Kassidy Kelley

The AI black box problem leads to bias and ethical concerns. Allowing for transparency, glass box testing is a reliable method of developing explainable AI.

Using a machine learning algorithms cheat sheet can help enterprises determine which algorithms to use to address specific use cases.

For the millions of people who are hard of hearing, speech-to-text advancements have improved their ability to complete daily tasks -- but the tech still has a long way to go.

The future of gameplay is reliant on the usage and perfection of Emotional AI and its ability to create and emulate realistic and human relationships.

Existing employees are in the best position to suggest process improvements through automation, and executives need to utilize the employee experience to drive AI strategy.

Automating the processes behind machine learning can take a business from zero to AI fast. Take a look inside the rise of autoML and the enterprises that are ripe to gain business value from using automated machine learning tools.

AutoML is the latest vendor trend promising to revolutionize enterprise AI implementations. How can enterprises choose which autoML platform fits their needs best? Experts say analyzing the initial problem, diving into the tools offered and evaluating your data science team is a start.

Retailers are chasing data-driven process automation in hopes of boosting sales and streamlining margins. But how do you decide which processes to automate or vendor to implement?

The GDPR grace period has ended. Fines are slowly being reported and Google was hit with the first multimillion-dollar penalty. With the tight restriction on data, how will GDPR and AI growth and development work together? Two experts sound off on the complications of GDPR and AI implementation.