Beyond the “I” in AI€¦ · © 2019 The MathWorks, Inc. 1 Beyond the “I” in AI Richard...
Transcript of Beyond the “I” in AI€¦ · © 2019 The MathWorks, Inc. 1 Beyond the “I” in AI Richard...
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© 2019 The MathWorks, Inc. 1
Beyond the “I” in AI
Richard RovnerVP Marketing, MathWorks
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Watt Steam Engine
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Artificial intelligence is a transformative technology
based on McKinsey’s latest AI forecast – September 2018
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AI has tremendous potential to increase productivity
3x
AI 4x
2x
=
McKinsey Global Institute, September 2018
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But, AI is struggling
Why Most AI Projects FailOct, 2017
Most AI Projects Fail. Here’s How to Make Yours Successful.July, 2018
3 Common Reasons Artificial Intelligence Projects FailMay, 2018
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There are many ways Artificial Intelligence can fail
No data scientists
Not enough data
Too much data
Problem is a poor fit for AI
Low ROI
Beyond the skill of the team
Incomplete tools
Can’t interact with other systems
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AI is more than just the intelligence of the algorithm
Intelligence
Interaction
Insights
Implementation
Apply domain expertise
Operate within their environment
Span the entire design workflow
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Intelligence
Interaction
Insights
Implementation
Apply domain expertise
Operate within their environment
Span the entire design workflow
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Bring human insights into AI
Select data
Make tradeoffs
Evaluate results
AI
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Bring human insights into AI
• We are the domain experts
• Shortage of data scientists
• We need the right tools
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Improving New Zealand Dairy Processing• University of Auckland
• Auckland University of Technology
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Continuous Plant Process
Powdered Milk
Goal: Detect a bad product earlier
Days later
Raw Milk
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Data
Powdered Milk
Raw Milk
Goal: Detect a bad product earlier
AI modelPlant Process Predict Results
Near real-time
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Data
Powdered Milk
Raw Milk
They had lots of data
Plant Process
• Millions of data points
• 6 years
• 3 plants
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Data
Powdered Milk
Raw Milk
But…
AI modelPlant Process
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They had three key insights
1. Need to build a separate model for each plant
Plants behaved differentlyfrom each another
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They had three key insights
1. Need to build a separate model for each plant
2. Plant’s operating state changes each year
Each year was like a completely different plant
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True
Cla
ss
Predicted Class
Bulk density prediction results were inaccurate
• Many false positives
• Unused classes
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They had three key insights
1. Need to build a separate model for each plant
2. Plant’s operating state changes each year
3. Training data was biased
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True
Cla
ss
Predicted Class
100%
90%
83%
93%
93%
93%
97%
10%
10%
3%3%
3%
3%
3%
3% 3%
3%
3%
Resampling data resulted in higher predictive accuracy
• Resampled data
• Reduced the number of bins
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© 2019 The MathWorks, Inc. 21https://imgur.com/gallery/8B5Tx
「MATLABによる非常に生産的なデータの可視化と解析の素早さを目の当たりにした時の、業界関係者の驚きを隠せない表情を見るのは
非常に誇らしく思います。この結果が彼らの仮説を検証し、プロセス改
善の新しいアイディアを生み出す事に大いに貢献してくれました。」
- David Wilson, Industrial Information and Control Centre
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To succeed with AI …
Combine AI model buildingwith scientific and engineering insights.
Use tools that span boththe science and engineering and the data science.
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Intelligence
Interaction
Insights
Implementation
Apply domain expertise
Operate within their environment
Span the entire design workflow
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Intelligence
Interaction
Insights
Implementation
Apply domain expertise
Operate within their environment
Span the entire design workflow
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Implementation is about designing the solution
TestingData analysis
Reporting
Concept DevelopmentPrototypingDeployment
Requirements buildingModeling & simulationVerification & validation
Research Manufacturing Autonomous System
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“Deliver on the promise of self-driving cars today.”
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Voyage’s goal was to quickly get to market
1. Target retirement communities
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Voyage’s goal was to quickly get to market
1. Target retirement communities
2. Use off-the-shelf components wherever possible
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Voyage’s goal was to quickly get to market
1. Target retirement communities
2. Use off-the-shelf components wherever possible
3. Bring in the right software tools across the entire workflow
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Voyage completed their AI system first
AIPerception
System
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Connect the AI to the rest of the system
AIPerception
System
Vehicle ControlSystem
Vehicle Dynamics
Environment
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Started with Simulink example
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Injected simulated vehicles to interact with while driving
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Deployed controller as ROS node and generated code
Robotics System ToolboxEmbedded Coder
Robot Operating System
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Train your AI faster with tight simulation loops
Field Data Algorithms
Usage
Better
SyntheticData
Simulated Usage
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Use simulation for data synthesis
Simulation-based workflow
Simulate Auto-label
Traditional deep learning workflow
Record Label
AI model
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“Simulink + ROS allowed us to deploy a Level 3 autonomous vehicle in less than 3 months.”− Alan Mond, Voyage
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To succeed with AI …
Use tool chains that span the entire design workflow.
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Intelligence
Interaction
Insights
Implementation
Apply domain expertise
Operate within their environment
Span the entire design workflow
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Intelligence
Interaction
Insights
Implementation
Apply domain expertise
Operate within their environment
Span the entire design workflow
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Interaction within complex environments
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What was the larger system the vehicle had to operate in?
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“Proactive patient care”
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Statistics and Machine Learning ToolboxSignal Processing ToolboxMATLAB CoderEmbedded Coder
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EarlySense’s AI can predict critical events before they happen
ContinuousMonitoring
EarlyDetection
EarlyIntervention
BetterOutcomes
AI AI
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Dashboards at nurses’ stations and on hallway monitors
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Alerts on hand-held devices carried by staff
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Address problems before they become emergencies
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To succeed with AI …
Design how our systems will integrate and interact within their environment.
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AI is a transformative technology But AI projects can and do fail
Success requires more than just intelligence
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Intelligence
Interaction
Insights
Implementation
Apply domain expertise
Operate within their environment
Span the entire design workflow
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Intelligence
Interaction
Insights
Implementation
Apply domain expertise
Operate within their environment
Span the entire design workflow
How will you apply AI to your projects?
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Go Beyond the “I” in AI