The author is a Silicon Valley based IIoT industry analyst & co-founder of ArcInsight Research Partners, a research & advisory group. He has also trained with business strategy consulting firms, with additional qualifications in decision analytics, Bayesian-learning approaches & risk-assessment. I wrote about GE's Predix Platform last year in the context of its immensely successful Minds Machines Conferences where the company showcased a very compelling story about its quiet but steady transformation into a digital industrial company. This is a far cry from creating avatars in a consumer or mobile context. Industrial stakes are very high.

Intel is bolstering its artificial intelligence efforts by acquiring Nervana Systems, a two-year-old startup considered among the leaders in developing machine learning technology. Nervana has built an extensive machine learning system, which runs the gamut from an open-sourced software platform all the way down to an upcoming customized computer chip. The platform is used for everything from analyzing seismic data to find promising places to drill for oil to looking at plant genomes in search of new hybrids. Intel intc declined to disclose the purchase price for the deal, which is expected to close in about one month. After transitioning from mainframes to PCs to servers to cloud-based data centers, computing is about to make another transition, says Intel vice president Jason Waxman, who runs the data center solutions group.

Josh Patterson currently runs a consultancy in the big data machine learning / deep learning space. Previously Josh worked as a Principal Solutions Architect at Cloudera and as a machine learning / distributed systems engineer at the Tennessee Valley Authority where he broughtHadoop into the smart grid with the openPDC project. Josh has a Masters in Computer Science from the University of Tennessee at Chattanooga where he did published research on mesh networks (tinyOS) and social insect optimization algorithms. Josh has over 17 years in software development and is very active in the open source space contributing to projects such as deeplearning4j, Apache Mahout, Metronome, IterativeReduce, openPDC, and JMotif.

Spreading processes are often modelled as a stochastic dynamics occurring on top of a given network with edge weights corresponding to the transmission probabilities. Knowledge of veracious transmission probabilities is essential for prediction, optimization, and control of diffusion dynamics. Unfortunately, in most cases the transmission rates are unknown and need to be reconstructed from the spreading data. Moreover, in realistic settings it is impossible to monitor the state of each node at every time, and thus the data is highly incomplete. We introduce an efficient dynamic message-passing algorithm, which is able to reconstruct parameters of the spreading model given only partial information on the activation times of nodes in the network. The method is generalizable to a large class of dynamic models, as well to the case of temporal graphs.

Load disaggregation based on aided linear integer programming (ALIP) is proposed. We start with a conventional linear integer programming (IP) based disaggregation and enhance it in several ways. The enhancements include additional constraints, correction based on a state diagram, median filtering, and linear programming-based refinement. With the aid of these enhancements, the performance of IP-based disaggregation is significantly improved. The proposed ALIP system relies only on the instantaneous load samples instead of waveform signatures, and hence does not crucially depend on high sampling frequency. Experimental results show that the proposed ALIP system performs better than the conventional IP-based load disaggregation system.

For the casual internet user, a quick Google search is often all it takes to find plenty of information on any particular topic. But for specialized financial research, analysts often find themselves laboriously searching proprietary databases, regulatory filings, and paywalled sources that aren't even indexed by the big search engines, says Jack Kokko, the founder and CEO of financial search engine company AlphaSense. That's why he and cofounder and CTO Raj Neervannan created AlphaSense, which applies natural language processing and machine learning techniques to let users find relevant information in financial documents. "It started from my first job out of college as an analyst at Morgan Stanley, where I was, as every analyst, going through these huge piles of paper on my desk and trying to find information very manually--nights and days spent toiling through that information and still fearing that I'm missing a lot," Kokko says. The San Francisco-based company takes in information from thousands of licensed data sources, as well as public web sources like news reports, and automatically processes them to extract meaning on a sentence-by-sentence level.

A three-day weekend means more time to spend with family and friends, to go out and explore the world, and to relax from the pressures of working life. Imagine if, rather than a few times a year, we had a three-day weekend every week. Writing in The Conversation, Alex Williams, a lecturer in Sociology at City University London, explains why it may be the best way to reduce our environmental impact. A reduction in working hours generally correlates with marked reductions in energy consumption, as economists David Rosnick and Mark Weisbrot have argued. In fact, if Americans simply followed European levels of working hours, for example, they would see an estimated 20 per cent reduction in energy use – and hence in carbon emissions.

For the casual internet user, a quick Google search is often all it takes to find plenty of information on any particular topic. But for specialized financial research, analysts often find themselves laboriously searching proprietary databases, regulatory filings, and paywalled sources that aren't even indexed by the big search engines, says Jack Kokko, the founder and CEO of financial search engine company AlphaSense. That's why he and cofounder and CTO Raj Neervannan, created AlphaSense, which applies natural language processing and machine learning techniques to let users find relevant information in financial documents. "It started from my first job out of college as an analyst at Morgan Stanley, where I was, as every analyst, going through these huge piles of paper on my desk and trying to find information very manually--nights and days spent toiling through that information and still fearing that I'm missing a lot," Kokko says. The San Francisco-based company takes in information from thousands of licensed data sources, as well as public web sources like news reports, and automatically processes them to extract meaning on a sentence-by-sentence level.

We had a pretty incredible line-up for Data Day Texas 2016 -- and we intend to exceed your expectations again for 2017. Tell us whom you want to see, what topics you want to learn about, and let us make it happen. Please share your thoughts at suggestions@datadaytexas.com. If you wish to propose a talk or workshop, please visit the Data Day Proposals page. Her commercial applications of data science include developing predictive maintenance models for oil and gas pipelines at Deep Signal, and designing/building a platform for real-time model application, data storage, and model building at WibiData.

Machine learning is nipping at the heels of conventional physical modeling of air quality predictions in more and more places. The latest is Johannesburg, South Africa, where computer engineer Tapiwa M. Chiwewe at the newly opened IBM Research lab is adapting IBM's air quality prediction software to local needs and adding new capabilities. The work is an expansion of the so-called Green Horizons initiative, in which IBM researchers partnered with Chinese government researchers and officials, starting two years ago. Last month, Chiwewe presented some of the Johannesburg lab's first results, involving ground-level ozone level predictions, at the 14th International Conference on Industrial Informatics in Poitiers, France. "You can do a lot of physics to understand how ozone is found in different places," he says, "but what we did is we just collected a lot of data and trained these machines on it and they were able to predict [local ozone levels] without any knowledge of how ozone works in the atmosphere."