A vector database is an organized collection of vector embeddings that can be created, read, updated, and deleted at any point in time. Vector embeddings represent chunks of data, such as text or images, as numerical values.
]]>A convolutional neural network is a type of deep learning network used primarily to identify and classify images and to recognize objects within images.
]]>Retrieval-augmented generation enhances large language model prompts with relevant data for more practical, accurate responses.
]]>Large language models (LLMs) are a class of generative AI models built using transformer networks that can recognize, summarize, translate, predict, and generate language using very large datasets. LLMs have the promise of transforming society as we know it, yet training these foundation models is incredibly challenging. This blog articulates the basic principles behind LLMs…
]]>Download this free eBook to learn more about LLMs and how they are powering use cases such as chatbots, global translation, and summarization.
]]>Generative AI is primed to transform the world’s industries and to solve today’s most important challenges. To enable enterprises to take advantage of the possibilities with generative AI, NVIDIA has launched NVIDIA AI Foundations and the NVIDIA NeMo framework, powered by NVIDIA DGX Cloud. NVIDIA AI Foundations are a family of cloud services that provide enterprises with a simplified…
]]>Edge computing is the practice of processing data physically closer to its source.
]]>Smart spaces are delivering unprecedented value, creating a continuous flow of information between physical and digital worlds. By incorporating technologies such as the Internet of Things (IoT), cloud computing, machine learning, and AI at the edge, world-class businesses can capture digital data and turn them into actionable insights. However, the process is complicated with edge…
]]>A proof-of-concept (POC) is the first step towards a successful edge AI deployment. Companies adopt edge AI to drive efficiency, automate workflows, reduce cost, and improve overall customer experiences. As they do so, many realize that deploying AI at the edge is a new process that requires different tools and procedures than the traditional data center. Without a clear understanding of…
]]>Timing is everything, especially when it impacts your customer experiences, bottom line, and production efficiency. Edge AI can help by delivering real-time intelligence and increased privacy in intermittent, low bandwidth, and low cost environments. By 2025, according to Gartner®, 75% of data will be created and processed at the edge, outside the traditional data center or cloud.1…
]]>Edge AI is the deployment of AI applications in devices throughout the physical world. It’s called “edge AI” because the AI computation is done near the user at the edge of the network, close to where the data is located, rather than centrally in a cloud computing facility or private data center.
]]>Since its inception, artificial intelligence (AI) has transformed every aspect of the global economy through the ability to solve problems of all sizes in every industry. NVIDIA has spent the last decade empowering companies to solve the world’s toughest problems such as improving sustainability, stopping poachers, and bettering cancer detection and care. What many don’t know is that behind…
]]>Over the last decades, organizations of all sizes across the world have flocked to implement video management systems (VMS) that tie together the components of a video network infrastructure. By allowing businesses to easily capture, record, store, retrieve, view, and analyze video collected from their cameras, VMS can improve their operations, increase visibility, and enhance safety.
]]>Nearly every organization is enticed by the ability to use cameras to understand their businesses better. Approximately 1 billion video cameras—the ultimate Internet of Things (IoT) sensors—are being used to help people around the world live better and safer. But, there is a clear barrier to success. Putting the valuable data collected by these cameras to use requires significant human…
]]>Sign up for Edge AI News to stay up to date with the latest trends, customers use cases, and technical walkthroughs. Nearly all enterprises today develop or adopt application software that codifies the processing of information such as invoices, human resource profiles, or product specifications. An entire industry has risen to deploy and execute these enterprise applications both in…
]]>Whether your organization is new to data science or has a mature strategy in place, many come to a similar realization: Most data does not originate at the core. Scientists often want access to amounts of data that are unreasonable to securely stream to the data center in real time. Whether the distance is 10 miles or thousands of miles, the bounds of traditional IT infrastructure are simply…
]]>Editor’s note: Interested in GPU Operator? Register for our upcoming webinar on January 20th, “How to Easily use GPUs with Kubernetes”. NVIDIA GPU Operator allows organizations to easily scale NVIDIA GPUs on Kubernetes. By simplifying the deployment and management of GPUs with Kubernetes, the GPU Operator enables infrastructure teams to scale GPU applications error-free, within minutes…
]]>Approximately 1 billion video cameras—the ultimate Internet of Things (IoT) sensors—have been deployed throughout the world’s cities and spaces to help us live better and safer. Optimizing AI-enabled video analytics is critical for frictionless retail, streamlined inventory management, traffic engineering in smart cities, optical inspection on factory floors, patient care in healthcare facilities…
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