Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.
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This is a detailed, candid walkthrough of the IRIS AI Studio platform. I speak out loud on my thoughts while trying different examples, some of which fail to deliver expected results - which I believe is a need for such a platform to explore different models, configurations and limitations. This will be helpful if you're interested in how to build 'Chat with PDF' or data recommendation systems using IRIS DB and LLM models.
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New to machine learning? Watch this video on InterSystems Developers YouTube to understand the basic concepts of machine learning and how it provides value in applications around the world today:
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While we're working on a new data product supporting the analytics development process, we'd like to test some of the UX (User eXperience) design elements on a real audience. If you've got some battle scars from earlier analytics work and are interested in participating, please complete this survey and we'll get in touch when we have something to show!
Feel free to share this survey with your data-savvy friends and colleagues if you think they match the profile.
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As said in the previous article about the iris-fhir-generative-ai experiment, the project logs all events for analysis. Here we are going to discuss two types of analysis covered by analytics embedded in the project:
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In it, Anton provides a great, high-level introduction to machine learning and shows why you don't need to be a "unicorn" data scientist to start using machine learning to your advantage!
I got access to the new Bing this morning and thought I would try out some things on it. Below are a series of screenshots of what I searched and the results. Please share your experiences here too.
Traditional keyword-based search struggles with nuanced, domain-specific queries. Vector search, however, leverages semantic understanding, enabling AI agents to retrieve and generate responses based on context—not just keywords.
This article provides a step-by-step guide to creating an Agentic AI RAG (Retrieval-Augmented Generation) application.
Implementation Steps:
Create Agent Tools
Add Ingest functionality: Automatically ingests and index documents (e.g., InterSystems IRIS 2025.1 Release Notes).
Episode 17 of Data Points features a roundtable conversation with Carmen Logue, Benjamin De Boe, and Thomas Dyar about the Analytics & AI area of the InterSystems technology stack. Learn from these product experts about the various technologies and partnerships that exist within the Analytics & AI space at InterSystems, how some customers use these tools, and what might be coming in the future.
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ChatIRIS Health Coach, a GPT-4 based agent that leverages the Health Belief Model as a psychological framework to craft empathetic replies. This article elaborates on the backend architecture and its components, focusing on how InterSystems IRIS supports the system's functionality.
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Join us on Wednesday, June 25, to explore innovative, real-world solutions built with InterSystems IRIS, AI, LLMs, and intelligent agent technologies — directly from the developers who created them:
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In our latest episode of Data Points, I had a conversation with @Thomas Dyarabout AI Link, which helps bridge the gap between data scientists and business analysts. Our conversation talks about how AI Link fits with IntegratedML and Adaptive Analytics, as well, as what new features are on the horizon for IntegratedML. Take a listen!
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