Artificial intelligence (AI) will create new job opportunities, not destroy them. AI will displace some jobs but will create new ones. The main aim of this article is intended to focus the minds of our political and business leaders as they consider what strategies to pursue to grow the economy (GDP), business activity and stimulate job creation whilst also taking into account the growing challenges of the environment with climate change mitigation increasingly on the agenda. Let's start by reviewing the types of AI and where we are now. Narrow AI: the field of AI where the machine is designed to perform a single task and the machine gets very good at performing that particular task.

An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications, examples of which include highly automated manufacturing cells or autonomously interacting harvesting machines. Energy efficiency is key in such edge environments, since they are often based on an infrastructure that consists of wireless and battery-run devices, e.g., e-tractors, drones, Automated Guided Vehicle (AGV)s and robots. The total energy consumption draws contributions from multiple iIoTe technologies that enable edge computing and communication, distributed learning, as well as distributed ledgers and smart contracts. This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption. Finally, the paper provides a vision for integrating these enabling technologies in ...

Artificial Intelligence (AI) has made significant progress in the past 5 years and is playing a more and more important role in power system analysis and control. It is foreseeable that the future power system transient stability simulations will be deeply integrated with AI. However, the existing power system dynamic simulation tools are not AI-friendly enough. In this paper, a general design of an AI-oriented power system transient stability simulator is proposed. It is a parallel simulator with a flexible application programming interface so that the simulator has rapid simulation speed, neural network supportability, and network topology accessibility. A prototype of this design is implemented and made public based on our previously realized simulator. Tests of this AI-oriented simulator are carried out under multiple scenarios, which proves that the design and implementation of the simulator are reasonable, AI-friendly, and highly efficient.

NYSEG, an electric and gas utility that serves areas of the Capital Region not served by National Grid, is developing a new computer-based outage prediction system that will use artificial intelligence. New York State Electric & Gas says it is developing what it is calling an "outage prediction model," essentially a software program that will use machine learning or artificial intelligence -- AI -- to predict outages during storm events. NYSEG and its parent company, Avangrid, along with its sister utility, RG&E, short for Rochester Gas & Electric, are working with researchers at the University at Albany and the University of Connecticut on developing the AI system. The system will use AI to analyze weather forecasts to predict -- or guess -- which parts of the electrical grid will be hit hardest by storms. That way the utility can prepare to deploy resources to those areas in advance.

We consider a networked linear dynamical system with $p$ agents/nodes. We study the problem of learning the underlying graph of interactions/dependencies from observations of the nodal trajectories over a time-interval $T$. We present a regularized non-casual consistent estimator for this problem and analyze its sample complexity over two regimes: (a) where the interval $T$ consists of $n$ i.i.d. observation windows of length $T/n$ (restart and record), and (b) where $T$ is one continuous observation window (consecutive). Using the theory of $M$-estimators, we show that the estimator recovers the underlying interactions, in either regime, in a time-interval that is logarithmic in the system size $p$. To the best of our knowledge, this is the first work to analyze the sample complexity of learning linear dynamical systems driven by unobserved not-white wide-sense stationary (WSS) inputs.

Parallel to the rising debates over sustainable energy and artificial intelligence solutions, the world is currently discussing the ethics of artificial intelligence and its possible negative effects on society and the environment. In these arguments, sustainable AI is proposed, which aims at advancing the pathway toward sustainability, such as sustainable energy. In this paper, we offered a novel contextual topic modeling combining LDA, BERT, and Clustering. We then combined these computational analyses with content analysis of related scientific publications to identify the main scholarly topics, sub-themes, and cross-topic themes within scientific research on sustainable AI in energy. Our research identified eight dominant topics including sustainable buildings, AI-based DSSs for urban water management, climate artificial intelligence, Agriculture 4, the convergence of AI with IoT, AI-based evaluation of renewable technologies, smart campus and engineering education, and AI-based optimization. We then recommended 14 potential future research strands based on the observed theoretical gaps. Theoretically, this analysis contributes to the existing literature on sustainable AI and sustainable energy, and practically, it intends to act as a general guide for energy engineers and scientists, AI scientists, and social scientists to widen their knowledge of sustainability in AI and energy convergence research.

A new artificial intelligence system could assess tipping points in the world's ecosystems, and act as an early warning system to help stop "runaway climate change", researchers have said. Climate tipping points are a particular threat to life on Earth, as when they are reached, they can set off chain reactions of climate-altering processes, supercharging global heating and rapidly exacerbating the existing climate crisis. Examples include the melting of the Arctic permafrost, which could release massive amounts of the potent greenhouse gas methane, which would generate further rapid heating; the breakdown of ocean current systems, which would cause almost immediate major changes to global weather patterns; and ice sheet disintegration, which could lead to rapid sea-level rises. Using a "deep-learning" algorithm, the researchers examined thresholds beyond which rapid or irreversible change happens in a system. Chris Bauch, professor of applied mathematics at the University of Waterloo ...

Like politics or religion, artificial intelligence is a topic that elicits strong opinions. Many in the environmental and sustainability communities sing its praises as a technology for combating climate change, citing its superhuman ability to optimize the integration of renewables into electric grids, or to detect deforestation and other threats to biodiversity, or drive corporate resilience planning using extreme weather models. The list of potential applications is long. The energy management system developed by cold storage warehouse company Lineage Logistics is one of my favorite examples to extol: When I wrote about it a couple of years ago, the company had managed to cut power consumption in half for facilities where it was deployed, saving customers at least $4 million along the way. In fact, it's unusual to find a big business that isn't at least thinking about using AI as a resource for automating all manner of tasks that would take homo sapiens far longer to handle manually (if they could handle it at all).

According to a former Google executive, the singularity is coming. And, what's more, he says that it poses a major threat to humanity. Mo Gawdat, formerly the Chief Business Officer for Google's moonshot organization, which was called Google X at the time, issued his warning in a new interview with The Times. In it, he said that he believes that artificial general intelligence (AGI), the sort of all-powerful, sentient AI seen in science fiction like Skynet from "The Terminator," is inevitable -- and that once it's here, humanity may very well find itself staring down an apocalypse brought forth by godlike machines. Gawdat told The Times that he had his frightening revelation while working with AI developers at Google X who were building robot arms capable of finding and picking up a small ball.

Mars has been a prime candidate for planetary exploration of the solar system because of the science discoveries that support chances of future habitation on this planet. Martian caves and lava tubes like terrains, which consists of uneven ground, poor visibility and confined space, makes it impossible for wheel based rovers to navigate through these areas. In order to address these limitations and advance the exploration capability in a Martian terrain, this article presents the design and control of a novel coaxial quadrotor Micro Aerial Vehicle (MAV). As it will be presented, the key contributions on the design and control architecture of the proposed Mars coaxial quadrotor, are introducing an alternative and more enhanced, from a control point of view concept, when compared in terms of autonomy to Ingenuity. Based on the presented design, the article will introduce the mathematical modelling and automatic control framework of the vehicle that will consist of a linearised model of a co-axial quadrotor and a corresponding Model Predictive Controller (MPC) for the trajectory tracking. Among the many models, proposed for the aerial flight on Mars, a reliable control architecture lacks in the related state of the art. The MPC based closed loop responses of the proposed MAV will be verified in different conditions during the flight with additional disturbances, induced to replicate a real flight scenario. In order to further validate the proposed control architecture and prove the efficacy of the suggested design, the introduced Mars coaxial quadrotor and the MPC scheme will be compared to a PID-type controller, similar to the Ingenuity helicopter's control architecture for the position and the heading.