In this study we consider adaptive power beaming with fiber-array laser transmitter system in presence of atmospheric turbulence. For optimization of power transition through the atmosphere fiber-array is traditionally controlled by stochastic parallel gradient descent (SPGD) algorithm where control feedback is provided via radio frequency link by an optical-to-electrical power conversion sensor, attached to a cooperative target. The SPGD algorithm continuously and randomly perturbs voltages applied to fiber-array phase shifters and fiber tip positioners in order to maximize sensor signal, i.e. uses, so-called, "blind" optimization principle. In opposite to this approach a perspective artificially intelligent (AI) control systems for synthesis of optimal control can utilize various pupil- or target-plane data available for the analysis including wavefront sensor data, photo-voltaic array (PVA) data, other optical or atmospheric parameters, and potentially can eliminate well-known drawbacks of SPGD-based controllers. In this study an optimal control is synthesized by a deep neural network (DNN) using target-plane PVA sensor data as its input. A DNN training is occurred online in sync with control system operation and is performed by applying of small perturbations to DNN's outputs. This approach does not require initial DNN's pre-training as well as guarantees optimization of system performance in time. All theoretical results are verified by numerical experiments.

With the growing availability of large-scale datasets, and the popularization of affordable storage and computational capabilities, the energy consumed by AI is becoming a growing concern. To address this issue, in recent years, studies have focused on demonstrating how AI energy efficiency can be improved by tuning the model training strategy. Nevertheless, how modifications applied to datasets can impact the energy consumption of AI is still an open question. To fill this gap, in this exploratory study, we evaluate if data-centric approaches can be utilized to improve AI energy efficiency. To achieve our goal, we conduct an empirical experiment, executed by considering 6 different AI algorithms, a dataset comprising 5,574 data points, and two dataset modifications (number of data points and number of features). Our results show evidence that, by exclusively conducting modifications on datasets, energy consumption can be drastically reduced (up to 92.16%), often at the cost of a negligible or even absent accuracy decline. As additional introductory results, we demonstrate how, by exclusively changing the algorithm used, energy savings up to two orders of magnitude can be achieved. In conclusion, this exploratory investigation empirically demonstrates the importance of applying data-centric techniques to improve AI energy efficiency. Our results call for a research agenda that focuses on data-centric techniques, to further enable and democratize Green AI.

Deploying advanced imaging solutions to robotic and autonomous systems by mimicking human vision requires simultaneous acquisition of multiple fields of views, named the peripheral and fovea regions. Low-resolution peripheral field provides coarse scene exploration to direct the eye to focus to a highly resolved fovea region for sharp imaging. Among 3D computer vision techniques, Light Detection and Ranging (LiDAR) is currently considered at the industrial level for robotic vision. LiDAR is an imaging technique that monitors pulses of light at optical frequencies to sense the space and to recover three-dimensional ranging information. Notwithstanding the efforts on LiDAR integration and optimization, commercially available devices have slow frame rate and low image resolution, notably limited by the performance of mechanical or slow solid-state deflection systems. Metasurfaces (MS) are versatile optical components that can distribute the optical power in desired regions of space. Here, we report on an advanced LiDAR technology that uses ultrafast low FoV deflectors cascaded with large area metasurfaces to achieve large FoV and simultaneous peripheral and central imaging zones. This technology achieves MHz frame rate for 2D imaging, and up to KHz for 3D imaging, with extremely large FoV (up to 150{\deg}deg. on both vertical and horizontal scanning axes). The use of this disruptive LiDAR technology with advanced learning algorithms offers perspectives to improve further the perception capabilities and decision-making process of autonomous vehicles and robotic systems.

Highly accurate different horizon-based wind speed forecasting facilitates a better modern power system. This paper proposed a novel astute hybrid wind speed forecasting model and applied it to different horizons. The proposed hybrid forecasting model decomposes the original wind speed data into IMFs (Intrinsic Mode Function) using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). We fed the obtained subseries from ICEEMDAN to the transformer network. Each transformer network computes the forecast subseries and then passes to the fusion phase. Get the primary wind speed forecasting from the fusion of individual transformer network forecast subseries. Estimate the residual error values and predict errors using a multilayer perceptron neural network. The forecast error is added to the primary forecast wind speed to leverage the high accuracy of wind speed forecasting. Comparative analysis with real-time Kethanur, India wind farm dataset results reveals the proposed ICEEMDAN-TNF-MLPN-RECS hybrid model's superior performance with MAE=1.7096*10^-07, MAPE=2.8416*10^-06, MRE=2.8416*10^-08, MSE=5.0206*10^-14, and RMSE=2.2407*10^-07 for case study 1 and MAE=6.1565*10^-07, MAPE=9.5005*10^-06, MRE=9.5005*10^-08, MSE=8.9289*10^-13, and RMSE=9.4493*10^-07 for case study 2 enriched wind speed forecasting than state-of-the-art methods and reduces the burden on the power system engineer.

Saudi's state-owned oil company announced today its plan to support artificial intelligence (AI) research in the kingdom. Aramco signed a memorandum of understanding with King Abdullah University of Science and Technology north of Jeddah to create a new research center for AI at the educational institution. The center will focus on research, development and marketing of AI technologies, the official Saudi Press Agency reported. The center will "provide a joint framework for working with Saudi Aramco in a close way to develop proper and quick solutions for the needs of the energy sector, among other relevant sectors," said university vice president Donal Bradley. Why it matters: Saudi Arabia is seeking to diversify its economy and rely less on oil as per the Vision 2030 initiative. The kingdom has had some success in this regard.

Data centers use an estimated 200 terawatt hours (TWh) of electricity annually, equal to roughly 50% of all electricity currently used for all global transport, and a worse-case-scenario model troublingly predicts that data centers alone could account for roughly 8% of global electricity consumption by 2030. This may seem shocking to some. But what may be equally shocking (to some) is that edge computing and machine learning will actually play a key role in reducing the carbon footprint of data centers. Through edge computing and machine learning, we can actually significantly reduce the amount of time--and hence, power--data centers need to use to process data. Let's explore why this is so important and how and why it will happen.

Consumers' customer service needs from the utilities sector are typically both sporadic and urgent. Someone needs service turned on, or off, or transferred – or they are reporting an outage and want to know when service will be restored. Conversational AI has transformed these interactions, meaning that gone are the days of frustrating call centre queues, confusing online menu options, and outdated FAQs. Being able to handle these inquiries swiftly and confidently is key to delivering a positive customer experience. Conversational AI can help manage customer care in contact centres by streamlining the service process, reducing the impact of high call volumes on contact centre morale during outages, and making routine tasks associated with utility accounts easier and faster to navigate.

AI-powered camera traps are being used for more than just documenting and monitoring animals -- they have also been a crucial tool in protecting the local wildlife from poachers, such is the case in Gabon in Central Africa. Congo, and Congo Basin, in particular, offer incredible biodiversity with roughly 400 species of mammals and 1,000 species of birds that reside in the largest area of forest preserve -- 80% of Gabon is covered in forests -- out of all African nations, reports Appsilon. Out of these diverse species are endangered wildlife -- elephants, bonobos, lowland gorillas, and chimpanzees, which are at the forefront of the country's so-called "Green Gabon" movement. It seeks to develop sustainable logging while preserving wildlife, with the help of various tracking systems using satellite imagery as well as camera traps on the ground. To help maintain Gabon's biodiversity, researchers from the University of Stirling in the United Kingdom have begun using a new kind of camera trap.

Glint was founded in 2020 with the mission of accelerating the adoption of floating solar energy across the globe. The company has seen floating solar go from a niche market to becoming the third pillar in solar energy along with utility-scale and rooftop solar. With such advantages as no land use, the potential for lower algae growth and lower water evaporation, and a higher yield from the water cooling, floating solar is one of the fastest-growing energy sectors globally and part of the solution to decarbonizing the world.

In this work, a Gaussian process regression(GPR) model incorporated with given physical information in partial differential equations(PDEs) is developed: physics-assisted Gaussian processes(PAGP). The targets of this model can be divided into two types of problem: finding solutions or discovering unknown coefficients of given PDEs with initial and boundary conditions. We introduce three different models: continuous time, discrete time and hybrid models. The given physical information is integrated into Gaussian process model through our designed GP loss functions. Three types of loss function are provided in this paper based on two different approaches to train the standard GP model. The first part of the paper introduces the continuous time model which treats temporal domain the same as spatial domain. The unknown coefficients in given PDEs can be jointly learned with GP hyper-parameters by minimizing the designed loss function. In the discrete time models, we first choose a time discretization scheme to discretize the temporal domain. Then the PAGP model is applied at each time step together with the scheme to approximate PDE solutions at given test points of final time. To discover unknown coefficients in this setting, observations at two specific time are needed and a mixed mean square error function is constructed to obtain the optimal coefficients. In the last part, a novel hybrid model combining the continuous and discrete time models is presented. It merges the flexibility of continuous time model and the accuracy of the discrete time model. The performance of choosing different models with different GP loss functions is also discussed. The effectiveness of the proposed PAGP methods is illustrated in our numerical section.