Google says that it will not "build custom AI/ML algorithms to facilitate upstream extraction in the oil and gas industry," the company announced on Tuesday. This represents a small but significant win for climate activists. Google's comment coincided with the release of a new Greenpeace report highlighting the role of the three leading cloud-computing services--Google Cloud, Amazon Web Services, and Microsoft Azure--in helping companies find and extract oil and gas. Greenpeace notes that extracting known fossil fuel reserves would already be sufficient to push the world over 2 degrees of warming. Uncovering additional reserves will ultimately lead to even more warming.

Gaussian processes~(Kriging) are interpolating data-driven models that are frequently applied in various disciplines. Often, Gaussian processes are trained on datasets and are subsequently embedded as surrogate models in optimization problems. These optimization problems are nonconvex and global optimization is desired. However, previous literature observed computational burdens limiting deterministic global optimization to Gaussian processes trained on few data points. We propose a reduced-space formulation for deterministic global optimization with trained Gaussian processes embedded. For optimization, the branch-and-bound solver branches only on the degrees of freedom and McCormick relaxations are propagated through explicit Gaussian process models. The approach also leads to significantly smaller and computationally cheaper subproblems for lower and upper bounding. To further accelerate convergence, we derive envelopes of common covariance functions for GPs and tight relaxations of acquisition functions used in Bayesian optimization including expected improvement, probability of improvement, and lower confidence bound. In total, we reduce computational time by orders of magnitude compared to state-of-the-art methods, thus overcoming previous computational burdens. We demonstrate the performance and scaling of the proposed method and apply it to Bayesian optimization with global optimization of the acquisition function and chance-constrained programming. The Gaussian process models, acquisition functions, and training scripts are available open-source within the "MeLOn - Machine Learning Models for Optimization" toolbox~(https://git.rwth-aachen.de/avt.svt/public/MeLOn).

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system. To address this problem, we propose a distributed intelligent resource scheduling (DIRS) framework, which includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. More specifically, we first introduce a novel multi-agent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Secondly, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel L\'evy flight search. Finally, an imitation acceleration scheme is presented to pre-train all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. Extensive simulations are conducted to demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes.

Google has announced it will no longer build custom AI or machine learning tools for oil and gas companies. The move comes in response to a report from Greenpeace called'Oil in the Cloud,' which identified the tech giant as one of three main tech companies helping fossil fuel companies expand their extraction projects. While Google still has a number of current contracts it says it will honor, a company spokesperson said that moving forward it will no longer build custom AI or machine learning algorithms'to facilitate upstream extraction in the oil and gas industry.' The spokesperson pointed out that the company receives just $65million in annual revenue through Google Cloud from oil and gas companies, less than one percent of the total revenue from cloud services. Greenpeace was encouraged by Google's response and called on other major tech companies to follow suit, saying it has already had'productive conversations' with many.

At the University of Toronto, Ted Sargent runs a test kitchen of sorts. His team, composed of researchers and students, develops recipes, measures and mixes ingredients carefully, and then evaluates the aftermath. The concoctions mostly--if not always--turn out to be inedible. Fortunately, though, flavor is not the point. Their goal is to invent recipes to "upgrade" the greenhouse gas into useful materials, says Sargent, an electrical engineer.

Google has announced that it will stop developing AI tools for oil and gas companies following a report from Greenpeace which criticised the search giant as well as Microsoft and Amazon. The latest report just shows that while firms may wish to present themselves as pro-renewable energy, if you scratch beneath the surface, things aren't as rosy as they appear to be. Responding to Google's decision to cut these ties with gas and oil firms, Elizabeth Jardim, senior corporate campaigner at Greenpeace USA, said: "While Google still has legacy contracts with oil and gas firms that we hope they will terminate, we welcome Google's move to no longer create custom solutions for upstream oil and gas extraction." "We hope Microsoft and Amazon will quickly follow with commitments to end AI partnerships with oil and gas firms, as these contracts contradict their stated climate goals and accelerate the climate crisis." According to the Greenpeace report, Microsoft has the most contracts with gas and oil companies and offers AI capabilities in all phases of oil production.

Gaussian processes (GPs) are a class of Kernel methods that have shown to be very useful in geoscience and remote sensing applications for parameter retrieval, model inversion, and emulation. They are widely used because they are simple, flexible, and provide accurate estimates. GPs are based on a Bayesian statistical framework which provides a posterior probability function for each estimation. Therefore, besides the usual prediction (given in this case by the mean function), GPs come equipped with the possibility to obtain a predictive variance (i.e., error bars, confidence intervals) for each prediction. Unfortunately, the GP formulation usually assumes that there is no noise in the inputs, only in the observations. However, this is often not the case in earth observation problems where an accurate assessment of the measuring instrument error is typically available, and where there is huge interest in characterizing the error propagation through the processing pipeline. In this letter, we demonstrate how one can account for input noise estimates using a GP model formulation which propagates the error terms using the derivative of the predictive mean function. We analyze the resulting predictive variance term and show how they more accurately represent the model error in a temperature prediction problem from infrared sounding data.

Mixed integer linear programs are commonly solved by Branch and Bound algorithms. A key factor of the efficiency of the most successful commercial solvers is their fine-tuned heuristics. In this paper, we leverage patterns in real-world instances to learn from scratch a new branching strategy optimised for a given problem and compare it with a commercial solver. We propose FMSTS, a novel Reinforcement Learning approach specifically designed for this task. The strength of our method lies in the consistency between a local value function and a global metric of interest. In addition, we provide insights for adapting known RL techniques to the Branch and Bound setting, and present a new neural network architecture inspired from the literature. To our knowledge, it is the first time Reinforcement Learning has been used to fully optimise the branching strategy. Computational experiments show that our method is appropriate and able to generalise well to new instances.

Google says it will no longer build custom artificial intelligence tools for speeding up oil and gas extraction, separating itself from cloud computing rivals Microsoft and Amazon. A statement from the company Tuesday followed a Greenpeace report that documents how the three tech giants are using AI and computing power to help oil companies find and access oil and gas deposits in the U.S. and around the world. The environmentalist group says Amazon, Microsoft and Google have been undermining their own climate change pledges by partnering with major oil companies including Shell, BP, Chevron and ExxonMobil that have looked for new technology to get more oil and gas out of the ground. But the group applauded Google on Tuesday for taking a step away from those deals. "While Google still has a few legacy contracts with oil and gas firms, we welcome this indication from Google that it will no longer build custom solutions for upstream oil and gas extraction," said Elizabeth Jardim, senior corporate campaigner for Greenpeace USA.

Greenpeace's report says Microsoft appears to be leading the way with the most oil and contracts, "offering AI capabilities in all phases of oil production." Amazon's contracts are more focused on pipelines, shipping and fuel storage, according to the report. Their tools have been deployed to speed up shale extraction, especially from the Permian Basin of Texas and New Mexico.