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…

Rationalizing the effort of our intensive care staff by predicting patient condition indicators in real time is a challenge because of an impressive number of externally imposed and internally unavoidable restrictions. Patient data privacy requirements, variety of types and lack of standardization within one type of equipment, limited availability of “pure” examples of this or that patient condition patterns – just to name a few complexity factors.

Computational approaches to model…

Author: Sergey Lukyanchikov

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…

ML Toolkit for InterSystems IRIS, besides Python/R/Julia, allows orchestrating cloud-based advanced analytics services, such as Microsoft Azure Data Factory and Machine Learning:…

IRIS ML Toolkit adds the power of IntegratedML to further extend convergent scenario coverage into the area of automated feature and model type/parameter selection. The previous "manual" pipelines now collaborate within the same analytic process with "auto" pipelines that are based on automation frameworks, such as H2O.…

Choose your format to work on the same analytical process among graphical composer (IRIS BPL), notebook (Jupyter) or IDE (IRIS Studio/Atelier):

This platform-centric approach to developing analytical tools and solutions aiming to maximize the advantages of combining multiple analytic toolsets (AI/ML, BI, SQL, Quantum, IoT, MapReduce, NLP,…