The technology industry likes to talk about how automation is set to change the world. Chatbots present a new way of interacting with software, self-driving cars promise to reshape our cities, and the increasing capability of AI to handle ever more complex and "human" tasks could reshape our economy. But amid all the futurism, one thing gets lost: actual robots. London startup Automata Technologies is one of those hoping to reverse the trend. The company makes a tabletop robotic arm, which it hopes will democratise access to automation for every industry by costing a fraction of the tens of thousands of dollars a typical industrial robot costs today – under £5,000 up front, or under £500 a month for a "robotics as a service" package.

Garry Kasparov and DeepMind's CEO Demis Hassabis discuss Garry's new book "Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins ", his chess match with IBM Deep Blue and his thoughts on the future of AI in the world of chess. Event moderated by Demis Hassabis, CEO, DeepMind of Google. In May 1997, the world watched as Garry Kasparov, the greatest chess player in the world, was defeated for the first time by the IBM supercomputer Deep Blue. It was a watershed moment in the history of technology: machine intelligence had arrived at the point where it could best human intellect. It wasn't a coincidence that Kasparov became the symbol of man's fight against the machines.

In the 1950s, scientists began working to build machines capable of imitating intelligent human behaviour. Progress accelerated recently when we entered a new phase of machine learning--one that has led to a dramatic decrease in the cost of prediction. Moore's Law (the 18-month doubling of transistor intensity on microprocessors) was the driver of the previous phase--a revolution in technology hardware that advanced mobile innovation and smartphone adoption. Increased connectivity and scalable cloud-based storage catalysed a step change in the amount of data we collected and consumed. Information taken from sensors, images, videos, and other digital sources is being used to generate a more accurate view of real-time context.

Antshares, the first open-source blockchain platform developed in China, announced a complete rebranding of its blockchain solution into NEO. NEO is China's answer to Ethereum, a currency that has seen a 3,275% increase since the beginning of the year, particularly since the inception of the Ethereum Enterprise Alliance. It has one major difference that give it a competitive edge. Unlike Ethereum, which uses its own programming language: Solidity, NEO will support nearly all programming languages, greatly lowering the difficulty for developers to write smart contracts. This means that NEO is far more open to developers, as they won't need to learn a new coding language.

We present a database of parliamentary debates that contains the complete record of parliamentary speeches from D\'ail \'Eireann, the lower house and principal chamber of the Irish parliament, from 1919 to 2013. In addition, the database contains background information on all TDs (Teachta D\'ala, members of parliament), such as their party affiliations, constituencies and office positions. The current version of the database includes close to 4.5 million speeches from 1,178 TDs. The speeches were downloaded from the official parliament website and further processed and parsed with a Python script. Background information on TDs was collected from the member database of the parliament website. Data on cabinet positions (ministers and junior ministers) was collected from the official website of the government. A record linkage algorithm and human coders were used to match TDs and ministers.

Disagreement-based approaches generate multiple classifiers and exploit the disagreement among them with unlabeled data to improve learning performance. Co-training is a representative paradigm of them, which trains two classifiers separately on two sufficient and redundant views; while for the applications where there is only one view, several successful variants of co-training with two different classifiers on single-view data instead of two views have been proposed. For these disagreement-based approaches, there are several important issues which still are unsolved, in this article we present theoretical analyses to address these issues, which provides a theoretical foundation of co-training and disagreement-based approaches. Keywords: machine learning, semi-supervised learning, disagreement-based learning, co-training, multi-view classification, combination 1. Introduction Learning from labeled training data is well-established in traditional machine learning, but labeling the data is time-consuming, sometimes may be very expensive since it requires human efforts. In many practical applications, unlabeled data can be obtained abundantly and cheaply.

Interpreting gradient methods as fixed-point iterations, we provide a detailed analysis of those methods for minimizing convex objective functions. Due to their conceptual and algorithmic simplicity, gradient methods are widely used in machine learning for massive data sets (big data). In particular, stochastic gradient methods are considered the de- facto standard for training deep neural networks. Studying gradient methods within the realm of fixed-point theory provides us with powerful tools to analyze their convergence properties. In particular, gradient methods using inexact or noisy gradients, such as stochastic gradient descent, can be studied conveniently using well-known results on inexact fixed-point iterations. Moreover, as we demonstrate in this paper, the fixed-point approach allows an elegant derivation of accelerations for basic gradient methods. In particular, we will show how gradient descent can be accelerated by a fixed-point preserving transformation of an operator associated with the objective function.

There has been a growing interest in using nonparametric regression methods like Gaussian Process (GP) regression for system identification. GP regression does traditionally have three important downsides: (1) it is computationally intensive, (2) it cannot efficiently implement newly obtained measurements online, and (3) it cannot deal with stochastic (noisy) input points. In this paper we present an algorithm tackling all these three issues simultaneously. The resulting Sparse Online Noisy Input GP (SONIG) regression algorithm can incorporate new noisy measurements in constant runtime. A comparison has shown that it is more accurate than similar existing regression algorithms. When applied to nonlinear black-box system modeling, its performance is competitive with existing nonlinear ARX models. Keywords: Nonlinear system identification, Gaussian processes, regression, machine learning, sparse methods.

Fuzzy controllers are efficient and interpretable system controllers for continuous state and action spaces. To date, such controllers have been constructed manually or trained automatically either using expert-generated problem-specific cost functions or incorporating detailed knowledge about the optimal control strategy. Both requirements for automatic training processes are not found in most real-world reinforcement learning (RL) problems. In such applications, online learning is often prohibited for safety reasons because online learning requires exploration of the problem's dynamics during policy training. We introduce a fuzzy particle swarm reinforcement learning (FPSRL) approach that can construct fuzzy RL policies solely by training parameters on world models that simulate real system dynamics. These world models are created by employing an autonomous machine learning technique that uses previously generated transition samples of a real system. To the best of our knowledge, this approach is the first to relate self-organizing fuzzy controllers to model-based batch RL. Therefore, FPSRL is intended to solve problems in domains where online learning is prohibited, system dynamics are relatively easy to model from previously generated default policy transition samples, and it is expected that a relatively easily interpretable control policy exists. The efficiency of the proposed approach with problems from such domains is demonstrated using three standard RL benchmarks, i.e., mountain car, cart-pole balancing, and cart-pole swing-up. Our experimental results demonstrate high-performing, interpretable fuzzy policies.

In this blog post, I am going to teach you how to train a Bayesian deep learning classifier using Keras and tensorflow. I will then cover two techniques for including uncertainty in a deep learning model and will go over a specific example using Keras to train fully connected layers over a frozen ResNet50 encoder on the cifar10 dataset. With this example, I will also discuss methods of exploring the uncertainty predictions of a Bayesian deep learning classifier and provide suggestions for improving the model in the future. This post is based on material from two blog posts (here and here) and a white paper on Bayesian deep learning from the University of Cambridge machine learning group. If you want to learn more about Bayesian deep learning after reading this post, I encourage you to check out all three of these resources. Thank you to the University of Cambridge machine learning group for your amazing blog posts and papers.