We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. When it comes to climate change and greenhouse gas emissions (GHGs), the United States is moving backwards, according to a report released earlier this year by the Rhodium Group, an independent research organization. The report says, "… progress in reducing U.S. GHG emissions was reversed in 2021, moving from 22.2% below 2005 levels in 2020 to only 17.4% in 2021, putting the U.S. even further off track from achieving its 2025 and 2030 climate targets." The U.S. Securities and Exchange Commission (SEC) took on the trend and recently detailed newly proposed rules that would require companies -- both foreign and domestic that are registered with the SEC -- to report climate impact and emissions information. The proposal aims to bring standardization via policy to what has, until now, been largely optional -- unlike the EU, which established similar reporting requirements in 2014.

Mining, oil and gas, and chemical manufacturing companies haven't yet started to exploit AI, but they can close the gap by applying lessons from other industries. Artificial Intelligence (AI) solutions have established a strong record of unlocking value across a range of sectors, but continuous process industries such as mining, oil and gas production, and chemical manufacturing have been slower to embrace the technology. The good news is that the relative lack of adoption means AI still offers significant untapped value--if companies take the steps necessary to implement it. By applying the lessons that have worked in other heavy industries, companies can generate gains of 15% or more in efficiency, throughput, reduced waste, and other metrics. Based on our experience working with several process-industry clients, capturing value through AI requires a three-part solution: collecting the right data, making that data available and accessible, and revamping the company culture to embrace new ways of working.

The variable and stochastic character of wind energy distinguish it from other renewable resources. As a result, wind energy generation forecasting is critical for power system reliability and balancing supply and demand. This article will look at how machine learning has made wind energy more predictable and recent advancements in this field. Wind energy has gained a lot of attention because of its abundant resources and efficient power-producing technology. However, large-scale strong and uncontrollable wind could undermine the stability of the power grid due to the uncertainty and randomness of the wind.

In this paper, we address the problem of convergence of sequential variational inference filter (VIF) through the application of a robust variational objective and Hinf-norm based correction for a linear Gaussian system. As the dimension of state or parameter space grows, performing the full Kalman update with the dense covariance matrix for a large scale system requires increased storage and computational complexity, making it impractical. The VIF approach, based on mean-field Gaussian variational inference, reduces this burden through the variational approximation to the covariance usually in the form of a diagonal covariance approximation. The challenge is to retain convergence and correct for biases introduced by the sequential VIF steps. We desire a framework that improves feasibility while still maintaining reasonable proximity to the optimal Kalman filter as data is assimilated. To accomplish this goal, a Hinf-norm based optimization perturbs the VIF covariance matrix to improve robustness. This yields a novel VIF- Hinf recursion that employs consecutive variational inference and Hinf based optimization steps. We explore the development of this method and investigate a numerical example to illustrate the effectiveness of the proposed filter.

To further strengthen our commitment to providing industry-leading coverage of data technology, VentureBeat is excited to welcome Andrew Brust and Tony Baer as regular contributors. COVID-19 has led to a dramatic acceleration in the adoption and implementation of digital transformation initiatives. Nowhere was this more obvious than in customer experience (CX). Organizations have been quick to adopt new technologies such as chatbots powered by artificial intelligence (AI) to fulfill customer expectations of timely response to queries and problem resolution. Chatbots are an example of how AI can be used to augment human capabilities, providing a convenient way for customers to interact with organizations 24/7.

RIYADH: The UAE's share of Saudi non-oil exports dropped to 14.8 percent in February, down from 17 percent the previous month, according to initial data by the General Authority for Statistics. Despite the fall, it is still the leading destination for the Kingdom's non-oil exports. The drop is partly due to a decline in transport equipment exports. The equipment, which made up 30.7 percent of UAE's share of exports in February, fell to SR1.11 billion ($0.3 billion), from 1.42 billion in January. Machinery and electrical equipment fell to SR687 million, from SR752 million respectively.

Large-scale machine learning and data mining applications require computer systems to perform massive matrix-vector and matrix-matrix multiplication operations that need to be parallelized across multiple nodes. The presence of straggling nodes--computing nodes that unpredictably slow down or fail--is a major bottleneck in such distributed computations. Ideal load balancing strategies that dynamically allocate more tasks to faster nodes require knowledge or monitoring of node speeds as well as the ability to quickly move data. Recently proposed fixed-rate erasure coding strategies can handle unpredictable node slowdown, but they ignore partial work done by straggling nodes, thus resulting in a lot of redundant computation. We propose a rateless fountain coding strategy that achieves the best of both worlds--we prove that its latency is asymptotically equal to ideal load balancing, and it performs asymptotically zero redundant computations. Our idea is to create linear combinations of the m rows of the matrix and assign these encoded rows to different worker nodes. The original matrix-vector product can be decoded as soon as slightly more than m row-vector products are collectively finished by the nodes. Evaluation on parallel and distributed computing yields as much as three times speedup over uncoded schemes. Matrix-vector multiplications form the core of a plethora of scientific computing and machine learning applications that include solving partial differential equations, forward and back propagation in neural networks, computing the PageRank of graphs, etcetera. In the age of Big Data, most of these applications involve multiplying extremely large matrices and vectors and the computations cannot be performed efficiently on a single machine. This has motivated the development of several algorithms that seek to speed up matrix-vector multiplication by distributing the computation across multiple computing nodes.

Large-scale distributed parallel computing has become necessary for handling machine learning and other algorithms with ever-increasing complexity and data requirements. For example, Google TensorFlow can execute distributed algorithms that require thousands of computing nodes to work simultaneously. However, computing systems suffer from random fluctuations in service times. Power management, software or hardware failures, maintenance, and resource sharing are the primary causes of service time variability. Failures and maintenance are inevitable, and power management is crucial for reducing energy consumption.

AI techniques have been leveraged that combine known biophysical, machine learning, and reinforcement learning models to effectively predict the constructs' impact on the host and vice versa, but there is much room for improvement.

We introduce a data-driven approach to building reduced dynamical models through manifold learning; the reduced latent space is discovered using Diffusion Maps (a manifold learning technique) on time series data. A second round of Diffusion Maps on those latent coordinates allows the approximation of the reduced dynamical models. This second round enables mapping the latent space coordinates back to the full ambient space (what is called lifting); it also enables the approximation of full state functions of interest in terms of the reduced coordinates. In our work, we develop and test three different reduced numerical simulation methodologies, either through pre-tabulation in the latent space and integration on the fly or by going back and forth between the ambient space and the latent space. The data-driven latent space simulation results, based on the three different approaches, are validated through (a) the latent space observation of the full simulation through the Nystr\"om Extension formula, or through (b) lifting the reduced trajectory back to the full ambient space, via Latent Harmonics. Latent space modeling often involves additional regularization to favor certain properties of the space over others, and the mapping back to the ambient space is then constructed mostly independently from these properties; here, we use the same data-driven approach to construct the latent space and then map back to the ambient space.