Boasting a guaranteed five-year life cycle, whilst offering twice the bandwidth and FP32 performance over the previous generation, these new MXM GPU cards feature second-generation Ray Tracing Cores, and third-generation Tensor Cores. This improved AI inference capability will help accelerate high-performance edge computing and AI application development in industrial applications including machine automation and machine vision, driver assistance, and public safety systems. MXM GPU cards meet the demands of fanless computing in harsh environments Unlike the standard PCI Express graphics accelerators that populate high-performance computing applications, Advantech's MXM modules are designed specifically to meet harsh environments, with the intention of being integrated into edge systems that have been tested and certified to handle excessive vibration and shock conditions adequately. With its compact size and ruggedised design, the new MXM GPU cards come in the following two form factors. The SKY-MXM-A500, SKY-MXM-A1000 and SKY-MXM-A2000 are MXM 3.1 Type A versions with an 82 x 70mm footprint whilst the SKY-MXM-A4500 is MXM 3.1 Type B version with a larger form factor of 82 x 105mm.

Mphasis accelerates the world-leading Quantum Computing Ecosystem in partnership with the University of Calgary and the Government of Alberta. The Quantum Lab is set to accelerate the development of quantum skills in the city to enable job creation. CALGARY, AB, June 9, 2022 – Mphasis, (BSE: 526299; NSE: MPHASIS), an Information Technology (IT) solutions provider specializing in cloud and cognitive services, today joined the Government of Alberta and the University of Calgary to announce the launch of the world-leading Quantum City – Canada. Quantum city will further establish Alberta as a leading technology hub and will accelerate the development of the quantum ecosystem in Calgary. The partnership aims to utilize the synergy between academia, industry, and government to put the process of ideation to market at the forefront.

SANTA CLARA, CA, USA, Feb 15, 2022 – AMD (NASDAQ: AMD) announced the completion of its acquisition of Xilinx in an all-stock transaction. The acquisition, originally announced on October 27, 2020, creates the industry's high-performance and adaptive computing leader with significantly expanded scale and the strongest portfolio of leadership computing, graphics and adaptive SoC products. AMD expects the acquisition to be accretive to non-GAAP margins, non-GAAP EPS and free cash flow generation in the first year. "The acquisition of Xilinx brings together a highly complementary set of products, customers and markets combined with differentiated IP and world-class talent to create the industry's high-performance and adaptive computing leader," said AMD President and CEO Dr. Lisa Su. "Xilinx offers industry-leading FPGAs, adaptive SoCs, AI engines and software expertise that enable AMD to offer the strongest portfolio of high-performance and adaptive computing solutions in the industry and capture a larger share of the approximately $135 billion market opportunity we see across cloud, edge and intelligent devices." Former Xilinx CEO Victor Peng will join AMD as president of the newly formed Adaptive and Embedded Computing Group (AECG).

In 2018, the smart sensor market was valued at $30.82 billion and is expected to reach $85.93 billion by the end of 2024, registering an increase of 18.82% per year during the forecast period 2019-2024. With the growing roles that IoT applications, vehicle automation, and smart wearable systems play in the world's economies and infrastructures, MEMS sensors are now perceived as fundamental components for various applications, responding to the growing demand for performance and efficiency. Connected MEMS devices have found applications in nearly every part of our modern economy, including in our cities, vehicles, homes, and a wide range of other "intelligent" systems. As the volume of data produced by smart sensors rapidly increases, it threatens to outstrip the capabilities of cloud-based artificial intelligence (AI) applications, as well as the networks that connect the edge and the cloud. In this article, we will explore how on-edge processing resources can be used to offload cloud applications by filtering, analyzing, and providing insights that improve the intelligence and capabilities of many applications.

Foxconn, a global leader in smart manufacturing, is joining Socionext, a major provider of advanced SoC solutions for video and imaging systems, and leading artificial intelligence (AI) chipmaker Hailo to launch the next-generation AI processing solution for video analytics at the edge. Foxconn has combined its high-density, fan-less, and highly efficient edge computing solution, "BOXiedge ", with Socionext's high-efficiency parallel processor "SynQuacer " SC2A11, and the Hailo-8 deep learning processor. The new combination provides market-leading energy efficiency for standalone AI inference nodes, benefiting applications including smart cities, smart medical, smart retail, and industrial IoT. In a global AI market forecasted by research firm IDC to approach $98.4 billion in revenue in 2023, this joint solution helps address the need for cost-effective multiprocessing capabilities required in video analytics, image classifications, and object segmentation. The robust, high-efficiency product is capable of processing and analyzing over 20 streaming camera input feeds in real-time, all at the edge.

Apache Spark (click here) is a general engine for large-scale analytical data processing which includes a powerful Machine Learning Engine (MLE). The GridGain in-memory computing platform (click here), built on Apache Ignite (to visit: click here), includes a comprehensive set of computing solutions including a data grid, compute grid, SQL grid, streaming, and acceleration solutions for Hadoop and Spark. GridGain and Spark are both in-memory computing solutions but they target different use cases. In many cases, they can be used together to achieve superior machine learning performance and functionality. GridGain can distribute and cache data in RAM across multiple servers to deliver unprecedented processing speed and massive application scalability.

Stackelberg games form the core of a number of tools deployed for computing optimal patrolling strategies in adversarial domains, such as the US Federal Air Marshall Service and the US Coast Guard. In traditional Stackelberg security game models the attacker knows only the probability that each target is covered by the defender, but is oblivious to the detailed timing of the coverage schedule. In many real-world situations, however, the attacker can observe the current location of the defender and can exploit this knowledge to reason about the defender’s future moves. We show that this general modeling framework can be captured using adversarial patrolling games (APGs) in which the defender sequentially moves between targets, with moves constrained by a graph, while the attacker can observe the defender’s current location and his (stochastic) policy concerning future moves. We offer a very general model of infinite-horizon discounted adversarial patrolling games. Our first contribution is to show that defender policies that condition only on the previous defense move (i.e., Markov stationary policies) can be arbitrarily suboptimal for general APGs. We then offer a mixed-integer non-linear programming (MINLP) formulation for computing optimal randomized policies for the defender that can condition on history of bounded, but arbitrary, length, as well as a mixed-integer linear programming (MILP) formulation to approximate these, with provable quality guarantees. Additionally, we present a non-linear programming (NLP) formulation for solving zero-sum APGs. We show experimentally that MILP significantly outperforms the MINLP formulation, and is, in turn, significantly outperformed by the NLP specialized to zero-sum games.

This article describes an effort to develop a knowledge-based financial marketing consultant system. Financial marketing is an excellent vehicle for both research and application in artificial intelligence (AI). This domain differs from the great majority of previous expert system domains in that there are no well-defined answers (in traditional sense); the goal here is to obtain satisfactory arguments to support the conclusions made. A large OPS5-based system was implemented as an initial prototype. We present the organization and principles underlying this system and offer our ongoing research directions. The experience gained in the initial prototyping effort is currently being used to further expert systems research and to develop an extensive system that ultimately can be used by the marketing organization.