Geek Night 17.0 - Artificial Intelligence and Machine Learning
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Transcript of Geek Night 17.0 - Artificial Intelligence and Machine Learning
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Artificial intelligence Machine learning
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Lee Sedol vs. AlphaGo
Silver, David, et al. "Mastering the game of Go with deep neural networks and tree search." Nature (2016)
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“At least a decade to go before a computer can beat a human expert”
Not very long AGo!
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1000 = 103
100000000000000000000000000000000000000000000000 = 1047
100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 = 10170
Why this assessment?
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What is AI?
https://xkcd.com/329/
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AI Winter
Series of setbacks from 70s till 90s
High Expectations
Failure of LISP machines
Failure of expert systems
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AI Spring
IMAGE: GETTY IMAGES/ISTOCKPHOTO
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AI Spring
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AI Spring
Domingos, Pedro. "A few useful things to know about machine learning." Communications of the ACM 55.10 (2012)
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What is ML?
“Field of study that gives computers the ability to learn without being explicitly
programmed”
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Types of ML problems
Machine learning
Unsupervised learning
Supervised learning
Reinforcement learning
Regression Classification
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Supervised Learning
Spoonfeeding labelled examples
Numerical values or Discrete class labels
Machine has to be ‘trained’ using a large corpus of ‘training data’
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Regression
Training data
Hypothesis
Choosing optimum ‘hypothesis’ from training data
Hypothesis chosen has minimum ‘cost’
Typically used in financial applications, like predicting stock prices or likely monetary value of products
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Classification
Height
Width
Decision boundary
Finding decision boundaries based on the labels of the training data
Non-linear decision boundaries require complex classifiers like SVMs and neural nets
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Classification applications
Spam filtering
Optical Character Recognition
Pedestrian detection
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Unsupervised Learning (Clustering)
Training data is not labelled
Grouping based on density (DBSCAN, OPTICS), cluster centers (K-Means) or probability distribution (GMM)
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Clustering applications
Grouping similar news items Kharinov, M. "Hierarchical pixel clustering for image segmentation." arXiv preprint (2014).
Pixel clustering for segmentation
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Reinforcement Learning
Teaching a machine by ‘rewarding’ it for good ‘actions’ and ‘punishing’ it for bad ones
Attempt is to explore the entire state space for a problem and get the best actions corresponding to each state, also known as ‘policy’
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Reinforcement Learning applications
Reinforcement learning used for AlphaGo
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Deep Learning
Capturing abstractions using a multi-level or ‘network’ approach
Each level or ‘layer’ composed of many simple processing units
The internal abstractions are often the best features to use for the problem, so no feature engineering is required
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Artificial Neural Networks (ANNs)Deep networks composed of
artificial neurons
Inspired by biological neurons
Activation function is typically sigmoid, can be tanh or ReLu
The method used to train a network is called ‘backpropagation’
Traditional neural networks with all signals propagating in one direction are called ‘feedforward’ networks
Structure of a typical biological neuron
Typical artificial neuron
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Artificial Neural Networks (ANNs) contd.
Rectifier functionLogistic function
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Artificial Neural Networks (ANNs) contd.
Sigmoid function Typical feedforward neural network
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Recurrent Neural Networks (RNNs)
Hidden layers feed back into themselves
Can be used to model sequences and for use as associative memory
Can take input sequences of arbitrary length using the concept of ‘attention’
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RNN applications (with links)Automatic music generation (Site has source code link)
Handwriting synthesis (Site has paper and source code links)
Intelligent personal assistants like Siri, Google Now, Cortana
Automatic image captioning
Sunspring
LSTM that generates poems
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Learning Resources
Good courses or tutorials for ML
Coursera ML by Andrew NgDatacamp ML courseUdacity Deep Learning
Learning by doing
KaggleTopcoder Data science
Good video lectures for ML
Gilbert Strang lectures on Linear AlgebraNando de Freitas Deep Learning
Some people I follow in ML
Andrej Karpathy Peter NorvigAlex Graves Fei Fei LiAndrew Ng
Some good blogs on ML
WildMLIAmTraskKarpathy’s blog
And finally there’s Google Scholar. Read lots of
research papers and try to implement them!
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Thank YouHappy Learning :D