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Artificial Intelligence & BusinessPresentation to: Analytics & Technology Summit, Wilmington, [email protected] 22, 2016
Copyright Design Economics, llc 2016. All rights reserved.
Special thanks to Dr. Steve Omohundro Chris Vaughn, Ruth Fisher, Lloyd Nirenberg, and Melanie Swan for comments, insight and perspective. Any mistakes remain my own.
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Illustration: Artificial Intelligent Diagnosis(Stylized)
US Medical SchoolRead JournalsAnalyze DataDiagnose
OncologistUS Medical School
Diagnose
OncologistSloan Kettering Hospital
Read JournalsAnalyze DataDiagnose
Artificial Intelligence
Diagnose
Oncologist
(US AI model)
Artificial Intelligence
Thai Hospital
Past Present
Sent To
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Artificial IntelligenceThe science of building machines able to take actions in an environment that will achieve desired outcomes using scarce resources.
Steve Omohundro, Ph.D.
What is Artificial Intelligence
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• 1/3 of Software, IT firms; >$10 billion invested
Google Microsoft IBM Baidu Facebook Intel Amazon Others
• All Auto Companies, Most Agriculture services, misc.
• Most other industries:mainly
leaders (1% of companies) GE Siemens American Express Bloomberg …
Leading Companies Investing in AI
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1,000 Artificial Intelligence Startups
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• What’s Going On?
• Noteworthy Artificial Intelligence Technologies
• Business Implications
Agenda
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Sample Application: Automated Anesthesiologist
Characteristics: Model Built of Sub-Models More / less-detailed variants Parameterized (Leverages ‘IOT’)
An Implicit or Explicit Part of Every Quantitative Analysis
AI: An Inherently Systematic Set of ToolsA Model of the ‘World’: Explicit or Implicit Core of Any Analysis
Applied Mathematical Models in Human Physiology,
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‘What Does a Leaf Look Like?’• Concept is Complex• Different Perspectives• Exceptions• Complicating Factors
Limited human:• Data Entry• Model Building• Data Processing
Develop equations equivalent to human ‘concepts’
AI: Generally Includes Neural Net Approaches Conceptual Handling of High-dimension problems & Automated Modeling
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Neural Networks Basics
0
1
S
Xi = Input from Neuron i
wi = Weight
Do Neurons Fire?
S = 1 is ‘fire’
Learning Data
NeuronsNeuron
Neuron
Neuron
Neuron
Neuron
NeuronNeuron
Neuron
Initial layers respond to sub-elements
Later layers respond to refined concepts
Neuron
Click to Play
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Image Recognition:
• Facebook People• Check, Form Processing• People’s Backs• Series of objects• Recognize Nudity• ID Objects Around Cars• ‘Bin-picking’ > 90% accurate• Catching Liars (better than
humans)• Cancer Detection (ID’ed new
approach)• Create Sound Basis Visual
What Can Neural Networks Do? Image Recognition
Click to PlaySee 10:46
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Natural Language: • Google Translator• Digital Assistants• Legal Research• Academic/ Tech Research• Teaching Assistant• Poetry Writing• Fiction Writing• Movie Script Writing• Writing Tone Improver
Click to Play
What Can Neural Networks Do? Natural Language
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General Pattern Recognition:
Financial Trading Product Recommending NIH Data Music Development Product System Design, Rec Manage complex physics Winning Conceptual ‘Go’* Design Artwork Jazz Jams ID Embryonic Cell State Generating Speech Brainwaves to Human Faces
Click to Play1:50
What Can Neural Networks Do? MUCH MORE!
* Illustrates ability for computers to think creatively & conceptually
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Clippy the Microsoft Assistant
Seemless Human-Machine Interaction (Driving)
• Cost of Interruption• Response Time• Cost of non-interruption• Surprising Information
Health System Understanding (Not AI)• Reconstruct Metabolic Paths• Cigarette Smoke Exposure• Intra-Cell Biologic Pathways• Decoding the Genome
Diagnosis / Treatment (AI):• Personalized Lung Cancer
Survival and Treatment• Fraud Detection• Psychiatric Illness Diagnosis• Online Self-Diagnostic Tool• Automated Image Annotation• Automated Image Text Rerieval
• Patient-specific drug prescription
AI: Frequently Leverages Statistical & Logical ToolsFor Lower-Dimension Problems (with Uncertainty)
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‘Lights’ for Black Box Models
Emotion Technology
Persuasive Technology
Statistics + Machine Learning
Methods for Volatile Process
Re-application Approaches
Maximizing Outcome Value Objective Function Design Objective Maximization
End-to-end Networks, Focus
Structuring Memory, Info
Anticipation
Select Development Areas
RAPID Ongoing AI Capability Growth
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Artificial Intelligence Limitations
Must Have Engaged, Understanding, Org.Limited to data available Data Quality, Comparability Weak in low-data pockets Over-generalize False-correlations Unclear what is ‘right’ if humans are unclear (NN)Can Break rules & lawsAre Integrated, Static (NN) 1 Change, Changes ALL Unclear change in model vs w/in model uncertainty Miss Reactions to UsTechnical Limitations
Uncertain what they’ll do Problem is shrinking (NN) Re-applyication is challenging Problem is shrinking (NN) Replicable, trickable
(NN) Neural Network Limits
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Artificial Intelligence StrengthsNO incremental cost
Alternate creativity
Customize every output
Fast (can be)
Work anywhere (no body)
Know all we know
Alternate Creativity
Excellent Pattern Recognition
Not Afraid of New / Complex
Leverage computer process, memory improvements
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• What’s Going On?
• Noteworthy Artificial Intelligence Technologies
• Business & Social Implications
Agenda
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Automation
Personnel Strategies becomePersonnel + Machine HR & IT Close Partners
Empower less-skilled;‘Rock stars’ created
Computational Thinking Everywhere Math / Logic critical Interpersonal skills ever more important… for now
Humans and Computers Together: Case of The Movie Industry
Annotated Scene Review
• Analyze Compositions: visual, audio• Recommend 10 trailer scenes probabilistically
• Development time reduction: 10-30 days 3 days
Trailer Production Support
IBM WatsonGoogle Deep Mind
+ Evaluate new projects (accept, reject, budget)+ Identifies screenwriter’s deficiencies
Pacific Aurora Project
(Developer Interview)
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Business Planning and Artificial Intelligence: Case of The Auto Industry
Confusion with Fleet Management• Spent $0.5- 2 BB for Uber, etc. minority shares• Now experimenting w/ internal ‘fleet business’• Fleet economics may be unattractive for many
Massive Autonomous Driving Investment• ‘80%’ is simple; massive, overlapping investment Auto industry, tech industry, entrepreneurs• Commercial ~2020, adjusted by infrastructure, laws
Re-imagining rider & driver entertainment• Apple spent $5 BB on ‘cars’ R&D• Virtual Reality, augmented reality, & furniture
At stake:3.8 MM Driving Jobs$1 TR / yr people time
At stake:>1 MM auto man. jobsUrban (parking)
At stake:$3-60 BB / Yr Mkting
Revolutionary technology shifts + AI-Sourced (+ others) + Challenging to foresee
Re-defining other industry dynamics Critical to forecast Challenging to forecast
Potentially reversing, dynamic patterns
Require new AI methods & approaches
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Irreplaceable strategic perspective– not just a ‘strategic partner’
Proactive data stewards key to automation plans
Machine Learning / AI algorithm sourcing : Packaged with cloud service? 3rd Party Markets? Internally Developed?
Information Technology: Strategic Data and Service Selection
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Know All We Know, Dream Deep Customer, Business, Competitor Understanding Fully Leveraged
Developing IP Neural Net Techniques Training Data Trained Nets Other ‘Big Data’ Models
Collaborate, Jointly Develop Update ‘Firm’s Meaning
Custom Production ‘Anywhere’ ‘Neural Netted’ Production Specs Generalized Machinery, Robots 3D Printed
Innovation, IP, & Efficiency Re-Defined
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1. Industry leaders and startups driving AI forward
2. Rapid AI progress, using neural nets & other tools + Leveraging growing computer power to learn features + Incredible capabilities– concerning limitations + Need the right data, complementary techniques
3. Key Business Function Thinking is Evolving + Personnel Strategies become People + Machine Plans + Business Planning impacted by New Technology Wave + IT the Source of Strategic Insights + Data, Algorithms require re-defining ‘the firm’ & innovation
Summary
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What Can Neural Networks Do? PredictionAdversarial Networks Create Video from Single Photos
Click to Play