By AI Trends Staff Efforts to further AI transparency and fairness got a boost recently with the naming of Prof. Judea...

(Submitted on 2 Apr 2020) Abstract: Existing question answering systems can only predict answers without explicit reasoning processes, which hinder their...

By John P. Desmond, AI Trends Editor Since the White House issued a “call to action” to AI researchers to help...

(Submitted on 31 Mar 2020) Abstract: A small subset of explainability techniques developed initially for image recognition models has recently been...

Artificial Intelligence (AI) has been finding its way into the financial services industry for some time, and there’s no denying that the technology...

In this code pattern we demonstrate a way to monitor your AI models in an application using Watson OpenScale in IBM Cloud Pak for Data. This will be demonstrated with an example of a Telecomm Call Drop Prediction Model.

In this code pattern we demonstrate how to create a model to predict call drops. With the help of an interactive dashboard, we use a time series model to better understand call drops. As a benefit to telecom providers and their customers, it can be used to identify issues at an earlier stage, allowing more time to take the necessary measures to mitigate problems.

In this Code Pattern, we will use German Credit data to train, create, and deploy a machine learning model using IBM Watson Machine Learning on IBM Cloud Pak for Data. We will create a data mart for this model with Watson OpenScale and configure OpenScale to monitor that deployment, then inject seven days' worth of historical records and measurements for viewing in the OpenScale Insights dashboard.