Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques offers exciting possibilities for advancing the capacity and speed of established methods and also for making substantial and serendipitous discoveries. Beyond vast amounts of complex data ubiquitous in many modern scientific fields, the study of the ocean poses a combination of unique challenges that ML can help address. The observational data available is largely spatially sparse, limited to the surface, and with few time series spanning more than a handful of decades. Important timescales span seconds to millennia, with strong scale interactions and numerical modelling efforts complicated by details such as coastlines. This review covers the current scientific insight offered by applying ML and points to where there is imminent potential. We cover the main three branches of the field: observations, theory, and numerical modelling. Highlighting both challenges and opportunities, we discuss both the historical context and salient ML tools. We focus on the use of ML in situ sampling and satellite observations, and the extent to which ML applications can advance theoretical oceanographic exploration, as well as aid numerical simulations. Applications that are also covered include model error and bias correction and current and potential use within data assimilation. While not without risk, there is great interest in the potential benefits of oceanographic ML applications; this review caters to this interest within the research community.

The development of artificial intelligence (AI) technologies has far exceeded the investigation of their relationship with society. Sociotechnical inquiry is needed to mitigate the harms of new technologies whose potential impacts remain poorly understood. To date, subfields of AI research develop primarily individual views on their relationship with sociotechnics, while tools for external investigation, comparison, and cross-pollination are lacking. In this paper, we propose four directions for inquiry into new and evolving areas of technological development: value--what progress and direction does a field promote, optimization--how the defined system within a problem formulation relates to broader dynamics, consensus--how agreement is achieved and who is included in building it, and failure--what methods are pursued when the problem specification is found wanting. The paper provides a lexicon for sociotechnical inquiry and illustrates it through the example of consumer drone technology.

Reliable and frequent population estimation is key for making policies around vaccination and planning infrastructure delivery. Since censuses lack the spatio-temporal resolution required for these tasks, census-independent approaches, using remote sensing and microcensus data, have become popular. We estimate intercensal population count in two pilot districts in Mozambique. To encourage sustainability, we assess the feasibility of using publicly available datasets to estimate population. We also explore transfer learning with existing annotated datasets for predicting building footprints, and training with additional `dot' annotations from regions of interest to enhance these estimations. We observe that population predictions improve when using footprint area estimated with this approach versus only publicly available features.

We test the performance of deep deterministic policy gradient (DDPG), a deep reinforcement learning algorithm, able to handle continuous state and action spaces, to learn Nash equilibria in a setting where firms compete in prices. These algorithms are typically considered model-free because they do not require transition probability functions (as in e.g., Markov games) or predefined functional forms. Despite being model-free, a large set of parameters are utilized in various steps of the algorithm. These are e.g., learning rates, memory buffers, state-space dimensioning, normalizations, or noise decay rates and the purpose of this work is to systematically test the effect of these parameter configurations on convergence to the analytically derived Bertrand equilibrium. We find parameter choices that can reach convergence rates of up to 99%. The reliable convergence may make the method a useful tool to study strategic behavior of firms even in more complex settings. Keywords: Bertrand Equilibrium, Competition in Uniform Price Auctions, Deep Deterministic Policy Gradient Algorithm, Parameter Sensitivity Analysis

Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency. A promising approach to forecast the temporal variability of solar irradiance resulting from the cloud cover dynamics, is based on the analysis of sequences of ground-taken sky images. Despite encouraging results, a recurrent limitation of current Deep Learning approaches lies in the ubiquitous tendency of reacting to past observations rather than actively anticipating future events. This leads to a systematic temporal lag and little ability to predict sudden events. To address this challenge, we introduce ECLIPSE, a spatio-temporal neural network architecture that models cloud motion from sky images to predict both future segmented images and corresponding irradiance levels. We show that ECLIPSE anticipates critical events and considerably reduces temporal delay while generating visually realistic futures.

Sonoma County will bolster its nascent network of fire-lookout cameras with artificial intelligence that aims to automatically identify potential wildfire starts and provide alerts even when no one is watching. County officials announced the program Wednesday after awarding a $300,000 contract to Alchera, Inc., a South Korea-based company that develops algorithms for visual artificial intelligence systems. The technology, which is promising but still in development, is meant to automate Sonoma County's alert-and-warning efforts to provide more of a heads-up in case a wildfire starts, said Chris Godley, the county's emergency management director. "This is really designed to help us catch those extremely early starts, so it gives us that much more time to investigate and, if need be, respond," Godley said. Most of the funding for the new technology comes from a $2.7 million grant the county received from the Federal Emergency Management Agency, with the county chipping in about $75,000.

Imagine earning your engineering degree in 50 minutes? Sandia National Laboratories has developed a new machine-learning algorithm capable of performing simulations for materials scientists nearly 40,000 times faster than normal, according to a Sandia press release. Their results, published in the January issue of a journal called npj Computational Materials, could herald a dramatic acceleration in the development of new technologies for optics, aerospace, energy storage, and potentially medicine while simultaneously saving laboratories money on computing costs, according to the study. The research, funded by the U.S. Department of Energy's Basic Energy Sciences program, was conducted at the Center for Integrated Nanotechnologies, a Department of Energy user research facility jointly operated by Sandia and Los Alamos national labs. Sandia researchers used machine learning to accelerate a computer simulation that predicts how changing a design or fabrication process, such as tweaking the amounts of metals in an alloy, will affect a material.

In this article we will take a look at the 10 best artificial intelligence stocks for 2021. You can skip our detailed analysis of the AI industry's outlook for 2021 and some of the major growth catalysts for AI stocks and go directly to 5 Best Artificial Intelligence Stocks for 2021. Artificial intelligence is a buzzword increasingly being used by companies around the world that seek to project themselves at the forefront of cutting-edge research that promises to transform the lives of humans. As the word loses its meaning, it is important for investors to understand what artificial intelligence is and what companies stand to gain from breakthroughs in the new technology. Market estimates suggest that the artificial intelligence industry will witness a compound annual growth of more than 40% in the first half of this decade. Artificial intelligence, in the simplest words, uses data analytics to perform tasks that would otherwise be performed by humans.

Large optimization problems with hard constraints arise in many settings, yet classical solvers are often prohibitively slow, motivating the use of deep networks as cheap "approximate solvers." Unfortunately, naive deep learning approaches typically cannot enforce the hard constraints of such problems, leading to infeasible solutions. In this work, we present Deep Constraint Completion and Correction (DC3), an algorithm to address this challenge. Specifically, this method enforces feasibility via a differentiable procedure, which implicitly completes partial solutions to satisfy equality constraints and unrolls gradient-based corrections to satisfy inequality constraints. We demonstrate the effectiveness of DC3 in both synthetic optimization tasks and the real-world setting of AC optimal power flow, where hard constraints encode the physics of the electrical grid. In both cases, DC3 achieves near-optimal objective values while preserving feasibility. Traditional approaches to constrained optimization are often expensive to run for large problems, necessitating the use of function approximators. Neural networks are highly expressive and fast to run, making them ideal as function approximators. However, while deep learning has proven its power for unconstrained problem settings, it has struggled to perform well in domains where it is necessary to satisfy hard constraints at test time. For example, in power systems, weather and climate models, materials science, and many other areas, data follows well-known physical laws, and violation of these laws can lead to answers that are unhelpful or even nonsensical.

Cloud computing enables remote execution of users' tasks. The pervasive adoption of cloud computing in smart cities' services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in a cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to tradeoff the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm.