Scaremongers play on the idea that robots will simply replace people on the job. In fact, they can become our essential collaborators, freeing us up to spend time on less mundane and mechanical challenges. Rodney Brooks points out how valuable this could be as the number of working-age adults drops and the number of retirees swells. He introduces us to Baxter, the robot with eyes that move and arms that react to touch, which could work alongside an aging population -- and learn to help them at home, too.

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.

In this paper we consider the problems of supervised classification and regression in the case where attributes and labels are functions: a data is represented by a set of functions, and the label is also a function. We focus on the use of reproducing kernel Hilbert space theory to learn from such functional data. Basic concepts and properties of kernel-based learning are extended to include the estimation of function-valued functions. In this setting, the representer theorem is restated, a set of rigorously defined infinite-dimensional operator-valued kernels that can be valuably applied when the data are functions is described, and a learning algorithm for nonlinear functional data analysis is introduced. The methodology is illustrated through speech and audio signal processing experiments.

Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been considered separately, assuming that data coding and ranking are two independent and irrelevant problems. However, is there any internal relationship between sparse coding and ranking score learning? If yes, how to explore and make use of this internal relationship? In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm. To explore the local distribution in the sparse code space, and also to bridge coding and ranking problems, we assume that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function. By considering the local approximation error of ranking scores, the reconstruction error and sparsity of sparse coding, and the query information provided by the user, we construct a unified objective function for learning of sparse codes, the dictionary and ranking scores. We further develop an iterative algorithm to solve this optimization problem.

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.

The alliance is the latest by IBM in a bid to harness Watson's cognitive learning capabilities to benefit millions of college students and professors. The announcement follows a separate agreement announced at the end of June between IBM and Blackboard, and the roll out of an IBM Watson-enabled app for Apple earlier this month, among other initiatives. For Pearson, the alliance represents a chance to combine its global offering of digital learning products with IBM's cognitive learning platform in an effort to give students a more immersive learning experience with their college courses. And it promises to give instructors greater insights about how well students are navigating through their courses. To accomplish that, Watson will essentially ingest and analyze all of Pearson courseware.

Business owners, be forewarned: The AccTech (accounting technology) bots are taking over. These bots are lines of code that grab information and communicate with humans about your business operations. They know (almost) everything before you've even whispered the thought, and they might want to take your bookkeeper's job -- or maybe just work alongside her. I had a recent conversation with Jan Haugo, CEO and vice president of the Institute of Certified Bookkeepers USA (ICBUSA), who emphasized all the ways emerging technologies will transform the role of small business bookkeeping. In particular, we discussed how machine learning and artificial intelligence will enable accountants to interact with bots the same way they would with a human co-worker.

These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design proprietary tools. When you click on any of the 40 links below, you will find a selection of articles related to the entry in question. Most of these articles are hard to find with a Google search, so in some ways this gives you access to the hidden literature on data science, machine learning, and statistical science. Many of these articles are fundamental to understanding the technique in question, and come with further references and source code. Starred techniques (marked with a *) belong to what I call deep data science, a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics.

Arvid Tchivzhel, Director of Product Development, Mather Economics, Arvid Tchivzhel, a director with Mather Economics oversees the delivery and operations for all Mather Economics consulting engagements, along wit... The old adage is as true as ever in the world of open source technology: "Those who do not learn history are doomed to repeat it." Numerous surveys, articles, listicles and case studies address best practices for companies wishing to implement a Big Data project and make a return on their investment. Machine learning is the new buzzword to take hold among executives (and in the marketing materials of enterprise consultants). Before plunging into the world of machine learning, firms should pause and learn from the mistakes made in the implementation of Big Data projects over the last five years.

In part one of this series last week, we discussed the emerging ecosystem of machine learning applications and what promise those portend. But of course, as with any emerging application area (although to be fair, machine learning is not new), there are bound to be some barriers. Even in analytically sophisticated organizations, machine learning often operates in "silos of expertise." For example, the financial crimes unit in a bank may use advanced techniques to catch anti-money laundering; the credit risk team uses completely different and incompatible tools to predict loan defaults and set risk-based pricing; while treasury uses still other tools to predict cash flow. Meanwhile, customer service and branch operations do not use machine learning at all because they lack the critical mass of specialists and software.