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Raycatch has introduced'DeepSolar 2.0,' a fully automated, AI-based diagnostic software program for cost-optimized maintenance and monitoring of photovoltaic power plants. Raycatch, which is backed by BayWa r.e., developed the next-generation of DeepSolar, its AI-based Software as a Service (SaaS) solution. The software supports solar plant owners by providing them with comprehensive ROI information and data-driven operational insights. In addition, the diagnostic system can identify the sources behind technical issues, outline issue resolutions, evaluate costs and make prioritized recommendations based on plant owners' respective needs. DeepSolar is a diagnostic software program for cost-optimized maintenance of PV power plants.

Sign in to report inappropriate content. Greg Powers, Ph.D, vice president of Technology at Halliburton, discusses how automation and machine learning will transform the oil and gas industry and bring new efficiencies to increase productivity and reduce costs.

INDIA, August 16th, 2019 – IDC announced India winners of the third IDC Digital Transformation Awards (DXa) 2019 and named IFFCO Tokio General Insurance, the 2019 Digital Transformer for India last weekend. Now on its third year, IDC's DX Awards honors the achievements of organizations that have successfully digitalized one or multiple areas of their business through the application of digital and disruptive technologies. Other winners include: Cairn Oil & Gas, Escorts Ltd., L&T Hydrocarbon Engineering Limited, ReArk Digital Preservations Pvt. Ltd., Tata SIA Airlines Limited and The Federal Bank Ltd, who all distinguished themselves for their discernible and measurable excellence in their digital transformation (DX) efforts across the five DX masteries and significant efforts to transform or disrupt the market. Eva Au, Managing Director of IDC Asia/Pacific says, "There is clearly an increasing adoption of third platform technologies and innovation accelerators as enterprises race to transform for the future. The winning projects for 2019 India IDC Digital Transformation Awards mirror this trend with inclusion of AI, IoT, Robotics and analytics to achieve operational efficiency and customer satisfaction. These organizations are successfully thriving with a digitally-native culture, using insights at scale, and deliver new models of customer engagements, all of which are enabled by an intelligent, empowered and agile workforce to evolve into the Future Enterprise."

Maharashtra has put into action Artificial Intelligence to alleviate agricultural hazards by making use of analysed data to fill in any clefts. The project so named, is the Maha Agri Tech project and had become operational in January this year. The Artificial Intelligence (AI) being employed in the first phase are the satellite images, based on mining data together from by the Maharashtra Remote Sensing Application Centre (MRSAC) and the National Remote Sensing Centre (NRSC) in Hyderabad. Moving on to its second phase, (in the upcoming rabid season) a yield model would be constructed wherein, data sets from different data providers will be amalgamated to create a territorial database of soil nutrients, rainfall, moisture stress and a few other relevant factors. This as a consequence will promote location specific consultation to farmers.

Advances in machine learning and the falling cost of storing data are key factors in big oil's motivation to harness the potential of AI.

Due in part to the growing sources of data about past sequences of decisions and their outcomes - from marketing to energy management to healthcare - there is increasing interest in developing accurate and efficient algorithms for off-policy policy evaluation. For Markov Decision Processes, this problem was addressed (Precup et al., 2000) early on by importance sampling (IS)(Rubinstein, 1981), a method prone to large variance due to rare events (Glynn, 1994; L'Ecuyer et al., 2009). The per-decision importance sampling estimator of Precup et al. (2000) tries to mitigate this problem by leveraging the temporal structure - earlier rewards cannot depend on later decisions - of the domain. While neither importance sampling (IS) nor per-decision IS (PDIS) assumes the underlying domain is Markov, more recently, a new class of estimators (Hallak and Mannor, 2017; Liu et al., 2018; Gelada and Bellemare, 2019) has been proposed that leverages the Markovian structure. In particular, these approaches propose performing importance sampling over the stationary state-action distributions induced by the corresponding Markov chain for a particular policy. By avoiding the explicit accumulation of likelihood ratios along the trajectories, it is hypothesized that such ratios of stationary distributions could substantially reduce the variance of the resulting estimator, thereby overcoming the "curse of horizon" (Liu et al., 2018) plaguing off-policy evaluation. The recent flurry of empirical results shows significant performance improvements over the alternative methods on a variety of simulation domains. Yet so far there has not been a formal analysis of the accuracy of IS, PDIS, and stationary state-action IS which will strengthen our understanding of their properties, benefits and limitations.

In all types of disasters, from earthquakes to armed conflicts, aid workers need accurate and timely data such as damage to buildings and population displacement to mount an effective response. Remote sensing provides this data at an unprecedented scale, but extracting operationalizable information from satellite images is slow and labor-intensive. In this work, we use machine learning to automate the detection of building damage in satellite imagery. We compare the performance of four different convolutional neural network models in detecting damaged buildings in the 2010 Haiti earthquake. We also quantify how well the models will generalize to future disasters by training and testing models on different disaster events.

We are rapidly approaching an era of gigantic jobloss. AI will replace many jobs as it gets cheaper. Robots will be doing jobs because every manufacturer can get them. This process is unavoidable because the global market is at the same time funded by a small number of wealthy people, who buy stuff from companies that compete agains many others, who are incentiviced to eliminate human assistence in the production process. The empoverishing effect of automatic production is already ongoing.

During the construction of BBVA's current headquarters in Madrid, criteria was used to ensure its maximum energy efficiency and minimum environmental impact. Together with the use of recycled, sustainable material; the inclusion of extensive green areas; and a sprinkler system that uses rainwater, 50,000 sensors were installed at the bank's headquarters to detect and collect data about the status of the facilities, the environmental conditions, and the proximity of people. BBVA's new corporate headquarters in Madrid have become an architectural and sustainability landmark. Architects Jacques Herzog and Pierre de Meuron have designed not only a smart but an environmentally and people friendly city, reflective of the financial group's global digital transformation strategy. "Once the complex was functioning and after analyzing all this data, we realized that it didn't have to be limited to properly managing the facilities, it could also further improve our energy efficiency and reduce costs," explains Borja Eugui Pemán, BBVA Head of Facility Management.