Artificial intelligence (AI) can distinguish a dog from a cat, but the billions of calculations needed to do so demand quite a lot of energy. The human brain can do the same thing while using only a small fraction of this energy. Could this phenomenon inspire us to develop more energy-efficient AI systems? Our computational power has risen exponentially, enabling the widespread use of artificial intelligence, a technology that relies on processing huge amounts of data to recognize patterns. When we use the recommendation algorithm of our favorite streaming service, we usually don't realize the gigantic energy consumption behind it.

Artificial intelligence (AI) engineers possess hands-on experience in machine-learning to help companies solve complex business problems by facilitating data-based decision-making and building new data-driven business models. With the use of techniques in computational intelligence, pattern recognition, and predictive analytics to create future-ready machine-learning applications, they can optimize and automate business processes with intelligent algorithms. While machine-learning development can help organizations drive business outcomes, it can also create breakthrough results and improve operational efficiency. That said, despite the global COVID-19 pandemic, artificial intelligence and machine learning have continued to make considerable progress. The latest breakthrough has seen engineers at the Swiss Center for Electronics and Microtechnology (CSEM) develop a new machine-learning method capable of cutting energy use by more than 20%.

COVID-19 has transformed how we travel, work and live. As we emerge from the pandemic, our transport, energy and internet patterns will again undergo a seismic shift, and so will the infrastructure systems that underlie them: our roads, railways, water supply, electrical grids and telecommunications. To plan and optimise these systems, operators need to forecast future usage. Forecasting energy demand and renewable energy generation, for instance, can help operators to avoid unnecessary use of fossil fuels. Artificial intelligence (AI), and more specifically machine learning (ML), can play a crucial role in making these forecasts, helping to guide the evolution of our infrastructure systems.

If you have finally decided to take the path from Excel-copy-and-paste to reproducible data science, then you will need to know the best route to take. The good news is that there is an abundance of free resources to get you there and awesome online communities to help you along the way. The bad news is that it can get overwhelming to pick which resources to take advantage of. This here is a no-nonsense guide that you can follow without regret, so you can spend less time worrying about the trail and more time trekking it. It's based on the lessons I learned when I went from a renewable energy project engineer who had never taken a statistics class to the head of a major data platform. At the trailhead for this journey, you can find an army of educated individuals doing data analysis by necessity, not passion.

We consider the stochastic shortest path planning problem in MDPs, i.e., the problem of designing policies that ensure reaching a goal state from a given initial state with minimum accrued cost. In order to account for rare but important realizations of the system, we consider a nested dynamic coherent risk total cost functional rather than the conventional risk-neutral total expected cost. Under some assumptions, we show that optimal, stationary, Markovian policies exist and can be found via a special Bellman's equation. We propose a computational technique based on difference convex programs (DCPs) to find the associated value functions and therefore the risk-averse policies. A rover navigation MDP is used to illustrate the proposed methodology with conditional-value-at-risk (CVaR) and entropic-value-at-risk (EVaR) coherent risk measures.

To shift the computational burden from real-time to offline in delay-critical power systems applications, recent works entertain the idea of using a deep neural network (DNN) to predict the solutions of the AC optimal power flow (AC-OPF) once presented load demands. As network topologies may change, training this DNN in a sample-efficient manner becomes a necessity. To improve data efficiency, this work utilizes the fact OPF data are not simple training labels, but constitute the solutions of a parametric optimization problem. We thus advocate training a sensitivity-informed DNN (SI-DNN) to match not only the OPF optimizers, but also their partial derivatives with respect to the OPF parameters (loads). It is shown that the required Jacobian matrices do exist under mild conditions, and can be readily computed from the related primal/dual solutions. The proposed SI-DNN is compatible with a broad range of OPF solvers, including a non-convex quadratically constrained quadratic program (QCQP), its semidefinite program (SDP) relaxation, and MATPOWER; while SI-DNN can be seamlessly integrated in other learning-to-OPF schemes. Numerical tests on three benchmark power systems corroborate the advanced generalization and constraint satisfaction capabilities for the OPF solutions predicted by an SI-DNN over a conventionally trained DNN, especially in low-data setups.

Time series prediction with neural networks have been focus of much research in the past few decades. Given the recent deep learning revolution, there has been much attention in using deep learning models for time series prediction, and hence it is important to evaluate their strengths and weaknesses. In this paper, we present an evaluation study that compares the performance of deep learning models for multi-step ahead time series prediction. Our deep learning methods compromise of simple recurrent neural networks, long short term memory (LSTM) networks, bidirectional LSTM, encoder-decoder LSTM networks, and convolutional neural networks. We also provide comparison with simple neural networks use stochastic gradient descent and adaptive gradient method (Adam) for training. We focus on univariate and multi-step-ahead prediction from benchmark time series datasets and compare with results from from the literature. The results show that bidirectional and encoder-decoder LSTM provide the best performance in accuracy for the given time series problems with different properties.

In this paper, we present a provably correct controller synthesis approach for switched stochastic control systems with metric temporal logic (MTL) specifications with provable probabilistic guarantees. We first present the stochastic control bisimulation function for switched stochastic control systems, which bounds the trajectory divergence between the switched stochastic control system and its nominal deterministic control system in a probabilistic fashion. We then develop a method to compute optimal control inputs by solving an optimization problem for the nominal trajectory of the deterministic control system with robustness against initial state variations and stochastic uncertainties. We implement our robust stochastic controller synthesis approach on both a four-bus power system and a nine-bus power system under generation loss disturbances, with MTL specifications expressing requirements for the grid frequency deviations, wind turbine generator rotor speed variations and the power flow constraints at different power lines.

Computer programming has grown to become one of the valuable subjects in every kids' education. However, while there is a high demand for programming skills in the current digital age, the growing sophistication of artificial intelligence has increasingly overtaken routine coding opportunities. With AI, programming has reduced to drag and drop tasks, signaling automation of various coding tasks. Therefore, as you encourage your kids to learn to code, you should also consider data science, which similarly has long-term employment opportunities. Being among the "sexiest jobs of the 21st Century, teaching data science is overly important.

Join our XPotential Community, future proof yourself with courses from XPotential University, connect, watch a keynote, or browse my blog. The world of maintenance might sound dull, but when the aircraft of the future have autonomous robot snakes and cockroaches from Rolls Royce fixing them all of a sudden things get a little bit more interesting. Now, in another giant leap forward for robot bug-kind a company called BladeBUG in the UK have unveiled a bug-like robot that, like human wing walkers, performs "blade walks" along the blades of operational offshore wind turbines. "[The new robo-bugs] open the door to autonomous inspection and repair of wind turbines, improving the efficiency of the blades and reducing risk for rope access technicians," said Chris Cieslak, founder and director of BladeBUG. "[Our robot] uses a patent-pending six-legged design with suction cup feet, which means each of the legs can move and bend independently. This is significant because it enables the robot to walk on the blade's changing curved surface, as well as inside the blade, tower, or hub of the turbine."