Deep learning is a new name for an approach to AI called neural networks, which have been going in and out of fashion for more than 70 years. Neural networks were first proposed in 1944 by Warren McCullough and Walter Pitts, two researchers who moved to MIT in 1952 as founding members of what’s sometimes called the first cognitive science department.
Neural networks were a major area of research in both neuroscience and computer science until 1969, when, according to computer science lore, they were killed off by the MIT mathematicians Marvin Minsky and Seymour Papert, who became co-directors of the new MIT Artificial Intelligence Laboratory in 1970.
Neural networks are a means of doing machine learning, in which a computer learns to perform specific tasks by analysing training examples. Usually, these examples have been hand-labeled in advance. An object recognition system, for instance, might be fed thousands of labeled images of cars, houses, coffee cups, and so on, and it would find visual patterns in the images that consistently correlate with particular labels.
Modelled loosely on the human brain, a neural net consists of thousands or even millions of simple processing nodes that are densely interconnected. Most of today’s neural nets are organised into layers of nodes, and they’re “feed-forward,” meaning that data moves through them in only one direction. An individual node might be connected to several nodes in the layer beneath it, from which it receives data, and several nodes in the layer above it, to which it sends data.
Architecture and main types of neural networks
A typical neural network contains a large number of artificial neurons called units arranged in a series of layers.
- Input layer contains units (artificial neurons) which receive input from the outside world on which network will learn, recognise about or otherwise process.
- Output layer contains units that respond to the information about how it learned a task.
- Hidden layers are situated between input and output layers. Their task is to transform the input into something that output unit can use in some way.
- Perceptron has two input units and one output unit with no hidden layers, and is also called single layer perceptron.
- Radial Basis Function Network are similar to the feed-forward neural network except radial basis function is used as activation function of these neurons.
- Multilayer Perceptron networks use more than one hidden layer of neurons. These are also known as deep feed-forward neural networks.
- Recurrent Neural Network’s (RNN) hidden layer neurons have self-connections and thus possess memory. LSTM is a type of RNN.
- Hopfield Network is a fully interconnected network of neurons in which each neuron is connected to every other neuron. The network is trained with input pattern by setting a value of neurons to the desired pattern. Then its weights are computed. The weights are not changed. Once trained for one or more patterns, the network will converge to the learned patterns.
- Boltzmann Machine Network are similar to Hopfield network except some neurons are for input, while others are hidden. The weights are initialized randomly and learn through back-propagation algorithm.
- Convolutional Neural Network (CNN) derives its name from the “convolution” operator. The primary purpose of Convolution in case is to extract features from an input image/video. Convolution preserves the spatial relationship between pixels by learning about image/video features using small squares of input data.
Of these, let’s have a very brief review of CNNs and RNNs, as these are the most commonly used.
CNN
- CNNs are ideal for image and video processing.
- CNN takes a fixed size input and generate fixed-size outputs.
- Use CNNs to break a component (image/video) into subcomponents (lines, curves, etc.).
- CNN is a type of feed-forward artificial neural network – variation of multilayer perceptrons, which are designed to use minimal amounts of preprocessing.
- CNNs use connectivity pattern between its neurons as inspired by the organization of the animal visual cortex, whose neurons are arranged in such a way that they respond to overlapping regions tiling the visual field.
- CNN looks for the same patterns on all the different subfields of the image/video.
RNN
- RNNs are ideal for text and speech analysis.
- RNN can handle arbitrary input/output lengths.
- Use RNNs to create combinations of subcomponents (image captioning, text generation, language translation, etc.)
- RNN, unlike feedforward neural networks, can use its internal memory to process arbitrary sequences of inputs.
- RNNs use time-series information, i.e. what is last done will impact what done next.
- RNN, in the simplest case, feed hidden layers from the previous step as an additional input into the next step and while it builds up memory in this process, it is not looking for the same patterns.
A type of RNN are LSTM and GRU. The key difference between GRU and LSTM is that a GRU has two gates (reset and update) whereas an LSTM has three gates (input, output and forget). GRU is similar to LSTM in that both utilise gating information to solve vanishing gradient problem. GRU’s performance is on par with LSTM, but computationally more efficient.
- GRUs train faster and perform better than LSTMs on less training data if used for language modelling.
- GRUs are simpler and easier to modify, for example adding new gates in case of additional input to the network.
- In theory, LSTMs remember longer sequences than GRUs and outperform them in tasks requiring modelling long-distance relations.
- GRUs expose complete memory, unlike LSTM
- It’s recommended to train both GRU and LSTM and see which is better.
Deep learning frameworks
There are several frameworks that provide advanced AI/ML capabilities. How do you determine which framework is best for you?
The below figure summarises most of the popular open source deep network repositories. The ranking is based on the number of stars awarded by developers in GitHub (as of May 2017).
Google’s TensorFlow is a library developed at Google Brain. TensorFlow supports a broad set of capabilities such as image, handwriting and speech recognition, forecasting and natural language processing (NLP). Its programming interfaces includes Python and C++ and alpha releases of Java, GO, R, and Haskell API will soon be supported.
Caffe is the brainchild of Yangqing Jia who leads engineering for Facebook AI. Caffe is the first mainstream industry-grade deep learning toolkit, started in late 2013. Due to its excellent convolutional model, it is one of the most popular toolkits within the computer vision community. Speed makes Caffe perfect for research experiments and commercial deployment. However, it does not support fine granularity network layers like those found in TensorFlow and Theano. Caffe can process over 60M images per day with a single Nvidia K40 GPU. It’s cross-platform and supports C++, Matlab and Python programming interfaces and has a large user community that contributes to their own repository known as “Model Zoo.” AlexNet and GoogleNet are two popular user-made networks available to the community.
Caffe 2 was unveiled in April 2017 and is focused on being modular and excelling at mobile and at large scale deployments. Like TensorFlow, Caffe 2 will support ARM architecture using the C++ Eigen library and continue offering strong support for vision-related problems, also adding in RNN and LSTM networks for NLP, handwriting recognition, and time series forecasting.
MXNet is a fully featured, programmable and scalable deep learning framework, which offers the ability to both mix programming models (imperative and declarative) and code in Python, C++, R, Scala, Julia, Matlab and JavaScript. MXNet supports CNN and RNN, including LTSM networks and provides excellent capabilities for imaging, handwriting and speech recognition, forecasting and NLP. It’s considered the world’s best image classifier, and supports GAN simulations. This model is used in Nash equilibrium to perform experimental economics methods. Amazon supports MXNet, planning to use it in existing and upcoming services whereas Apple is rumorred to be also using it.
Theano architecture lacks the elegance of TensorFlow, but provides capabilities like symbolic API supports looping control, so-called scan, which makes implementing RNNs easy and efficient. Theano supports many types of convolutions for hand writing and image classification including medical images. Theano uses 3D convolution/pooling for video classification. It can process natural language processing tasks, including language understanding, translation, and generation. Theano supports GAN.
As you know, goal of AI learning is generalisation, but one major issue is that data alone will never be enough, no matter how much of it is available. AI systems need both data and they need to learn based on data in order to generalise.
So let’s look at how AI systems learn. But before we do that, what are the few different and prevalent AI approaches?
Neural networks model a brain learning by example―given a set of right answers, a neural network learns the general patterns. Reinforcement Learning models a brain learning by experience―given some set of actions and an eventual reward or punishment, it learns which actions are ‘good’ or ‘bad,’ as relevant in context. Genetic Algorithms model evolution by natural selection―given some set of agents, let the better ones live and the worse ones die.
Usually, genetic algorithms do not allow agents to learn during their lifetimes, while neural networks allow agents to learn only during their lifetimes. Reinforcement learning allows agents to learn during their lifetimes and share knowledge with other agents.
Consider learning a Boolean function of (say) 100 variables from a million examples. There are 2100 ^ 100 examples whose classes you don’t know. How do you figure out what those classes are? In the absence of further information, there is no way to do this that beats flipping a coin. This observation was first made (in somewhat different form) by David Hume over 200 years ago, but even today many mistakes in ML stem from failing to appreciate it. Every learner must embody some knowledge/assumptions beyond the data it’s given in order to generalise beyond it.
This seems like rather depressing news. How then can we ever hope to learn anything? Luckily, the functions we want to learn in the real world are not drawn uniformly from the set of all mathematically possible functions. In fact, very general assumptions—like similar examples having similar classes, limited dependences, or limited complexity—are often enough to do quite well, and this is a large part of why ML has been so successful to date.
AI systems use induction, deduction, abduction and other methodologies to collect, analyse and learn from data, allowing generalisation to happen.
Like deduction, induction (what learners do) is a knowledge lever: it turns a small amount of input knowledge into a large amount of output knowledge. Induction (despite its limitations) is a more powerful lever than deduction, requiring much less input knowledge to produce useful results, but it still needs more than zero input knowledge to work.
Abduction is sometimes used to identify faults and revise knowledge based on empirical data. For each individual positive example that is not derivable from the current theory, abduction is applied to determine a set of assumptions that would allow it to be proven. These assumptions can then be used to make suggestions for modifying the theory. One potential repair is to learn a new rule for the assumed proposition so that it could be inferred from other known facts about the example. Another potential repair is to remove the assumed proposition from the list of antecedents of the rule in which it appears in the abductive explanation of the example – parsimonious covering theory (PCT). Abductive reasoning is useful in inductively revising existing knowledge bases to improve their accuracy. Inductive learning can be used to acquire accurate abductive theories.
One key concept in AI is classifier. Generally, AI systems can be divided into two types: classifiers (“if shiny and yellow then gold”) and controllers (“if shiny and yellow then pick up”). Controllers also include classify-ing conditions before inferring actions. Classifiers are functions that use pattern matching to determine a closest match. They can be tuned according to examples known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as data set. When a new observation is made, it is classified based on previous experience.
Classifier performance depends greatly on the characteristics of the data to be classified. The most widely used classifiers use kernel methods to be trained (i.e. to learn). There is no single classifier that works best on all given problems – “no free lunch“. Determining an optimal classifier for a given problem is still more an art than science.
The following formula sums up the process of AI learning.