Machine learning (ML) is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" with data, without being explicitly programmed.
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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See how InterSystems IRIS Adaptive Analytics can be used to aggregate and query billions of records from a virtual cube, applying machine learning and analytics to that data.
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Keywords: COVID-19, Medical Imaging, Deep Learning, PACS Viewer, and HealthShare.
Purpose
We are all gripped by this unprecedented Covid-19 pandemic. While supporting our customers in battlefields by any means, we also observed various fighting fronts against Covid-19 by leveraging today's AI powers.
Following up the previous part, it's time to take advantages for IntegratedML VALIDATION MODEL statement, to provide information in order to monitor your ML models. You can watch it in action here
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In this first installment of InterSystems IRIS 2020.1 Tech Talks, we put the spotlight on data science, machine learning (ML), and analytics. InterSystems IntegratedMLTM brings automated machine learning to SQL developers. We'll show you how this technology supports feature engineering and chooses the most appropriate ML model for your data, all from the comfort of a SQL interface. We'll also talk about what's new in our open analytics offerings. Finally, we'll share some big news about InterSystems Reports, our "pixel-perfect" reporting option. See how you can now generate beautiful reports and export to PDF, Excel, or HTML.
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This series of articles would cover Python Gateway for InterSystems Data Platforms. Execute Python code and more from InterSystems IRIS. This project brings you the power of Python right into your InterSystems IRIS environment:
Interested in predictive modeling? This exercise shows how IntegratedML® works, and you can get practice creating a model with sample patient data. Try it out and share what you think!
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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.
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In this article, I am trying to identify the multiple areas to develop the features we can able to do using python and machine learning.
Each hospital is every moment trying to improve its quality of service and efficiency using technology and services.
The healthcare sector is one of the very big and vast areas of service options available and python is one of the best technology for doing machine learning.
In every hospital, humans will come with some feelings, if this feeling will understand using technology is make a chance to provide better service.
Good news! One more upcoming in-person event is nearby.
We're pleased to invite you to join "J On The Beach", an international rendezvous for developers and DevOps around Big Data technologies.A fun conference to learn and share the latest experiences, tips & tricks related to Big Data technologies, and, the most important part, it’s On The Beach!
🗓 April 27-29, 2022
📍Málaga, Spain
This year, InterSystems is a Gold Sponsor of the JOTB.
We're more than happy to invite you and your colleagues to our InterSystems booth for a personal conversation. As always, there will be some surprises on it... 😁
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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”).
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