How RAG Will Usher In the Next Generation of LLMs and Generative AI
Retrieval-augmented generation may provide a big step forward in addressing many of the issues that keep enterprises from adopting AI.
Retrieval-augmented generation may provide a big step forward in addressing many of the issues that keep enterprises from adopting AI.
Retrieval-augmented generation may provide a big step forward in addressing many of the issues that keep enterprises from adopting AI.
Almost every customer we talk to today has questions about Large Language Models. In this blog post, we’ll share the 5 most common questions we receive and walk through how we address each one.
In this interview written for Jiemian News, our co-founder Jeff Hawkins shares his insights on ChatGPT, the brain-based approach to building intelligent AI systems, and the future of humanity in an increasingly AI-integrated world.
Built on a foundation of two decades of neuroscience research and breakthroughs in AI technology, we have developed a cutting-edge AI platform that uses neuroscience principles to process large amounts of language data quickly and accurately. Read on to learn how you can use our product to build sophisticated language-based applications with no machine learning experience required.
Generative AI is an exciting and transformative technology, which will continue to gain adoption across a wide range of use cases. However, the associated compute costs are significant. Using Numenta’s AI platform, which is deployed directly into customer infrastructure, these costs can be reduced by up to 60X, allowing enterprises of all sizes to fully exploit the game-changing technology.
This is a joint blog post co-authored by Numenta and Intel on accelerating Large Language Models with long sequence lengths. Numenta running on the Intel Xeon CPU Max Series delivers 20x inference acceleration compared to other CPUs.
On January 10, as part of Intel’s 4th Gen Xeon Scalable processors launch, we announced that our technology improves low-latency BERT-Large inference throughput by over two orders of magnitude.