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What is AI

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Hack/Reduce recently hosted John T. Langton, Director of Applied Data Science at Wolters Kluwer, who spoke at its quarterly dinner event for Boston technologists about the application of AI in the enterprise.

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What is AI

  1. 1. AI: Intro and Use Cases John T. Langton June 27, 2019
  2. 2. Who am I?  Ph.D. in computer science  Director of Data Science at WK  PI on multiple DoD projects  Founder of VisiTrend: cyber ml  Several peer reviewed publications and speaking engagements 2 John T. Langton
  3. 3. Agenda  Putting AI hype in perspective  What is AI?  An example of how AI works  AI use cases  Questions 3
  4. 4. AI: the hype 4 Bloomberg: number of AI mentions in corporate earnings statements
  5. 5. AI Hype Cycle 5 Gartner says ML is is 2-5 years to adoption
  6. 6. AI Reality Machine learning has been in production for years, and it’s ubiquitous  Siri in 2011  Google search  Netflix recommendations  Image search  Elevators  Thermostats  Rice cookers AI from Adoption to Disruption 6
  7. 7. Multiple AI Hype Cycles: the story of AI Winters As soon as AI solves something, but it’s not a walking, talking robot, then it is no longer considered AI. AI from Adoption to Disruption 7
  8. 8. AI from Adoption to Disruption 8 “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run” – Roy Amara
  9. 9.  AI is not new; it’s ubiquitous and in products that you use every day  What’s new are the application areas and scale of AI  Digitization of content  Advances in hardware Why AI and Why Now? Google’s TPU 9
  10. 10. So what is AI?  A set of algorithms (search + statistics)  Software that can program itself using data and/or axioms and a goal  Can adapt over time AI from Adoption to Disruption 10
  11. 11. The Birth of AI Antiquity 1800’s 1947 1956 AI from Adoption to Disruption 11 Aristotle covers syllogism and goal-regression planning Logical positivists: knowledge can be reasoned about mathematically Alan Turing: the Turing Test, machine learning and genetic algorithms McCarthy workshop at Dartmouth coined “artificial intelligence”
  12. 12. Most people think “Strong AI” but mean “Weak AI” What Do We Mean by AI? 12 •Ability to reason like a human •General problem solver •Passes Turing Test (Blade Runner) Strong or General AI •Well scoped applications such as predicting sepsis •Automation, detection, prediction •Data driven and often performs better than a human Weak or Narrow AI
  13. 13. General AI Requirements Turn into Narrow AI Subfields  Natural Language Processing (NLP): topic modeling, sentiment analysis  Perception: computer vision, speech recognition  Knowledge representation: linked-lists, ontologies, frames, logic statements  Reasoning: search, propositional and fuzzy logic, probabilistic reasoning  Planning: decision theory, constraint satisfaction, problem solving as search  Learning: machine learning, evolutionary computing AI from Adoption to Disruption 13
  14. 14. What is AI 14 NLP
  15. 15. Machine Learning (ML) Supervised Learning Continuous Target Variable Regression Forecasting Drug Utilization Categorical Target Variable Classification Predicting Sepsis Unsupervised Learning No Target Variable Clustering Customer Segmentation Association Rules Market Basket Analysis Reinforcement Learning Fitness Functions Genetic Algorithms Route Optimization Hidden Markov Models Spam Detection
  16. 16. Machine Learning is Pretty Big AI from Adoption to Disruption 16
  17. 17.  Expert systems  Discriminant analysis  Agents / multi-agent architectures (“Society of Mind”)  Evolutionary Algorithms  Case Based Reasoning  Fuzzy Logic  Constraint-based satisfaction  Game theory  Search-based Problem Solving (A*, Hill Climbing, Local Beam)  Pattern Recognition  Sequence learning, classification, and optimization  NLP: LDA, LSA, TF / IDF  … AI is Bigger than ML and Deep Learning 17
  18. 18.  Doctors – expert opinion from training, studies, dozens of cases  Studies and Randomized Controlled Trials – up to hundreds of patients in each  SIRS and qSOFA diagnosis systems – consensus of expert opinions + studies  Over a million patients are diagnosed with sepsis every year  OSU conducts study with 319,817 patients revealing flaws in SIRS and qSOFA  How do we leverage the scale of this data?! AI Example: predicting / detecting sepsis
  19. 19. 1,000,000 Gini = .5 p(s) = .5 p(s) = .7 Gini = .58 = 1-(.72 +.32) 714,000 p(s) = 0 Gini = 0 = 1-(12 +02) 286,000 AI Example: predicting Sepsis with CART PCT ≤ .2 ng/mL truefalse For each variable:  Compute data distribution  Find optimal split point(s)  Compute Gini index for each branch  Sum Gini indexes weighted by # of records for each branch
  20. 20. 1,000,000 Gini = .5 p(s) = .5 p(s) = .7 Gini = .58 = 1-(.72 +.32) 714,000 p(s) = .81 Gini = .69 = 1-(.812 +.192) 614,000 p(s) = 1 Gini = 0 = 1-(12 +02) 100,000 p(s) = 0 Gini = 0 = 1-(12 +02) 286,000 AI Example: predicting Sepsis with CART • Repeat for each variable • Choose variable with lowest Gini sum and split • Repeat until stopping criteria • Branch has Gini = 0 • No more variables • Maximum depth PCT ≤0.2 ng/mL systolic ≤ 90 mmHg truefalse false true
  21. 21.  Tree pruning  Other cost functions  Winnowing variables using dimensionality reduction (PCA, MDS)  Weighting classes and misclassification types  Sampling techniques for data points and variables (stratified sampling, bootstrapping)  Ensemble methods like boosting and rf  # of trees  Tree depth  Learning rate AI Example: more advanced methods
  22. 22. AI Evaluation Example: 10-fold Cross Validation
  23. 23. AI: Metrics of Evaluation
  24. 24. AI Use Cases  Cybersecurity  Anomaly detection  Static analysis of malware  Domain generating algorithm detection  Tax and Legal  Regulatory change management  Billing automation and reconciliation  Finance  Risk analysis  Projections and forecasting 24
  25. 25. Health AI Startups AI from Adoption to Disruption 25  Risk Analytics: readmission, interventions, quality measures  Medical Imaging: diabetic retinopathy, skin cancer, radiology  Text Analytics: terminology mapping, code extraction, document classification  Predictive Analytics: Clostridium Difficile, Sepsis, renal failure Source: www.cbinsights.com
  26. 26. AI from Adoption to Disruption 26  Companies:  Freenome, SOPHiA Genetics, Deep Genomics, FDNA, DeCODE, Verily Life Sciences (spun out of Google)  Precision medicine: gene therapy development and biomarker discovery  Drug discovery: predicting response to molecular compounds  Risk analytics: detecting genetic variants associated with disease AI + Genomics
  27. 27. AI and Radiology AI from Adoption to Disruption 27
  28. 28. AI and Radiology AI from Adoption to Disruption 28 FDA approves IDx-DR, AI-powered software to detect diabetic retinopathy Infervision uses AI to detect bleed volume in stroke patients
  29. 29. Concerns, Obstacles, and Challenges  Data  Is there enough?  Does it need to be labeled?  Is it noisy?  Class imbalanced?  Black box vs explainable AI  AI bias  Regulations  Evaluation (should AI drivers have less accidents than humans?) AI from Adoption to Disruption 29
  30. 30. Questions?  John T. Langton  http://JohnLangton.com AI from Adoption to Disruption 30

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