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Want to dive deeper into the concepts of vector searches and embeddings? Learn from an InterSystems expert:
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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Want to dive deeper into the concepts of vector searches and embeddings? Learn from an InterSystems expert:
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What components and libraries can you add to your retrieval-augmented generation (RAG) applications? Find out in this video:
Identifying Useful Components for Your Generative AI Application
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Play the new video on InterSystems Developers YouTube:
⏯ InterSystems IRIS Vector Search and the Python Ecosystem @ Global Summit 2024
In today's data landscape, businesses encounter a number of different challenges. One of them is to do analytics on top of unified and harmonized data layer available to all the consumers. A layer that can deliver the same answers to the same questions irrelative to the dialect or tool being used. InterSystems IRIS Data Platform answers that with and add-on of Adaptive Analytics that can deliver this unified semantic layer. There are a lot of articles in DevCommunity about using it via BI tools. This article will cover the part of how to consume it with AI and also how to put some insights
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Curious how a retrieval-augmented generation (RAG) application works? Take a look at this demonstration:
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We hope you enjoyed our Developer Community AI sweepstakes and learned something new while interacting with our DC AI. Now, it's time to announce the winner!
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Ready to create a retrieval-augmented generation (RAG) architecture for your next GenAI application? See how to get started:
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As you may know, our Developer Community AI has been out for over a month now 🎉 We hope you were curious enough to give it a try 😁 If you haven't yet, please do! Anyway, since it's still in beta, we're very interested in learning what you think about it, and we look forward to hearing your thoughts and experiences.
Since we value your time and effort, we will give away a cute prize to a random member of the Community who shares how DC AI helped you. To participate in this sweepstakes, you have to follow the guidelines:
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We’re thrilled to invite you to an exciting LinkedIn Live session dedicated to the most common myths and misconceptions surrounding AI!
🌐 Debunking AI Myths with Expert Insights 🌐
📅 Thursday, September 5th, 10 am EDT | 4 pm CEST
🗣 Presenters:
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Are you building generative AI applications? See how a retrieval-augmented generation (RAG) architecture can help:
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How can you get your data ready for generative AI applications? Get some key tips from an InterSystems expert:
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Are you using generative AI tools for development? Let InterSystems principles be your guiding star! 🌟 Learn about the company's approach:
I received some really excellent feedback from a community member on my submission to the Python 2024 contest. I hope its okay if I repost it here:
you build a container more than 5 times the size of pure IRIS
and this takes time
container start is also slow but completes
backend is accessible as described
a production is hanging around
frontend reacts
I fail to understand what is intended to show
the explanation is meant for experts other than me
The submission is here: https://openexchange.intersystems.com/package/IRIS-RAG-App
In the previous article we presented the d[IA]gnosis application developed to support the coding of diagnoses in ICD-10. In this article we will see how InterSystems IRIS for Health provides us with the necessary tools for the generation of vectors from the ICD-10 code list using a pre-trained language model, its storage and the subsequent search for similarities on all these generated vectors.
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Need to learn how to write better prompts for GenAI? This video from Learning Services introduces six key strategies:
The invention and popularization of Large Language Models (such as OpenAI's GPT-4) has launched a wave of innovative solutions that can leverage large volumes of unstructured data that was impractical or even impossible to process manually until recently. Such applications may include data retrieval (see Don Woodlock's ML301 course for a great intro to Retrieval Augmented Generation), sentiment analysis, and even fully-autonomous AI agents, just to name a few!
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Do you think Generative AI can make your life easier? See some potential use cases for GenAI in the latest video from Learning Services:
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How can you create a balanced approach to using Generative AI? Get help from InterSystems experts in the latest video from Learning Services:
Hi Community,
Watch this video to learn about the PainChek artificial intelligence technology, which assesses patient pain at the hospital bedside, leverages InterSystems IRIS interoperability to connect to third-party electronic medical record systems:
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Need an introduction to Generative AI? Learning Services is excited to announce the first video in a new series about GenAI basics:
Hey Community!
We're happy to share our newest learning path dedicated to the essentials of GenAI, accessible on the Learning Portal via your SSO:
🤖 Getting Started with Generative AI 🤖
Here is what you can expect:
Hi,
Is anyone using a 3rd party software for dictating notes, and has a way to integrate that with TrakCare?
We are exploring introducing an AI-scribe software for medical professionals note taking and wondering how to connect the two.
Thanks
Looking to get started with generative AI? Try two brand-new learning paths. In Getting Started with Generative AI (2h 45m), learn the basics of interacting with GenAI. Then, try Developing Generative AI Applications (2h) to start developing your own GenAI application. Plus, earn badges for completing each path!
We have a yummy dataset with recipes written by multiple Reddit users, however most of the information is free text as the title or description of a post. Let's find out how we can very easily load the dataset, extract some features and analyze it using features from OpenAI large language model within Embedded Python and the Langchain framework.
First things first, we need to load the dataset or can we just connect to it?
Hi All,
We invite you to join our next Meetup in Boston on June 25 5:30-7:30 pm.
>> RSVP here <<
Continuing with the series of articles on voice file management, we are going to see how we can convert text into audio and receive the file with the chosen voice.
We will also explore how a service from OpenAI can help us analyze a text and determine the mood expressed in it.
Let's analyze how you can create your own voice file and how it can “read” your feelings.
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.
In the previous article, we saw in detail about Connectors, that let user upload their file and get it converted into embeddings and store it to IRIS DB. In this article, we'll explore different retrieval options that IRIS AI Studio offers - Semantic Search, Chat, Recommender and Similarity.