artificial intelligence, machine learning, and deep learning...artificial intelligence, machine...
TRANSCRIPT
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
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Academia
Corporations
Governments
Introduction to Artificial intelligence
1. The technology
• Machine learning, deep learning, neural networks
2. Why now?
• Data, hardware, software
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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?
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Machine learning Make machines that can learn
• Why is learning useful?
• Program computers to be intelligent
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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
• …
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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
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Example Label
sevenfour
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Example Label
seven
two
Example Label
two
Example Label
Training: Process of learning using examples
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Example Label
Training: Process of learning using examples
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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
y = β0 + β1x1 + β2x2 + . . .+ ϵ<latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit>
Non-Linear regression model
y = f(β0 + β1x1 + β2x2 + . . .) + ϵ<latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit>
Non-Linear regression model
y = f(β0 + β1x1 + β2x2 + . . .) + ϵ<latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit>
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β0<latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit>
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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
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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
• Bayesian machine learning approaches sometimes provide the best of both worlds
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Why now?
• Availability of large datasets
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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
40
Hardware resources
• Before 2010: Use a faster computer
40
Hardware resources
• Before 2010: Use a faster computer
• Now: specialized hardware
40
Hardware resources
• Before 2010: Use a faster computer
• Now: specialized hardware
• Graphical processing units (GPU)
• Specialized hardware for linear algebra operations
40
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
45
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
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?
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)?
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)?
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!