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Deep Learning: Are GPUs the Only Way?

By Dick Weisinger

Deep Learning has enabled impressive advances in areas like healthcare, finance, machine vision, and self-driving cars. But to be successful, the algorithms need massive amounts of data and CPU cycles. Most Deep Learning projects today rely on specialized GPU processors

Data Privacy: Creating a Global Norm that Treats Data Privacy as a Human Right

By Dick Weisinger

Businesses and organizations have a responsibility to act as custodians of the data that they receive from their customers and members. Their task is to ensure that personal data is protected. Increasingly people are viewing the protection of personal information

Artificial Intelligence: Neuroevolution Points to More General Paths for Artificial Intelligence

By Dick Weisinger

The recent wave of artificial intelligence (AI) and deep learning has been powered by teaching machines to recognize patterns. The more data, the more power an algorithm has.  More data makes it possible for the AI algorithm to derive deep

Machine Learning: Lack of Reproducibility Threatens Credibility

By Dick Weisinger

Reproducibility  and repeatability form the foundation of scientific research. Science works best when researchers have enough information and understanding of the parameters of research that has been done previously so they can reproduce those results and work to build on

Deep Reinforced Learning: Making AI More Accessible by Bootstrapping

By Dick Weisinger

Machine Learning and Deep Learning have become popular tools in the arsenal of Artificial Intelligence (AI), but while AI has generated a lot of interest, one problem is that AI techniques are complex and not enough people are properly trained