Yesterday, The Guardian published a provocative opinion piece titled, "Are Robots Going To Steal Your Job? Probably." At first glance, the author's pessimism would seem justified. From robotic gardeners and farmers to robotic pizza delivery services, it seems like every day robots make new forays into jobs traditionally done by humans. But pause to consider technology in historical perspective. Pessimism about new technologies is not new.

This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system's dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenz 63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well.

Many important forms of data are stored digitally in XML format. Errors can occur in the textual content of the data in the fields of the XML. Fixing these errors manually is time-consuming and expensive, especially for large amounts of data. There is increasing interest in the research, development, and use of automated techniques for assisting with data cleaning. Electronic dictionaries are an important form of data frequently stored in XML format that frequently have errors introduced through a mixture of manual typographical entry errors and optical character recognition errors. In this paper we describe methods for flagging statistical anomalies as likely errors in electronic dictionaries stored in XML format. We describe six systems based on different sources of information. The systems detect errors using various signals in the data including uncommon characters, text length, character-based language models, word-based language models, tied-field length ratios, and tied-field transliteration models. Four of the systems detect errors based on expectations automatically inferred from content within elements of a single field type. We call these single-field systems. Two of the systems detect errors based on correspondence expectations automatically inferred from content within elements of multiple related field types. We call these tied-field systems. For each system, we provide an intuitive analysis of the type of error that it is successful at detecting. Finally, we describe two larger-scale evaluations using crowdsourcing with Amazon's Mechanical Turk platform and using the annotations of a domain expert. The evaluations consistently show that the systems are useful for improving the efficiency with which errors in XML electronic dictionaries can be detected.

Off-the-shelf Gaussian Process (GP) covariance functions encode smoothness assumptions on the structure of the function to be modeled. To model complex and non-differentiable functions, these smoothness assumptions are often too restrictive. One way to alleviate this limitation is to find a different representation of the data by introducing a feature space. This feature space is often learned in an unsupervised way, which might lead to data representations that are not useful for the overall regression task. In this paper, we propose Manifold Gaussian Processes, a novel supervised method that jointly learns a transformation of the data into a feature space and a GP regression from the feature space to observed space. The Manifold GP is a full GP and allows to learn data representations, which are useful for the overall regression task. As a proof-of-concept, we evaluate our approach on complex non-smooth functions where standard GPs perform poorly, such as step functions and robotics tasks with contacts.

We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other. We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source SentiStrength program. Specifically we make three contributions. Firstly we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example they use positive sentiment more often and negative sentiment less often. Secondly we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community. Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable to those obtained from our empirical dataset.

David Shing, or simply'Shingy,' is well-known in technology circles. And for good reason, he's AOL's energetic and up-beat'Digital Prophet' – oft jetting around the world to talk about the future of technology. But how long will Shingy's (and other similar roles) survive? Not all that long perhaps, if McCann's hiring of an artificially intelligent creative director in its Japanese office is anything to go by. Our biggest ever edition of TNW Conference is fast approaching!

Undoubtedly the most interesting conversation I had at SXSW was with Dr Ted Berger, who is working on a brain implant to make life better for people who have problems with long-term memory – and the science behind it is fascinating. Berger explained to me how people with conditions like epilepsy and alzheimers can suffer problems with their hippocampus, the part of the brain that turns short-term memories into longterm ones. Our biggest ever edition of TNW Conference is fast approaching! Essentially, our initial memories of an event are binary electrical codes that are filtered through the hippocampus to another part of the brain for longterm storage. What Berger, who was at SXSW as part of IEEE's Tech for Humanity series, is building is essentially a battery-powered prosthetic hippocampus.

For example, in Terminator XXVIII: Rise of the Earthlings (2051), a brave young android is tasked with saving the world from an army of killer humans sent from the future to destroy robotkind. Leading the human rebellion is Barry, an 18-stone unemployed bus driver from Caerphilly whose powers include the ability to eat a foot-long meatball marinara from Subway in under nine seconds. In the war zones of the future, robot generals will send human beings on to the battlefield to check for land mines and other unexploded devices. "Previously, this highly dangerous work was carried out by bomb disposal robots," explains Major-General Sir Optimus Prime. "Sending human beings instead will reduce the risk to robot life. We've lost too many good droids this way."

So how could a scalable retail artificial intelligence in the cloud – Brand AI – turn these challenges into unique opportunities for competitive advantage? But unlike today's arguably bland, soulless smartphone versions that focus on delivering simple functionality; Brand AI would have a unique, human character that reflects the retailer's values to inform its interactions and maturing relationship with an individual customer. Intended to be more than another'digital novelty', this disruptive form of customer engagement builds on and enhances a B&M's traditional brand as a trusted long term friend throughout the entire customer journey by offering compelling, timely presale insights, instant payment processing and effective after sales support and care. A customer is empowered to select what personal data they choose to share (or keep private) with the Brand AI to enrich their relationship. Social, location, wearable or browsing and buying behaviour data from complementary or even competing retailers could potentially be shared via its cloud platform.

Since the first computer-managed elements entered service in a General Motors auto manufacturing plant in 1961, almost every service and manufacturing industry in the world has benefited from increased automation provided -- to a greater or lesser degree -- by robotics. And, as industries become more deeply interconnected as a result of the demands of globalization and ubiquitous connectivity, so the very nature of robots will also evolve. However, increased proliferation of robots will bring as many new or accentuated risks as benefits, heightening the need for control over our creations. Today, there are many different types of robots in commercial and private use, with form factors varying considerably from the static to the fully mobile, from the microscopic to the truly huge and from the single function-specific design to the multi-function, modular types popularised by science fiction. Risks and threats posed by robots will also vary considerably.