Edge AI Technology Report: Generative AI Edition Chapter 3. Advances in model optimization techniques like pruning, quantization, and knowledge distillation pave the way for more efficient deployment of generative AI at the edge.
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ORGANIZATIONS. SHAPING THE INDUSTRY.
Berkeley Artificial Intelligence Research
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The Berkeley Artificial Intelligence Research (BAIR) Lab brings together UC...
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How BrainChip's Akida™ Edge Box Simplifies ML Deployment
Edge AI Technology Report: Generative AI Edition Chapter 1. Generative AI's demand for real-time insights is driving a shift from cloud to edge computing, enabling faster, local processing on devices and reducing cloud latency and bandwidth constraints.
Discover how AI is optimizing manufacturing by increasing quality control, reducing downtime and boosting efficiency.
Edge computing: a decentralized computing paradigm that processes data closer to the source, reducing latency and enabling more efficient use of resources.
Edge AI is the process of running artificial intelligence (AI) and machine learning (ML) algorithms on computing devices at the periphery of a network, rather than on large cloud servers.
Similar to visual data, collecting and curating sound data that accurately reflects real-world scenarios is a major hurdle in training effective machine learning models.
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Recently we launched new Edge Impulse functionality that shows just one of the powerful ways that LLMs can be utilized on the edge: using GPT-4o to train a 2,000,000x smaller model, that runs directly on device.
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Several factors contribute to the occurrence of retained surgical bodies. One common factor is the hectic nature of surgical environments, where numerous instruments and items are used during procedures.
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The latest large language models (LLMs), like the newly released ChatGPT-4o, are truly astonishing. They are multimodal, meaning they have multiple senses and can interpret text, images, and audio information and respond in a seemingly human-like manner.
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We are proud to announce that synthetic data generation is now available inside the Edge Impulse platform, enabling a new and efficient way to work with LLM-generated images, audio, and voice to enhance your edge AI models.
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Edge AI is the process of running artificial intelligence (AI) and machine learning (ML) algorithms on devices at the network’s edge (periphery), rather than on large cloud servers. In simple terms, edge AI is the fusion of devices and artificial intelligence.
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A deep dive into the convergence of generative AI and edge computing.
Smart manufacturing integrates advanced technologies such as the Internet of Things (IoT), big data analytics, and artificial intelligence (AI) to enhance efficiencies, improve product quality, and gain a competitive advantage.
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Weighing Latency, Privacy, and Scalability to Optimize Your AI Strategy
