artificial intelligence, machine learning, and deep learning...artificial intelligence, machine...

Artificial Intelligence, Machine Learning, and

Deep Learning

Laurent Charlin HEC Montréal

Mila, Quebec Artificial Intelligence Institute

Progressive Growing of GANs for Improved Quality, Stability, and Variation Karras et al., ICLR’18

Progressive Growing of GANs for Improved Quality, Stability, and Variation Karras et al., ICLR’18

All computer generated

Deep Visual-Semantic Alignments for Generating Image Descriptions A. Karpathy, L. Fei-Fei, CVPR’15

Deep Visual-Semantic Alignments for Generating Image Descriptions A. Karpathy, L. Fei-Fei, CVPR’15

Deep Visual-Semantic Alignments for Generating Image Descriptions A. Karpathy, L. Fei-Fei, CVPR’15

Automatic Translation

https://ai.googleblog.com/2016/11/zero-shot-translation-with-googles.html

Automatic Translation

https://ai.googleblog.com/2016/11/zero-shot-translation-with-googles.html

Mnih et al. Nature  Volume 518,  pages 529–533  (26 February 2015)

AI for video games

Esteva et al. Nature  volume 542,  pages 115–118 (02 February 2017)

AI for automatic skin cancer detection

9

Academia

Corporations

Governments

Introduction to Artificial intelligence

1. The technology

• Machine learning, deep learning, neural networks

2. Why now?

• Data, hardware, software

10

Artificial intelligence Make intelligent machines

Idea: Hugo Larochelle

Artificial intelligence Make intelligent machines

Machine learning Make machines that can learn

Idea: Hugo Larochelle

Artificial intelligence Make intelligent machines

Machine learning Make machines that can learn

Deep learning A set of machine learning techniques

based on neural networks

Idea: Hugo Larochelle

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Machine learning Make machines that can learn

• Why is learning useful?

12

Machine learning Make machines that can learn

• Why is learning useful?

• Program computers to be intelligent

12

Machine learning Make machines that can learn

• Why is learning useful?

• Program computers to be intelligent

• Imagine you want a computer to recognize digits:

• Describe what a “1” should look like

• Describe what a “2” should look like

• …

12

Machine learning Make machines that can learn

• Why is learning useful?

• Program computers to be intelligent

• Imagine you want a computer to recognize digits:

• Describe what a “1” should look like

• Describe what a “2” should look like

• …

12

Machine learning Make machines that can learn

• High variability

• Why is learning useful?

• Program computers to be intelligent

• Imagine you want a computer to recognize digits:

• Describe what a “1” should look like

• Describe what a “2” should look like

• …

12

Machine learning Make machines that can learn

• High variability

• Why is learning useful?

• Program computers to be intelligent

• Imagine you want a computer to recognize digits:

• Describe what a “1” should look like

• Describe what a “2” should look like

• …

12

Machine learning Make machines that can learn

• High variability

• Why is learning useful?

• Program computers to be intelligent

• Imagine you want a computer to recognize digits:

• Describe what a “1” should look like

• Describe what a “2” should look like

• …

12

Machine learning Make machines that can learn

• High variability

• Why is learning useful?

• Program computers to be intelligent

• Imagine you want a computer to recognize digits:

• Describe what a “1” should look like

• Describe what a “2” should look like

• …

12

Machine learning Make machines that can learn

• High variability

• Time consuming

• Lack of robustness

• Why is learning useful?

• Program computers to be intelligent

• Imagine you want a computer to recognize digits:

• Describe what a “1” should look like

• Describe what a “2” should look like

• …

12

Machine learning Make machines that can learn

• High variability

• Time consuming

• Lack of robustness

• Think of recognizing more complicated object (e.g., animals)

13

Machine learning Make machines that can learn

• How do children learn?

• Using “examples”

13

Machine learning Make machines that can learn

• How do children learn?

• Using “examples”

• Machine learning

• Present examples and labels to the computer

13

Machine learning Make machines that can learn

two

Example Label

sevenfour

two

Example Label

seven

two

Example Label

two

Example Label

Training: Process of learning using examples

two

Example Label

Training: Process of learning using examples

two

Example Label

Training: Process of learning using examples

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Testing: Evaluate the performance of the computer

(Statistical) model of the data

Linear regression model

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Non-Linear regression model

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Non-Linear regression model

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Neural Network model (a type of non-linear regression)

Biologically inspired models

Neural network model of the data Deep

Caption generation

The cat eats grass Le chat mange de l’herbe

Translation

The cat eats grass

Image generation

The cat eats grass

Speech recognition

Recommendation

Sales

How good is my advertisement?

Will my team make the playoffs?

…

Financial predictions

Buy / Sell / Hold

Financial predictions

Buy / Sell / Hold

Should bail be given?

…

Machine Learning and Statistics (in 1 slide)

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Machine Learning and Statistics (in 1 slide)

• Machine learning is most often about predictions

• Performance on out-of-sample data

• Hypothesis testing & confidence intervals rarely considered

29

Machine Learning and Statistics (in 1 slide)

• Machine learning is most often about predictions

• Performance on out-of-sample data

• Hypothesis testing & confidence intervals rarely considered

• Bayesian machine learning approaches sometimes provide the best of both worlds

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Why now?

• Availability of large datasets

31

Idea: Alain Tapp

Idea: Alain Tapp

Idea: Alain Tapp

Idea: Alain Tapp

Idea: Alain Tapp

Idea: Alain Tapp

• The number of examples required relates to the complexity of the task

• Recognizing digits 10K examples

• Recognizing objects in images 10M examples

• Chatbots ?

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four

• “Marketplace for work that requires human intelligence”. (http://mturk.com)

39

Example Label

four

• “Marketplace for work that requires human intelligence”. (http://mturk.com)

• Requesters propose tasks (HITs) to workers

39

Example Label

four

• “Marketplace for work that requires human intelligence”. (http://mturk.com)

• Requesters propose tasks (HITs) to workers

• Each task is worth a certain amount of money

39

Example Label

four

• “Marketplace for work that requires human intelligence”. (http://mturk.com)

• Requesters propose tasks (HITs) to workers

• Each task is worth a certain amount of money

• Mechanisms to ensure the quality of the results

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Example Label

Hardware resources

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Hardware resources

• Before 2010: Use a faster computer

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Hardware resources

• Before 2010: Use a faster computer

• Now: specialized hardware

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Hardware resources

• Before 2010: Use a faster computer

• Now: specialized hardware

• Graphical processing units (GPU)

• Specialized hardware for linear algebra operations

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Hardware resources

• Before 2010: Use a faster computer

• Now: specialized hardware

• Graphical processing units (GPU)

• Specialized hardware for linear algebra operations

• Tensorflow processing unit (TPU) or other even more-tailored hardware

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https://cloud.google.com/blog/big-data/2017/05/an-in-depth-look-at-googles-first-tensor-processing-unit-tpu

Performance in terms of number of predictions per seconds

Fast Computer

GPU

TPU

https://cloud.google.com/blog/big-data/2017/05/an-in-depth-look-at-googles-first-tensor-processing-unit-tpu

Performance in terms of number of predictions per seconds

Fast Computer

GPU

TPU

Software

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python, lua, C++

CPU, GPU, clusters

Neural Networks

Theano, Torch, Tensorflow

Libraries

Programing Languages

Computer

Models

Software

42

python, lua, C++

CPU, GPU, clusters

Neural Networks

Theano, Torch, Tensorflow

Libraries

Programing Languages

Computer

Models

Software

42

python, lua, C++

CPU, GPU, clusters

Neural Networks

Theano, Torch, Tensorflow

Libraries

Programing Languages

Computer

Models

Software

42

python, lua, C++

CPU, GPU, clusters

Neural Networks

Theano, Torch, Tensorflow

Libraries

Programing Languages

Computer

Models

• These libraries have had a transformative effect

• We can explore models much faster than we before

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Knowledge dissemination

• Data:

• Publishing dataset is becoming the new norm

• Most important datasets are publicly available

• Software:

• Libraries are open source

• Researchers are encouraged to share their code

• Ideas: interesting results are shared with the community in a matter of months (e.g., using arXiv.org)

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Takeaways

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Takeaways• Machine learning is a subfield of artificial intelligence

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Takeaways• Machine learning is a subfield of artificial intelligence

• Neural networks are machine learning models

• They learn from examples

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Takeaways• Machine learning is a subfield of artificial intelligence

• Neural networks are machine learning models

• They learn from examples

• Essential ingredients for neural nets

1. Large amounts of data

2. Specialized hardware

3. Software Stack

• Current neural nets are close to human performance in some domains

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Learning Visual Reasoning Without Strong Priors, Perez et al., 2017

Learning Visual Reasoning Without Strong Priors, Perez et al., 2017

How many yellow things are there?

Learning Visual Reasoning Without Strong Priors, Perez et al., 2017

How many yellow things are there? 2

Learning Visual Reasoning Without Strong Priors, Perez et al., 2017

How many yellow things are there?

2How many cyan things are there?

2

Learning Visual Reasoning Without Strong Priors, Perez et al., 2017

How many yellow things are there?

2How many cyan things are there?

2

Are there as many yellow things as cyan things?

Learning Visual Reasoning Without Strong Priors, Perez et al., 2017

How many yellow things are there?

2How many cyan things are there?

2

Are there as many yellow things as cyan things? No

Some Challenges for deep learning

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Some Challenges for deep learning

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Some Challenges for deep learning

1. How do we learn higher-level reasoning?

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Some Challenges for deep learning

1. How do we learn higher-level reasoning?

2. How much data do we need to solve more complicated tasks? E.g., chatbots

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Some Challenges for deep learning

1. How do we learn higher-level reasoning?

2. How much data do we need to solve more complicated tasks? E.g., chatbots

• Will we ever collect enough data?

47

Some Challenges for deep learning

1. How do we learn higher-level reasoning?

2. How much data do we need to solve more complicated tasks? E.g., chatbots

• Will we ever collect enough data?

3. How de we build systems we can trust (ethical, minimize bias)?

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Some Challenges for deep learning

1. How do we learn higher-level reasoning?

2. How much data do we need to solve more complicated tasks? E.g., chatbots

• Will we ever collect enough data?

3. How de we build systems we can trust (ethical, minimize bias)?

4. …

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How will AI impact the world?

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How will AI impact the world?

• Science fiction scenarios are unlikely

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How will AI impact the world?

• Science fiction scenarios are unlikely

• “Yes they [neural nets] can do great things, yes we can build companies around them, and yes they’ll change the economy but we are not there yet”

— Micheal I. Jordan (UC Berkeley)

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How will AI impact the world?

• Science fiction scenarios are unlikely

• “Yes they [neural nets] can do great things, yes we can build companies around them, and yes they’ll change the economy but we are not there yet”

— Micheal I. Jordan (UC Berkeley)

• Major economic disruptions?

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Thanks!