Multimodal

“Entering the Era of Multimodal Perception,” a Presentation from Connected Vision Advisors

Simon Morris, Serial Tech Entrepreneur and Start-Up Advisor at Connected Vision Advisors, presents the “Entering the Era of Multimodal Perception” tutorial at the May 2024 Embedded Vision Summit. Humans rely on multiple senses to quickly and accurately obtain the most important information we need. Similarly, developers have begun using multiple… “Entering the Era of Multimodal […]

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SiMa.ai Expands ONE Platform for Edge AI with MLSoC Modalix, a New Product Family for Generative AI

Industry’s first multi-modal, software-centric edge AI platform supports any edge AI model from CNNs to multi-modal GenAI and everything in between with scalable performance per watt SAN JOSE, Calif.–(BUSINESS WIRE)–SiMa.ai, the software-centric, embedded edge machine learning system-on-chip (MLSoC) company, today announced MLSoC™ Modalix, the industry’s first multi-modal edge AI product family. SiMa.ai MLSoC Modalix supports

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“Unveiling the Power of Multimodal Large Language Models: Revolutionizing Perceptual AI,” a Presentation from BenchSci

István Fehérvári, Director of Data and ML at BenchSci, presents the “Unveiling the Power of Multimodal Large Language Models: Revolutionizing Perceptual AI” tutorial at the May 2024 Embedded Vision Summit. Multimodal large language models represent a transformative breakthrough in artificial intelligence, blending the power of natural language processing with visual… “Unveiling the Power of Multimodal

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Multimodal AI is Having Its Moment In the Sun. Here’s Why It’s So Important

This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. Multimodal AI takes in different inputs like text, images or video, allowing digital assistants to better understand the world and you, and gets supercharged when it’s able to run on your device As smart as generative artificial

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“Multimodal LLMs at the Edge: Are We There Yet?,” An Embedded Vision Summit Expert Panel Discussion

Sally Ward-Foxton, Senior Reporter at EE Times, moderates the “Multimodal LLMs at the Edge: Are We There Yet?” Expert Panel at the May 2024 Embedded Vision Summit. Other panelists include Adel Ahmadyan, Staff Engineer at Meta Reality Labs, Jilei Hou, Vice President of Engineering and Head of AI Research at… “Multimodal LLMs at the Edge:

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May 2024 Embedded Vision Summit Opening Remarks (May 23)

Jeff Bier, Founder of the Edge AI and Vision Alliance, welcomes attendees to the May 2024 Embedded Vision Summit on May 23, 2024. Bier provides an overview of the edge AI and vision market opportunities, challenges, solutions and trends. He also introduces the Edge AI and Vision Alliance and the… May 2024 Embedded Vision Summit

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May 2024 Embedded Vision Summit Opening Remarks (May 22)

Jeff Bier, Founder of the Edge AI and Vision Alliance, welcomes attendees to the May 2024 Embedded Vision Summit on May 22, 2024. Bier provides an overview of the edge AI and vision market opportunities, challenges, solutions and trends. He also introduces the Edge AI and Vision Alliance and the… May 2024 Embedded Vision Summit

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“Understand the Multimodal World with Minimal Supervision,” a Keynote Presentation from Yong Jae Lee

Yong Jae Lee, Associate Professor in the Department of Computer Sciences at the University of Wisconsin-Madison and CEO of GivernyAI, presents the “Learning to Understand Our Multimodal World with Minimal Supervision” tutorial at the May 2024 Embedded Vision Summit. The field of computer vision is undergoing another profound change. Recently,… “Understand the Multimodal World with

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Snapdragon Powers the Future of AI in Smart Glasses. Here’s How

This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. A Snapdragon Insider chats with Qualcomm Technologies’ Said Bakadir about the future of smart glasses and Qualcomm Technologies’ role in turning it into a critical AI tool Artificial intelligence (AI) is increasingly winding its way through our

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Build VLM-powered Visual AI Agents Using NVIDIA NIM and NVIDIA VIA Microservices

This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Traditional video analytics applications and their development workflow are typically built on fixed-function, limited models that are designed to detect and identify only a select set of predefined objects. With generative AI, NVIDIA NIM microservices, and foundation

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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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