Unlock more accurate and meaningful AI outcomes with retrieval-augmented generation. Photo by No Revisions on UnsplashBy Eduardo Rojas Oviedo and Ezequiel Lanza The retrieval-augmented generation (RAG) process has gained popularity due to its potential to enhance the understanding of large language models (LLMs), providing them with context and helping to prevent hallucinations. The RAG process involves several steps, from ingesting documents in chunks to extracting context to prompting the LLM model with that context. While known to significantly improve predictions, RAG can occasio ..read more

Seamlessly inference from edge to cloud and across devices Photo by Sharad Bhat on UnsplashPresented by Ria Cheruvu & Dr. Paula Ramos Generative AI is revolutionizing how organizations create high-quality text and images. However, challenges such as high inference time, unsatisfactory customer experience, and intricated developer experience can pose obstacles to the seamless adoption of generative AI. Developers are often torn between using expensive machines, such as servers or cloud providers, and porting workloads to an edge device with optimized models. While the cloud o ..read more

Help meet scalability, security, and sustainability demands. Photo by SpaceX on UnsplashPresented by Dr. Malini Bhandaru Building an effective AI pipeline presents developers with unique scalability, security, and sustainability challenges. Developers must ensure that AI pipelines can scale to meet dynamic demands while balancing costs. Developers must also protect the data — which is often sensitive, proprietary, or regulated — used in AI models, as well as the valuable AI models themselves. Lastly, developers must build pipelines that use resources responsibly and that help advance ..read more

Hey Mr, DJ! Which large language model should I use? Photo by Marcela Laskoski on UnsplashPresented by Ezequiel Lanza — AI Open Source Evangelist (Intel) Developers working in artificial intelligence must make a pivotal decision at the start of any language project. Do you use a foundation model or a fine-tuned model? How do you decide? If you’ve ever planned a party, it’s a lot like choosing a DJ. Imagine you’re planning an event, and you need to pick a DJ for the evening entertainment. Do you go with someone who sticks to the tried-and-true hits or the one who dives deep into ..read more

New approaches focus on helping end users to understand AI. Photo by Sid Balachandran on UnsplashPresented by Ezequiel Lanza — AI Open Source Evangelist (Intel) As AI continues to revolutionize industries, convincing users that AI models can be trusted will play an important role in driving widespread adoption. Explainable AI (XAI) provides frameworks and tools to help users understand why models make certain decisions, enabling users to embrace AI with confidence. But while XAI approaches are proving to be useful for developers, how do we know we’re providing the explanations end users n ..read more

See how you can apply this to many use cases beyond retail. Photo by Matt Noble on UnsplashPresented by Antonio Martinez AI and computer vision (CV) applications have gained remarkable popularity in the retail world, driven by the need for retailers to enhance the customer experience, optimize operations, and stay competitive in a rapidly evolving market. Successfully deploying a CV initiative at the edge requires a deep understanding of the underlying hardware and how it affects AI model behavior. The automated self-checkout open source project developed by Intel provides a fra ..read more

A reusable framework for computer vision pipelines. Photo by Nathália Rosa on UnsplashAuthor : Dr. Jim Wang, Senior Software Engineer, Intel Computer vision (CV) pipelines use many AI models based on use case and complexity. In the retail checkout space, developers design and assemble their applications with models such as object detection, person recognition, instance segmentation, and more. The profile launcher, developed by Intel, introduces a repeatable framework for automated self-checkout use cases, enabling developers to easily configure and launch pipelines in a consiste ..read more

Learn the key to building trustworthy models. Photo by Ivan Torres on UnsplashPresented by Ezequiel Lanza — AI Open Source Evangelist (Intel) In the last five years, we’ve made big strides in the accuracy of complex AI models, but it’s still almost impossible to understand what’s going on inside. The more accurate and complicated the model, the harder it is to interpret why it makes certain decisions. Explainable AI (XAI) techniques provide the means to try to unravel the mysteries of AI decision-making, helping end users easily understand and interpret model predictions. This post explor ..read more

Build, deploy, and scale AI/ML applications. Photo by Martin Adams on UnsplashAuthor : Neethu Elizabeth Simon, IT/ML Senior Software Engineer, Intel Widespread adoption of machine learning (ML) requires a systematic and efficient approach to building AI/ML pipelines. New tools are needed to streamline the process. Two new open source projects — AI Connect for Scientific Data (AiCSD)*, developed by Intel, and BentoML* — make it easy to build, deploy, and scale AI/ML applications virtually anywhere, helping scientific researchers unlock groundbreaking use cases in chemistry, physics, b ..read more

An experiment demonstrating the improvements in Federated Learning when utilizing Synthetic Data. Photo by Claudio Schwarz on Unsplash In the rapidly evolving field of machine learning, federated learning offers a privacy-preserving method for training models on shared data across many collaborators who cannot share their data, typically for legal reasons (for example data protection laws, contractual restrictions, user consent issues, etc). Imagine Hospital A wants to build the best COVID-19 mortality prediction model to improve patient care. It turns out Hospital B also wants to tr ..read more