The Cognitive Computing market research encompasses an exhaustive analysis of the market outlook, framework, and socio-economic impacts. The report covers the accurate investigation of the market size, share, product footprint, revenue, and progress rate. Driven by primary and secondary researches, the Cognitive Computing market study offers reliable and authentic projections regarding the technical jargon. As per the latest business intelligence report published by Transparency Market Research, the Cognitive Computing market has been observing promising growth since the last few years. The report further suggests that the Cognitive Computing market appears to progress at an accelerating rate over the forecast period.

'We Care For The Planet' is what the Indian Government's first slide in their presentation regarding the initiatives they started on clean energy mentioned. Adhering to what the Government stated, India stands at #4 globally in wind power installed, #6 globally in solar power installed, and #5 globally in total renewable power installed. But, India, along with its renewable energy plans, intends to integrate Artificial Intelligence to speed up its clean energy initiative. Many tech companies are investing in cleantech startups to empower people in harnessing renewable energy through their AI-based services. It looks like India's vision for reaching the renewable energy capacity of 175GW is within reach.

Hudson Yards, a $25 billion urban complex on Manhattan's west side, is the city's most ambitious development since the rebuilding of the World Trade Center. When fully complete, the 28-acre site will include 16 towers of homes and offices, a hotel, a school, the highest outdoor observation deck in the Western Hemisphere, a performing arts center, Vessel and a shopping mall. Hudson Yards in New York and Sidewalk Labs' project in Toronto are test cases that will radically change the way our cities work through the use of data and the Internet of Things. As I discussed in a previous post, the Internet of Things has evolved to encompass a range of devices, from the smallest household appliance to self-driving cars. On a larger scale, smart city developments compound the benefits of IoT by collecting and analyzing data on usage patterns to create a reciprocal relationship between residents and their communities.

Deep learning and the Internet of Things (IoT) are two aspects of artificial intelligence (AI) that could potentially revolutionise the oil and gas industries. Having already made quite a storm in various other industries including consumer electronics, this couldn't come at a better time for the oil industry as it currently faces dramatic drops in the price of oil. While there is no doubt several AI practical applications already in place that will indeed help these industries improve the following are three have the potential to make a significant difference across the board. In the same way that bots are being used in customer service departments, field technicians can interact with diagnostic applications through voice controls. This is made possible through the use of deep learning and natural language processing algorithms and enables remote diagnostics at the touch of a button.

Turbulence is still one of the main challenges for accurately predicting reactive flows. Therefore, the development of new turbulence closures which can be applied to combustion problems is essential. Data-driven modeling has become very popular in many fields over the last years as large, often extensively labeled, datasets became available and training of large neural networks became possible on GPUs speeding up the learning process tremendously. However, the successful application of deep neural networks in fluid dynamics, for example for subgrid modeling in the context of large-eddy simulations (LESs), is still challenging. Reasons for this are the large amount of degrees of freedom in realistic flows, the high requirements with respect to accuracy and error robustness, as well as open questions, such as the generalization capability of trained neural networks in such high-dimensional, physics-constrained scenarios. This work presents a novel subgrid modeling approach based on a generative adversarial network (GAN), which is trained with unsupervised deep learning (DL) using adversarial and physics-informed losses. A two-step training method is used to improve the generalization capability, especially extrapolation, of the network. The novel approach gives good results in a priori as well as a posteriori tests with decaying turbulence including turbulent mixing. The applicability of the network in complex combustion scenarios is furthermore discussed by employing it to a reactive LES of the Spray A case defined by the Engine Combustion Network (ECN).

We propose a statistical model to understand people's perception of their carbon footprint. Driven by the observation that few people think of CO2 impact in absolute terms, we design a system to probe people's perception from simple pairwise comparisons of the relative carbon footprint of their actions. The formulation of the model enables us to take an active-learning approach to selecting the pairs of actions that are maximally informative about the model parameters. We define a set of 18 actions and collect a dataset of 2183 comparisons from 176 users on a university campus. The early results reveal promising directions to improve climate communication and enhance climate mitigation.

Artificial Intelligence and Machine Learning are increasingly seen as key technologies for buildin g more decentralised and resilient energy grids, but researchers must consider the ethical and social implications of their use E nergy grids are undergoing rapid changes, requiring new ways both to process the large amounts of data generated from the power system, but also - increasingly - to take smart operational decisions [1]. On the data side, the UK and most EU countries have committed to a target of offering a smart meter to every home by 2020 [ 2 ], with similar monitoring being installed in other parts of the energy network. This has led to some to refer to a "data tsunami", requiri ng development of new machine learning techniques to deal with the e nsuing challenge of extracting useful information from this data - often in real time. Another trend is the use of AI techniques (such as those from multi - agent systems, computational gam e theory and decision making under uncertainty) to take autonomous allocation and control decisions. This is driven increasingly by the moves towards more decentralised energy systems, where prosumers (consumers with own micro - generation and storage) can g enerate and source their own electricity through peer - to - peer (P2P) trading in local energy markets and community energy schemes.

Recurrent neural networks (RNN) have been successfully applied to various sequential decision-making tasks, natural language processing applications, and time-series predictions. Such networks are usually trained through back-propagation through time (BPTT) which is prohibitively expensive, especially when the length of the time dependencies and the number of hidden neurons increase. To reduce the training time, extreme learning machines (ELMs) have been recently applied to RNN training, reaching a 99\% speedup on some applications. Due to its non-iterative nature, ELM training, when parallelized, has the potential to reach higher speedups than BPTT. In this work, we present \opt, an optimized parallel RNN training algorithm based on ELM that takes advantage of the GPU shared memory and of parallel QR factorization algorithms to efficiently reach optimal solutions. The theoretical analysis of the proposed algorithm is presented on six RNN architectures, including LSTM and GRU, and its performance is empirically tested on ten time-series prediction applications. \opt~is shown to reach up to 845 times speedup over its sequential counterpart and to require up to 20x less time to train than parallel BPTT.

We present a scoring approach for speaker verification that mimics the standard PLDA-based backend process used in most current speaker verification systems. However, unlike the standard backends, all parameters of the model are jointly trained to optimize the binary cross-entropy for the speaker verification task. We further integrate the calibration stage inside the model, making the parameters of this stage depend on metadata vectors that represent the conditions of the signals. We show that the proposed backend has excellent out-of-the-box calibration performance on most of our test sets, making it an ideal approach for cases in which the test conditions are not known and development data is not available for training a domain-specific calibration model.

This paper presents a convolutional neural network (CNN) which can be used for forecasting electricity load profiles 36 hours into the future. In contrast to well established CNN architectures, the input data is one-dimensional. A parameter scanning of network parameters is conducted in order to gain information about the influence of the kernel size, number of filters, and dense size. The results show that a good forecast quality can already be achieved with basic CNN architectures. The method works not only for smooth sum loads of many hundred consumers, but also for the load of apartment buildings.