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.

Experts are warning that skilled jobs will soon start disappearing because of the rise of artificial intelligence. So far, robots have mainly been replacing manual labor, performing routine and intensive tasks. But smarter machines are putting more skilled professions at risk. Robots are likely to be performing 45% of manufacturing tasks by 2025, versus just 10% today, according to a study by Bank of America. And the rise of artificial intelligence will only accelerate that process as the number of devices connected to the Internet doubles to 50 billion by 2020.

The internet, and more importantly how we consume data from the web, has evolved at an incredible pace in recent years. One thing that has been steadily growing, and is only now really starting to make the headlines is Machine Learning. "Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the Web. It would understand exactly what you wanted, and it would give you the right thing. However, we can get incrementally closer to that, and that is basically what we work on."

This week Stephen Wolfram, founder and chief executive of Wolfram Research, announced a new component of the Wolfram Language for programming called ImageIdentify. Wolfram also introduced a new website, dubbed The Wolfram Language Image Identification Project, that demonstrates the language's new capabilities. The new site lets you upload images and get inferences and definitions in response. You can provide feedback, which should help it become more accurate. You can hit buttons like "Great!," "Could be better," "Missed the point," and "What the heck?!" After you choose one, the service offers a few more guesses, and a text box where you can type in a tag.