This document provides an overview of machine learning and artificial intelligence. It discusses how machine learning works by having algorithms learn from large amounts of data to make decisions without being explicitly programmed. The document outlines the step-by-step machine learning process of defining an objective, preparing data, training a solution using an algorithm, and testing the model. It also discusses how AI can be used for tasks like vision, communication, and games. Examples of real-world AI applications are also provided.

1. Journey to AI
Cassie Kozyrkov,
Chief Decision Scientist, Google Cloud
@quaesita

2. Confidential & Proprietary 2
An unprecedented
opportunity
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AI in action
At 10 minutes?
It’s random luck
AI in action
At 10 minutes?
It’s random luck
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AI in action
At 120 minutes?
It’s flawless
AI in action
At 120 minutes?
It’s flawless
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AI in action
At 240 minutes?
Strategy emerges
AI in action
At 240 minutes?
Strategy emerges
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6. Artificial intelligence
Machine learningNeural networks
Models
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Machine learning is an approach
to making lots of small decisions

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Machine learning vs traditional programming
Information AnswerRECIPE
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Traditional programming: Handcrafted recipe
?
(recipe)
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Traditional programming: Handcrafted recipe
0 1 0 1 1 0 1 0 1 0 0 1
(information) (answer)
CODE
(recipe)
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Machine learning: Recipe learned from data
(information) (answer)
0 1 0 1 1 0 1 0 1 0 0 1
(information) (answer)
0 1 1 1 1 0
1 0 1 0 0 0
0 1 1 0 1 0
0 0 1 0 0 0
1 0 0 0 0 1
0 1 0 1 1 1
1 0 0 1 0 1
1 1 0 1 1 1
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(information) (answer)
0 1 0 1 1 0 1 0 1 0 0 1
(information) (answer)
0 1 1 1 1 0
1 0 1 0 0 0
0 1 1 0 1 0
0 0 1 0 0 0
?
(recipe)
1 0 0 0 0 1
0 1 0 1 1 1
1 0 0 1 0 1
1 1 0 1 1 1
ML MODEL
(recipe)
Machine learning: Recipe learned from data
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Step-by-step
machine learning
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Step 1
Define your objective.

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Define your objective
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Step 1: Define your objective
?
(information)
?
(answer)
?
(recipe)
Think about desired outputs
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Step 1: Define your objective
?
(information)
Y / N
(answer)
?
(recipe)
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I’d like:
Y/N with at least 80% accuracy.
Step 1: Define your objective
?
(information)
Y / N
(answer)
?
(recipe)
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?
(information)
Y / N
(answer)
?
(recipe)
I’d like:
Y/N with at least 80% accuracy.
Step 1: Define your objective
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Step 2
Wrangle your data.

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Wrangle your data
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Get some data to learn from
?
(information)
Y / N
(answer)
?
(recipe)
Identify some useful inputs
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Y / N
(answer)
?
(recipe)
Age
Review Score
(information)
Get some data to learn from
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100
50
30
20
Split your data
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Data inputs
Wine classification
Age in years
Reviewscore
4
2
0
-2
2 4 6 8
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Use a line?
Wine classification
Age in years
Reviewscore
4
2
0
-2
2 4 6 8
Use a line?
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Step 3
Train your solution

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Train your solution
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Train your solution
?
(information)
Y / N
(answer)
Pick an algorithm!
?
(recipe)
Age
Review Score
(information)
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Train your solution
Wine classification
Age in years
Reviewscore
4
2
0
-2
2 4 6 8
Support Vector Classifier
Decision Tree
Neural Network
Algorithm selection:
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Wine classification
Age in years
Reviewscore
4
2
0
-2
2 4 6 8
Support Vector Classifier
Decision Tree
Neural Network
Algorithm selection:
Train your solution
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Support Vector Classifier
Algorithm selected:
Wine classification
Age in years
Reviewscore
4
2
0
-2
2 4 6 8
Run ML algorithm
Train your solution
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Support Vector Classifier
Algorithm selected:
Run ML algorithm
Wine classification
Age in years
Reviewscore
4
2
0
-2
2 4 6 8
Train your solution
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Get model (recipe)
Wine classification
Age in years
Reviewscore
4
2
0
-2
2 4 6 8
Train your solution
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Get model (recipe)
Train your solution
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New data? Use recipe!
Train your solution
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New data? Use recipe!
Train your solution
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Support Vector Classifier
Decision Tree
Neural Network
Algorithm selection:
Wine classification
Age in years
Reviewscore
4
2
0
-2
2 4 6 8
Train your solution
@quaesita

39. Confidential & Proprietary
Support Vector Classifier
Decision Tree
Neural Network
Algorithm selection:
Wine classification
Age in years
Reviewscore
4
2
0
-2
2 4 6 8
Train your solution
@quaesita

40. Confidential & Proprietary
Run ML algorithm
Wine classification
Age in years
Reviewscore
4
2
0
-2
2 4 6 8
Decision Tree
Algorithm selected:
Train your solution
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41. Confidential & Proprietary
Decision Tree
Algorithm selected:
Run ML algorithm
Wine classification
Age in years
Reviewscore
4
2
0
-2
2 4 6 8
Train your solution
@quaesita

42. Confidential & Proprietary
Decision Tree
Algorithm selected:
Run ML algorithm
Wine classification
Age in years
Reviewscore
4
2
0
-2
2 4 6 8
Train your solution
@quaesita

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Get model (recipe)
Wine classification
Age in years
Reviewscore
4
2
0
-2
2 4 6 8
Train your solution
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Get model (recipe)
Train your solution
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New data? Use recipe!
Train your solution
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New data? Use recipe!
Train your solution
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Decision tree
Which model do we trust?
Support vector classifier
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Step 4
Test your model.

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Test your model
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100
50
30
20
Test with new data
Training set
Validation set
Test set
Fitting the model
Soft interim evals
Rigorous final eval

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Take some fresh data
Testing data
Age in years
Reviewscore
4
2
0
-2
2 4 6 8

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Apply each model to fresh data
Support vector classifier Decision tree

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Get classifications
Support vector classifier Decision tree

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Compare with the correct answers
Truth
Age in years
Reviewscore
4
2
0
-2
2 4 6 8

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Get model performance
Support vector classifier Decision tree
Test Accuracy: 100% Test Accuracy: 75%

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And the winner is...
Statistical Output (CI): [80%, 100%] Statistical Output (CI): [51%, 90%]
Support vector classifier Decision tree

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Testing is crucial!
It’s the key to responsible ML and AI.
Make sure your solution actually works
on relevant, new data.
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Applied ML is easier than
most people think
Tinker and have fun, try out anything you like,
but assess performance carefully.
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Success takes data
You need separate datasets for
training, validation, and testing.
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Machine learning is
an approach to making repeated
decisions that involves algorithmically
finding patterns in data and using
these to make recipes that deal
correctly with brand new data.
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What is AI?
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62. Vision
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63. Communication
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Playing games

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AI tasks
Information Answer?
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AI tasks
Cat!?
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AI tasks
Cat!?
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AI tasks
Too difficult to write explicit instructions for
Complicated!
? Cat!
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AI succeeds at very
complicated tasks that
programmers can’t write
instructions for by hand
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Data Compute
Sophisticated
algorithms
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Science fiction becomes reality
71
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72. Confidential & ProprietaryGoogle Cloud Platform 72
Deep learning
Geoff Hinton: engineering of artificial neural networks.
Sophisticated
algorithms

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Deep Learning

74. Confidential & Proprietary 74Proprietary + Confidential
GPUs
Researchers began to notice that
neural network mathematics closely
resembled the algorithms to shade
pixels in graphics cards (GPUs)
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75. Confidential & ProprietaryGoogle Cloud Platform 75
Massive labeled datasets
Fei-Fei Li: enabling learning through data.
Data

76. Confidential & ProprietaryGoogle Cloud Platform 76
Hardware and compute
Jeff Dean: TensorFlow and success at scale.
Compute

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The future is here

78. Vision
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Google Photos

80. Image Classification
AI fixes satellite image maps automatically.
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81. Communication
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Communication
Google Cloud Platform Text-to-Speech

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Smart reply
in Inbox
12%
of all responses
sent on mobile

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Playing games

85. Self-driving cars
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86. Cooling data centers
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• Predictive inventory planning
• Recommendation engines
• Upsell and cross-channel marketing
• Market segmentation and targeting
• Customer ROI and lifetime value
Retail
• Alerts and diagnostics from real-time
patient data
• Disease identification and risk stratification
• Patient triage optimization
• Proactive health management
• Healthcare provider sentiment analysis
Healthcare and Life Sciences
• Aircraft scheduling
• Dynamic pricing
• Social media – consumer feedback
and interaction analysis
• Customer complaint resolution
• Traffic patterns and congestion
management
Travel and Hospitality
• Risk analytics and regulation
• Customer Segmentation
• Cross-selling and up-selling
• Sales and marketing campaign
management
• Credit worthiness evaluation
Financial Services
• Predictive maintenance or
condition monitoring
• Warranty reserve estimation
• Propensity to buy
• Demand forecasting
• Process optimization
• Telematics
Manufacturing
• Power usage analytics
• Seismic data processing
• Carbon emissions and trading
• Customer-specific pricing
• Smart grid management
• Energy demand and supply
optimization
Energy, Feedstock and
Utilities
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88. ML PlatformML Pre-Trained APIs
Cloud
Vision
Cloud Natural
Language
Cloud
Translation
Cloud
Speech
Cloud Video
Intelligence
ML Accelerators
Cloud GPU Cloud TPU
Cloud ML
Engine
Cloud
Dataproc
Cloud
Dataflow
Google
BigQuery
Cloud Job
Discovery
ML Professional
Services & PartnersML & Data Science Tools
ML Libraries
Cloud
Datalab
Cloud
Dataprep
ASL
Google Cloud AI
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Decision
making
Domain
expertise
Analytics
Reliability
EngineeringSteps to success
1. Define success
2. Prepare your data
3. Try out different models
4. Test on lots of new data
5. Use your solution!
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90. Your AI journey awaits!
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