By the mid-20th century, both physicists and lyricists were scratching their heads over the issue of artificial intelligence. What would it be like? What kinds of risks would it bear for humanity? Now we know the answer to the first question. Artificial intelligence is a computer program.

Technological advances like artificial intelligence, machine learning and big data are impacting the power sector as well, making it imperative for producers to re-skill resources, a senior official of Tata Power said. "With technological disruptions like artificial intelligence, machine learning, big data, augmented reality etc. that are impacting the power sector as well, it has become imperative to re-train and re-skill resources for emerging careers where the demand is more than the supply," Tata Power Chief Human Resource Officer Jayant Kumar said. Addressing the seventh Power HR Round Table organised by the University of Petroleum & Energy Studies (UPES), Kumar said, "Curiosity, courage and comfort with technology are the traits of a future digital worker." CEOs and HR heads of leading Indian companies in the power sector such as Tata Power, Power Grid, GMR Energy, Adani Green Energy, and DB Power deliberated on various challenges faced by the sector today and possible solutions, UPES said in a statement. "Power distribution companies (Discoms), due to their nature of work, have access to huge consumer data and they can use this data to foray into other allied businesses just the way cab aggregators are delivering food or e-commerce companies providing video-on-demand services," Ashis Basu, CEO (Corporate), GMR Energy, suggested.

Welcome to the first post of my series Deep Learning for Rookies by me, a rookie. I'm writing as a reinforcement learning strategy to process and digest the knowledge better. But if you are a deep learning rookie, then this is for you as well because we can learn together as rookies!

On the two-year anniversary of the Paris climate accord, the world's government, civic and business leaders are coming together in Paris to discuss one of the most important issues and opportunities of our time, climate change. I'm excited to lead the Microsoft delegation at these meetings. While the experts' warnings are dire, at Microsoft we believe technology advances can help us better understand and address the environmental issues facing our planet. That's why we're announcing in Paris that we are broadening our AI for Earth program with an expanded strategic plan and committing $50 million over the next five years to put artificial intelligence technology in the hands of individuals and organizations around the world who are working to protect our planet. At Microsoft, we believe artificial intelligence is a game changer.

To further enhance its research capabilities Eco Marine Power announced today that it will begin using the Neural Network Console provided by Sony Network Communications Inc., as part of a strategy to incorporate Artificial Intelligence (AI) into various ongoing ship related technology projects including the further development of the patented Aquarius MRE (Marine Renewable Energy) and EnergySail. The Neural Network Console is an integrated development environment using deep learning for AI creation and has been used in deep learning applied technology development within Sony since 2015. Various functions are included such as recognition technology and a full-fledged GUI (graphical user interface) and these allow for deep learning programs to be developed. Deep learning refers to a form of machine learning that uses neural networks modelled after the human brain and is notable for its high versatility with applications in a wide variety of fields including signal processing, and robotics. An initial area of focus will be on studying how the Neural Network Console and AI can assist with the development of the automated control system for EMP's EnergySail.

In a guest blogpost, Peter Pugh-Jones, head of technology at SAS UK & Ireland, reflects on how the analytics industry is evolving and what organisations need in a data-driven economy. Check out the latest findings on how the hype around artificial intelligence could be sowing damaging confusion. Also, read a number of case studies on how enterprises are using AI to help reach business goals around the world. You forgot to provide an Email Address. This email address doesn't appear to be valid.

The study of deep recurrent neural networks (RNNs) and, in particular, of deep Reservoir Computing (RC) is gaining an increasing research attention in the neural networks community. The recently introduced deep Echo State Network (deepESN) model opened the way to an extremely efficient approach for designing deep neural networks for temporal data. At the same time, the study of deepESNs allowed to shed light on the intrinsic properties of state dynamics developed by hierarchical compositions of recurrent layers, i.e. on the bias of depth in RNNs architectural design. In this paper, we summarize the advancements in the development, analysis and applications of deepESNs.

Inference, prediction and control of complex dynamical systems from time series is important in many areas, including financial markets, power grid management, climate and weather modeling, or molecular dynamics. The analysis of such highly nonlinear dynamical systems is facilitated by the fact that we can often find a (generally nonlinear) transformation of the system coordinates to features in which the dynamics can be excellently approximated by a linear Markovian model. Moreover, the large number of system variables often change collectively on large time- and length-scales, facilitating a low-dimensional analysis in feature space. In this paper, we introduce a variational approach for Markov processes (VAMP) that allows us to find optimal feature mappings and optimal Markovian models of the dynamics from given time series data. The key insight is that the best linear model can be obtained from the top singular components of the Koopman operator. This leads to the definition of a family of score functions called VAMP-r which can be calculated from data, and can be employed to optimize a Markovian model. In addition, based on the relationship between the variational scores and approximation errors of Koopman operators, we propose a new VAMP-E score, which can be applied to cross-validation for hyper-parameter optimization and model selection in VAMP. VAMP is valid for both reversible and nonreversible processes and for stationary and non-stationary processes or realizations.

Companies say the new tools make them more efficient and give employees more opportunities to do new kinds of work. But the software also is starting to take on management tasks that humans have long handled, such as scheduling and shepherding strategic projects. Researchers say the shift could lead to narrower roles for some managers and displace others.

Kyoto Protocol (KP) like strategic agreements on energy resources reflects the need for long run forecasting of renewable energy time series fluctuations and mitigate the problems of environment degradation due to emission exhausts from nonrenewable resources [1]. Photovoltaic systems for industrial and domestic uses require the distribution of grid connected power systems with solar radiation as the main energy source. However direct conversion of solar to electrical energy is costly and has relatively low efficiency [2]. Coupled with grid stability issues concerning scheduling and assets optimization for short-term (monthly)and long-term (yearly) forecasting requires guaranteed knowledge of solar radiation instabilities at local weather stations. All this information is based on satellite observations and data from ground stations, with uncertainty in geographic and time availability of data, and data sampling rate posing significant forecast granularity. To assess the PV plant operation dependability on global solar radiation (GSR), good measurement of GSR using a high class radiometer and correct controlling of the instrument through correct maintenance policy is essential.