New post

#Artificial Intelligence (AI)

3

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.

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.

2 2
2 392

Hey Community,

Don't miss the latest videos on InterSystems Developers YouTube channel:

⏯ IntegratedML Update from the Field

https://www.youtube.com/embed/2-i2Z7aukSc
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

1 3
0 260

Maternal Risk can be measured from some parameters well known to the medical community. In this way, in order to help the medical community and computerized systems, especially AI, the scientist Yasir Hussein Shakir published a very useful dataset for training ML algorithms in the detection/prediction of Maternal Risk.

Open Exchange app
4 0
1 263

Kidney Disease can be discovered from some parameters well known to the medical community. In this way, in order to help the medical community and computerized systems, especially AI, the scientist Akshay Singh published a very useful dataset for training ML algorithms in the detection/prediction of kidney disease. This publication can be found on the largest and best known data repository for ML, Kaggle at https://www.kaggle.com/datasets/akshayksingh/kidney-disease-dataset.

Open Exchange app
2 0
0 367

Diabetes can be discovered from some parameters well known to the medical community. In this way, in order to help the medical community and computerized systems, especially AI, the National Institute of Diabetes and Digestive and Kidney Diseases published a very useful dataset for training ML algorithms in the detection/prediction of diabetes. This publication can be found on the largest and best known data repository for ML, Kaggle at https://www.kaggle.com/datasets/mathchi/diabetes-data-set.

Open Exchange app
6 1
1 382

Hi Community,

Don't miss the new video on InterSystems Developers YouTube:

⏯ Exploring Machine Learning with R Language Gateway

https://www.youtube.com/embed/GcN2wD-3FnA
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

2 0
0 277

Hey Community,

Enjoy watching the new video on InterSystems Developers YouTube channel:

⏯ Creating Virtual Models with InterSystems IRIS Adaptive Analytics

https://www.youtube.com/embed/E9jBHljk9To
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

1 0
0 220

Hi Community,

Please welcome the new video from #VSummit20:

⏯ Showcasing Health Insight & IntegratedML for Healthcare Applications

https://www.youtube.com/embed/q8lC2a6eFgY
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

1 0
0 276

Hi Community,

We're pleased to invite you to the online meetup with the winners of the InterSystems AI contest!

Date & Time: Friday, July 30, 2021 – 11:00 AM EDT

What awaits you at this Virtual Meetup?

  • Our winners' bios.
  • Short demos on their applications.
  • An open discussion about technologies being used. Q&A. Plans for the next contests.

https://www.youtube.com/embed/27vE9o9U3nM
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

4 2
0 319

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
1 696

Fixing the terminology

A robot is not expected to be either huge or humanoid, or even material (in disagreement with Wikipedia, although the latter softens the initial definition in one paragraph and admits virtual form of a robot). A robot is an automate, from an algorithmic viewpoint, an automate for autonomous (algorithmic) execution of concrete tasks. A light detector that triggers street lights at night is a robot. An email software separating e-mails into “external” and “internal” is also a robot. Artificial intelligence (in an applied and narrow sense, Wikipedia interpreting it differently again) is algorithms for extracting dependencies from data. It will not execute any tasks on its own, for that one would need to implement it as concrete analytic processes (input data, plus models, plus output data, plus process control). The analytic process acting as an “artificial intelligence carrier” can be launched by a human or by a robot. It can be stopped by either of the two as well. And managed by any of them too.

6 0
0 352

Hi Community,

Please welcome the new video on InterSystems Developers YouTube:

⏯ Best Practices for In-Platform AI

https://www.youtube.com/embed/K2xm6LIVA6U
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

1 0
0 159

Hi Developers!

Here're the technology bonuses for the InterSystems IRIS AI contest that will give you extra points in the voting.

IntegratedML usage - 4 points

Use InterSystems IntegratedML in you AI/ML solution. Here is the template that uses it.

Be sure that the IRIS version is not less than 2021. The latest ML images with ZPM are:

intersystemsdc/iris-ml-community:2021.1.0.215.0-zpm
intersystemsdc/irishealth-ml-community:2021.1.0.215.0-zpm

R Gateway and Python gateway usage - 4 points

InterSystems IRIS 2021 release contains two new features - R gateway and Python gateway. Here is the template on how to use the R gateway. Here is a short demo of how to use it.

Embedded Python usage - 4 points

Embedded Python is a very new feature of InterSystems IRIS that gives you the option to use python as a "first-class citizen" in backend business logic development with InterSystems classes. Short demo of Embedded Python.

Embedded python could be used in "on-demand" images that could be delivered via InterSystems Early Access Program (EAP) if you refer to python-interest@intersystems.com.

Here is the template package on how to use Embedded Python deployable with ZPM. Don't forget to change the image to the one you get from the Early Access program.

PMML usage - 4 points

PMML - Predictive Modelling Markup Language - can be used to build AI/ML solutions with InterSystems IRIS. Check with documentation.

There is an example in Open Exchange on how to use PMML.

0 0
0 309

Hi Community,

Please welcome the new video on InterSystems Developers YouTube:

⏯ Machine Learning with IntegratedML & Data Robot

https://www.youtube.com/embed/BRyWD39Sado
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

2 0
0 326

Hi Community,

We're pleased to invite all the developers to the upcoming InterSystems AI Contest Kick-Off Webinar! The topic of this webinar is dedicated to the InterSystems AI programming contest.

During the webinar, we will demo how to load data into IRIS, how to deal with it using ODBC/JDBC and REST, and how to use special AI/ML features of IRIS: IntegratedML, DataRobot, R Gateway, Embedded Python, PMML.

Date & Time: Monday, June 28 — 11:00 AM EDT

Speakers:
🗣 @Aleksandar Kovacevic, InterSystems Sales Engineer
🗣 @Théophile.Thierry, InterSystems Intern
🗣 @Bob Kuszewski, Product Manager - Developer Experience, InterSystems
🗣 @Evgeny Shvarov, InterSystems Developer Ecosystem Manager

https://www.youtube.com/embed/4vC1gp1PeUw
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

4 1
0 361

Hi Community,

Please welcome the new video on InterSystems Developers YouTube:

⏯ Getting Started with IntegratedML

https://www.youtube.com/embed/f7A-wbNkIic
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

2 0
0 406

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.

https://5e18edf067eb59-03854285.castos.com/player/408391
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

1 0
1 198

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
1 647