This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. In this series, we’ll build a data-centric pipeline using Tenyks to debug and fix a model trained with the NVIDIA TAO Toolkit. Part 1. We demystify the NVIDIA ecosystem and define a data-centric pipeline tailored for a […]
This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. How generative AI (GenAI)-powered “agents” will change the way you interact with the digital world The rise of artificial intelligence (AI) opens the door to a vast array of possibilities. AI-powered agents will be the key to
Qualcomm CEO Cristiano Amon at Web Summit: GenAI is the New UI Read More +
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Building a multimodal retrieval augmented generation (RAG) system is challenging. The difficulty comes from capturing and indexing information from across multiple modalities, including text, images, tables, audio, video, and more. In our previous post, An Easy Introduction
An Easy Introduction to Multimodal Retrieval-augmented Generation for Video and Audio Read More +
While being closed off from the real world is an experience achievable with virtual reality (VR) headsets, augmented reality (AR) offers images and data combined with real-time views to create an enriched and computing-enhanced experience. IDTechEx‘s portfolio of reports, including “Optics for Virtual, Augmented and Mixed Reality 2024-2034: Technologies, Players and Markets“, explore the latest
Virtual and Augmented Reality: The Rise and Drawbacks of AR Read More +
This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. In the rapidly evolving world of generative artificial intelligence (GenAI), the focus has traditionally been on large, complex models that require significant computational resources. However, a new trend is emerging: the development and deployment of small, efficient
Why Smaller, More Accurate GenAI Models Put Safety Front and Center Read More +
At CES® 2025, Macnica Americas is proud to spotlight the transformative potential of generative AI in embedded vision solutions, showcasing its role as a leader in imaging and vision solutions. Collaborating with innovative partners such as iENSO and Ambarella, Macnica is enabling next-generation advancements in IoT, surveillance, consumer electronics, and beyond. iENSO, a trusted solution
Revolutionizing Embedded Vision: Macnica Americas at CES 2025 Read More +
Axelera AI – a leading edge-inference company – and Arduino, the global leader in open-source hardware and software, today announced a strategic partnership to make high-performance AI at the edge more accessible than ever, building advanced technology solutions based on inference and an open ecosystem. This furthers Axelera AI’s strategy to democratize artificial intelligence everywhere.
Axelera AI Partners with Arduino to Extend the Full Power of AI to the Edge Read More +
For more information, visit https://www.idtechex.com/en/research-report/hardware-for-hpc-and-ai-2025-2035-technologies-markets-forecasts/1058. HPC hardware market to reach US$581 billion by 2035 at a CAGR of 13.6%. This report examines the high-performance computing (HPC) and AI hardware markets. It provides an overview of the overall HPC market in the exascale era, including analysis of major supercomputers like El Capitan. Detailed overview of technologies
Hardware for HPC and AI 2025-2035: Technologies, Markets and Forecasts Read More +
The Jetson Orin Nano Super delivers up to a 1.7x gain in generative AI performance, supporting popular models for hobbyists, developers and students. NVIDIA is taking the wraps off a new compact generative AI supercomputer, offering increased performance at a lower price with a software upgrade. The new NVIDIA Jetson Orin Nano Super Developer Kit,
NVIDIA Unveils Its Most Affordable Generative AI Supercomputer Read More +
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. A retrieval-augmented generation (RAG) application has exponentially higher utility if it can work with a wide variety of data types—tables, graphs, charts, and diagrams—and not just text. This requires a framework that can understand and generate responses
An Easy Introduction to Multimodal Retrieval-augmented Generation Read More +
Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.
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