Investigations have been performed into using clustering methods in data mining time-series data from smart meters. The problem is to identify patterns and trends in energy usage profiles of commercial and industrial customers over 24-hour periods, and group similar profiles. We tested our method on energy usage data provided by several U.S. power utilities. The results show accurate grouping of accounts similar in their energy usage patterns, and potential for the method to be utilized in energy efficiency programs.

We propose new metrics on sets, ontologies, and functions that can be used in various stages of probabilistic modeling, including exploratory data analysis, learning, inference, and result interpretation. These new functions unify and generalize some of the popular metrics on sets and functions, such as the Jaccard and bag distances on sets and Marczewski-Steinhaus distance on functions. We then introduce information-theoretic metrics on directed acyclic graphs drawn independently according to a fixed probability distribution and show how they can be used to calculate similarity between class labels for the objects with hierarchical output spaces (e.g., protein function). Finally, we provide evidence that the proposed metrics are useful by clustering species based solely on functional annotations available for subsets of their genes. The functional trees resemble evolutionary trees obtained by the phylogenetic analysis of their genomes.

Semidefinite programming has become a key optimization tool in many areas of applied mathematics, signal processing and machine learning. SDPs often arise naturally from the problem structure, or are derived as surrogate optimizations that are relaxations of difficult combinatorial problems [7, 1, 8]. In spite of the importance of SDPs in principle--promising efficient algorithms with polynomial runtime guarantees--it is widely recognized that current optimization algorithms based on interior point methods can handle only relatively small problems. Thus, a considerable gap exists between the theory and applicability of SDP formulations. Scalable algorithms for semidefinite programming, and closely related families of nonconvex programs more generally, are greatly needed. A parallel development is the surprising effectiveness of simple classical procedures such as gradient descent for large scale problems, as explored in the recent machine learning literature. In many areas of machine learning and signal processing such as classification, deep learning, and phase retrieval, gradient descent methods, in particular first order stochastic optimization, have led to remarkably efficient algorithms that can attack very large scale problems [3, 2, 10, 6]. In this paper we build on this work to develop first-order algorithms for solving the rank minimization problem under random measurements and a closely related family of semidefinite programs. Our algorithms are efficient and scalable, and we prove that they attain linear convergence to the global optimum under natural assumptions.

A good friend recently told me that it takes a special kind of nerd to appreciate what Google's AlphaGo did to international Go champion Lee Sedol: a nerd that is both a Go nerd and a computer nerd. For Go nerdiness, I am recently enamored with the massively complex game that has exponentially more outcomes and dimensions than chess. As for the tech nerdiness, many of us assumed that after DeepBlue beat Kasparov in chess, any other game was a foregone conclusion. But actually, it's taken twenty years for a computer to rise to the level of top-ranked Go players, because high-level Go incorporates less calculation of a limited set of future outcomes and far more intuition. Challenges like this are not just an interesting competition.

Attention all 18- to 24-year-olds in the U.S.: Tay is online now, and she wants to chat with you. That, at least, is according to a new Web page for the artificially intelligent bot, which was created by Microsoft to learn more about how people converse. The bot is now on hand to chat with you on Twitter as well as Kik and GroupMe; it's also on Facebook, Instagram and Snapchat. The more you chat with it, the smarter it gets, Microsoft says, leading to a personalized experience. If you share information with Tay, the bot will track your nickname, gender, favorite food, ZIP code and relationship status. It may also use that data to search on your behalf or to create a simple profile for you.

Artificial intelligence is certainly no longer considered science fiction--or a source of expensive R&D efforts with unmet potential--by major players in the technology sector.1 Instead, we are in the midst of a real-world paradigm shift: the final stages of a decades-long transition from the scientific discipline known as artificial intelligence (and its various sub-disciplines) into an array of applied cognitive technologies made more widely available through innovative enterprise architectures unique to the business culture of the technology sector. The technology sector's interest in these technologies (figure 1)2 has exploded in the last several years. Networking companies, semiconductor manufacturers, hardware companies, IT providers, software providers, Internet players--just about every technology subsector has seen a substantial upsurge of activity in this space. In fact, the race to invest in artificial intelligence has been described as "the latest Silicon Valley arms race."3 Since 2012, there have been 100 mergers and acquisitions (M&A) within the technology sector involving cognitive technology companies, products, and services.4 And this rush of M&A activity is not the only sign of the industry's interest. Many capabilities that were only just emerging a few years ago are now essentially mature and becoming "democratized" and more readily available for business applications. As a result, leading companies are using cognitive technologies to enhance their existing products and services, as well as to open up new markets. What is interesting is that the assertive actions of the sector's leaders do not mirror the wholesale adoption of these technologies across the industry. Many technology sector companies have yet to turn their attention to how cognitive technologies are changing their sector or how they--or their competitors--may be able to implement these technologies in their strategy or operations.

Data scientists are considered to have the hottest job right now, but a new study suggests they're little more than "digital janitors" who spend most of their time cleaning data to prepare it for analysis. That's according to CrowdFlower, a crowdsourcing company, which surveyed 80 data scientists with varying levels of experience. While an advanced degree is usually required for the position, a full 60 percent of respondents said they spend most of their time cleaning and organizing data, leaving little for analytical tasks like building training sets and refining algorithms. "You have your hardest-to-hire resource spending most of their time cleaning data," said Lukas Biewald, CrowdFlower's cofounder and CEO. Cleaning and organizing data, as it turns out, is also data scientists' least favorite part of the job, according to more than half of CrowdFlower's respondents.

Billionaire philanthropist Paul Allen has announced a 100 million commitment over 10 years to fund scientific endeavors at the "frontiers of bioscience" that he describes as having major implications for humankind. An initial set of grants, announced Wednesday, will go to Stanford and Tufts universities for the creation of new research centers and to individual scientists with unconventional approaches to projects in tissue regeneration, antibiotic resistance, gene editing and the development of brain circuitry. Allen said his commitment grew out of a realization that the biological sciences are at a critical point in history, with technology now able to take the field in a more quantitative direction than ever before. New tools can manipulate DNA, next-generation microscopes measure and create images of the tiniest parts of living systems, and super-powerful computers are able to make sense of massive amounts of data. "What I believe is that this is potentially a game-changer for our understanding of complex biological systems," Allen said.

Our 5-day bootcamp is an opportunity for anyone to learn the fundamentals of data science and engineering. Bootcamp attendees are trained in machine learning concepts, deploy predictive models, complete an IoT (internet of things) hack day and enter a Kaggle competition.

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