Newswire) GBT Technologies Inc. (OTCPINK: GTCH) ("GBT", or the "Company"), a company specializing in the development of Internet of Things (IoT) and Artificial Intelligence (AI) enabled networking and tracking technologies, including its GopherInsight wireless mesh network technology platform and its Avant! AI, for both mobile and fixed solutions, announced that it started R&D activities for IC (Integrated Circuit) Micro-Solar cell structures, targeted to empower its 3D, multi-planner microchip. As announced before, GBT has filed for an innovative 3D, multi-planner microchip patent in the US (serial number 16292388), and internationally (PCT/50266). GBT's invention relates to the field of integrated circuits (IC), and more specifically to 3D, multi-dimensional, multi-planar microchips. This patent is a derivative of the GopherInsight platform, which is a new, fully integrated circuit technology platform being developed by GBT.

The artificial intelligence industry has skyrocketed in recent years, powering technologies behind smart speakers and self-driving cars, but that growth is coming at a cost. Researchers at the University of Massachusetts Amherst recently conducted a study assessing the energy consumption required to train several common large AI models. The study revealed that the training process can emit over 626,000 pounds of carbon dioxide, nearly 5x the lifetime emissions of an average car, or the equivalent of about 300 round-trip flights between New York and San Francisco. The benefits from the advancements in AI and other emerging technologies at the expense of the environment are simply not worth it, say many industry experts who are urging big tech companies to ramp up their sustainability efforts. Failing to do so could leave the companies' reputations at risk, they said.

Being virtually hooked up to a human could help robots respond to disasters or other situations that would put human responders' lives at risks. The researchers say that a system like this could be used to help in robotic clean-up operations such as the one after the Fukushima Daiichi nuclear power plant disaster in Japan in 2011. Humans could have guided robots to navigate around the site more accurately, from a safe distance. And while there's currently no machine learning involved in the process, Ramos believes the data captured from the system could be used to help train autonomous robots.

It's no secret that participating in energy markets and developing strategies requires processing massive amounts of data whether generating, selling, or buying power. Consider a trading and marketing company like DTE, Exelon or Vistra who participate in nearly all markets in North America and beyond. To turn data into insights that can improve productivity and cut costs, energy players -- from startup renewables companies to major utility giants -- are turning to artificial intelligence (AI) to improve the accessibility and efficiency of the energy market. The use of AI in energy operations has picked up the pace in recent years and is now moving beyond back office automation toward projects that generate useful information for front office. Lessons learned from yesterday, last week, last month or even last year can shape the daily decisions made by traders.

VS: When I founded METRON in 2013, I envisioned creating a digital solution to help industrial energy providers better manage their energy consumption in the most sustainable and efficient way. METRON opens the way for this new paradigm by helping industrial clients leverage untapped industrial data and connect to decentralized energy resources. For METRON, energy optimization of the world's industrial plants is the real opportunity to address sustainable challenges. Our disruptive AI approach is the combination of Machine Learning and knowledge bases. Our AI-driven platform gets insights into the factory's operations and transforms them into real savings by suggesting an actionable energy management strategy. We unlock new energy-saving opportunities by optimizing all the industrial untapped data.

San Jose, CA, Oct. 30, 2019 (GLOBE NEWSWIRE) -- via NEWMEDIAWIRE --Sonasoft Corp. (OTCQB: SSFT), a leader in innovative artificial intelligence (AI), today announced that it has won a contract with Padmini VNA, a manufacturer of products for original equipment manufacturers, for the Company's artificial intelligence (AI) solution, NuGene. The OEM manufacturer will deploy NuGene across an army of robots by O&M Robotics, a partner of Padmini VNA, enabling these robots to autonomously navigate and clean solar panels, allowing for millions of dollars in maintenance to be saved. Solar panels' efficiency can diminish by as much as 20% in domestic installations and as high as 60% in commercial installations. The number is too large to be ignored especially for commercial installations. This situation has encouraged the need for an efficient and cost-effective system to clean the surface of solar panels.

As much as we'd like to think that we're entering an era of autonomous robots, they're actually still pretty helpless. To keep them from falling down all the time, a human's fast reflexes could be the solution. But the human has to feel what the robot is feeling -- and that's just what these researchers are testing. Bipedal robots are excellent in theory for navigating human environments, but naturally are more prone to falling than quadrupedal or wheeled robots. Although they often have sophisticated algorithms that help keep them upright, in some situations those just might not be enough.

We present a new feature extraction method for complex and large datasets, based on the concept of transport operators on graphs. The proposed approach generalizes and extends the many existing data representation methodologies built upon diffusion processes, to a new domain where dynamical systems play a key role. The main advantage of this approach comes from the ability to exploit different relationships than those arising in the context of e.g., Graph Laplacians. Fundamental properties of the transport operators are proved. We demonstrate the flexibility of the method by introducing several diverse examples of transformations. We close the paper with a series of computational experiments and applications to the problem of classification of hyperspectral satellite imagery, to illustrate the practical implications of our algorithm and its ability to quantify new aspects of relationships within complicated datasets.

For joint inference over multiple variables, a variety of structured prediction techniques have been developed to model correlations among variables and thereby improve predictions. However, many classical approaches suffer from one of two primary drawbacks: they either lack the ability to model high-order correlations among variables while maintaining computationally tractable inference, or they do not allow to explicitly model known correlations. To address this shortcoming, we introduce `Graph Structured Prediction Energy Networks,' for which we develop inference techniques that allow to both model explicit local and implicit higher-order correlations while maintaining tractability of inference. We apply the proposed method to tasks from the natural language processing and computer vision domain and demonstrate its general utility.

$\varepsilon$-greedy is a policy used to balance exploration and exploitation in many reinforcement learning setting. In cases where the agent uses some on-policy algorithm to learn optimal behaviour, it makes sense for the agent to explore more initially and eventually exploit more as it approaches the target behaviour. This shift from heavy exploration to heavy exploitation can be represented as decay in the $\varepsilon$ value, where $\varepsilon$ depicts the how much an agent is allowed to explore. This paper proposes a new approach to this $\varepsilon$ decay where the decay is based on feedback from the environment. This paper also compares and contrasts one such approach based on rewards and compares it against standard exponential decay. The new approach, in the environments tested, produces more consistent results that on average perform better.