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Augmented intelligence pietro_leo_sole24_ore_school

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Augmented intelligence intro prepared for Sole 24 ore Business School

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Augmented intelligence pietro_leo_sole24_ore_school

  1. 1. Artificial Intelligence to make precise decisions July 13, 2017 Pietro Leo Executive Architect & CTO Chief scientist, and research strategist IBM Italy IBM Academy of Technology Leadership Team pieroleo.com
  2. 2. DATA INFORMATION KNOWLEDGE WISDOM
  3. 3. DECISION
  4. 4. July 18,20176
  5. 5. July 18,20177
  6. 6. July 18,20178
  7. 7. July 18,20179
  8. 8. 10 You shared your position with me and can guess your mobility need. I can take you where you need to be Just enjoy your new experience. Stay safe as in your friend’s home I know what is needed for you, even before you order it Please, come with me and stay by me. I know your content I can take care of all your digital life
  9. 9. 11 Video: http://www.digitaltrends.com/home/grush-toothbrush-wins-americas-greatest-makers/
  10. 10. http://www.grushgamer.com/
  11. 11. HYPERDATA WORLD
  12. 12. Source: http://www.bloomberg.com/video/meet-the-world-s-most-connected-man- Vs~LzkbkR7yhjza~7nji1g.html Meet the World's Most Connected Man
  13. 13. 16 Image source: http://personalexcellence.co/blog/i deal-beauty/
  14. 14. 17 Image source: http://personalexcellence.co/blog/i deal-beauty/ City Lifestyle ZIPcode Costal vs Inland Marital status Generation Location Family Size Gender Income Level Competitors Age Loyalty & Card Activity Revenue Size Life Stages Eductation Legal status Sector Industry
  15. 15. 18 Image source: http://personalexcellence.co/blog/i deal-beauty/ City Lifestyle ZIPcode Costal vs Inland Marital status Generation Location Family Size Gender Income Level Competitors Age Loyalty & Card Activity Revenue Size Life Stages Eductation Legal status Sector Industry Subscriptions Date on Site Wish List Size of Network Check-ins App usage duration Number of Apps on Device Deposits/Withdrawals Device Usage Purchase History Following Followers Likes Number of Hashtags used History of Hashtags Search Strings entered Sequence of visits Time/Day log in Time spent on site Time spent on page Frequency of Search Videos Viewed Photos liked
  16. 16. 19 Image source: http://personalexcellence.co/blog/i deal-beauty/ City Lifestyle ZIPcode Costal vs Inland Marital status Generation Location Family Size Gender Income Level Competitors Age Loyalty & Card Activity Revenue Size Life Stages Eductation Legal status Sector Industry Subscriptions Date on Site Wish List Size of Network Check-ins App usage duration Number of Apps on Device Deposits/Withdrawals Device Usage Purchase History Following Followers Likes Number of Hashtags used History of Hashtags Search Strings entered Sequence of visits Time/Day log in Time spent on site Time spent on page Frequency of Search Videos Viewed Photos liked Sentiment Tone Euphemisms Hedonism Extroversion Face Recognition Openess Colloquialism Reasoning Strategies Language Modeling Dialog Intent Latent Semantic Analysis Phonemes Ontology Analysis Linguistics Image Tags Question Analysis Self-transcendent Affective Status
  17. 17. 20 Image source: http://personalexcellence.co/blog/i deal-beauty/ City Lifestyle ZIPcode Costal vs Inland Marital status Generation Location Family Size Gender Income Level Competitors Age Loyalty & Card Activity Revenue Size Life Stages Eductation Legal status Sector Industry Subscriptions Date on Site Wish List Size of Network Check-ins App usage duration Number of Apps on Device Deposits/Withdrawals Device Usage Purchase History Following Followers Likes Number of Hashtags used History of Hashtags Search Strings entered Sequence of visits Time/Day log in Time spent on site Time spent on page Frequency of Search Videos Viewed Photos liked Sentiment Tone Euphemisms Hedonism Extroversion Face Recognition Openess Colloquialism Reasoning Strategies Language Modeling Dialog Intent Latent Semantic Analysis Phonemes Ontology Analysis Linguistics Image Tags Question Analysis Self-transcendent Affective Status X-rays (CT scans) sound (ultrasound), magnetism (MRI), Radioactive (SPECT, PET) light (endoscopy, OCT) Bio-Images Clinical/Biochemical DataMicrobiome EnvironmentDNA Proteome Steps Nutrition Genetics Runs Food Source: Bipartisan Policy Center, “F” as in Fat: HowObesity Threatens America’s Future (TFAH/RWJF, Aug. 2013) Internet of Body BMI
  18. 18. Rapid growth of exogenous data is transforming healthcare 6 Terabytes 60% Exogenous Factors 1100 Terabytes Volume, Variety, Velocity, Veracity: Educational records, Employment Status, Social Security Accounts, Mental Health Records, Caseworker Files, Fitbits, Home Monitoring Systems, and more… 0.4 Terabytes Electronic Medical / Health Records, Physician Management Systems, Claims Systems and more… 30% Genomics Factors 10% Clinical Factors IBM Watson Health // SOURCE: ©2015 J.M. McGinnis et al., “The Case for More Active Policy Attention to Health Promotion,” Health Affairs 21, no. 2 (2002):78–93 Data Generated per Life
  19. 19. Leveraging Exogenous Data for Chronic Care 60% Exogenous Factors 30% Genomics Factors 10% Clinical Factors SOURCE: ©2015 J.M. McGinnis et al., “The Case for More Active Policy Attention to Health Promotion,” Health Affairs 21, no. 2 (2002):78–93 Glucose Monitoring Calorie Intake Stress Levels Physical Activity Other vital signs Social Interaction Affinity (retail) Sleep Pattern
  20. 20. > 2.5 Trillion PDF Files in the World Majority with public and private enterprises and institutions. Enterprise HYPERDATA 23 Multi-Modal Rich data: Text, Tables, Images, Audio, Video, Formats, Hierarchy….
  21. 21. PRECISION
  22. 22. Leveraging the Explosion of Data in Medicine An Impossible Task Without Analytics and New advanced Artificial Intelligence Computing Models 1000 FactsperDecision 10 100 1990 2000 2010 2020 Human Cognitive Capacity Electronic Health Records (Clinical Data) Internet of Things (Exogenous Data) The Human Genome (Genomic Data) Capturing the Value of Data: Big Changes Ahead Medical error—the third leading cause of death in the US Source: BMJ 2016; 353 doi: http://dx.doi.org/10.1136/bmj.i2139 (Published 03 May 2016) Cite this as: BMJ 2016;353:i2139
  23. 23. 26 Body Mass Index (BMI) Mass (weight - Kg) / height (cm) x height (cm) You are “Normal” if your BMI is between 18.5 and 24.99 Adolphe Quetelet, 1832
  24. 24. 27 Practice Pearls: • BMI - Body mass index is a strong and independent risk factor for being diagnosed with type 2 diabetes mellitus • Type 2 diabetes risk may be incrementally higher in those with a higher body mass index • Understanding the risk factors helps to shorten the time to diagnosis and treatment How precise could be a “simple” signal
  25. 25. © 2017 International Business Machines Corporation The way to find information The way to make precise decisions BigData++
  26. 26. © 2017 International Business Machines Corporation Technology ingredients to make precise decisions: driving new Capability for Business Artificial Intelligence Range of techniques including natural language understanding, knowledge, reasoning and planning, for advanced tasks Cognitive Computing Leverage a combination decision-making and reasoning strategies over deep domain models and evidence-based explanations, using AI/ Machine Learning tools. Machine Learning Statistical analysis for pattern recognition to make data-driven predictions
  27. 27. © 2017 International Business Machines Corporation Research at the heart of core AI Comprehension: From video and text to rich human perception Learning and Reasoning: From scalable machine learning to making a case Interaction: Understanding language, tone, emotion and context “A green bird sitting on top of a bowl”
  28. 28. Hype Cycle for Emerging Technologies, 2016 (Gartner)
  29. 29. https://www.ibm.com/annualreport/2016/images/dow nloads/IBM-Annual-R eport-2016.pdf Augmenting DECISIONS
  30. 30. Assistant Tools Collaborator Coach Mediator Emerging types of Cognitive Systems Augment Decision Making is opening to new forms of collaboration between humans and machines
  31. 31. 34 Radiologist Oncologist Sales Assistant Tax Advisor
  32. 32. 35 Chef Designer Musicist Movie Director
  33. 33. Opportunity for decision-making support 2025 Augmenting decisions opens new opportunities on top of traditional IT Traditional global IT spend Source: IBM analysis presented to the Investor Briefings ~$2T ~$1.2T
  34. 34. 37 Top outcomes from cognitive initiatives vary by industry Finance 49% Increased market agility 46% Improved customer service 43% Increased customer engagement 43% Improved productivity & efficiency 42% Improved security & compliance, reduced risk Retail 56% Personalized customer / user experience 56% Increased customer engagement 56% Improved decision making & planning 56% Reduced costs 55% Improved customer service Health 66% Accelerated innovation of new products / services 66% Improved productivity & efficiency 64% Improved security & compliance, reduced risk 62% Reduced costs 59% Improved customer service Manufacturing 64% Improved decision making & planning 58% Improved productivity & efficiency 54% Improved security & compliance, reduced risk 52% Improved customer service 49% Enhanced thelearning experience Government/Education 54% Personalized customer / user experience 50% Improved customer service 37% Improved decision making & planning 36% Improved productivity & efficiency 33% Increased customer engagement Professional Services 40% Reduced costs 36% Personalized customer/user experience 36% Improved customer service 36% Expanded ecosystem 34% Accelerated innovation of new products / services % achieving outcome with cognitive Source: An IBM study of over 600 early cognitive adopters - 2016 Full report: http://www.ibm.com/cognitive/advantage-reports/
  35. 35. IBM Watson is the most advanced Artificial Intelligence & Machine Learning platform to support Decision Making in Business Toward a Precise Decision Making to reduce the wasteful spend as well as the risk in every industry Watson : Cognitive System
  36. 36. IBM Cognitive Computing 45 Nazioni 100+ Applications già nel mercato 6.000 Ricercatori e Specialisti in IBM 8 Lingue 200 Università organizzano corsi su Watson 500+ Partners Che integranoWatson API & Hybrid Cognitive Frameworks 20 Industrie 80.000 Sviluppatori costruiscono applicazioni con Watson Watson Health 5.000 Dipendenti,6B$ di investimento Watson Internet Of Things 1000 Dipendenti, 3B$di investimento Watson Finantial Services 3 Unità di Business Verticali 200M Cittadini 60M Pazienti 30B Immagini 1.2M Abstract Medici 60+ Soluzioni
  37. 37. Who: Current top players (prevalent) competitive directions and approaches Personalized Service / Content Aggregation Industry-oriented / Professions Specific Outcomes via cognitive Solutions Core Business Cognitive / Enhance Experiences IBM (Health, Finance, …) API SERVICES / PLATFORM AWS Microsoft Goggle Amazon (Alexa) Facebook IBM BlueMix
  38. 38. 41
  39. 39. 42 Keyword Extraction, Entity Extraction, Sentiment Analysis, Concept Tagging, Conversation Intents Entities Dialogues Personality Big5 Personality Traits Needs Values Language Tone Emotion Social propensities Language styles Translate Conversational News Custom TranslationPatents Language Deep Understanding Relation Extraction, Taxonomy Classification, Author Extraction….. Custom Analysis Speech-to-text Custom pronunciations Voice Transformation Expressive Voice Voice synthesis Keyword Spotting Telephony Broadband Vision Face Recognition Image Similarity Image Classification Custom eyes Source: https://www.ibm.com/watson/developercloud/services-catalog.html WATSON Kind of skills
  40. 40. 43 https://www.technologyreview.com/s/603895/customer-service-chatbots-are-about-to-become-frighteningly-realistic/ The movements of Soul Machines’sdigital facesare produced by simulating the anatomy and mechanicsof muscles and other tissues of th human face. Soul Machines The avatarscan read the facial expressionsof a person talking to them, using a device’s front- facing camera Soul Machines made NADIA, a chatbot for the Australian government to help people get information about disability services.
  41. 41. 44
  42. 42. 45 Conversation
  43. 43. 46 I am going to New York next May Man Walking, go around vest Where and When will you be using this jacket? I'll find a jacket that fits those conditions.Are you looking for a men's or women'sjacket? Okay, I got it. What will you use this jacket for? What styles are you looking for? Conversation https://www.thenorthface.com/xps
  44. 44. 47 I am going to New York next May Where and When will you be using this jacket? I'll find a jacket that fits those conditions. Are you looking for a men's or women's jacket? https://www.thenorthface.com/xps Man Okay, I got it. What will you use this jacket for? Walking, go around What styles are you looking for? vest
  45. 45. 48 It will be more and more a bots vs bots marketing battle! Our personal BOTS will buy for us, #Brands should convince them NOT us!
  46. 46. © 2017 International Business Machines Corporation Watson Oncology A collaboration between IBM and Memorial Sloan Kettering (MSK). Watson for Oncology utilizes MSK curated literature and rationales, as well as over 290 medical journals, over 200 textbooks, and 12 million pages of text to support decisions. • Analyzes the patient's medical record • Identifies potential evidence-backed treatment options • Finds and provides supporting evidence from a wide variety of sources
  47. 47. 50
  48. 48. 51 The Medical Sieve § Build a fast anomaly detection engine –Quickly filters irrelevant images –Highlights disease-depicting regions –Flags coincidental diagnosis § Intended as a radiology assistant –Clinicians still do the diagnosis –Machine reduces workload –Machine performs triage/decision support Given history of the patient and images of a study Is there an anomalous image here? If so, where is the anomaly ? Describe the anomaly The Medical Sieve
  49. 49. © 2017 International Business Machines Corporation 86% Accuracy
  50. 50. © 2017 International Business Machines Corporation • Identification of masses in breast MRI images >93% (1) • Detection of calcified plaques in coronary arteries from CT images > 90% (2) • Automatic Detection of Aortic Dissection in Contrast-Enhanced CT > 83% (3) • Melanoma recognition in Dermoscopic Images >84% Roc curve (sensivity >95%) IBM Research Works from the International Symposium on Biomedical Imaging 2017 (1) Hadad, Omer at ali - (2) Tang, Hui at ali. (3) Dehghan, Ehsan at ali (4) Moradi, Mehdi at ali. (5) Ben-Ari, Rami at ali. (6) Roy, Pallab at ali. (7) edai, Suman at ali (8) Coleccala et ali. • Labeling Doppler images with aortic stenosis >78% (4) • Detection of Architectural distortion in Mammograms > 80% (5) • Diabetic Retinopathy detection in Colour Fundus Images >86% (6) • Multi-Stage Segmentation of the Fovea in Retinal Fundus Images Error <14 pixel (7)
  51. 51. © 2017 International Business Machines Corporation
  52. 52. 7/18/175 I.R.C.C.S. CASA SOLLIEVO della SOFFERENZA Opera di San Pio da Pietrelcina
  53. 53. 7/18/175
  54. 54. 7/18/175
  55. 55. 7/18/175
  56. 56. 5
  57. 57. 7/18/17 Stories
  58. 58. © 2017 International Business Machines Corporation Memories are a bridge among generations 7/18 Tales
  59. 59. © 2017 International Business Machines Corporation
  60. 60. Weather is the secret to understanding how consumers feel… and cook A brand able to gain a spot in the daily routines and rituals of consumers creates a not only a relationbut a deep intimacywith them 63
  61. 61. https://watsonads.com Watson Ads 16
  62. 62. 65 CREATIVE COMPUTING
  63. 63. 66 MARCHESA A dress that think JASONGRECH Fashion zeitgeist
  64. 64. Food Knowledge Database Combinatorial Designer Cognitive Assessor Dynamic Planner Peer Produced Inspiration Set Novel Customized Recipe Cognitive Cooking System 67 How does Cognitive Cooking work? Raw Data - Recipes - Recipes contexts - Chemical/Flavour Data - Hedonic psychophysics - Background knowledge (e.g. Wikipedia for regional cuisines, etc) ... - Bayesian surprise - Flavor Pleasantness ... Data-driven Decisions 106 >1015-23
  65. 65. Watson Chef with Bon Appétit Live at: https://www.ibmchefwatson.com/tupler
  66. 66. 69
  67. 67. 70 Creations from the Cognitive Collection – Designed by JASONGRECH and IBM Watson Source: https://www.ibm.com/blogs/think/2016/08/cognitive-fa
  68. 68. 71 Source: https://www.ibm.com/blogs/think/2016/08/cognitive-fa
  69. 69. Source: https://www.ibm.com/blogs/think/2016/08/cognitive-movie-trailer/ 1) A visual analysis 2) An audio analysis 3) An analysis of eachscene’s composition IBM Research Takes Watson to Hollywood with the First “Cognitive Movie Trailer”
  70. 70. Watson /Presentation Title / Date74 Watson Platform
  71. 71. 75 IBM Cognitive Cloud | Electrolux Digital Summit 2017 Cloud Infrastructure A highly scalable, security enabled infrastructure Data Tools to prepare data for cognitive AI Cognitive building blocks for developers Applications, solutions and services Targeted solutions for enterprise businesses IBM delivers an architecture engineered for disruption Cognitive Systems leverage machine learning to predict meaning in features of human language (spoken, written, visual) and related forms of human reasoning
  72. 72. 76 IBM Cognitive Cloud | Electrolux Digital Summit 2017 Cloud Infrastructure A highly scalable, security enabled infrastructure Data Tools to prepare data for cognitive AI Cognitive building blocks for developers Applications, solutions and services Targeted solutions for enterprise businesses Ingestion ConversationAPI Storage Analytics Deployment Governance Watson Health Solutions Watson Cyber Security Weather IBM Services & Ind. Solutions Watson Virtual Agent Watson Explore and Discover IBM Risk and Compliance Asset Mgmt. (Maximo) Visual Recognition API Discovery API Speech API Compare and Comply API Document Conversion API DLaaS API Nat Language Understanding API Nat Language Classifier API Tone Analyzer API Personal Insight API Knowledge Query API IBM delivers an architecture engineered for disruption Cloud Integration Networking Security Core Enterprise Infrastructure Cognitive Systems Virtual Servers File StorageObject Storage Cognitive Micro-services DevOps Tooling ISV Solutions Client Solutions
  73. 73. 77 IBM Cognitive Cloud | Electrolux Digital Summit 2017 Data analyticsServe modelTrain model Cognitive technologies transform data into augmented intelligence that drives differentiated experiences and outcomes Cognitive micro-services driven tooling Curate Training data Conversation API Tone Analyzer API Document Conversion API Discovery API Personal Insight API Nat Language Understanding API Compare & Comply API Visual Recognition API Nat Language Classifier API DLaaS API Speech API Knowledge Query API AI https://developer.ibm.com/academic/ https://www.ibm.com/developerworks/
  74. 74. 78 IBM Cognitive Cloud | Electrolux Digital Summit 2017 IBM Academic Initiative https://developer.ibm.com/ac ademic/ References Bluemix https://www.ibm.com/cloud- computing/bluemix/
  75. 75. 79 CLOSING
  76. 76. Chief Artificial Intelligence Officer Chief Data Scientist Chief Information Officer Chief Data Officer DATA INFORMATION KNOWLEDGE WISDOM “A number” “A STREET number” “A map of a City” “A GPS root recommendation to go from A to B”
  77. 77. https://www.theguardian.com/technology/2016/sep/08/artificial-intelligence-beauty-contest-doesnt-like-black-people
  78. 78. https://www.partnershiponai.org/
  79. 79. Cognitive Principles 1. Purpose 2. Transparency 3. Skills Source: https://www.ibm.com/ibm/responsibility/ibm_policies.html The purpose of AI and cognitive systems developed and applied by the IBM companyis to augment human intelligence. The IBM company will make clear: a) When and what purpose of a cognitive solution; b) Major Data Used; c) Protect Customer Data & Insightsownership. IBM company will workto help students, workers and citizens acquire the skills and knowledge to engage safely, securely and effectively in a relationship with cognitive systems, and to perform the new kinds of work and jobs that will emerge in a cognitive economy.
  80. 80. Thank you for your attention. Pietro Leo Executive Architect & CTO Chief scientist, and research strategist IBM Italy IBM Academy of Technology Leadership Team pieroleo.com
  81. 81. July 18,201785 1st Place Image Source: COCO Challenge https://www.ibm.com/blogs/bluemix/2016/12/watsons-image- captioning-accuracy/ 1st Place Speech Watson says: “A green bird sitting on top of a bowl” IBM Leadership in AI to understand our world Watson error rate: 5.5% Source: Switchboard conversational corpus https://www.ibm.com/blogs/watson/2017/03/reachi ng-new-records-in-speech-recognition/
  82. 82. 86 IBM Cognitive Cloud | Electrolux Digital Summit 2017 Data analyticsServe modelTrain model Ready to use Affective Computing services in the Watson Platform Cognitive micro-services driven tooling Curate Training data Conversation API Tone Analyzer API Document Conversion API Discovery API Personal Insight API Nat Language Understanding API Compare & Comply API Visual Recognition API Nat Language Classifier API DLaaS API Speech API Knowledge Query API AI = Affective Service Emotional Tone: joy, fear, sadness, disgust, anger Social Tone: openness, conscientiousness, extraversion, agreeableness, emotional range or neuroticism Language Tone: Analytical, confidence, tentative Customer Engagement Tone: Sad, Frustrated, Satisfied, Excited, Polite, Impolite, Sympathetic
  83. 83. 87 IBM Cognitive Cloud | Electrolux Digital Summit 2017 Data analyticsServe modelTrain model Ready to use Affective Computing services in the Watson Platform Cognitive micro-services driven tooling Curate Training data Conversation API Document Conversion API Discovery API Personality Insight API Nat Language Understanding API Compare & Comply API Visual Recognition API Nat Language Classifier API DLaaS API Speech API Knowledge Query API AI = Affective Service Big Five dimensions Emotional Range, Consciousness, Openness, Introversion/Extroversion, Agreeableness, Big Five facets (30 sub dimensions) Needs Structure, Curiosity, Challenge, Ideal, Stability Values Stimulation, Tradition, Helping others, Taking pleasure in life, Achievement Tone Analyzer API
  84. 84. 88 IBM Cognitive Cloud | Electrolux Digital Summit 2017 Data analyticsServe modelTrain model Ready to use Affective Computing services in the Watson Platform Cognitive micro-services driven tooling Curate Training data Conversation API Tone Analyzer API Document Conversion API Discovery API Personality Insight API Nat Language Understanding API Compare & Comply API Visual Recognition API Nat Language Classifier API DLaaS API Speech API Knowledge Query API AI = Affective Service Expressiveness GoodNews, Apology, Uncertainty Voice Transformation Young, Soft Custom: Pitch, pitch range,, glottal tension, breathiness, rate timbre (sunrise, Breeze)
  85. 85. 89 IBM Cognitive Cloud | Electrolux Digital Summit 2017 Data analyticsServe modelTrain model Ready to use Affective Computing services in the Watson Platform Cognitive micro-services driven tooling Curate Training data Conversation API Tone Analyzer API Document Conversion API Discovery API Personality Insight API Nat Language Understanding API Compare & Comply API Visual Recognition API Nat Language Classifier API DLaaS API Speech API Knowledge Query API AI = Affective Service Emotions joy, fear, sadness, disgust, anger Target Emotions for Entities (24 main types of entities) (433 subtypes) Custom entities Keywords

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