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.

1. AI: Intro and Use Cases
John T. Langton
June 27, 2019

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
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John T. Langton

3. Agenda
 Putting AI hype in perspective
 What is AI?
 An example of how AI works
 AI use cases
 Questions
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4. AI: the hype
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Bloomberg: number of AI mentions in corporate earnings statements

5. AI Hype Cycle
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Gartner says ML is is
2-5 years to adoption

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. 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. 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.  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
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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. 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. Most people think “Strong AI” but mean “Weak AI”
What Do We Mean by AI?
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•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. 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. What is AI
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NLP

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. Machine Learning is Pretty Big
AI from Adoption to Disruption 16

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
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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. 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. 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.  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. AI Evaluation Example: 10-fold Cross Validation

23. AI: Metrics of Evaluation

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
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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. 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. AI and Radiology
AI from Adoption to Disruption 27

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. 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. Questions?
 John T. Langton
 http://JohnLangton.com
AI from Adoption to Disruption 30