The company says that its system could cut the payback time for installing LED lights from around five years to three. The system uses "self-learning algorithms" and sensors to enable a streetlight to alter its brightness in response to changes in crime patterns, traffic, people movement and weather. The firm says it would be able to dim lights when bad weather keeps people indoors, or in areas where there are complaints about light pollution. It would also shine brighter in response to particular incidents, such as a traffic accident. "Like the dawn of the Internet, we're just scratching the surface of what's possible when cities intelligently connect scores of new urban data sources," said Seeta Hariharan, the general manager TCS' Digital Software & Solutions Group.

Belief propagation (BP) is an iterative method to perform approximate inference on arbitrary graphical models. Whether BP converges and if the solution is a unique fixed point depends on both the structure and the parametrization of the model. To understand this dependence it is interesting to find \emph{all} fixed points. In this work, we formulate a set of polynomial equations, the solutions of which correspond to BP fixed points. To solve such a nonlinear system we present the numerical polynomial-homotopy-continuation (NPHC) method. Experiments on binary Ising models and on error-correcting codes show how our method is capable of obtaining all BP fixed points. On Ising models with fixed parameters we show how the structure influences both the number of fixed points and the convergence properties. We further asses the accuracy of the marginals and weighted combinations thereof. Weighting marginals with their respective partition function increases the accuracy in all experiments. Contrary to the conjecture that uniqueness of BP fixed points implies convergence, we find graphs for which BP fails to converge, even though a unique fixed point exists. Moreover, we show that this fixed point gives a good approximation, and the NPHC method is able to obtain this fixed point.

ALAMO is a computational methodology for leaning algebraic functions from data. Given a data set, the approach begins by building a low-complexity, linear model composed of explicit non-linear transformations of the independent variables. Linear combinations of these non-linear transformations allow a linear model to better approximate complex behavior observed in real processes. The model is refined, as additional data are obtained in an adaptive fashion through error maximization sampling using derivative-free optimization. Models built using ALAMO can enforce constraints on the response variables to incorporate first-principles knowledge. The ability of ALAMO to generate simple and accurate models for a number of reaction problems is demonstrated. The error maximization sampling is compared with Latin hypercube designs to demonstrate its sampling efficiency. ALAMO's constrained regression methodology is used to further refine concentration models, resulting in models that perform better on validation data and satisfy upper and lower bounds placed on model outputs.

Knoema is the latest data provider to add a conversational interface. The McLean, Virginia-based company has launched an artificial intelligence (AI)-powered chatbot called Yodatai, which it describes as the first digital assistant for public and corporate data. The name, CEO Vladimir Bougay told me, is a shortening of "your data AI." His company provides access to industry, governmental and market data from thousands of providers, including the US Census, the US Department of Energy and other sources around the world. Additionally, Yodatai has been integrated with product analytics platform Amplitude and can access other databases via API.

The ebb and flow of legal rules when it comes to flying a drone, whether it's a cheap mainstream model or something a little more intense, is confusing. It also differs depending on country, and even state. When it comes to Texas, both the House and the Senate are pushing a bill that could attach jail sentences to any pilot found guilty of flying something over oil and gas drilling facilities, as well as telecomms infrastructure and concentrated animal feeding operations -- factory farms. Politicians want these structures added to a "critical infrastructure" list, where flying a drone lower than 400 feet aboveit would be a Class B misdemeanor and could even mean up to 180 days in jail. The facilities would join a list that includes power plants, dams and other refineries, but critics say the additions would affect the public's First Amendment rights.

We show how the discovery of robust scalable numerical solvers for arbitrary bounded linear operators can be automated as a Game Theory problem by reformulating the process of computing with partial information and limited resources as that of playing underlying hierarchies of adversarial information games. When the solution space is a Banach space $B$ endowed with a quadratic norm $\|\cdot\|$, the optimal measure (mixed strategy) for such games (e.g. the adversarial recovery of $u\in B$, given partial measurements $[\phi_i, u]$ with $\phi_i\in B^*$, using relative error in $\|\cdot\|$-norm as a loss) is a centered Gaussian field $\xi$ solely determined by the norm $\|\cdot\|$, whose conditioning (on measurements) produces optimal bets. When measurements are hierarchical, the process of conditioning this Gaussian field produces a hierarchy of elementary bets (gamblets). These gamblets generalize the notion of Wavelets and Wannier functions in the sense that they are adapted to the norm $\|\cdot\|$ and induce a multi-resolution decomposition of $B$ that is adapted to the eigensubspaces of the operator defining the norm $\|\cdot\|$. When the operator is localized, we show that the resulting gamblets are localized both in space and frequency and introduce the Fast Gamblet Transform (FGT) with rigorous accuracy and (near-linear) complexity estimates. As the FFT can be used to solve and diagonalize arbitrary PDEs with constant coefficients, the FGT can be used to decompose a wide range of continuous linear operators (including arbitrary continuous linear bijections from $H^s_0$ to $H^{-s}$ or to $L^2$) into a sequence of independent linear systems with uniformly bounded condition numbers and leads to $\mathcal{O}(N \operatorname{polylog} N)$ solvers and eigenspace adapted Multiresolution Analysis (resulting in near linear complexity approximation of all eigensubspaces).

Collecting data is good and collecting Big Data is better, but analyzing Big Data is not easy. It requires knowledge of enterprise search engines for making content from different sources like enterprise databases, social media, sensor data, etc. searchable to a defined audience. ElasticSearch, Apache Solr, and Sphinx are some of the free and open-source enterprise search software. While applications of ElasticSearch have advanced to Machine Learning, in this blog, I have tried to summarize a simple use case for Big Data analysis using ElasticSearch. ElasticSearch recently added Machine Learning algorithms to its enterprise stack for the purpose of finding anomalies in time-series log data.

The excited states of polyatomic systems are rather complex, and often exhibit meta-stable dynamical behaviors. Static analysis of reaction pathway often fails to sufficiently characterize excited state motions due to their highly non-equilibrium nature. Here, we proposed a time series guided clustering algorithm to generate most relevant meta-stable patterns directly from ab initio dynamic trajectories. Based on the knowledge of these meta-stable patterns, we suggested an interpolation scheme with only a concrete and finite set of known patterns to accurately predict the ground and excited state properties of the entire dynamics trajectories. As illustrated with the example of sinapic acids, the estimation error for both ground and excited state is very close, which indicates one could predict the ground and excited state molecular properties with similar accuracy. These results may provide us some insights to construct an excited state force field with compatible energy terms as traditional ones.

This survey outlines a general and modular theory for proving approximation guarantees for equilibria of auctions in complex settings. This theory complements traditional economic techniques, which generally focus on exact and optimal solutions and are accordingly limited to relatively stylized settings. We highlight three user-friendly analytical tools: smoothness-type inequalities, which immediately yield approximation guarantees for many auction formats of interest in the special case of complete information and deterministic strategies; extension theorems, which extend such guarantees to randomized strategies, no-regret learning outcomes, and incomplete-information settings; and composition theorems, which extend such guarantees from simpler to more complex auctions.

Utilities house enormous datasets that defy traditional analysis, for which machine-learning could be of great benefit. When machine-learning is applied to IoT data, utility companies are able to realise the next generation power grid that can eventually handle billions of endpoints on utility networks autonomously. Pacific Gas and Electric's (PG&E) emerging technologies leader Tom Martin and Paul Doherty, corporate relations, discuss how machine learning and data science is being leveraged for asset maintenance and the integration of distributed energy resources (DER). MSEI: What does machine-learning mean to PG&E? What is your definition of machine-learning? TM: Machine-learning at PG&E is the ability to use analytics to drive optimisation in our operations.