyte esterase urine culture: E. Coli heart rate: 88 bpm
    19. 19. From battling humans at Jeopardy! to transforming business Tech Support Financial Services Investment and retirement planning, institutional trading and decision support. Contact center support and services. Enterprise knowledge management. Consumer marketing. Public Safety, Improved Information Sharing, Security, Fraud and Abuse Prevention IBM Watson has the capabilities to address business and societal challenges IBM Watson Healthcare Diagnostic/Treatment Assistance, Evidenced-Based Insights, Collaborative Medicine Government
    20. 20. <ul><li>See Watson in action at an IBM Lab, Briefing Center or Analytics Solution Center </li></ul><ul><li>Learn more at: </li></ul> . (Tweet #ibmwatson ) Learn more at: