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Taming LLMs: Strategies and Tools for Controlling Responses

Algorithms, Articles, Software, Tools, Tryolabs /

This article was originally published at Tryolabs’ website. It is reprinted here with the permission of Tryolabs. In the ever-evolving landscape of natural language processing, the advent of Large Language Models (LLMs) has ushered in a new era of possibilities and challenges. While these models showcase remarkable capabilities in generating human-like text, the potential for […]

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AI Disruption is Driving Innovation in On-device Inference

Aerospace and Defense, Articles, Automotive, Entertainment, Industrial Vision (Computer Vision), Multimodal, Robotics /

This article was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. How the proliferation and evolution of generative models will transform the AI landscape and unlock value. The introduction of DeepSeek R1, a cutting-edge reasoning AI model, has caused ripples throughout the tech industry. That’s because its performance is on

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From Brain to Binary: Can Neuro-inspired Research Make CPUs the Future of AI Inference?

Algorithms, Articles, Processors, Software, Tools, Tryolabs /

This article was originally published at Tryolabs’ website. It is reprinted here with the permission of Tryolabs. In the ever-evolving landscape of AI, the demand for powerful Large Language Models (LLMs) has surged. This has led to an unrelenting thirst for GPUs and a shortage that causes headaches for many organizations. But what if there

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DALL-E vs Gemini vs Stability: GenAI Evaluations

Algorithms, Articles, Software, Tenyks, Tools /

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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Enhance Multi-camera Tracking Accuracy by Fine-tuning AI Models with Synthetic Data

Algorithms, Articles, NVIDIA, Object Identification, Object Tracking, Processors, Software, Tools /

This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Large-scale, use–case-specific synthetic data has become increasingly important in real-world computer vision and AI workflows. That’s because digital twins are a powerful way to create physics-based virtual replicas of factories, retail spaces, and other assets, enabling precise simulations

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Generate Traffic Insights Using YOLOv8 and NVIDIA JetPack 6.0

Algorithms, Articles, Automotive, NVIDIA, Processors, Software, Tools /

This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Intelligent Transportation Systems (ITS) applications are becoming increasingly valuable and prevalent in modern urban environments. The benefits of using ITS applications include: Increasing traffic efficiency: By analyzing real-time traffic data, ITS can optimize traffic flow, reducing congestion and

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NVIDIA DeepStream 7.0 Milestone Release for Next-gen Vision AI Development

Algorithms, Articles, NVIDIA, Processors, Software, Tools /

This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. NVIDIA DeepStream is a powerful SDK that unlocks GPU-accelerated building blocks to build end-to-end vision AI pipelines. With more than 40+ plugins available off-the-shelf, you can deploy fully optimized pipelines with cutting-edge AI Inference, object tracking, and seamless

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Quantization of Convolutional Neural Networks: Quantization Analysis

Algorithms, Articles, Software, Tools /

See “Quantization of Convolutional Neural Networks: Model Quantization” for the previous article in this series. In the previous articles in this series, we discussed quantization schemes and the effect of different choices on model accuracy. The ultimate choice of quantization scheme depends on the available tools. TFlite and Pytorch are the most popular tools used

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Quantization of Convolutional Neural Networks: Model Quantization

Algorithms, Articles, Software, Tools /

See “From Theory to Practice: Quantizing Convolutional Neural Networks for Practical Deployment” for the previous article in this series. Significant progress in Convolutional Neural Networks (CNNs) has focused on enhancing model complexity while managing computational demands. Key advancements include efficient architectures like MobileNet1, SqueezeNet2, ShuffleNet3, and DenseNet4, which prioritize compute and memory efficiency. Further innovations

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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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