Renewable

Deniz Kalaslioglu is the Co-Founder & CTO of Soar Robotics a cloud-connected Robotic Intelligence platform for drones. You have over 7 years of experience in operating AI-back autonomous drones. Could you share with us some of the highlights throughout your career? Back in 2012, drones were mostly perceived as military tools by the majority. On the other hand, the improvements in mobile processors, sensors and battery technology had already started creating opportunities for consumer drones to become mainstream.

What lies ahead for the data center industry in 2020? At Data Center Frontier our eyes are on the horizon, and we're constantly talking with industry thought leaders to get their take on key trends. Our crystal ball did pretty well last year, so it's time to look ahead at what's in store for 2020. We've identified eight themes that will shape the data center business this year. We'll be writing in more depth about many of these trends in coming weeks, but this list provides a high-level view of the topics that we believe will be relevant this year. In 2020 the explosive growth of data will be felt like never before.

The MyFood Family Smart Greenhouse got a lot of traction at CES 2020, so much that the unit exhibited at the FrenchTech pavilion was sold during the show! Given that the price tag for such a large model (Family model, 22 m² / 242 ft²) varies between 8,600 to 22,000 euros (VAT incl.), that sale was a great validation of the product and the technology powering it. Mickaël Gandecki, co-founder, CTO and Managing Partner, MyFood, told me that the Chef of the Tao restaurant at the Venitian Hotel was so impressed that he wanted his team to visit the smart solar aquaponic greenhouse during CES. Founded in 2015 by Mickaël Gandecki, Matthieu Urban and Johan Nazaraly, MyFood aims to fight the damages caused by industrial agriculture by bringing food production back home, off-grid, using 90% less water, and without pesticide. The company's mission statement reads: "Our ambition: to make it possible to produce at home a healthy, diverse and ultra-fresh diet all-year-round. Reconnect with nature and enjoy a sense of well-being."

We propose a novel Bayesian approach to modelling nonlinear alignments of time series based on latent shared information. We apply the method to the real-world problem of finding common structure in the sensor data of wind turbines introduced by the underlying latent and turbulent wind field. The proposed model allows for both arbitrary alignments of the inputs and non-parametric output warpings to transform the observations. This gives rise to multiple deep Gaussian process models connected via latent generating processes. We present an efficient variational approximation based on nested variational compression and show how the model can be used to extract shared information between dependent time series, recovering an interpretable functional decomposition of the learning problem.

Nigeria has a population of 200m people yet only half have access to electricity. Without electricity, there are no computers or the internet. There are no fridges to keep food fresh. There is no electric water pump. There is nowhere to charge a mobile phone.

Artificial intelligence, big data analytics, and machine learning are revolutionizing renewable energy sector and allowing the companies to improve their overall customer experience by means of automating work processes. Optimization and predictions are two major factors on which the energy sector heavily depends. The energy industry also produces vast amounts of data, and to turn this data into insights, major energy players are turning to AI. The historical data collected by power plants can now be combined with weather and satellite data through advancements in big data, AI, and machine learning. Consequently, solar and wind forecasting technology can predict weather conditions well in advance.

Samsung has shown off an 8K QKED bezel-less TV that is 99 per cent screen and ultra-thin – only 15mm. Fellow South Korean rival LG has its own set of OLED TVs that double as'a piece of art' thanks to an outer edge that mimics a picture frame and the ability to display HD art pieces when not in use. Sony unveiled a concept connected car loaded with sensors and technology from its audio/visual business as part of its own push into mobility. Panasonic had as part of its CES showcase a miniature, battery-powered prototype fire engine that can transport the same level of equipment as a full-sized fire engine but at a fraction of the cost and energy. Lenovo has showcased its foldable PC with a 13.3-inch screen that it says is more durable than Samsung's Galaxy Fold.

Market intelligence firm Tractica has released a new report which explores the global market for artificial intelligence (AI) software between 2018 and 2025. According to the study, the annual global AI software revenue will grow from $10.1 billion to $126.0 billion during the forecast period. More than 330 AI software use cases will contribute to market growth across 28 Industries. The strongest enterprise AI opportunities are in the automotive, consumer, financial services, telecommunications and retail industries. The top five sectors dominating the AI software market have clear business cases for incorporating AI unlike other industries with pie-in-the-sky use cases that may not generate a return on investment for many years.

Researchers at the University of Central Florida think artificial intelligence could help move that process forward faster. The team's work so impressed …

The neural network (NN)-based direct uncertainty quantification (UQ) methods have achieved the state of the art performance since the first inauguration, known as the lower-upper-bound estimation (LUBE) method. However, currently-available cost functions for uncertainty guided NN training are not always converging and all converged NNs are not generating optimized prediction intervals (PIs). Moreover, several groups have proposed different quality criteria for PIs. These raise a question about their relative effectiveness. Most of the existing cost functions of uncertainty guided NN training are not customizable and the convergence of training is uncertain. Therefore, in this paper, we propose a highly customizable smooth cost function for developing NNs to construct optimal PIs. The optimized average width of PIs, PI-failure distances and the PI coverage probability (PICP) are computed for the test dataset. The performance of the proposed method is examined for the wind power generation and the electricity demand data. Results show that the proposed method reduces variation in the quality of PIs, accelerates the training, and improves convergence probability from 99.2% to 99.8%.