It's one of our most distinctive features, but it seems that your voice isn't safe from cybercriminals, if a recent case is anything to go by. In the case, cybercriminals developed an AI that mimicked a CEO's voice so well, that it was able to scam an energy firm out of hundreds of thousands of pounds. The Wall Street Journal reported the scam, which happened back in March, and saw criminals swindle a staggering $243,000 (£201,000). The fraudsters used AI to mimic a chief executive from the German parent company of an unnamed UK energy firm. This voice was so believable that the UK-based CEO was tricked into making a large transfer of money to the chief executive, via a Hungarian supplier.

According to a release issued earlier this month by the Los Alamos National Laboratory (LANL), researchers have developed a machine learning approach called transfer learning that lets them model novel materials by learning from data collected about millions of other compounds. The new approach can be applied to new molecules in milliseconds, enabling research into a far greater number of compounds over much longer timescales. The new technique, called ANI-1ccx potential, promises to advance the capabilities of researchers in many fields and improve the accuracy of machine learning-based potentials in future studies of metal alloys and detonation physics. "Our quantum mechanical calculations to create ANI-1ccx potential were conducted over two years with time split on the Comet supercomputer at the San Diego Supercomputer Center and the Badger supercomputer at LANL," said Olexandr Isayev, paper author and a pharmacy professor at the University of North Carolina at Chapel Hill. "We chose these two supercomputers to train our neural networks as there are few machines that can run these – due to the high memory and core requirements."

The energy sector is under constant development, and more of significant inventions and innovations are yet to come. The energy use has always been involved in other industries like agriculture, manufacturing, transportation, and many others. Thus these industries tend to enlarge the amount of energy they consume every day. Energy seems to be very demanding in terms of new technologies application and development of new energy sources. The rapid development of the energy sector and utilities directly influences social development.

LIVERMORE, CALIFORNIA – Today, U.S. Secretary of Energy Rick Perry and Founder of the Weill Family Foundation, Mr. Sandy Weill, signed a Memorandum of Understanding to formally initiate a public-private partnership for artificial intelligence (AI), neurological disorders, and related subjects. The partnership will apply DOE-fueled AI capabilities to advance transformative scientific opportunities in biomedical and public health research. The MOU will foster collaboration to demonstrate AI based research breakthroughs that span from basic science focused on a better understanding of how the brain functions, to clinical and translational research focused on developing novels methods for preventing, treating, and repairing damage caused by diseases and disorders of the brain. "Artificial Intelligence has the power to literally change the world we live in by tackling some of the biggest problems facing humanity – from improving our environment, to advancing our understanding of the cosmos; from increasing cyber security to improving crop production," said U.S. Secretary of Energy Rick Perry. "This Memorandum of Understanding between the Department of Energy and Weill Family Foundation will advance groundbreaking AI research and development in health sciences that will enhance our overall security and improve our quality of life."

In recent years the power systems research community has seen an explosion of work applying operations research techniques to challenging power network optimization problems. Regardless of the application under consideration, all of these works rely on power system test cases for evaluation and validation. However, many of the well established power system test cases were developed as far back as the 1960s with the aim of testing AC power flow algorithms. It is unclear if these power flow test cases are suitable for power system optimization studies. This report surveys all of the publicly available AC transmission system test cases, to the best of our knowledge, and assess their suitability for optimization tasks. It finds that many of the traditional test cases are missing key network operation constraints, such as line thermal limits and generator capability curves. To incorporate these missing constraints, data driven models are developed from a variety of publicly available data sources. The resulting extended test cases form a compressive archive, NESTA, for the evaluation and validation of power system optimization algorithms.

Gaussian Conditional Random Fields (GCRF), as a structured regression model, is designed to achieve higher regression accuracy than unstructured predictors at the expense of execution time, taking into account the objects similarities and the outputs of unstructured predictors simultaneously. As most structural models, the GCRF model does not scale well with large networks. One of the approaches consists of performing calculations on factor graphs (if it is possible) rather than on the full graph, which is more computationally efficient. The Kronecker product of the graphs appears to be a natural choice for a graph decomposition. However, this idea is not straightforwardly applicable for GCRF, since characterizing a Laplacian spectrum of the Kronecker product of graphs, which GCRF is based on, from spectra of its factor graphs has remained an open problem. In this paper we apply new estimations for the Laplacian eigenvalues and eigenvectors, and achieve high prediction accuracy of the proposed models, while the computational complexity of the models, compared to the original GCRF model, is improved from $O(n_{1}^{3}n_{2}^{3})$ to $O(n_{1}^{3} + n_{2}^{3})$. Furthermore, we study the GCRF model with a non-Kronecker graph, where the model consists of finding the nearest Kronecker product of graph for an initial graph. Although the proposed models are more complex, they achieve high prediction accuracy too, while the execution time is still much better compare to the original GCRF model. The effectiveness of the proposed models is characterized on three types of random networks where the proposed models were consistently away more accurate than the previously presented GCRF model for multiscale networks [Jesse Glass and Zoran Obradovic. Structured regression on multiscale networks. IEEE Intelligent Systems, 32(2):23-30, 2017.].

Power load forecast with Machine Learning is a fairly mature application of artificial intelligence and it is indispensable in operation, control and planning. Data selection techniqies have been hardly used in this application. However, the use of such techniques could be beneficial provided the assumption that the data is identically distributed is clearly not true in load forecasting, but it is cyclostationary. In this work we present a fully automatic methodology to determine what are the most adequate data to train a predictor which is based on a full Bayesian probabilistic model. We assess the performance of the method with experiments based on real publicly available data recorded from several years in the United States of America.

"I don't care how much power, brilliance or energy you have, if you don't harness it and focus it on a specific target, and hold it there you're never going to accomplish as much as your ability warrants." Cheap computing power, plethora of data and Cloud-based services have made Artificial Intelligence based applications fairly easy to implement. While AI powered retail, ride-sharing and travel apps have made out lives easier, it is worthwhile to look at AI applications where it has made a difference between night and day in the developing world. Cotton is a commercial crop and is considered the backbone of the country's textile Industry. Over 50% of the cotton balls are affected by a pest called Pink cotton ball which has resulted in extensive crop damage and financial loss to the farmers.

The global Artificial Intelligence in Agriculture Market is valued at USD 432.2 million in 2016, and is expected to grow with a CAGR of 22.5% by 2025. The forecast period considered for market analysis and compiling detailed market research report is 2017-2025. Global Artificial Intelligence in Agriculture Market Research Report by Report Ocean offers competitive landscape, data, trends, information, and exclusive vital statistics of the market. The global Artificial Intelligence in Agriculture Market report is an in-depth study and analysis of various market parameters. This market research report studies market provides detailed analysis of various regions such as North America, Europe, Asia-Pacific, Latin America and Rest of the World.

Tesla and SpaceX CEO Elon Musk and Alibaba founder and Chairman Jack Ma kicked off the 2019 World Artificial Intelligence Conference in Shanghai, China, with an informal debate about AI and its implications to humanity. Throughout their conversation, Musk and Ma touched on several topics, from jobs, the need for educational reform, moving to Mars, and how humans' way of life can improve in the future. The two billionaires have vastly differing points of view concerning artificial intelligence. While Musk is cautious about AI considering the dangers it may pose to humanity, Ma is far more optimistic. "I don't think AI is a threat," Ma said, responding to the Tesla CEO's introductory points.