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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

Deep Learning and Artificial Intelligence: Algorithms Will Change How Health Care is Delivered

By Dick Weisinger

Machines are beginning to outperform humans at highly-specific tasks that often take humans years to master.  A Deep Learning algorithm at Stanford, for example, recently was trained to be able to detect pneumonia from x-rays of patients, and the accuracy

Artificial Intelligence Databases: Speeding Training for Machine and Deep Learning

By Dick Weisinger

One of Artificial Intelligence’s biggest roadblocks is data preparation and training.¬† Collecting and cleaning data and then training software on data to recognize patterns and develop some amount of insight takes a lot of time. New techniques to speed up