New machine learning architectures continue to attract attention as the race continues to provide the most effective acceleration architectures for the cloud and the edge. The focus isn’t just on hardware, but also the software tools. Semiconductor Engineering brought this topic to us in their article, “ML Focus Shifting Toward Software.”
Will software abstraction eventually will win out over hardware details in determining who the future winners are? Only time will tell.
In health care, artificial intelligence (AI) and machine learning technologies have the potential to transform the status quo by deriving new and important insights from the vast amount of data generated during the delivery of health care every day.
Today, all eyes are on AI and companies are trying to find the best architecture for machine learning accelerators that will work either in the cloud or at the edge. However, most organizations have little visibility and knowledge on how AI systems make the decisions they do, and as a result, how the results are being applied in the various fields that AI and machine learning are being applied. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.
Melody K. Smith
Sponsored by Access Innovations, changing search to found.
