With a wave of investment, a raft of new products, and a rising tide of enterprise deployments, artificial intelligence is making a splash in the Internet of Things (IoT). Companies crafting an IoT strategy, evaluating a potential new IoT project, or seeking to get more value from an existing IoT deployment may want to explore a role for AI. Artificial intelligence is playing a growing role in IoT applications and deployments,12 a shift apparent in the behavior of companies operating in this area. Venture capital investments in IoT start-ups that are using AI are up sharply. Companies have acquired dozens of firms working at the intersection of AI and IoT in the last two years.

Like most sectors, the solar industry is rapidly embracing ways to analyze and crunch data in order to lower the cost of solar energy and to open up new markets for their technology. The rise of data tools--algorithms, machine learning, sensors--are driving investments in, and acquisitions of, solar startups, while entrepreneurs are launching new companies that are using data to solve various solar industry problems. Meanwhile, big companies are spending money on tracking, monitoring and evaluating data from solar projects worldwide, helping to lower the cost of generating energy from the sun. It shouldn't come as a surprise that the solar sector is the latest to embrace the value of data. Other traditionally non-digital sectors, like the auto industry, oil and gas, and agriculture are turning to managing data as a necessity to keep their technology competitive and their companies in business.

This is pretty evident from the new technologies that have been emerging day-by-day such as Face-ID which has revolutionized the way we secure information in our mobile phones. Self-driving cars had been a myth, but now they are very much a reality, the adoption of which can be seen by governments throughout the world. Data science is a field wherein ground-breaking research is happening at a much faster pace, in comparison to any other emergent technologies ever before. The time between contemplating a research idea and actually implementing it has come down significantly. . This is also fueled by the immense amount of resources freely available to everyone – which essentially enables even a normal person to contribute to research in their own way.

In recent years, RTB(Real Time Bidding) becomes a popular online advertisement trading method. During the auction, each DSP(Demand Side Platform) is supposed to evaluate current opportunity and respond with an ad and corresponding bid price. It's essential for DSP to find an optimal ad selection and bid price determination strategy which maximizes revenue or performance under budget and ROI(Return On Investment) constraints in P4P(Pay For Performance) or P4U(Pay For Usage) mode. We solve this problem by 1) formalizing the DSP problem as a constrained optimization problem, 2) proposing the augmented MMKP(Multi-choice Multi-dimensional Knapsack Problem) with general solution, 3) and demonstrating the DSP problem is a special case of the augmented MMKP and deriving specialized strategy. Our strategy is verified through simulation and outperforms state-of-the-art strategies in real application. To the best of our knowledge, our solution is the first dual based DSP bidding framework that is derived from strict second price auction assumption and generally applicable to the multiple ads scenario with various objectives and constraints.

A novel quantile Fourier neural network is presented for nonparametric probabilistic forecasting. Prediction are provided in the form of composite quantiles using time as the only input to the model. This effectively is a form of extrapolation based quantile regression applied for forecasting. Empirical results showcase that for time series data that have clear seasonality and trend, the model provides high quality probabilistic predictions. This work introduces a new class of forecasting of using only time as the input versus using past data such as an autoregressive model. Extrapolation based regression has not been studied before for probabilistic forecasting.

At the intersection of machine learning and energy consumption stands an incredibly powerful force with the potential to transform the way we globally produce and consume energy. So powerful in fact, that the concept of merging machine learning and renewable resources has been named the "energy internet" by economic theorist and author Jeremy Rifkin or "digital efficiency" by Intel and GE. Going green with machine learning solutions can drastically improve the way we consume energy, in terms of lower operational costs, more efficient production, better use of natural resources and lower environmental impacts. Last year, Google, with the help of its U.K.-based subsidiary DeepMind, reduced the amount of energy used to cool its data centers by 40%. By introducing machine learning to compensate for the nonlinear interactions between equipment and environment, and using the unique architecture and environment of each data center, this decrease saves Google millions of dollars each year.

To the editor: News about the environment has been so sad lately. On the front page of Friday's Los Angeles Times, you had a story about park rangers being killed by elephant poachers in Congo, and on the Opinion page you had an article about Congress opening up the Arctic National Wildlife Refuge in Alaska to oil drilling.

Reconstruction of structure and parameters of an Ising model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning. The focus of the research community shifted towards developing universal reconstruction algorithms which are both computationally efficient and require the minimal amount of expensive data. We introduce a new method, Interaction Screening, which accurately estimates the model parameters using local optimization problems. The algorithm provably achieves perfect graph structure recovery with an information-theoretically optimal number of samples, notably in the low-temperature regime which is known to be the hardest for learning. The efficacy of Interaction Screening is assessed through extensive numerical tests on synthetic Ising models of various topologies with different types of interactions, as well as on a real data produced by a D-Wave quantum computer. This study shows that the Interaction Screening method is an exact, tractable and optimal technique universally solving the inverse Ising problem.

New research has posited that artificial intelligence will increasingly automate operations for the wind and solar industries, boosting their efficiencies in areas such as decision making and planning, condition monitoring, robotics, and inspections. The new position paper published this week by DNV GL -- international accredited registrar and classification society headquartered near Oslo -- entitled Making Renewables Smarter: The benefits, risks, and future of artificial intelligence in solar and wind, outlines the advances being made in robotics, inspections, supply chain, and the way we work and showcases a variety of opportunities for the solar and wind industries to embrace artificial intelligence (AI) applications to improve their efficiency. "The use of artificial intelligence in industries continues at an impressive rate -- in manufacturing, engineering, healthcare, transportation, finance, telecommunications, services, and energy," the authors of the report explain. "Artificial intelligence's ability to use machine learning to analyse historical and new data, make predictions, control physical operations, and make decisions at increasingly higher levels is having an immense impact." The report explores ways in which AI applications like machine learning can impact the efficiency levels of areas involved in the wind and solar industries such as decision making and planning, condition monitoring, robotics, inspections, certifications and supply chain optimization, as well as the way technical work is carried out.

In April, it was reported that 69-year‑old Tom Patterson, an American who fell gravely ill with an antibiotic-resistant acinetobacter infection, had been brought out of a two-month coma by an injected cocktail of bacteriophages, tiny viruses that specifically attack and kill bacteria. The story is a testament to Patterson's wife (Steffanie Strathdee, a scientist), who searched for alternative therapies when conventional treatments failed, to his physician, Robert Schooley, who used an untested treatment, and to a large band of phage scientists, led by Ryland Young of Texas A&M University and Theron Hamilton of the US Naval Academy. Their long-term, and sometimes unfashionable, research work meant that phages were available in their labs for the rescue attempt. Because a mixed-phage cocktail was used, no one is sure what tipped the balance, but, importantly, it worked. The Eliava Institute in Tbilisi, Georgia has dispensed phage therapy for years, but it was little tried in the west until recently.