The Sunday Telegraph reports that Chancellor Philip Hammond will announce a pay rise for nurses after "bowing to pressure" from Conservative MPs and cabinet colleagues. The Sunday Times also leads on the Budget, with a picture of what could be a playful Mr Hammond apparently deep in thought and scratching his head. In an interview with the paper, he says he will confirm plans to build 300,000 new homes every year - equivalent to a city the size of Leeds. He is quoted as saying "he will do whatever it takes to get builders building". "Driverless cars by 2021" is the Sunday Express's front page headline.

Mindfire, a new foundation with the goal of "decoding the mind" to help develop true artificial intelligence (AI) is launching November 17th in Zurich, Switzerland. Futurism spoke with the founder of Starmind and president of the foundation, Pascal Kaufmann to learn more about its goals and the path to reach them. "We cannot achieve True AI until we understand actual intelligence. Intelligence has evolved as a means of nature to successfully guide us through an ever-changing environment. This gave rise to behavior, emotions, and consciousness. These critical factors must be taken into account in how we develop AI. This is the purpose of the Mindfire Foundation," he explains.

Driverless cars will be on Britain's roads by 2021 as a result of sweeping regulatory reforms that will put the UK in the forefront of a post-Brexit technological revolution, chancellor Philip Hammond will say this week. In his budget on Wednesday Hammond will allow driverless cars to be tested without any human operator inside or outside the car, and without the legal constraints and rules that apply in many other EU nations, and much of the US. The move – welcomed by the UK motor industry – is part of an attempt by Hammond and the Treasury to project a more upbeat message about the prospects for the UK economy after Brexit, and focus on opportunities as well as the risks. Carmakers have warned that they may have to move at least some production abroad if there is no deal to keep Britain inside the EU single market and customs union, at least for a two-year transition period. But Mike Hawes, chief executive of the Society of Motor Manufacturers and Traders, said it was good news that the government was taking a lead by making the UK attractive to those seeking to develop, test and build an entirely new generation of cars.

McCullough also addresses the fact that Penrose's argument rests on the assumption that human reasoning is consistent and that human beings can be sure of their own consistency. He argues that this assumption is not beyond doubt and presents a thought experiment in order to show how inconsistencies could turn up even during careful and justified reasoning. He proposes to imagine an interrogator asking questions that can be answered by yes or no, and an experimental subject that can answer these questions by pressing a'yes' button or a'no' button. If the interrogator asks the question "Will you push the'no' button", then this question cannot be answered truthfully. The subject knows that the true answer is'no', but he cannot communicate this answer by pressing the'no' button.

Modeling sequential data has become more and more important in practice. Some applications are autonomous driving, virtual sensors and weather forecasting. To model such systems so called recurrent models are used. In this article we introduce two new Deep Recurrent Gaussian Process (DRGP) models based on the Sparse Spectrum Gaussian Process (SSGP) and the improved variational version called Variational Sparse Spectrum Gaussian Process (VSSGP). We follow the recurrent structure given by an existing DRGP based on a specific sparse Nystr\"om approximation. Therefore, we also variationally integrate out the input-space and hence can propagate uncertainty through the layers. We can show that for the resulting lower bound an optimal variational distribution exists. Training is realized through optimizing the variational lower bound. Using Distributed Variational Inference (DVI), we can reduce the computational complexity. We improve over current state of the art methods in prediction accuracy for experimental data-sets used for their evaluation and introduce a new data-set for engine control, named Emission. Furthermore, our method can easily be adapted for unsupervised learning, e.g. the latent variable model and its deep version.

Wednesday's Budget will include plans to make the UK the best place to manufacture and road-test driverless cars, the chancellor has said. Writing in the Sun newspaper, Mr Hammond said investment in "exciting new technologies", including in driverless cars, will be announced. This will "prepare the ground" for the cars to be on UK roads by 2021, he said. Mr Hammond said the "inventors dream" will soon become a reality. The technology that allows cars to become more autonomous has been increasing in recent years, with all the main manufacturers now offering some element of driverless technology, including self-parking features and cruise control on motorways.

There are two big tech topics in banking these days, namely Artificial Intelligence (AI) and Blockchain. These two tech titans are changing the back office of banks into intelligent shared structures that will be mind-blowing a decade from now. I've written a lot about Blockchain, and feel that it's a slow-burn as it needs an awful lot of agreements before it will come to fruition. A bit like Brexit, Blockchain has too many counterparties involved in agreeing how it will happen, which is why it will not be until the next decade that we really start to see this technology transform clearing and settlement. Short-term, sure, Blockchain technologies or, rather, Distributed Ledger Technologies (DLT) will have an impact in bilateral arrangements but that will be piecemeal compared to the transformation that will come when it can replace paper-based identities with digital ones.

Using conditional probability gives Bayes Nets strong analytical advantages over traditional regression-based models. This adds to several advantages we discussed in an earlier article. But what is conditional probability and what makes it different? In short, conditional probability means that the effects of one variable depend on, of flow from, the distribution of another variable (or others). The complete state of one variable determines how another acts.

We live in an age of rapid technological advances where artificial intelligence (AI) is a reality, not a science fiction fantasy. Every day we rely on algorithms to communicate, do our banking online, book a holiday - even introduce us to potential partners. Driverless cars and robots may be the headline makers, but AI is being used for everything from diagnosing illnesses to helping police predict crime hot spots. As machines become more advanced, how does society keep pace when deciding the ethics and regulations governing technology? Al Jazeera talks to Stephen Roberts, professor of Machine Learning at the University of Oxford, United Kingdom, on the role machine learning plays in our lives today - and in the future.

Among the vast business applications of artificial intelligence, Slator has been keeping a close eye on neural machine translation (MT). However, the boundaries between MT and broader tech like natural language processing (NLP) are sometimes fuzzy. The services resulting from these technologies are often adjacent: translation on one side and chatbots on another. In fact, some companies combine them into a single service--multilingual chatbots, for instance. This is why a recent slew of significant funding rounds in the NLP space has caught our attention. In June 2017, Italy-based venture incubator H-Farm acquired language technology services provider CELI, also headquartered in Italy, in a leveraged buyout of 100% of its shares.