AI & Machine Learning

Data Quality Dashboard is an information management tool that visually tracks, analyzes, and displays key performance indicators metrics. They can be customized to meet the specific needs of a business and it shows how much trust you can put in your data.

Machine learning models, especially ones using neural networks, work in mysterious ways. There are hidden layers in deep neural networks that connect themselves in ways that make sense for your training data but have no input from us humans.

Computer vision models need to be trained on vast data sets, and synthetic data—images generated using the same CGI software as big budget movies and games—can train that AI without compromising anyone’s personal information.

A data trust that is set up as a fiduciary for the data providers could make it much easier for firms to safely share data by instituting a new way for governing the collection, processing, access, and utilization of the data.

Getting business employees to understand how Artificial Intelligence (AI) works is often the critical point of success or failure for AI projects that work inside the business process.

Organisations need to clearly understand the risks and evaluate requirements before investing in a Data Science team that is expected to mainly develop data-driven insights. You may be better off investing in a different flavour of Data Science — the one that builds intelligent systems for automated decisions making.

Debugging means finding the source of a problem in your code and resolving it. Debugging your code or resolving errors can be one of the hardest things to do. Follow some simple procedures to determine the source of your problem.

A Data Scientist’s blindness to the data lifecycle can cause their machine learning project to fail. This article shares five recommendations to support advanced analytics, machine learning, and model deployment across all the stages of project planning.

The field of quantum machine learning is still in its infancy, but already some successful applications have been published and it is expected that will provide more opportunity in the future.

Information contained in User-Agent strings can be efficiently represented using low-dimensional embeddings, and then employed in downstream Machine Learning tasks.

The triggerless backdoor can provide new directions in research on adversarial machine learning. Like every other technology that finds its way into the mainstream, machine learning will present its own unique security challenges, and we still have a lot to learn.