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.
Elements of language learning - an analysis of how different elements of lang...
What is AI
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
2
John T. Langton
3. Agenda
Putting AI hype in perspective
What is AI?
An example of how AI works
AI use cases
Questions
3
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
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
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
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