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David Barber - Deep Nets, Bayes and the story of AI
- 1. Deep Nets, Bayes and the story of AI (continued) David Barber
- 2. Table of Contents History of the AI dream How do brains work? Connectionism AutoDiff Fantasy Machines Probability Directed Graphical Models Variational Generative Models Reinforcement Learning Outlook
- 3. Intelligent Machinery 1948 Turing and Champernowne ‘paper and pencil’ chess.
- 4. Intelligent Machinery 1951 Prinz mate-in-two moves chess machine. 1952 Strachey programs first computer draughts algorithm.
- 5. Learning Machines 1951 Oettinger makes first program that ‘learns’. 1955 Samuel adds ‘learning’ to his draughts algorithm.
- 6. Logical Intelligence 1968 Risch’s algorithm for integration in calculus 1972 Prolog for general logical reasoning 1997 Deep Blue defeats Kasparov
- 7. Other forms of intelligence But is this getting us to where we’d like to be? Selfridge-Shannon film clip Speech Recognition Visual Processing Natural Language modelling Planning and decision in uncertain environments Perhaps a different approach would be useful.
- 8. Table of Contents History of the AI dream How do brains work? Connectionism AutoDiff Fantasy Machines Probability Directed Graphical Models Variational Generative Models Reinforcement Learning Outlook
- 9. Astonishing Hypothesis: Crick “A person’s mental activities are entirely due to the behaviour of nerve cells and the molecules that make them up and influence them.”
- 10. Neurons
- 11. Visual Pathway
- 12. Information Processing in Brains Neurons RealWorld Layer 1 Layer 2 High−level Concepts Feature Hierarchical; Modular; Binary; Parallel; Noisy
- 13. Table of Contents History of the AI dream How do brains work? Connectionism AutoDiff Fantasy Machines Probability Directed Graphical Models Variational Generative Models Reinforcement Learning Outlook
- 14. Artificial Neuron (Perceptron) weight 7 output neuron neuron 1 neuron 2 neuron 3 neuron 4 neuron 7 neuron 6 neuron 5 inputs weight 1
- 15. Training an artificial neural network Want to generalise to new images with high accuracy.
- 16. Artificial Network 1957 Rosenblatt’s perceptron perceptron film clip
- 17. Connectionism 1960 Realised a perceptron can only solve simple tasks. 1970 Decline in interest. 1980 New computing power made training multilayer networks feasible. outputinputs Each node (or ‘neuron’) computes a function of a weighted combination of parental nodes: hj = σ( i wijhi)
- 18. Neural Networks and Deep Learning Historical Problems with Neural Nets (1990s) NNs are difficult to train (many local optima?). Particularly difficult to train a NN with a large number of layers (say larger than around 10). ‘Gradient Diffusion Problem’ – difficult to assign responsibility of errors to individual ‘neurons’. Machine Learning (up to 2006) A large section of the machine learning community abandoned NNs. More principled and computationally better understood techniques (SVMs and related convex methods) replaced them. Bayesian AI (1990s onwards) From mid 1990s there was a realisation that pattern recognition is not sufficient for all AI purposes. Uncertainty and reasoning are not naturally representable using standard feed-forward nets. Explosion in more ‘symbolic’ Bayesian AI.
- 19. Deep Learning NNs have resurged in interest in the last few years (Hinton, Bengio, . . . ) Also called ‘deep learning’. Sense that very complex tasks (object recognition, learning complex structure in data) requires going beyond simple (convex) statistical techniques. The brain uses hierarchical distributed processing and it is likely to be for a good reason. Many problems have a hierarchical structure: images are made of parts; language is hierarchical, etc. Why now? New computing resources (GPU processing) Availability of large amount of data means that we can train nets with many parameters (1010 ). Recent evidence suggests local optima are not particularly problematic.
- 20. Autoencoder y1 y2 y3 y4 y5 h1 h2 h3 h4 h5 y1 y2 y3 y4 y5 h6 h7 h8 The bottleneck forces the network to try to find a low dimensional representation of the data. Useful for unsupervised learning.
- 21. Autoencoder on MNIST digits (Hinton 2006 Science) Figure : Reconstructions using H = 30 components. From the Top: Original image, Autoencoder1, Autoencoder2, PCA 60,000 training images (28 × 28 = 784 pixels). Use a form of autoencoder to find a lower (30) dimensional representation. At the time, the special layerwise training procedure was considered fundamental to the success of this approach. Now not deemed necessary, provided we use a sensible initialisation.
- 22. Google Cats 10 Million Youtube video frames (200x200 pixel images). Use a specialised autoencoder with 9 layers (1 billion weights). 2000 computers + two weeks of computing. Examine units to see what images they most respond to.
- 23. Google Autoencoder From Nando De Freitas
- 24. Convolutional NNs CNNs are particularly popular in image processing Often the feature maps correspond (not to macro features such as bicycles) but micro features. For example, in handwritten digit recognition they correspond to small constituent parts of the digits. These are used then to process the image into a representation that is better for recognition.
- 25. NNs in NLP Bag of Words We have D words in a dictionary, aardvark,. . .,zorro so that we can relate each word with its dictionary index. We can also think of this as a Euclidean embedding e: aardvark → eaardvark = 1 0 ... 0 , zorro → ezorro = 0 0 ... 1 Word Embeddings Idea is to replace the Euclidean embeddings e with embeddings (vectors) v that are learned. Objective is, for example, next word prediction accuracy. These are often called ‘neural language models’.
- 26. NNs in NLP Each word w in the dictionary has an associated embedding vector vw. Usually around 200 dimensional vectors are used. Consider the sentence: the cat sat on the mat and that we wish to predict the word on given the two preceding cat sat and two succeeding words the mat We can use a network that has inputs vcat, vsat, vthe, vmat The output of the network is a probability over all words in the dictionary p(w| {vinputs}). We want p(w = on|vcat, vsat, vthe, vmat) to be high. The overall objective is then to learn all the word embeddings and network parameters subject to predicting the word correctly based on the context.
- 27. Word Embeddings Given a word (France, for example) we can find which words w have embedding vectors closest to vFrance. From Ronan Collabert (2011).
- 28. Word Embeddings There appears to be a natural ‘geometry’ to the embeddings. For example, there are directions that correspond to gender. vwoman − vman ≈ vaunt − vuncle vwoman − vman ≈ vqueen − vking From Mikolov (2013).
- 29. Word Embeddings: Analogies Given a relationship, France-Paris, we get the ‘relationship’ embedding v = vParis − vFrance Given Italy we can calculate vItaly + v and find the word in the dictionary which has closest embedding to this (it turns out to be Rome!). From Mikolov (2013).
- 30. Word Embeddings: Constrained Embeddings We can learn embeddings for English words and embeddings for Chinese words. However, when we know that a Chinese and English word have a similar meaning, we add a constraint that the word embeddings vChineseWord and vEnglishWord should be close. We have only a small amount of labelled ‘similar’ Chinese-English words (these are the green border boxes in the above; they are standard translations of the corresponding Chinese character). We can visualise in 2D (using t-SNE) the embedding vectors. See Socher (2013)
- 31. Word Embeddings: Constrained Embeddings
- 32. Recursive Nets and Embeddings Stanford Sentiment Treebank. Consists of parsed sentences with sentiment labels (−−, −, 0, +, ++) for each node (phrase) in the tree. 215,000 labelled phrases (obtained from three humans).
- 33. Recursive Nets and Embeddings Idea is to recursively combine embeddings such that they accurately predict the sentiment at each node.
- 34. Recursive Nets and Embeddings Training We have a softmax classifier for each node in the tree, to predict the sentiment of the phrase beneath this node in the tree. The weights of this classifier are shared across all nodes. At the leaf nodes at the bottom of the tree, the inputs to the classifiers are the word embeddings. The embeddings are combined by another network g with common parameters, which forms the input to the sentiment classifier. We then learn all the embeddings, shared classifier parameters and shared combination parameters to maximise the classification accuracy. Prediction For a new movie review, the review is first parsed using a standard grammar tree parser. This forms the tree which can be used to recursively form the sentiment class label for the review. Currently the best sentiment classifier. Socher (2013)
- 35. Recursive Nets and Embeddings õ õ ð ð α¹»® ð ܱ¼¹»® õ õ ð · õ ð ±²» õ 𠱺 õ õ 𠬸» õ õ ð ³±¬ õ ½±³°»´´·²¹ 𠪿®·¿¬·±² ð ð ±² ð 𠬸· 𠬸»³» ð ò ¥ ð ð α¹»® ð ܱ¼¹»® ¥ ¥ ð · ¥ ð ±²» ¥ 𠱺 ¥ ¥ 𠬸» ¥ ¥ ¥ ´»¿¬ õ ½±³°»´´·²¹ 𠪿®·¿¬·±² ð ð ±² ð 𠬸· 𠬸»³» ð ò õ ð × õ õ õ ´·µ»¼ ð ð 𠻪»®§ ð ð ·²¹´» ð ³·²«¬» ð 𠱺 ð 𠬸· ð ð ò ¥ ð × ¥ ¥ ð ð ¼·¼ ð ²ù¬ ð ð ´·µ» ð ð ð ¿ ð ð ·²¹´» ð ³·²«¬» ð 𠱺 ð 𠬸· ð ð ò ¥ ð ׬ ¥ ¥ ð ð ù 𠶫¬ ¥ õ ·²½®»¼·¾´§ ¥ ¥ ¼«´´ ð ò ð ð ׬ ð ð ð ð ð ù õ ¥ ²±¬ ¥ ¥ ¼«´´ ð ò Ú·¹«®» çæ ÎÒÌÒ °®»¼·½¬·±² ±º °±·¬·ª» ¿²¼ ²»¹¿¬·ª» ø¾±¬¬±³ ®·¹¸¬÷ »²¬»²½» ¿²¼ ¬¸»·® ²»¹¿¬·±²ò
- 36. Recurrent Nets x1 x2 x3 h1 h2 h3 y1 y2 y3 A A A C C C B B RNNs are used in timeseries applications The basic idea is that the hidden units at time ht (and possibly output yt) depend on the previous state of the network ht−1, xt−1, yt−1 for inputs xt and outputs yt. In the above network, I ‘unrolled the net through time’ to give a standard NN diagram. I omitted the potential links from xt−1, yt−1 to ht.
- 37. Handwriting Generation using a RNN Some training examples.
- 38. Handwriting Generation using a RNN Some generated examples. Top line is real handwriting, for comparison. See Alex Grave’s work.
- 39. Table of Contents History of the AI dream How do brains work? Connectionism AutoDiff Fantasy Machines Probability Directed Graphical Models Variational Generative Models Reinforcement Learning Outlook
- 40. Reasons research in deep learning has exploded Much greater compute power. (GPU) Much larger datasets. AutoDiff. What is AutoDiff? AutoDiff takes a function f(x) and returns an exact value (up to machine accuracy) for the gradient gi(x) ≡ ∂ ∂xi f x Note that this is not the same as a numerical approximation (such as central differences) for the gradient. One can show that, if done efficiently, one can always calculate the gradient in less than 5 times the time it takes to compute f(x).
- 41. Reverse Differentiation A useful graphical representation is that the total derivative of f with respect to x is given by the sum over all path values from x to f, where each path value is the product of the partial derivatives of the functions on the edges: df dx = ∂f ∂x + ∂f ∂g dg dx x f g∂f ∂x dg dx ∂f ∂g Example For f(x) = x2 + xgh, where g = x2 and h = xg2 x f gh2x + gh 2x xh 2gx xg g2 f (x) = (2x + gh) + (g2 xg) + (2x2gxxg) + (2xxh) = 2x + 8x7
- 42. Reverse Differentiation Consider f(x1, x2) = cos (sin(x1x2)) We can represent this computationally using an Abstract Syntax Tree (AST): x1 x2 f1 f2 f3 f1(x1, x2) = x1x2 f2(x) = sin(x) f3(x) = cos(x) Given values for x1, x2, we first run forwards through the tree so that we can associate each node with an actual function value.
- 43. Reverse Differentiation x1 x2 f1 f2 f3 df3 dx1 = ∂f3 ∂f2 df2 dx1 = ∂f3 ∂f2 df2 df1 df3 df1 df1 dx1 Similarly, df3 dx2 = ∂f3 ∂f2 df2 df1 df3 df1 df1 dx2 The two derivatives share the same computation branch and we want to exploit this.
- 44. Reverse Differentiation x1 x2 f1 f2 f3 ∂f1 ∂x1 = x2 ∂f1 ∂x2 = x1 ∂f2 ∂f1 = cos(f1) ∂f3 ∂f2 = − sin(f2) 1. Find the reverse ancestral (backwards) schedule of nodes (f3, f2, f1, x1, x2). 2. Start with the first node n1 in the reverse schedule and define tn1 = 1. 3. For the next node n in the reverse schedule, find the child nodes ch (n). Then define tn = c∈ch(n) ∂fc ∂fn tc 4. The total derivatives of f with respect to the root nodes of the tree (here x1 and x2) are given by the values of t at those nodes. This is a general procedure that can be used to automatically define a subroutine to efficiently compute the gradient. It is efficient because information is collected at nodes in the tree and split between parents only when required.
- 45. Limitations of forward reasoning World Representation Recognising patterns (perceptron style) is only one form of intelligence. Solving chess problems is another and requires complex reasoning using some form of internal model. The world is noisy and information may be conflicting. Recognised that new approaches are required.
- 46. Table of Contents History of the AI dream How do brains work? Connectionism AutoDiff Fantasy Machines Probability Directed Graphical Models Variational Generative Models Reinforcement Learning Outlook
- 47. Limitations of forward reasoning World Representation Models help us to fantasise about the world.
- 48. Models
- 49. Models
- 50. Burglar Problem
- 51. Creaks and Bumps Creak Bump
- 52. Burglar Model pos1 pos2 pos3 pos4 snd1 snd2 snd3 snd4 pos - position in kitchen snd – sound
- 53. Finding the Burglar creak! creak! bump! creak! bump! bump! creak! bump! bump! bump! creak! bump!
- 54. Finding the Burglar creak! creak! bump! creak! bump! bump! creak! bump! bump! bump! creak! bump!
- 55. Finding the Burglar creak! creak! bump! creak! bump! bump! creak! bump! bump! bump! creak! bump!
- 56. Stubby Fingers
- 57. Stubby Fingers int1 int2 int3 int4 hit1 hit2 hit3 hit4 int - intended key hit – hit key
- 58. Stubby Fingers: errors a b c d e f g h i j k l m n o p q r s t u v w x y z a b c d e f g h i j k l m n o p q r s t u v w x y z 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55
- 59. Stubby Fingers: language a b c d e f g h i j k l m n o p q r s t u v w x y z a b c d e f g h i j k l m n o p q r s t u v w x y z 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
- 60. Stubby Fingers Given the typed sequence cwsykcak what is the most likely word that this corresponds to? List the 200 most likely hidden sequences Discard those that are not in a standard English dictionary Take the most likely proper English word as the intended typed word
- 61. Speech Recognition: raw signal 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 −0.2 −0.15 −0.1 −0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 Time
- 62. ‘neural’ representation 10 20 30 40 50 60 70 80 5 10 15 20 25
- 63. Speech Recognition pho1 pho2 pho3 pho4 aud1 aud2 aud3 aud4 pho: phoneme (letter) aud: audio signal (neural representation)
- 64. Medical Diagnosis tumour flu meningitis headache fever appetite x-ray Combine known medical knowledge with patient specific information.
- 65. Table of Contents History of the AI dream How do brains work? Connectionism AutoDiff Fantasy Machines Probability Directed Graphical Models Variational Generative Models Reinforcement Learning Outlook
- 66. Probability Why Probability? Probability is a logical calculus of uncertainty. Natural framework to use in models of physical systems, such as the Ising Model (1920) and in AI applications, such as the HMM (Baum 1966, Stratonovich 1960). The need for structure We often want to make a probabilistic description of many objects (electron spins, neurons, customers, etc. ). Typically the representational and computational cost of probabilistic models grows exponentially with the number of objects represented. Without introducing strong structural limitations about how these objects can interact, probability is a non-starter. For this reason, computationally ‘simpler’ alternatives (such as fuzzy logic) were introduced to try to avoid some of these difficulties – however, these are typically frowed on by purists.
- 67. Graphical Models We can use graphs to represent how objects can probabilistically interact with each other. Graphical Models and then a marriage between Graph and Probability theory. Many of the quantities that we would like to compute in a probability distribution can then be related to operations on the graph. The computational complexity of operations can often be related to the structure of the graph. Graphical Models are now used as a standard framework in Engineering, Statistics and Computer Science. Graphical Models are used to perform reasoning under uncertainty and are therefore widely applicable.
- 68. Uses in Industry Microsoft: used to estimate the skill distribution of players in online games (the worlds largest graphical model?!). Hospitals use Belief Nets to encode knowledge about diseases and symptoms to aid medical diagnosis. Google, Microsoft, Facebook: used in many places, including advertising, video game prediction, speech recognition. Used to estimate inherent desirability of products in consumer retail. Microsoft and others: Attempt to go beyond simple A/B testing by uses Graphical Models to model the whole company/user relationship.
- 69. Conditional Probability and Bayes’ Rule The probability of event x conditioned on knowing event y (or more shortly, the probability of x given y) is defined as p(x|y) ≡ p(x, y) p(y) = p(y|x)p(x) p(y) (Bayes’ rule) Throwing darts p(region 5|not region 20) = p(region 5, not region 20) p(not region 20) = p(region 5) p(not region 20) = 1/20 19/20 = 1 19 Interpretation p(A = a|B = b) should not be interpreted as ‘Given the event B = b has occurred, p(A = a|B = b) is the probability of the event A = a occurring’. The correct interpretation should be ‘p(A = a|B = b) is the probability of A being in state a under the constraint that B is in state b’.
- 70. Battleships Assume there are 2 ships, 1 vertical (ship 1) and 1 horizontal (ship 2), of 5 pixels each. Can be placed anywhere on the 10×10 grid, but cannot overlap. Let s1 is the origin of ship 1 and s2 the origin of ship 2 Data D is a collection of query ‘hit’ or ‘miss’ responses. p(s1, s2|D) = p(D|s1, s2)p(s1, s2) p(D) Let X be the matrix of pixel occupancy p(X|D) = s1,s2 p(X, s1, s2|D) = s1,s2 p(X|s1, s2)p(s1, s2|D) demoBattleships.m
- 71. Table of Contents History of the AI dream How do brains work? Connectionism AutoDiff Fantasy Machines Probability Directed Graphical Models Variational Generative Models Reinforcement Learning Outlook
- 72. Belief Networks (Bayesian Networks) A belief network is a directed acyclic graph in which each node has associated the conditional probability of the node given its parents. The joint distribution is obtained by taking the product of the conditional probabilities: p(A, B, C, D, E) = p(A)p(B)p(C|A, B)p(D|C)p(E|B, C) p(E|B, C) A B C D E
- 73. Example – Part I Sally’s burglar Alarm is sounding. Has she been Burgled, or was the alarm triggered by an Earthquake? She turns the car Radio on for news of earthquakes. Choosing an ordering Without loss of generality, we can write p(A, R, E, B) = p(A|R, E, B)p(R, E, B) = p(A|R, E, B)p(R|E, B)p(E, B) = p(A|R, E, B)p(R|E, B)p(E|B)p(B) Assumptions: The alarm is not directly influenced by any report on the radio, p(A|R, E, B) = p(A|E, B) The radio broadcast is not directly influenced by the burglar variable, p(R|E, B) = p(R|E) Burglaries don’t directly ‘cause’ earthquakes, p(E|B) = p(E) Therefore p(A, R, E, B) = p(A|E, B)p(R|E)p(E)p(B)
- 74. Example – Part II: Specifying the Tables B A E R p(A|B, E) Alarm = 1 Burglar Earthquake 0.9999 1 1 0.99 1 0 0.99 0 1 0.0001 0 0 p(R|E) Radio = 1 Earthquake 1 1 0 0 The remaining tables are p(B = 1) = 0.01 and p(E = 1) = 0.000001. The tables and graphical structure fully specify the distribution.
- 75. Example Part III: Inference Initial Evidence: The alarm is sounding p(B = 1|A = 1) = E,R p(B = 1, E, A = 1, R) B,E,R p(B, E, A = 1, R) = E,R p(A = 1|B = 1, E)p(B = 1)p(E)p(R|E) B,E,R p(A = 1|B, E)p(B)p(E)p(R|E) ≈ 0.99 Additional Evidence: The radio broadcasts an earthquake warning: A similar calculation gives p(B = 1|A = 1, R = 1) ≈ 0.01. Initially, because the alarm sounds, Sally thinks that she’s been burgled. However, this probability drops dramatically when she hears that there has been an earthquake.
- 76. Markov Models For timeseries data v1, . . . , vT , we need a model p(v1:T ). For causal consistency, it is meaningful to consider the decomposition p(v1:T ) = T t=1 p(vt|v1:t−1) with the convention p(vt|v1:t−1) = p(v1) for t = 1. v1 v2 v3 v4 Independence assumptions It is often natural to assume that the influence of the immediate past is more relevant than the remote past and in Markov models only a limited number of previous observations are required to predict the future.
- 77. Markov Chain Only the recent past is relevant: p(vt|v1, . . . , vt−1) = p(vt|vt−L, . . . , vt−1) where L ≥ 1 is the order of the Markov chain p(v1:T ) = p(v1)p(v2|v1)p(v3|v2) . . . p(vT |vT −1) For a stationary Markov chain the transitions p(vt = s |vt−1 = s) = f(s , s) are time-independent (‘homogeneous’). v1 v2 v3 v4 (a) v1 v2 v3 v4 (b) Figure : (a): First order Markov chain. (b): Second order Markov chain.
- 78. Markov Chains v1 v2 v3 v4 p(v1, . . . , vT ) = p(v1) initial T t=2 p(vt|vt−1) Transition State transition diagram Nodes represent states of the variable v and arcs non-zero elements of the transition p(vt|vt−1) 1 2 34 56 7 8 9
- 79. Most probable and shortest paths 1 2 34 56 7 8 9 The shortest (unweighted) path from state 1 to state 7 is 1 − 2 − 7. The most probable path from state 1 to state 7 is 1 − 8 − 9 − 7 (assuming uniform transition probabilities). The latter path is longer but more probable since for the path 1 − 2 − 7, the probability of exiting state 2 into state 7 is 1/5.
- 80. Equilibrium distribution It is interesting to know how the marginal p(xt) evolves through time: p(xt = i) = j p(xt = i|xt−1 = j) Mij p(xt−1 = j) p(xt = i) is the frequency that we visit state i at time t, given we started from p(x1) and randomly drew samples from the transition p(xτ |xτ−1). As we repeatedly sample a new state from the chain, the distribution at time t, for an initial distribution p1(i) is pt = Mt−1 p1 If, for t → ∞, p∞ is independent of the initial distribution p1, then p∞ is called the equilibrium distribution of the chain: p∞ = Mp∞ The equil. distribution is proportional to the eigenvector with unit eigenvalue of the transition matrix.
- 81. PageRank Define the matrix Aij = 1 if website j has a hyperlink to website i 0 otherwise From this we can define a Markov transition matrix with elements Mij = Aij i Ai j If we jump from website to website, the equilibrium distribution component p∞(i) is the relative number of times we will visit website i. This has a natural interpretation as the ‘importance’ of website i. For each website i a list of words associated with that website is collected. After doing this for all websites, one can make an ‘inverse’ list of which websites contain word w. When a user searches for word w, the list of websites that contain word is then returned, ranked according to the importance of the site.
- 82. Hidden Markov Models The HMM defines a Markov chain on hidden (or ‘latent’) variables h1:T . The observed (or ‘visible’) variables are dependent on the hidden variables through an emission p(vt|ht). This defines a joint distribution p(h1:T , v1:T ) = p(v1|h1)p(h1) T t=2 p(vt|ht)p(ht|ht−1) For a stationary HMM the transition p(ht|ht−1) and emission p(vt|ht) distributions are constant through time. v1 v2 v3 v4 h1 h2 h3 h4 Figure : A first order hidden Markov model with ‘hidden’ variables dom(ht) = {1, . . . , H}, t = 1 : T. The ‘visible’ variables vt can be either discrete or continuous.
- 83. The classical inference problems Filtering (Inferring the present) p(ht|v1:t) Prediction (Inferring the future) p(ht|v1:s) t > s Smoothing (Inferring the past) p(ht|v1:u) t < u Likelihood p(v1:T ) Most likely path (Viterbi alignment) argmax h1:T p(h1:T |v1:T ) For prediction, one is also often interested in p(vt|v1:s) for t > s.
- 84. Inference in Hidden Markov Models Belief network representation of a HMM: h1 h2 h3 h4 v1 v2 v3 v4 Filtering, Smoothing and Viterbi are all computationally efficient, scaling linearly with the length of the timeseries (but quadratically with the number of hidden states). The algorithms are variants of ‘message passing on factor graphs’ Algorithm guaranteed to work if the graph is singly-connected. Huge research effort in the last 15 years to apply message passing for approximate inference in multiply-connected graphs (eg low-density parity-check codes).
- 85. HMMs for speech recognition ht is the phoneme at time t. p(ht|ht−1) – language model. p(vt|ht) – speech signal model.
- 86. Deep Nets and HMMs h1 h2 h3 h4 v1 v2 v3 v4 Recently companies including Google have made big advances in speech recognition. The breakthrough is to model p(vt|ht) as a Gaussian whose mean is some function of the phoneme µ(ht; θ). This function is a deep neural network, trained on a large amount of data. Goldrush at the moment to find similar breakthrough applications of deep networks in reasoning systems.
- 87. Table of Contents History of the AI dream How do brains work? Connectionism AutoDiff Fantasy Machines Probability Directed Graphical Models Variational Generative Models Reinforcement Learning Outlook
- 88. Generative Model h1 h2 v1 v2 v3 v4 It is natural to consider that objects (images for example) can be constructed on the basis of a low dimensional representation. Note that this is a Graphical Model, not a Function The latent variables h can be sampled from, using p(h) and then an image sampled from p(v|h). One cannot use an autoencoder to generate new images. The bad news Inference (computing p(h|v) and parameter learning) is intractable in these models. Statisticians typically use sampling as an approximation. Very popular in ML to use a variational method – much faster for inference.
- 89. Variational Inference Consider a distribution p(v|θ) = h p(v|h, θ)p(h) and that we wish to learn θ to maximise the probability this model generates observed data. log p(v|θ) ≥ − q(h|v, φ) log q(h|v, φ) + h q(h|v, φ)p(v|h, θ) + const. Idea is to choose a ‘variational’ distribution q(h|v, φ) such that we can either calculate analytically the bound, or sample it efficiently. We then jointly maximise the bound wrt φ and θ. We can parameterise p(v|h, θ) using a deep network. Very popular approach – see ‘variational autoencoder’ and also attention mechanisms. Extension to semi-supervised method using p(v) = h c p(v|h, c)p(c)p(h)
- 90. DRAW
- 91. Table of Contents History of the AI dream How do brains work? Connectionism AutoDiff Fantasy Machines Probability Directed Graphical Models Variational Generative Models Reinforcement Learning Outlook
- 92. Reinforcement Learning Can we teach computers to play Atari video games?
- 93. Deep Reinforcement Learning Given a state of the world, W and a set of possible actions A, we need to decide which action to taken for any state of W that will be best for our long term goals. Problem is that the number of pixel states is enormous. Need to learn a low dimensional representation of the screen (use a deep generative model). Learn then which action to take given the low dimensional representation. Tetris Google
- 94. Table of Contents History of the AI dream How do brains work? Connectionism AutoDiff Fantasy Machines Probability Directed Graphical Models Variational Generative Models Reinforcement Learning Outlook
- 95. Outlook Machine Learning is in a boom period. Renewed interest and hope in creating AI. Combine new computational power with suitable hierarchical representations. Impressive state of the art results in Speech Recognition, Image Analysis, Game Playing. Challenges Improve understanding of optimisation for deep learning. Learn how to more efficiently exploit computational resources. Learn how to exploit massive databases. Improve interaction between reinforcement learning and representation learning. Marry non-symbolic (neural) with symbolic (Bayesian reasoning) Emphasis is on scalability. Feel free to contact me at UCL or at my AI company reinfer https:://reinfer.io