The next decade will be a journey towards building an intelligent world during which we will witness dreams of the past turn into inventions of the day and features of science fiction emerge as daily life utilities. Exploration and innovation will be the driving force of this new future. Our quality of life at home and work will be greatly improved. Our lives in 2030 will face marked improvements including more plentiful food, larger living spaces, renewable energy, and greater efficiency and security. In fact, nearly all repetitive and dangerous work will be done by machines.

Quantitative workflows utilizing real-time data to constrain ahead-of-bit uncertainty have the potential to improve geosteering significantly. Fast updates based on real-time data are essential when drilling in complex reservoirs with high uncertainties in pre-drill models. However, practical assimilation of real-time data requires effective geological modeling and mathematically robust parameterization. We propose a generative adversarial deep neural network (GAN), trained to reproduce geologically consistent 2D sections of fluvial successions. Offline training produces a fast GAN-based approximation of complex geology parameterized as a 60-dimensional model vector with standard Gaussian distribution of each component. Probabilistic forecasts are generated using an ensemble of equiprobable model vector realizations. A forward-modeling sequence, including a GAN, converts the initial (prior) ensemble of realizations into EM log predictions. An ensemble smoother minimizes statistical misfits between predictions and real-time data, yielding an update of model vectors and reduced uncertainty around the well. Updates can be then translated to probabilistic predictions of facies and resistivities. The present paper demonstrates a workflow for geosteering in an outcrop-based, synthetic fluvial succession. In our example, the method reduces uncertainty and correctly predicts most major geological features up to 500 meters ahead of drill-bit.

Deep neural networks have become a popular algorithm for a variety of applications using mobile devices including smart phones but also recently expanding to connected and autonomous vehicles (CAVs), robotics, or even unmanned aerial vehicles (UAVs), and other smart infrastructure. Convolutional Neural Networks (CNNs) have been demonstrated to provide solutions to these problems with relatively high accuracy. While there have been many proposals to improve the performance and energy efficiency of CNN inference, these algorithms are too compute and data intensive to execute directly on mobile nodes typically operating with limited computational and energy capabilities. Thus, edge servers, now being deployed often in conjunction with advanced (e.g., 5G) wireless networks, have become a popular target to accelerate CNN inference. Moreover, due to their deployment in the field, edge servers must operate under size, weight, and power (SWaP) constraints, while serving many concurrent requests from mobile clients. Thus, to accelerate CNNs, these edge servers often use energy-efficient accelerators, reduced precision, or both to achieve fast response time while balancing requests from multiple clients and maintaining a low operational energy cost. Recently, there has been a trend to push online training to edge server nodes to avoid communicating large datasets from edge to cloud servers [1]. However, online training typically requires much higher precision and floating-point computation compared to inference. Unfortunately, the proliferation of computing, both the mobile devices, and the edge servers themselves, can come at the expense of negative environmental impacts.

We consider minimizing a twice-differentiable, $L$-smooth, and $\mu$-strongly convex objective $\phi$ over an $n\times n$ positive semidefinite matrix $M\succeq0$, under the assumption that the minimizer $M^{\star}$ has low rank $r^{\star}\ll n$. Following the Burer--Monteiro approach, we instead minimize the nonconvex objective $f(X)=\phi(XX^{T})$ over a factor matrix $X$ of size $n\times r$. This substantially reduces the number of variables from $O(n^{2})$ to as few as $O(n)$ and also enforces positive semidefiniteness for free, but at the cost of giving up the convexity of the original problem. In this paper, we prove that if the search rank $r\ge r^{\star}$ is overparameterized by a constant factor with respect to the true rank $r^{\star}$, namely as in $r>\frac{1}{4}(L/\mu-1)^{2}r^{\star}$, then despite nonconvexity, local optimization is guaranteed to globally converge from any initial point to the global optimum. This significantly improves upon a previous rank overparameterization threshold of $r\ge n$, which is known to be sharp if $\phi$ is allowed to be nonsmooth and/or non-strongly convex, but would increase the number of variables back up to $O(n^{2})$. Conversely, without rank overparameterization, we prove that such a global guarantee is possible if and only if $\phi$ is almost perfectly conditioned, with a condition number of $L/\mu<3$. Therefore, we conclude that a small amount of overparameterization can lead to large improvements in theoretical guarantees for the nonconvex Burer--Monteiro factorization.

A study published last month in the peer-reviewed journal Nature Climate Change sought to understand the potential impact of artificial intelligence on climate change. AI has both positive and negative effects on the climate, according to study co-author David Rolnick, Assistant Professor of Computer Science at McGill University and a Core Academic Member of Mila – Quebec AI Institute. "AI affects the climate in many ways, both positive and negative, and most of these effects are poorly quantified," he explained. "For example, AI is being used to track and reduce deforestation, but AI-based advertising systems are likely making climate change worse by increasing the amount that people buy." "Climate change should be a key consideration when developing and assessing AI technologies," The researchers highlighted that engineers, policymakers and scientists can all contribute to using AI to achieve climate goals, McGill University noted. For instance, AI-based autonomous vehicles can help reduce carbon emissions if used for public transportation but could increase emissions if used for personal transportation.

In recent years, hardware accelerators based on field-programmable gate arrays (FPGAs) have been widely adopted, thanks to FPGAs' extraordinary flexibility. However, with the high flexibility comes the difficulty in design and optimization. Conventionally, these accelerators are designed with low-level hardware descriptive languages, which means creating large designs with complex behavior is extremely difficult. Therefore, high-level synthesis (HLS) tools were created to simplify hardware designs for FPGAs. They enable the user to create hardware designs using high-level languages and provide various optimization directives to help to improve the performance of the synthesized hardware. However, applying these optimizations to achieve high performance is time-consuming and usually requires expert knowledge. To address this difficulty, we present an automated design space exploration tool for applying HLS optimization directives, called Chimera, which significantly reduces the human effort and expertise needed for creating high-performance HLS designs. It utilizes a novel multi-objective exploration method that seamlessly integrates active learning, evolutionary algorithm, and Thompson sampling, making it capable of finding a set of optimized designs on a Pareto curve with only a small number of design points evaluated during the exploration. In the experiments, in less than 24 hours, this hybrid method explored design points that have the same or superior performance compared to highly optimized hand-tuned designs created by expert HLS users from the Rosetta benchmark suite. In addition to discovering the extreme points, it also explores a Pareto frontier, where the elbow point can potentially save up to 26\% of Flip-Flop resource with negligibly higher latency.

However, years of bold proclamations have resulted in AI becoming overhyped, with reality often falling short of the world-altering promises. The coming years will be more about practical uses of AI, as businesses ensure return on investment by using AI to address specific cases. Power Technology's artificial intelligence in power dashboard covers all you need to know about this emerging technology and its impact on the sector. The power sector, especially in Europe is expected to be impacted due to gas availability and price issues. Utilities will have to look for alternate sources of gas or shift to other sources of generation.

AJ Abdallat is CEO of Beyond Limits, a leader in artificial intelligence and cognitive computing. Artificial intelligence (AI) is thought to be instrumental to the complex phase confronting critical infrastructure and its sectors. Every industry is facing the mounting necessity to become more agile, resourceful and sustainable. As a result of those pressures, entities in charge of systems that are essential in our everyday lives have made substantial strides toward constructive transformation and smarter digital initiatives. Ambitions for smart cities with intelligent critical infrastructure are no exception.

There has been a growing realisation among businesses in recent years that becoming environmentally sustainable is a must, not a choice. Customers, investors and employees and industry regulators are all putting pressure on them to act before the climate crisis worsens to calamitous levels. Alongside this, the willingness of companies to publicise their progress in reducing their ecological impact is increasing. More than 3,400 organisations, with a combined market cap of £21.4tn, have registered their support for the Task Force on Climate-Related Financial Disclosures since it published its first reporting recommendations in 2017, for instance. AI has a key role to play in helping firms to hit the ambitious net-zero CO2 emissions targets they are setting themselves. The Global AI Adoption Index 2022, IBM's latest annual survey of uptake, found that two-thirds of the 7,500 IT chiefs it polled were either using AI to achieve sustainability goals or planning to do so.

The NPV is calculated by a machine–learning (ML) proxy model trained to approximate the NPV that would be calculated from a reservoir simulator run.