Arthur C. Clarke famously stated that "any sufficiently advanced technology is indistinguishable from magic." No current technology embodies this statement more than neural networks and deep learning. And like any good magic it not only dazzles and inspires but also puts fear into people's hearts. One known property of artificial neural networks (ANNs) is that they are universal function approximators. This means that any mathematical function can be represented by a neural network.

Democratic Gov. Gina Raimondo said in a statement Tuesday that she is holding the company accountable because it is "unfairly demanding more money to complete an 8-year-old computer upgrade" and is "trying to double the price of this project." She referred to a 2013 pledge made by CEO Meg Whitman to devote more resources to get the project done in a way state residents deserved.

Microsoft hosts its Future Decoded event on an annual basis at London's ExCeL center in the fast-regenerating'docklands' area. But was this year's event just another set of polished executives striding around talking about so-called'business transformation', or were there guts and substance of any kind? The firm in fact devoted much of its opening statements and arguments to discuss intelligent machines, neural networks and Artificial Intelligence (AI). By way of introduction, Microsoft UK CEO Cindy Rose leads the software firm's British operations. The New York Law School educated Rose explained some of the company's new business models and detailed the firm's approach to now operating datacenters in the UK itself -- and this is always important for so-called'data residency' and data sovereignty.

Algorithms are hidden heroes of the modern world. Whether they are making sure you see your best friend's social posts, recommending you new music or suggesting your next favourite film, they have become central to our lives. However, we have only seen the start of algorithms' impact. There will be no sector left untouched by this oncoming revolution and the fashion industry is no exception. The technology is already making waves, playing a vital role in recommendation engines, which display items of clothing based on the user's browsing history.

Since the Industrial Revolution, the pace of technological advancement has been something of a mixed blessing when it comes to job creation (or retraction). As major industries moved from the age of manual labor into the era of automation (around 1760-1840, if you want to get geeky about it), the world as we know it has irrevocably changed everything about the way we work, with an undisputable trend towards improved operational efficiencies, enhanced worker productivity and outcomes not previously possible in earlier agrarian ages. If you missed that part of your Freshman year history class or still think Jethro Tull was the name of a singer in the eponymous rock group (they were named after the inventor of the seed plow, for the record), let me refresh your memory. In a short span of a hundred years, humans witnessed a boom in innovation and advancement that was unrivalled in human history. We talk a lot about the concepts of "disruption" and "innovation," but advances like the Bessemer process of steel manufacture, the Cotton Gin, the photograph, the telegram, the automatic weapon and sundry other advancements fundamentally altered everything from the way we moved from water wheels to steam engines to the way we now purchase clothes.

Artificial intelligence has the opportunity to affect almost every aspect of how we live and consume. I struggle to think of a single industry that could not be transformed through AI technologies, leading to huge gains in effectiveness or efficiency. Yet it's sometimes hard to see a place for AI in particular businesses or organizations, especially if there's an established product, workflow, and way of thinking. Because of this, it can be easy to overlook the opportunities afforded by some of the most revolutionary technologies of our age. To see these opportunities, it is important to remember that powerful AI methods have not been around for very long.

BOT TO THE FUTURE: Mastercard wants to make commerce'more conversational' Mastercard has unveiled plans to launch artificial intelligence (AI) bots for its merchant and bank partners, allowing consumers to use chat, messaging and natural language interfaces to shop and manage their finances. Mastercard KAI, the payments giant's bot for banks, will allow consumers to ask the bot questions about their accounts, review purchase history, monitor spending levels and receive contextual offers. Meanwhile, the Mastercard Bot for Merchants will allow consumer to shop and transact on messaging platforms and then check out with the Masterpass global digital payment service. According to research firm Gartner, nearly US$2bn in online sales will be performed exclusively through mobile digital assistants by the end of 2016. Kiki Del Valle, SVP at Mastercard, explained to NFC World the company was aiming to make commerce "more conversational by combining secure digital payments and artificial intelligence technology".

Towards this end, researchers at MIT are investigating ways of making artificial neural networks more transparent in their decision-making. As they stand now, artificial neural networks are a wonderful tool for discerning patterns and making predictions. But they also have the drawback of not being terribly transparent. The beauty of an artificial neural network is its ability to sift through heaps of data and find structure within the noise. This is not dissimilar from the way we might look up at clouds and see faces amidst their patterns.

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

In the course of this three-part series on the challenges and opportunities for enterprise machine learning, we have worked to define the landscape and ecosystem for these workloads in large-scale business settings and have taken an in-depth look at some of the roadblocks on the path to more mainstream machine learning applications. In this final part of the series, we will turn from pointing to the problems and look at the ways the barriers can be removed, both in terms of leveraging the technology ecosystem around machine learning and addressing more difficult problems, most notably, how to implement the human side of machine learning in an organization. For now, however, let's start looking at solutions at the top of the technology side with the sheer performance and workflow possibilities. Logically, if we want to reduce the cycle time for machine learning radically, it makes sense to attack the most time-consuming tasks. As we noted previously, data scientists spend most of their time collecting and cleaning data, so it makes sense to focus effort on simplifying and expediting this task.