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…

In many cases, for an enterprise to build its digital business technology platform, it must modernize its traditional data and analytics architecture. A modern data and analytics platform should be built on a services-based principles and architecture.…

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:…

Of late, a buzzword in marketing and analytics circles is CDP or Customer Data Platform. Chief Marketing Officers now have this new weapon in their arsenal to serve customers in an even better, faster and more granular manner. 



A CDP is a “data unifying software”. Adding it on top of your martech stack helps manage your…

Summary:  McKinsey says platform companies will represent 30% of global business revenue by next year (2020).  In Part 1 of this article we started to lay out some important lessons learned and examples for you to follow.  Here are the rest.

McKinsey says platform companies will represent 30% of global…

Summary:  McKinsey says platform companies will represent 30% of global business revenue by next year (2020).  Here are some lessons and examples to help mature companies evaluate where they can create AI/ML-enabled platforms to remain competitive.  This is a long topic so this will be Part 1 of 2.

As data…

Summary:  A new business model strategy based around intermediary platforms powered by AI/ML is promising the most direct path to fastest growth, profitability, and competitive success.  Adopting this new approach requires a deep change in mindset and is quite different from just adopting AI/ML to optimize your current operations.

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Summary:  The shortage of data scientists is driving a growing number of developers to fully Automated Predictive Analytic platforms.  Some of these offer true One-Click Data-In-Model-Out capability, playing to Citizen Data Scientists with limited or no data science expertise.  Who are these players and what does it mean for the profession of data science?

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A business intelligence platform is used by healthcare organizations to build helpful healthcare applications that assist them in ensuring the provision of quality healthcare to patients. BI platforms help control healthcare costs and provide several benefits to healthcare organizations such as analysis capability, providing information on delivery, and integration. 

Healthcare BI platforms offer a very useful function called financial analytics. The availability of the financial…

Years ago, when I made a switch from procedural to object oriented programming, concepts such as function overloading fascinated me. The simplicity a single function changing and behaving based on the types of values passed to it spoke volumes to its simplicity and elegance. Being a bit nostalgic I am going to say “those were the good old days!”.  Today, the word Analytics is certainly an overloaded term in the data world!  It is used to define work done by data scientists to…