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.
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.
We’re looking for Python developers to participate in our Embedded Python Early Access Program! If you (or someone you know) are a Python developer and are interested, please contact us via the email address below.
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.
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”).
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!
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.
Keywords: COVID-19, Medical Imaging, Deep Learning, PACS Viewer, and HealthShare.
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.
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.
This is the third post of a series explaining how to create an end-to-end Machine Learning system.
Training a Machine Learning Model
When you work with machine learning is common to hear this work: training. Do you what training mean in a ML Pipeline?
Training could mean all the development process of a machine learning model OR the specific point in all development process
that uses training data and results in a machine learning model.
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: