Philip Hammond vowed to use the Budget to push for driverless cars on the road within years. The Chancellor has made clear his crucial financial package this week will be a rallying cry for Britain to take the lead on technology. Playing down concerns about the safety of self-driving vehicles, Mr Hammond said that after Brexit the UK to be'leading the next industrial revolution'. Alongside removing obstacles to autonomous cars, Mr Hammond is set to announce investment in robotics and 5G internet. He will unveil changes to regulations that will allow developers to test self-driving cars on UK roads for the first time.

Most businesses are planning to plough cash into artificial intelligence over the next two years as just one in ten leaders believes the UK to be a world leader in digital. Some 85 per cent of the more than 50 organisations surveyed by Deloitte, which collectively are worth more than £200bn, said they will invest in the technology along with the Internet of Things (IoT). AI was identified as potentially the most disruptive technology, though only 22 per cent have invested in it already. By 2020 more than half will put in more than £10m into digital technology, which also includes areas such as robotics, cloud blockchain and virtual and augmented reality, while a third will invest that much this year alone the Digital Disruption Index found. However, that amount is dwarfed by the size of overall IT budgets: a quarter of firms spend more than £75m each year. "The first edition of the index shows that few UK businesses are successfully exploiting digital technologies and ways of working," said Deloitte's digital transformation leaders Paul Thompson.

Nigel Toon, the cofounder and CEO of Graphcore, a semiconductor startup based in the U.K., recalls that only a couple of years ago many venture capitalists viewed the idea of investing in semiconductor chips as something of joke. "You'd take an idea to a meeting," he says, "and many of the partners would roll about on the floor laughing." Now some chip entrepreneurs are getting a very different reception. Instead of rolling on the floor, investors are rolling out their checkbooks. Venture capitalists have good reason to be wary of silicon, even though it gave Silicon Valley its name.

We study a classification problem where each feature can be acquired for a cost and the goal is to optimize the trade-off between classification precision and the total feature cost. We frame the problem as a sequential decision-making problem, where we classify one sample in each episode. At each step, an agent can use values of acquired features to decide whether to purchase another one or whether to classify the sample. We use vanilla Double Deep Q-learning, a standard reinforcement learning technique, to find a classification policy. We show that this generic approach outperforms Adapt-Gbrt, currently the best-performing algorithm developed specifically for classification with costly features.

Many popular random partition models, such as the Chinese restaurant process and its two-parameter extension, fall in the class of exchangeable random partitions, and have found wide applicability in model-based clustering, population genetics, ecology or network analysis. While the exchangeability assumption is sensible in many cases, it has some strong implications. In particular, Kingman's representation theorem implies that the size of the clusters necessarily grows linearly with the sample size; this feature may be undesirable for some applications, as recently pointed out by Miller et al. (2015). We present here a flexible class of non-exchangeable random partition models which are able to generate partitions whose cluster sizes grow sublinearly with the sample size, and where the growth rate is controlled by one parameter. Along with this result, we provide the asymptotic behaviour of the number of clusters of a given size, and show that the model can exhibit a power-law behavior, controlled by another parameter. The construction is based on completely random measures and a Poisson embedding of the random partition, and inference is performed using a Sequential Monte Carlo algorithm. Additionally, we show how the model can also be directly used to generate sparse multigraphs with power-law degree distributions and degree sequences with sublinear growth. Finally, experiments on real datasets emphasize the usefulness of the approach compared to a two-parameter Chinese restaurant process.

The partially observable hidden Markov model is an extension of the hidden Markov Model in which the hidden state is conditioned on an independent Markov chain. This structure is motivated by the presence of discrete metadata, such as an event type, that may partially reveal the hidden state but itself emanates from a separate process. Such a scenario is encountered in keystroke dynamics whereby a user's typing behavior is dependent on the text that is typed. Under the assumption that the user can be in either an active or passive state of typing, the keyboard key names are event types that partially reveal the hidden state due to the presence of relatively longer time intervals between words and sentences than between letters of a word. Using five public datasets, the proposed model is shown to consistently outperform other anomaly detectors, including the standard HMM, in biometric identification and verification tasks and is generally preferred over the HMM in a Monte Carlo goodness of fit test.

The ability to use a 2D map to navigate a complex 3D environment is quite remarkable, and even difficult for many humans. Localization and navigation is also an important problem in domains such as robotics, and has recently become a focus of the deep reinforcement learning community. In this paper we teach a reinforcement learning agent to read a map in order to find the shortest way out of a random maze it has never seen before. Our system combines several state-of-the-art methods such as A3C and incorporates novel elements such as a recurrent localization cell. Our agent learns to localize itself based on 3D first person images and an approximate orientation angle. The agent generalizes well to bigger mazes, showing that it learned useful localization and navigation capabilities.

Spatial understanding is a fundamental problem with wide-reaching real-world applications. The representation of spatial knowledge is often modeled with spatial templates, i.e., regions of acceptability of two objects under an explicit spatial relationship (e.g., "on", "below", etc.). In contrast with prior work that restricts spatial templates to explicit spatial prepositions (e.g., "glass on table"), here we extend this concept to implicit spatial language, i.e., those relationships (generally actions) for which the spatial arrangement of the objects is only implicitly implied (e.g., "man riding horse"). In contrast with explicit relationships, predicting spatial arrangements from implicit spatial language requires significant common sense spatial understanding. Here, we introduce the task of predicting spatial templates for two objects under a relationship, which can be seen as a spatial question-answering task with a (2D) continuous output ("where is the man w.r.t. a horse when the man is walking the horse?"). We present two simple neural-based models that leverage annotated images and structured text to learn this task. The good performance of these models reveals that spatial locations are to a large extent predictable from implicit spatial language. Crucially, the models attain similar performance in a challenging generalized setting, where the object-relation-object combinations (e.g.,"man walking dog") have never been seen before. Next, we go one step further by presenting the models with unseen objects (e.g., "dog"). In this scenario, we show that leveraging word embeddings enables the models to output accurate spatial predictions, proving that the models acquire solid common sense spatial knowledge allowing for such generalization.

Across the world, higher education is increasingly being judged through the lens of employability. More and more, politicians are asking universities how they are preparing students for work, and even tying their funding to their graduates' success in the workplace. In the West, this has mainly been a result of the squeeze on the public purse and – in some countries, at least – an accompanying rise in tuition fees. But there is also growing anxiety about the technological revolution's potential to replace large numbers of human workers with computers and robots if humans can't keep one step ahead in the race to acquire skills. So how well are universities meeting the challenge of preparing graduates for the digital age?

Written on 17 November 2017. About 30 percent of local institutions see AI playing an important role in their innovation plans, according to GFT Technologies' Digital Banking Expert Survey. By comparison, 23 percent of sector firms in the UK and Mexico see AI as crucial in their strategy, while only 17 percent of US banks perceive the technology as an important aspect of their overall plans, the study from the financial services vendor says. The survey covered 285 professionals from small to large retail banks based in Brazil, Germany, Italy, Mexico, Spain, Switzerland, the UK and the US. Brazilian firms may be enthusiastic about the potential of artificial intelligence for tasks such as automating customer service and achieving greater customer engagement, but the country still struggles with issues ranging from infrastructure, lack of qualified manpower and effective partnerships with AI vendors and fintechs - that means the number of real initiatives is still small.