New post

#Artificial Intelligence (AI)

4

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.

Learn more.

All time

Keywords: Anaconda, Jupyter Notebook, Tensorflow GPU, Deep Learning, Python 3 and HealthShare

1. Purpose and Objectives

This "Part I" is a quick record on how to set up a "simple" but popular deep learning demo environment step-by-step with a Python 3 binding to a HealthShare 2017.2.1 instance . I used a Win10 laptop at hand, but the approach works the same on MacOS and Linux.

4 0
2 1.2K

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.

9 3
1 1.1K

Last week saw the launch of the InterSystems IRIS Data Platform in sunny California.

For the engaging eXPerience Labs (XP-Labs) training sessions, my first customer and favourite department (Learning Services), was working hard assisting and supporting us all behind the scene.

11 3
0 1K

The last time that I created a playground for experimenting with machine learning using Apache Spark and an InterSystems data platform, see Machine Learning with Spark and Caché, I installed and configured everything directly on my laptop: Caché, Python, Apache Spark, Java, some Hadoop libraries, to name a few. It required some effort, but eventually it worked.

8 3
7 1K
Announcement
· Aug 30, 2018
Machine Learning 101 Presentation

View Machine Learning 101 recording at: https://videos.intersystems.com/detail/video/5827774460001/machine-learning-101?autoStart=true&q=machine%20learning.

In addition to our webinar on machine learning (https://community.intersystems.com/post/rescheduled-webinar-its-machine-learning-not-rocket-science-july-31-1100-am-edt), we are pleased to announce a basic introduction to machine learning presentation that provides an overview of the basic algorithms by @Don Woodlock, InterSystems VP of HealthShare Platforms.

4 6
2 954

1. IRIS RAG Demo

IRIS RAG Demo

This demo showcases the powerful synergy between IRIS Vector Search and RAG (Retrieval Augmented Generation), providing a cutting-edge approach to interacting with documents through a conversational interface. Utilizing InterSystems IRIS's newly introduced Vector Search capabilities, this application sets a new standard for retrieving and generating information based on a knowledge base.
The backend, crafted in Python and leveraging the prowess of IRIS and IoP, the LLM model is orca-mini and served by the ollama server.
The frontend is an chatbot written with Streamlit.

Open Exchange app
17 3
2 1K

Keywords: Jupyter Notebook, Tensorflow GPU, Keras, Deep Learning, MLP, and HealthShare

1. Purpose and Objectives

In previous"Part I" we have set up a deep learning demo environment. In this "Part II" we will test what we could do with it.

Many people at my age had started with the classic MLP (Multi-Layer Perceptron) model. It is intuitive hence conceptually easier to start with.

1 2
3 991

Hey Community!

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:

6 22
1 949

Keywords: IRIS, IntegratedML, Machine Learning, Covid-19, Kaggle

Purpose

Recently I noticed a Kaggle dataset for the prediction of whether a Covid-19 patient will be admitted to ICU. It is a spreadsheet of 1925 encounter records of 231 columns of vital signs and observations, with the last column of "ICU" being 1 for Yes or 0 for No. The task is to predict whether a patient will be admitted to ICU based on known data.

Open Exchange app
2 1
1 916
Discussion
· Sep 16, 2022
txt2img
Several models, such as DALL-E, Midjourney, and StableDiffusion, became available recently. All these models generate digital images from natural language descriptions. The most interesting one, in my opinion, is StableDiffusion which is open source - released barely a few weeks ago. There's now an entire community trying to leverage it for various use cases.
2 0
0 928

On this GitHub you can find all the information on how to use a HuggingFace machine learning / AI model on the IRIS Framework using python.

1. iris-huggingface

Usage of Machine Learning models in IRIS using Python; For text-to-text, text-to-image or image-to-image models.

Open Exchange app
6 5
1 753

Artificial intelligence has solved countless human challenges – and medical coding might be next.
As organizations prepare for ICD-11, medical coding is about to become more complicated. Healthcare organizations in the United States already manage 140,000+ codes in ICD-10. With ICD-11, that number will rise.
Some propose artificial intelligence as a solution. AI could aid computer-based medical coding systems, identifying errors, enhancing patient care, and optimizing revenue cycles, among other benefits.

1 0
0 835

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.

27 4
5 747

Hey Community,

We're pleased to invite you to the InterSystems AI+ML Summit 2021, which will be held virtually from January 25 to February 4! Join us for a two-week event that ranges from thought leadership to technical sessions and even 1:1 “Ask the Expert” sessions.

The sessions will be in both German and English. And this summit is free to attend!

See details below:

4 7
0 630

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.

29 4
0 687

This is my introduction to a series of posts explaining how to create an end-to-end Machine Learning system.

Starting with one problem

Our IRIS Development Community has several posts without tags or wrong tagged. As the posts keep growing the organization
of each tag and the experience of any community member browsing the subjects tends to decrease.

First solutions in mind

We can think some usual solutions for this scenario, like:

Open Exchange app
5 11
2 528

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!

Open Exchange app
6 8
1 571

Hey Community,

It's time for the first programming contest of the year, and there's a surprise so read on! Please welcome:

🏆 InterSystems AI Programming Contest: Vector Search, GenAI, and AI Agents 🏆

Duration: March 17 - April 6, 2025

Prize pool: $12,000 + a chance to be invited to the Global Summit 2025!

8 10
0 527

Hi all. We are going to find duplicates in a dataset using Apache Spark Machine Learning algorithms.

Note: I have done the following on Ubuntu 18.04, Python 3.6.5, Zeppelin 0.8.0, Spark 2.1.1

Introduction

In previous articles we have done the following:

0 0
1 723

Challenges of real-time AI/ML computations

We will start from the examples that we faced as Data Science practice at InterSystems:

  • A “high-load” customer portal is integrated with an online recommendation system. The plan is to reconfigure promo campaigns at the level of the entire retail network (we will assume that instead of a “flat” promo campaign master there will be used a “segment-tactic” matrix). What will happen to the recommender mechanisms? What will happen to data feeds and updates into the recommender mechanisms (the volume of input data having increased 25000 times)? What will happen to recommendation rule generation setup (the need to reduce 1000 times the recommendation rule filtering threshold due to a thousandfold increase of the volume and “assortment” of the rules generated)?
  • An equipment health monitoring system uses “manual” data sample feeds. Now it is connected to a SCADA system that transmits thousands of process parameter readings each second. What will happen to the monitoring system (will it be able to handle equipment health monitoring on a second-by-second basis)? What will happen once the input data receives a new bloc of several hundreds of columns with data sensor readings recently implemented in the SCADA system (will it be necessary, and for how long, to shut down the monitoring system to integrate the new sensor data in the analysis)?
  • A complex of AI/ML mechanisms (recommendation, monitoring, forecasting) depend on each other’s results. How many man-hours will it take every month to adapt those AI/ML mechanisms’ functioning to changes in the input data? What is the overall “delay” in supporting business decision making by the AI/ML mechanisms (the refresh frequency of supporting information against the feed frequency of new input data)?

4 0
1 722
Announcement
· Sep 12, 2019
Python Gateway 0.8 release

I'm happy to announce the latest Python Gateway release.

This is not an InterSystems product, it is community supported open source project.

Download new release from GitHub.

Now for the new features.

Fast transfer. Pass globals, classes and tables from InterSystems IRIS to Python with ease and speed (10x faster than old QueryExecute). Documentation.

Open Exchange app
2 8
0 524

Hi Everyone!

Very soon, almost every product and application will include artificial intelligence (AI).

On the afternoon of Wednesday, October 3, at the Global Summit 2018 in San Antonio we’re pulling together experts from InterSystems and from the front lines of the AI industry to discuss the current and future state-of-the-art for AI solutions.

Learn more about our Post-Summit Symposium: Artificial Intelligence and Machine Learning.

2 4
0 604

​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.

6 3
2 618

What is Distributed Artificial Intelligence (DAI)?

Attempts to find a “bullet-proof” definition have not produced result: it seems like the term is slightly “ahead of time”. Still, we can analyze semantically the term itself – deriving that distributed artificial intelligence is the same AI (see our effort to suggest an “applied” definition) though partitioned across several computers that are not clustered together (neither data-wise, nor via applications, not by providing access to particular computers in principle). I.e., ideally, distributed artificial intelligence should be arranged in such a way that none of the computers participating in that “distribution” have direct access to data nor applications of another computer: the only alternative becomes transmission of data samples and executable scripts via “transparent” messaging. Any deviations from that ideal should lead to an advent of “partially distributed artificial intelligence” – an example being distributed data with a central application server. Or its inverse. One way or the other, we obtain as a result a set of “federated” models (i.e., either models trained each on their own data sources, or each trained by their own algorithms, or “both at once”).

2 0
1 663