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#Generative AI (GenAI)

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Generative AI refers to algorithms and models in artificial intelligence that are capable of generating new data or content that is similar to existing data. These models are trained on large datasets and learn to generate new examples that mimic the patterns and characteristics of the original data.

We all know that having a set of proper test data before deploying an application to production is crucial for ensuring its reliability and performance. It allows to simulate real-world scenarios and identify potential issues or bugs before they impact end-users. Moreover, testing with representative data sets allows to optimize performance, identify bottlenecks, and fine-tune algorithms or processes as needed. Ultimately, having a comprehensive set of test data helps to deliver a higher quality product, reducing the likelihood of post-production issues and enhancing the overall user experience.

In this article, let's look at how one can use generative AI, namely Gemini by Google, to generate (hopefully) meaningful data for the properties of multiple objects. To do this, I will use the RESTful service to generate data in a JSON format and then use the received data to create objects.

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Article
· Jun 19, 2023 8m read
Open AI integration with IRIS

As you all know, the world of artificial intelligence is already here, and everyone wants to use it to their benefit.

There are many platforms that offer artificial intelligence services for free, by subscription or private ones. However, the one that stands out because of the amount of "noise" it made in the world of computing is Open AI, mainy thanks to its most renowned services: ChatGPT and DALL-E.

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Considering new business interest in applying Generative-AI to local commercially sensitive private data and information, without exposure to public clouds. Like a match needs the energy of striking to ignite, the Tech lead new "activation energy" challenge is to reveal how investing in GPU hardware could support novel competitive capabilities. The capability can reveal the use-cases that provide new value and savings.

Sharpening this axe begins with a functional protocol for running LLMs on a local laptop.

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Artificial Intelligence (AI) is getting a lot of attention lately because it can change many areas of our lives. Better computer power and more data have helped AI do amazing things, like improving medical tests and making self-driving cars. AI can also help businesses make better decisions and work more efficiently, which is why it's becoming more popular and widely used. How can one integrate the OpenAI API calls into an existing IRIS Interoperability application?

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Fun or No Fun - how serious is it?


Large language models are stirring up some phenomena in recent months. So inevitably I was playing ChatGPT too over last weekend, to probe whether it would be a complimentary to some BERT based "traditional" AI chatbots I was knocking up, or rather would it simply sweep them away.

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Artificial intelligence is not limited only to generating images through text with instructions or creating narratives with simple directions.

You can also make variations of a picture or include a special background to an already existing one.

Additionally, you can obtain the transcription of audio regardless of its language and the speed of the speaker.

So, let's analyze how the file management works.

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What is Unstructured Data?
Unstructured data refers to information lacking a predefined data model or organization. In contrast to structured data found in databases with clear structures (e.g., tables and fields), unstructured data lacks a fixed schema. This type of data includes text, images, videos, audio files, social media posts, emails, and more.

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Problem

Do you resonate with this - A capability and impact of a technology being truly discovered when it's packaged in a right way to it's audience. Finest example would be, how the Generative AI took off when ChatGPT was put in the public for easy access and not when Transformers/RAG's capabilities were identified. At least a much higher usage came in, when the audience were empowered to explore the possibilities.

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Problem

In a fast-paced clinical environment, where quick decision-making is crucial, the lack of streamlined document storage and access systems poses several obstacles. While storage solutions for documents exist (e.g, FHIR), accessing and effectively searching for specific patient data within those documents meaningfully can be a significant challenge.

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Hi Community

In this article, I will introduce my application IRIS-GenLab.

IRIS-GenLab is a generative AI Application that leverages the functionality of Flask web framework, SQLALchemy ORM, and InterSystems IRIS to demonstrate Machine Learning, LLM, NLP, Generative AI API, Google AI LLM, Flan-T5-XXL model, Flask Login and OpenAI ChatGPT use cases.

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Hey Community,

Watch the latest video on InterSystems Developers YouTube:

⏯ Using Vector Search for Generative AI

https://www.youtube.com/embed/IapbO3j1Qmw
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Hey Community,

We have more exciting news! The new InterSystems online programming contest dedicated to Generative AI, Vector Search and Machine Learning is starting very soon!

🏆 InterSystems Vector Search, GenAI and ML Contest 🏆

Duration: April 22 - May 19, 2024

Prize pool: $14,000

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Hi Community!

Just want to share with you an exercise I made to create "my own" chat with GPT in Telegram.

It became possible because of two components on Open Exchange: Telegram Adapter by @Nikolay Solovyev and IRIS Open-AI by @Kurro Lopez

So with this example you can setup your own chat with ChatGPT in Telegram.

Let's see how to make it work!

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Hi, Community!

How can you get your data ready for generative AI applications? Get some key tips from an InterSystems expert:

Preparing Your Data for Use with Generative AI

https://www.youtube.com/embed/rIsyOv4he2g?utm_source=youtube&utm_medium=social&utm_campaign=rIsyOv4he2g
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​Keywords: ChatGPT, COS, Lookup Table, IRIS, AI

Purpose


Here is another quick note before we move on to GPT-4 assisted automation journey. Below are some "little" helps ChatGPT had already been offering, here and there, during daily works.

And what could be the perceived gaps, risks and traps to LLMs assisted automation, if you happen to explore this path too. I'd also love to hear anyone's use cases and experiences on this front too.

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FHIR has revolutionized the healthcare industry by providing a standardized data model for building healthcare applications and promoting data exchange between different healthcare systems. As the FHIR standard is based on modern API-driven approaches, making it more accessible to mobile and web developers. However, interacting with FHIR APIs can still be challenging especially when it comes to querying data using natural language.

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Hi Community,

In this article, I will introduce my application iris-RAG-Gen .

Iris-RAG-Gen is a generative AI Retrieval-Augmented Generation (RAG) application that leverages the functionality of IRIS Vector Search to personalize ChatGPT with the help of the Streamlit web framework, LangChain, and OpenAI. The application uses IRIS as a vector store.

Application Features

  • Ingest Documents (PDF or TXT) into IRIS
  • Chat with the selected Ingested document
  • Delete Ingested Documents
  • OpenAI ChatGPT

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Our next Developer Meetup will take place on July 26, 17:30 pm at the CIC Venture Café in Cambridge.

Join us to learn Generative AI Use Cases + Reference Architecture in Healthcare, witness the demo of LLMs in Healthcare and share your thoughts on the topic.

RSVP here

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In recent years, artificial intelligence technologies for text generation have developed significantly. For example, text generation models based on neural networks can produce texts that are almost indistinguishable from texts written by humans.
ChatGPT is one such service. It is a huge neural network trained on a large number of texts, which can generate texts on various topics and be matched to a given context.

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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!

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