Algorithms

Multimodal Large Language Models: Transforming Computer Vision

This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. This article introduces multimodal large language models (MLLMs) [1], their applications using challenging prompts, and the top models reshaping computer vision as we speak. What is a multimodal large language model (MLLM)? In layman terms, a multimodal […]

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How Qualcomm is Catalyzing Retail’s AI Revolution

This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. Retail is embracing innovation — we’ll be showing off examples of these game-changing experiences at NRF 2025 With more places to shop than ever, physical stores are turning to AI and technology for a competitive edge. That

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How Au-Zone Technologies Plays a Key Role in the Ocean Cleanup Automated Debris Imaging System

This blog post was originally published at Au-Zone Technologies’ website. It is reprinted here with the permission of Au-Zone Technologies. Founded in 2012 in Rotterdam, The Netherlands, The Ocean Cleanup is developing and scaling technologies to rid the oceans of plastic with ocean cleanup systems and river interception technologies. With over 150 employees, The Ocean

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DALL-E vs Gemini vs Stability: GenAI Evaluations

This article was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. We performed a side-by-side comparison of three models from leading providers in Generative AI for Vision. This is what we found: Despite the subjectivity involved in Human Evaluation, this is the best approach to evaluate state-of-the-art GenAI Vision

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Harnessing the Power of LLM Models on Arm CPUs for Edge Devices

This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. In recent years, the field of machine learning has witnessed significant advancements, particularly with the development of Large Language Models (LLMs) and image generation models. Traditionally, these models have relied on powerful cloud-based infrastructures to deliver impressive

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AI On the Road: Why AI-powered Cars are the Future

This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. AI transforms your driving experience in unexpected ways as showcased by Qualcomm Technologies collaborations As automotive technology rapidly advances, consumers are looking for vehicles that deliver AI-enhanced experiences through conversational voice assistants and sophisticated user interfaces. Automotive

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Visual Intelligence at the Edge

This blog post was originally published at Au-Zone Technologies’ website. It is reprinted here with the permission of Au-Zone Technologies. Optimizing AI-based video telematics deployments on constrained SoCs platforms The demand for advanced video telematics systems is growing rapidly as companies seek to enhance road safety, improve operational efficiency, and manage liability costs with AI-powered

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NVIDIA JetPack 6.2 Brings Super Mode to NVIDIA Jetson Orin Nano and Jetson Orin NX Modules

This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. The introduction of the NVIDIA Jetson Orin Nano Super Developer Kit sparked a new age of generative AI for small edge devices. The new Super Mode delivered an unprecedented generative AI performance boost of up to 1.7x

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Improving Vision Model Performance Using Roboflow and Tenyks

This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. When improving an object detection model, many engineers focus solely on tweaking the model architecture and hyperparameters. However, the root cause of mediocre performance often lies in the data itself. ‍In this collaborative post between Roboflow and

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Federated Learning: Risks and Challenges

This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. In the first article of our mini-series on Federated Learning (FL), Privacy-First AI: Exploring Federated Learning, we introduced the basic concepts behind the decentralized training approach, and we also presented potential applications in certain domains. Undoubtedly, FL

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