Former New York Times cartographer Tim Wallace describes how his current firm, Santa Fe-based Descartes Labs, has built a machine learning model to identify tree canopy from satellite imagery thus making accurate mapping of trees and urban forests far more accessible to cities worldwide. "The ability to map tree canopy at a such a high resolution in areas that can't be easily reached on foot would be helpful for utility companies to pinpoint encroachment issues--or for municipalities to find possible trouble spots beyond their official tree census (if they even have one)," writes Wallace. For example, unexpected tree deserts can be identified and neighborhoods that would most benefit from a surge of saplings revealed."

As in many other sectors, artificial intelligence (AI) technologies are beginning to emerge in the chemical process industries (CPI). While AI-assisted solutions, and other associated technologies, such as robotic process automation (RPA), Internet of Things (IoT), automated drones and quantum computing, are still relatively new for many CPI applications, developers and users alike are realizing their potential benefits for expediting research and development (R&D), predictive maintenance, process optimization and more. Within its Smart Operations initiative, Henkel AG & Co. KGaA (Düsseldorf, Germany; www.henkel.com) is utilizing AI capabilities in its global process operations and supply chain. "We use AI to run efficient analyses of complex data arrays for achieving higher production performance, quick product innovation and scaleup for our self-adjusting production systems," explains Sandeep Sreekumar, global head of Adhesive Digital Operations at Henkel. "Our focus is not only on collecting internal manufacturing data, but also on actively working with customers on data collection opportunities during product usage to make improvements and adjust to changing customer needs," says Sreekumar.

A model-based optimal experiment design (OED) of nonlinear systems is studied. OED represents a methodology for optimizing the geometry of the parametric joint-confidence regions (CRs), which are obtained in an a posteriori analysis of the least-squares parameter estimates. The optimal design is achieved by using the available (experimental) degrees of freedom such that more informative measurements are obtained. Unlike the commonly used approaches, which base the OED procedure upon the linearized CRs, we explore a path where we explicitly consider the exact CRs in the OED framework. We use a methodology for a finite parametrization of the exact CRs within the OED problem and we introduce a novel approximation technique of the exact CRs using inner-and outer-approximating ellipsoids as a computationally less demanding alternative. The employed techniques give the OED problem as a finite-dimensional mathematical program of bilevel nature. We use two small-scale illustrative case studies to study various OED criteria and compare the resulting optimal designs with the commonly used linearization-based approach. We also assess the performance of two simple heuristic numerical schemes for bilevel optimization within the studied problems. Introduction At present, advanced industrial engineering and management strive for resource-and energy-efficient design and operation of systems, plants, and processes. Here a use of the model-based techniques is a leading paradigm. The employed models, whether mechanistic or data-based, include a finite number of parameters, whose values are related to the particular natural and system-wide phenomena and are thus commonly only known to belong to some interval or unknown completely.

Weather forecasting is usually solved through numerical weather prediction (NWP), which can sometimes lead to unsatisfactory performance due to inappropriate setting of the initial states. In this paper, we design a data-driven method augmented by an effective information fusion mechanism to learn from historical data that incorporates prior knowledge from NWP. We cast the weather forecasting problem as an end-to-end deep learning problem and solve it by proposing a novel negative log-likelihood error (NLE) loss function. A notable advantage of our proposed method is that it simultaneously implements single-value forecasting and uncertainty quantification, which we refer to as deep uncertainty quantification (DUQ). Efficient deep ensemble strategies are also explored to further improve performance. This new approach was evaluated on a public dataset collected from weather stations in Beijing, China. Experimental results demonstrate that the proposed NLE loss significantly improves generalization compared to mean squared error (MSE) loss and mean absolute error (MAE) loss. Compared with NWP, this approach significantly improves accuracy by 47.76%, which is a state-of-the-art result on this benchmark dataset. The preliminary version of the proposed method won 2nd place in an online competition for daily weather forecasting.

In this paper we consider the problem of how a reinforcement learning agent that is tasked with solving a sequence of reinforcement learning problems (a sequence of Markov decision processes) can use knowledge acquired early in its lifetime to improve its ability to solve new problems. We argue that previous experience with similar problems can provide an agent with information about how it should explore when facing a new but related problem. We show that the search for an optimal exploration strategy can be formulated as a reinforcement learning problem itself and demonstrate that such strategy can leverage patterns found in the structure of related problems. We conclude with experiments that show the benefits of optimizing an exploration strategy using our proposed approach.

We present a deep learning approach for vertex reconstruction of neutrino-nucleus interaction events, a problem in the domain of high energy physics. In this approach, we combine both energy and timing data that are collected in the MINERvA detector to perform classification and regression tasks. We show that the resulting network achieves higher accuracy than previous results while requiring a smaller model size and less training time. In particular, the proposed model outperforms the state-of-the-art by 4.00% on classification accuracy. For the regression task, our model achieves 0.9919 on the coefficient of determination, higher than the previous work (0.96).

BP Ventures, the oil major's venture-capital division, has invested US$5 million in an artificial intelligence firm in a bid to bolster its "digital capabilities" for oil and natural gas exploration. The oil and gas giant says it could save "several hundreds of millions" of euros a year with the use of artificial intelligence to boost oil-drilling efficiency. The UK-listed firm said it was using data to identify and exploit new fields and to squeeze more from existing sites. BP is exploring opportunities to apply machine learning and "cognitive computing" in its operations, with a focus on its upstream production, which centres around activities in "oil and natural gas exploration, field development and production". BP Ventures, set up in 2004, already invests about US$200 million a year in fledgeling tech enterprises in an attempt to keep track of innovations and the latest US$5 million will be spent at Belmont Technology, a three-year-old startup in Houston which has created tools to analyse geological information.

The traditional components of AI – perception, decision-making/cognition and action map up quite precisely with the business problems the Oil & Gas sector deals with- collect and process lots of data (perception); where to drill? This is my take as an outsider looking in. A few years ago I moved from Upstate New York to Texas. In a lighter vein that can be described as a move from "no fracking please, we're New York" to "Drill, baby, Drill". Time magazine described it well in a recent issue: How an Oil Boom in West Texas Is Reshaping the World .

For a long time, being out at sea meant being out of sight and out of reach. And all kinds of shenanigans went on as a result - countries secretly selling oil and other goods to countries they're not supposed to under international sanctions rules, for example, not to mention piracy and kidnapping. The problem is that captains can easily switch off the current way of tracking ships, called the Automatic Identification System (AIS), turning their vessels into "ghost ships". But now thousands of surveillance satellites have been launched into space, and artificial intelligence (AI) is being applied to the images they take. There's no longer anywhere to hide - even for ghost ships.

It is known that the Langevin dynamics used in MCMC is the gradient flow of the KL divergence on the Wasserstein space, which helps convergence analysis and inspires recent particle-based variational inference methods (ParVIs). But no more MCMC dynamics is understood in this way. In this work, by developing novel concepts, we propose a theoretical framework that recognizes a general MCMC dynamics as the fiber-gradient Hamiltonian flow on the Wasserstein space of a fiber-Riemannian Poisson manifold. The "conservation + convergence" structure of the flow gives a clear picture on the behavior of general MCMC dynamics. We analyse existing MCMC instances under the framework. The framework also enables ParVI simulation of MCMC dynamics, which enriches the ParVI family with more efficient dynamics, and also adapts ParVI advantages to MCMCs. We develop two ParVI methods for a particular MCMC dynamics and demonstrate the benefits in experiments.