Tag: Marvin Minsky

Brief overview: neural networks, architectures, frameworks

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

  1. CNNs are ideal for image and video processing.
  2. CNN takes a fixed size input and generate fixed-size outputs.
  3. Use CNNs to break a component (image/video) into subcomponents (lines, curves, etc.).
  4. CNN is a type of feed-forward artificial neural network – variation of multilayer perceptrons, which are designed to use minimal amounts of preprocessing.
  5. 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.
  6. CNN looks for the same patterns on all the different subfields of the image/video.

RNN

  1. RNNs are ideal for text and speech analysis.
  2. RNN can handle arbitrary input/output lengths.
  3. Use RNNs to create combinations of subcomponents (image captioning, text generation, language translation, etc.)
  4. RNN, unlike feedforward neural networks, can use its internal memory to process arbitrary sequences of inputs.
  5. RNNs use time-series information, i.e. what is last done will impact what done next.
  6. 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).

deep learning frameworks ranked via GitHub

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.

 

Reinforcement learning and its new frontiers

RL’s origins and historic context

RL copies a very simple principle from nature. The psychologist Edward Thorndike documented it more than 100 years ago. Thorndike placed cats inside boxes from which they could escape only by pressing a lever. After a considerable amount of pacing around and meowing, the animals would eventually step on the lever by chance. After they learned to associate this behaviour with the desired outcome, they eventually escaped with increasing speed.

Some of earliest AI researchers believed that this process might be usefully reproduced in machines. In 1951, Marvin Minsky, a student at Harvard who would become one of the founding fathers of AI, built a machine that used a simple form of reinforcement learning to mimic a rat learning to navigate a maze. Minsky’s Stochastic Neural Analogy Reinforcement Computer (SNARC), consisted of dozens of tubes, motors, and clutches that simulated the behaviour of 40 neurons and synapses. As a simulated rat made its way out of a virtual maze, the strength of some synaptic connections would increase, thereby reinforcing the underlying behaviour.

There were few successes over the next few decades. In 1992, Gerald Tesauro demonstrated a program that used the technique to play backgammon. It became skilled enough to rival the best human players, a landmark achievement in AI. But RL proved difficult to scale to more complex problems.

In March 2016, however, AlphaGo, a program trained using RL, won against one of the best Go players of all time, South Korea’s Lee Sedol. This milestone event opened again teh pandora’s box of research about RL. Turns out the key to having a strong RL is to combine it with deep learning.

Current usage and major methods of RL

Thanks to current RL research, computers can now automatically learn to play ATARI games, are beating world champions at Go, simulated quadrupeds are learning to run and leap, and robots learn how to perform complex manipulation tasks that defy explicit programming.

However, while RL saw its advancements accelerate, progress in RL has not been driven as much by new ideas or additional research as just by more of data, processing power and infrastructure. In general, there are four separate factors that hold back AI:

  1. Processing power (the obvious one: Moore’s Law, GPUs, ASICs),
  2. Data (in a specific form, not just somewhere on the internet – e.g. ImageNet),
  3. Algorithms (research and ideas, e.g. backprop, CNN, LSTM), and
  4. Infrastructure (Linux, TCP/IP, Git, AWS, TensorFlow,..).

Similarly for RL, for example for computer vision, the 2012 AlexNet (deeper and wider version of 1990’s Convolutional Neural Networks – CNNs). Or, ATARI’s Deep Q Learning is an implementation of a standard Q Learning algorithm with function approximation, where the function approximator is a CNN. AlphaGo uses Policy Gradients with Monte Carlo tree search (MCTS).

RL’s most optimal method vs. human learning

Generally, RL approaches can be divided into two core categories. The first focuses on finding the optimum mappings that perform well in the problem of interest. Genetic algorithm, genetic programming and simulated annealing have been commonly employed in this class of RL approaches. The second category is to estimate the utility function of taking an action for the given problem via statistical techniques or dynamic programming methods, such as TD(λ) and Q-learning. To date, RL has been successfully applied in many real-world complex applications, including autonomous helicopter, humanoid robotics, autonomous vehicles, etc.

Policy Gradients (PGs), one of RL’s most used methods, is shown to work better than Q Learning when tuned well. PG is preferred because there’s an explicit policy and a principled approach that directly optimises the expected reward.

Before trying PGs (canon), it is recommended to first try to use cross-entropy method (CEM) (normal gun), a simple stochastic hill-climbing “guess and check” approach inspired loosely by evolution. And if you really need to or insist on using PGs for your problem, use a variation called TRPO, which usually works better and more consistently than vanilla PG in practice. The main idea is to avoid parameter updates that change the policy dramatically, as enforced by a constraint on the KL divergence between the distributions predicted by old and the new policies on data.

PGs, however have few disadvantages: they typically converge to a local rather than a global optimum and they display inefficient and high variance while evaluating a policy. PGs also require lot of training samples, take lot of time to train, and are hard to debug debug when they don’t work.

PG is a fancy form of guess-and-check, where the “guess” refers to sampling rollouts from a current policy and encouraging actions that lead to good outcomes. This represents the state of the art in how we currently approach RL problems. But compare that to how a human might learn (e.g. a game of Pong). You show him/her the game and say something along the lines of “You’re in control of a paddle and you can move it up or down, and your goal is to bounce the ball past the other player”, and you’re set and ready to go. Notice some of the differences:

  • Humans communicate the task/goal in a language (e.g. English), but in a standard RL case, you assume an arbitrary reward function that you have to discover through environment interactions. It can be argued that if a human went into a game without knowing anything about the reward function, the human would have a lot of difficulty learning what to do but PGs would be indifferent, and likely work much better.
  • A human brings in a huge amount of prior knowledge, such as elementary physics (concepts of gravity, constant velocity,..), and intuitive psychology. He/she also understands the concept of being “in control” of a paddle, and that it responds to your UP/DOWN key commands. In contrast, algorithms start from scratch which is simultaneously impressive (because it works) and depressing (because we lack concrete ideas for how not to).
  • PGs are a brute force solution, where the correct actions are eventually discovered and internalised into a policy. Humans build a rich, abstract model and plan within it.
  • PGs have to actually experience a positive reward, and experience it very often in order to eventually shift the policy parameters towards repeating moves that give high rewards. On the other hand, humans can figure out what is likely to give rewards without ever actually experiencing the rewarding or unrewarding transition.

In games/situations with frequent reward signals that requires precise play, fast reflexes, and not much planning, PGs quite easily can beat humans. So once we understand the “trick” by which these algorithms work you can reason through their strengths and weaknesses.

PGs don’t easily scale to settings where huge amounts of exploration are difficult to obtain. Instead of requiring samples from a stochastic policy and encouraging the ones that get higher scores, deterministic policy gradients use a deterministic policy and get the gradient information directly from a second network (called a critic) that models the score function. This approach can in principle be much more efficient in settings with  high-dimensional actions where sampling actions provide poor coverage, but so far seems empirically slightly finicky to get working.

There is also a line of work that tries to make the search process less hopeless by adding additional supervision. In many practical cases, for instance, one can obtain expert trajectories from a human. For example AlphaGo first uses supervised learning to predict human moves from expert Go games and the resulting human mimicking policy is later fine-tuned with PGs on the “real” goal of winning the game.

RL’s new frontiers: MAS, PTL, evolution, memetics and eTL

There is another method called Parallel Transfer Learning (PTL), which aims to optimize RL in multi-agent systems (MAS). MAS are computer systems composed of many interacting and autonomous agents within an environment of interests for problem-solving. MAS have a wide array of applications in industrial and scientific fields, such as resource management and computer games.

In MAS, as agents interact with and learn from one another, the challenge is to identify suitable source tasks from multiple agents that will contain mutually useful information to transfer. In conventional MAS (cMAS), which are optimal for simple environments, actions of each agent are pre-defined for possible states in the environment. Normal RL methodologies have been used as the learning processes of (cMAS) agents through trial-and-error interactions in a dynamic environment.

In PTL, each agent will broadcast its knowledge to all other agents while deciding whose knowledge to accept based on the reward received from other agents vs. expected rewards it predicts. Nevertheless, agents in this approach tend to infer incorrect actions on unseen circumstances or complex environments.

However, for more complex or changing environments, it is necessary to endow the agents with intelligence capable of adapting to an environment’s dynamics. A complex environment, almost by definition, implies complex interactions and necessitated learning of MAS, which current RL methodologies are hard-pressed to meet. A more recent machine learning paradigm of Transfer Learning (TL) was introduced as an approach of leveraging valuable knowledge from related and well studied problem domains to enhance problem-solving abilities of MAS in complex environments. Since then, TL has been successfully used for enhancing RL tasks via methodologies such as instance transfer, action-value transfer, feature transfer and advice exchanging (AE).

Most RL systems aim to train a single agent or cMAS. Evolutionary Transfer Learning framework (eTL) aims to develop intelligent and social agents capable of adapting to the dynamic environment of MAS and more efficient problem solving. It’s inspired by Darwin’s theory of evolution (natural selection + random variation) by principles that govern the evolutionary knowledge transfer process. eTL constructs social selection mechanisms that are modelled after the principles of human evolution. It mimics natural learning and errors that are introduced due to the physiological limits of the agents’ ability to perceive differences, thus generating “growth” and “variation” of knowledge that agents have, thus exhibiting higher adaptability capabilities for complex problem solving. Essential backbone of eTL comprises of memetic automaton, which includes evolutionary mechanisms such as meme representation, meme expression, etc.

Memetics

 

The term “meme” can be traced back to Dawkins’ “The Selfish Gene”, where he defined it as “a unit of information residing in the brain and is the replicator in human cultural evolution.” For the past few decades, the meme-inspired science of Memetics has attracted increasing attention in fields including anthropology, biology, psychology, sociology and computer science. Particularly, one of the most direct and simplest applications in computer science for problem solving has become memetic algorithm. Further  research of meme-inspired computational models resulted in concept of memetic automaton, which integrates memes into units of domain information useful for problem-solving. Recently, memes have been defined as transformation matrixes that can be reused across different problem domains for enhanced evolutionary search. As with genes serving as “instructions for building proteins”, memes carry “behavioural instructions,” constructing models for problem solving.

 

Memetics in eTL

 

Meme representation and meme evolution form the two core aspects of eTL. It then undergoes meme expression and meme assimilation. Meme representation is related to what a meme is, while meme expression is defined for an agent to express its stored memes as behavioural actions, and meme assimilation captures new memes by translating corresponding behaviours into knowledge that blends into the agent’s mind-universe. The meme evolution processes (i.e. meme internal and meme external evolutions) comprise the main behavioural learning aspects of eTL. To be specific, meme internal evolution denotes the process for agents to update their mind-universe via self learning or personal grooming. In eTL, all agents undergo meme internal evolution by exploring the common environment simultaneously. During meme internal evolution, meme external evolution might happen to model the social interaction among agents mainly via imitation, which takes place when memes are transmitted. Meme external evolution happens whenever the current agent identifies a suitable teacher agent via a meme selection process. Once the teacher agent is selected, meme transmission occurs to instruct how the agent imitates others. During this process, meme variation facilitates knowledge transfer among agents. Upon receiving feedback from the environment after performing an action, the agent then proceeds to update its mind-universe accordingly.

 

eTL implementation with learning agents

 

There are two implementations of learning agents that take the form of neurally-inspired learning structures, namely a FALCON and a BP multilayer neural network. Specifically, FALCON is a natural extension of self-organizing neural models proposed for real-time RL, while BP is a classical multi-layer network that has been widely used in various learning systems.
  1. MASs with TL vs. MAS without TL: Most TL approaches outperform cMAS. This is due to TL endowing agents with capacities to benefit from the knowledge transferred from the better performing agents, thus accelerating the learning rate of the agents in solving the complex task more efficiently and effectively.
  2. eTL vs. PTL and other TL approaches: FALCON and BP agents with the eTL outperform PTL and other TL approaches due to the reason that, when deciding whether to accept  information broadcasted by the others, agents in PTL tend to make incorrect predictions on previously unseen circumstances. Further, eTL also demonstrates superiority in attaining higher success rates than all AE models thanks to meme selection operator of eTL, which considers a fusion of the “imitate-from-elitist” and “like-attracts-like” principles so as to give agents the option of choosing more reliable teacher agents over the AE model.

Conclusions

While popularisation of RL is traced back to Edward Thorndike and Marvin Minsky, it’s been inspired by nature and present with us humans since ages long gone. This is how we effectively teach children and want to now teach our computer systems, real (neural networks) or simulated (MAS).

RL reentered human consciousness and rekindled our interest again in 2016 when AlphaGo beat Go champion Lee Sedol. RL has, via its currently successful PGs, DQNs and other methodologies, already contributed and continues to accelerate, turn more intelligent and optimise humanoid robotics, autonomous vehicles, hedge funds, and other endeavours, industries and aspect of human life.

However, what is that optimises or accelerates RL itself? Its new frontiers represent PTLs, Memetics and a holistic eTL methodology inspired by natural evolution and spreading of memes. This latter evolutionary (and revolutionary!) approach is governed by several meme-inspired evolutionary operators (implemented using FALCON and BP multi-layer neural network), including meme evolutions.

The performance efficacy of eTL seems to have outperformed even most state-of-the-art MAS TL systems (PTL).

What future does RL hold? We don’t know. But the amount of research resources, experimentation and imaginative thinking will surely not disappoint us.