What you need to know to start an AI company?

What you need to know to start an AI company?
Mo Patel, Practice Director
Artificial Intelligence & Machine Learning
Teradata Analytics
Tech Club Lunch & Learn
November 10, 2016
The Mack Institute
The Wharton School
University of Pennsylvania

2 © 2016 Teradata
• What is Intelligence?
• How did we get here?
• Why is AI a hot topic right now?
• What is the AI startup landscape?
• What do you need for AI startup?
• What challenges are brought
forward by AI?
Agenda
Image: The Verge

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What is Intelligence?

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Studying Biological Neural Network to achieve sentient Artificial
Intelligence

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What is Machine Intelligence?
Teaching Machines to learn and detect patterns from data to
make future predictions
Learning Prediction
Intelligenc
e

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Machine Intelligence Design Patterns
Raw Data
Feature
Engineering
Modeling Testing Deploym
ent
Raw Data Learning
Hyper
Parameter
Optimization
Deploym
ent
Data
Rules
Learn-
ing
Deploy
ment
Feed
back
Unsupervised
Supervised
Reinforcement

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How Machine Learning is and has been used in the past decade?
Raw Data
Feature
Engineering
Model
Training
Model
Testing
Model
Packaging
Model
Deployment
Benefits:
 Proven & Tested
 Explainable
 Large Community
Drawbacks:
− Tedious Feature Engineering
− Prone to Input Data Drift & Shift
− Must limited # of Features
Ingest Modeling Production

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How is Machine Learning Changing?
Raw Data
Model Training: Deep Learning
Model
Testing
Model
Packaging
Model
Deployment
Benefits:
 Feature Learning
 Record Breaking Results
Drawbacks:
− Model Architecture Engineering
− Data Hungry
− Training Phase Time Intensive
Ingest Modeling Production

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How did we get here?

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First breakthroughs in Artificial Neural Network around WW2
• Logical Calculus for
Nervous Activity –
McCulloch & Pitt (1943)
• Intelligent Machinery –
Alan Turing (1948)
• Cornell Mark 1 Perceptron
– Frank Rosenblatt (1958)

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Artificial Neural Network models as Perceptron Models
• Simple Feed Forward Network
• Linear Classifier
Single Layer Perceptron Multi Layer Perceptron
• Feature Learning instead of Feature Engineering
• Highly dimensional data is organized into learning
features as network is connected from layer to
layer
• Solving Activation Function of hidden layer:
Gradient Descent (best weights) assisted by
Back Propagation (partial derivatives)

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• Simply adding more layers to a shallow Neural
Network does not significantly increase accuracy
after a certain point
• Complex structures needed that capture distinct
concepts in the data (e.g.: image decomposition)
There is a problem with Perceptron Model!

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Winter is Coming …
1974 DARPA drops funding for Speech Understanding
Research program at Carnegie Mellon University
1980s Expert Systems Hype & Crash

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Why is AI a hot topic right now?

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Spring is here! Data + Algorithms + …

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… GPUs …
NVIDIA DGX-1
$129K

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… breaking records …

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Deep Learning has become a topic of heavy interest in 2016
• What is Deep Learning?
– Evolution of multi-decade long research in Artificial Neural Networks that
mimic natural Neural Networks (e.g. The Human Brain)
– A step towards Artificial Intelligence (self aware, self learning system)
• Why Deep Learning Now?
– The Big Data Revolution: Variety of large data sets available for research
– Algorithmic Arms War: Leading Internet technology companies competing to
beat benchmarks
– On Demand Computing: Easy accessibility to massive scale advanced
hardware for experimentation (training) and production

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Deep Learning shatters the Perceptron Model barriers
• Enabled through increase in available
processing power and data (since 2006+)
• Deeper networks – breaking down
problems
• New types of layers and activations
• Nonsequential network architectures
• Unsupervised pre-training of networks
• Transfer learning

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Why Deep Learning is delivering better results than traditional
Machine Learning methods?
Deep LearningMachine Learning
Big Data
Feature
Engineering
Predictive
Modeling
Significant challenges in traditional ML methods for high
dimensional large datasets (i.e. image, audio, video,
natural language, documents, sensors)
Big Data
Feature
Learning
Predictive
Modeling
Data Scientist time is spent with Data
Engineers in Architecture Engineering
(Less complex than Feature
Engineering)

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What is the AI startup landscape?

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State of Machine Intelligence by Shivon Zilis & James Cham
https://hbr.org/2016/11/the-competitive-landscape-for-machine-intelligence

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What are my hardware options as a startup?
• AI applications require specialized hardware to achieve useable results
GPU & FPGA in Cloud
Source: Infoworld - Microsoft Distinguished Engineer Doug Burger

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What are my hardware options as a startup?
• AI applications require specialized hardware to achieve useable results

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But what about data?
Data is the hardest part
Open
Data Sets
Gamification
Crowd Sourcing
Simulation Transfer Learning
Pre-trained Models

39 © 2016 Teradata
What are the leading ethical issues in AI?
Unemployment Bias Machine-
Human
Relationship
Security Safety
https://www.weforum.org/agenda/2016/10/top-10-ethical-issues-in-artificial-intelligence/

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