The low-altitude economy (LAE) is gaining significant attention from academia and industry. Fortunately, LAE naturally aligns with near-field communications in extremely large-scale MIMO (XL-MIMO) systems. By leveraging near-field beamfocusing, LAE can precisely direct beam energy to unmanned aerial vehicles, while the additional distance dimension boosts overall spectrum efficiency. However, near-field communications in LAE still face several challenges, such as the increase in signal processing complexity and the necessity of distinguishing between far and near-field users. Inspired by the large language models (LLM) with powerful ability to handle complex problems, we apply LLM to solve challenges of near-field communications in LAE. The objective of this article is to provide a comprehensive analysis and discussion on LLM-empowered near-field communications in LAE. Specifically, we first introduce fundamentals of LLM and near-field communications, including the key advantages of LLM and key characteristics of near-field communications. Then, we reveal the opportunities and challenges of near-field communications in LAE. To address these challenges, we present a LLM-based scheme for near-field communications in LAE, and provide a case study which jointly distinguishes far and near-field users and designs multi-user precoding matrix. Finally, we outline and highlight several future research directions and open issues.

With the incorporation of Artificial intelligence, the contemporary preventive healthcare environment is witnessing tremendous advancements in the comprehension of disease propensity, pattern, and prediction diagnosis. FREMONT, CA: The increased need for individualized treatment and the rising pressure to reduce healthcare costs are two primary drivers of the healthcare market's expansion. In order to accurately predict diseases in their early stages based on past health records, healthcare systems are progressively adopting and integrating Artificial Intelligence (AI) and Machine Learning (ML) algorithms. Healthcare organizations widely adopt these technologies for rapid diagnosis and detection of different viral strains using individualized data to improve outbreak management. Better decision-making: AI-based solutions can streamline diagnostic and therapeutic operations by using large amounts of organized and unstructured medical data from various institutions.

It has been estimated that 1.7 million people die from Tuberculosis (TB), and more than 10.4 million new cases are reported every year worldwide. The global'End TB' strategy aims to eliminate the disease by 2030. However, realizing this goal would be challenging if there were to be a gap in treatment adherence to prescribed medication. In the context of TB and HIV coinfection, non-adherence to the medication has been associated with the incidence of drug resistance, prolonged infection, unsuccessful treatments, and death. Africa experiences a severe shortage of healthcare workers, making delivering proper healthcare difficult.

Today many organizations and brands are using machine learning/artificial intelligence programs and systems in their workspaces. Using these programs and systems an organization can easily increase its efficiency and improve its workflow. In simple terms, machine learning programs and systems can be very useful for an organization and can provide lots of benefits to it. Here's why you should use artificial intelligence programs and systems in your organization. The biggest advantage of using AI systems is that it helps in increasing productivity and efficiency.

Based around the Xilinx Versal AI Core series, the ADM-PA100 offers fully customizable IO and meets requirements for a range of markets including data center, machine learning, HPC, scientific instrumentation, and test and measurement. The Versal AI Core series includes an array of Xilinx AI engines (dedicated VLIW processors, capable of vector math processing at compute densities 5x higher than programmable logic), closely coupled with programmable logic allowing highly efficient implementation of custom coprocessing operations in this data flow. The Xilinx Versal series of devices also feature an on-chip programmable network on chip (NoC) that improves on-chip programmable logic routing in large designs), dedicated hardened IP for multi-rate 100G Ethernet, hardened PCIe Gen4 endpoints with DMA outside the programmable logic, hardened DDR4 memory controllers, built in ARM A72 and R5F CPUs, and programmable logic and DSP performance a generation on from UltraScale devices, says the company. Manuel Uhm, director of Silicon Marketing at Xilinx says, "The hardware adaptability and heterogeneous architecture of Versal AI Core ACAPs are a key advantage over traditional accelerators that typically focus on a subset of applications. This enables the creation of multiple domain specific architectures targeted to specific workloads. We're delighted that Alpha Data has chosen Versal AI Core series for its ADM-PA100 board to accelerate a breadth of workloads in cloud, networking, and edge markets."

As in any other manufacturing process, robotics and automation are already greatly involved in injection molding and bring considerable benefits to the table. According to statistics released by the European Plastics Machinery Organization EUROMAP, the number of sold injection molding machines equipped with robots rose from 18% in 2010 to almost a third of all injection machines sold with 32% by the first quarter of 2019. There is definitely a change in attitude in this trend, with a respectable number of plastic injection molders embracing robots to get ahead of their competition. Undoubtedly, there has been a serious upwards trend towards the use of robotics and automation in plastics processing. A significant part of this is driven by the demand for more flexible solutions, as the 6-axis industrial robots in precision molding, for example, are certainly more common nowadays than several years before.

This is a guest post from Quenton Hall, AI System Architect for Industrial, Scientific and Medical applications. One of the AI demo highlights at XDF2019 in San Jose was a high-performance inference demo leveraging Alveo. If you are familiar with Alveo and ML Suite, this might at first glance not seem that novel. However, what was indeed very novel was that this demonstration leveraged a brand-new inference engine. Whereas past Alveo ML inference implementations have leveraged the xDNN engine architecture, this latest demo implements a new version of the Xilinx DPU IP, specifically optimized for the Alveo U280 and Xilinx SSIT devices.

Microsoft expects to do more artificial intelligence research in Taiwan. Artificial brains threaten to outnumber real ones in Taiwan, as the island's prowess in artificial intelligence (AI) continues to grow. Global players such as Google, IBM and Microsoft have all expressed their intentions of developing either AI R&D centers or similar initiatives in Taiwan. These companies could have selected other tech-savvy locations in Asia like South Korea and Shenzhen, China, but they chose Taiwan. "Taiwan has a lot going for it with AI research," says William Foreman, president of the American Chamber of Commerce in Taipei.

Microsoft expects to do more artificial intelligence research in Taiwan. Artificial brains threaten to outnumber real ones in Taiwan, as the island's prowess in artificial intelligence (AI) continues to grow. Global players such as Google, IBM and Microsoft have all expressed their intentions of developing either AI R&D centers or similar initiatives in Taiwan. These companies could have selected other tech-savvy locations in Asia like South Korea and Shenzhen, China, but they chose Taiwan. "Taiwan has a lot going for it with AI research," says William Foreman, president of the American Chamber of Commerce in Taipei.

In this blog series, I've focused on how NetApp can help you streamline your artificial intelligence projects. With technologies and services for managing data everywhere, NetApp is well positioned to solve your AI data challenges. Built on our partnership with NVIDIA and powered by NVIDIA DGX supercomputers and NetApp all-flash storage, ONTAP AI lets you simplify, accelerate, and scale your AI data pipeline to gain deeper understanding in less time. Combining Data Fabric enabled NetApp storage with GPU-accelerated NVIDIA computing systems results in capabilities that aren't available from other turnkey AI solutions, on-premises or in the cloud. Here are five of the key advantages of ONTAP AI.