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Machine Learning and SLAs: Staying on Track by Predicting Usage Trends

By Dick Weisinger

Machine Learning (ML) algorithms spot patterns and trends from past data. Typically, the more data that can be fed into the algorithm, the better the prediction. One example application is how ML can be used to assist IT in more

AI and ML: Project Success Often Defined by Data Quality

By Dick Weisinger

Garbage in — Garbage out. Machine Learning (ML) and Artificial Intelligence (AI) algorithms often work by looking for patterns that occur across huge volumes of data, but dirty or poor data sets can throw a ringer into AI projects. Nathaniel

Machine Learning: Speeding up ML with Quantum Computers

By Dick Weisinger

Quantum Machine Learning is the use of Quantum Computers to do Machine Learning.  The Machine Learning techniques applied often are “classical” or do not significantly differ from standard Machine Learning, although the algorithms may be implemented to be optimized for

AI and Machine Learning Chip Sets: Is the Battle Against FPGAs and ASICs one that GPUs Ultimately Cannot Win?

By Dick Weisinger

GPUs have sped the development of machine learning and artificial intelligence in recent years.  GPUs are bandwidth optimized to be able to do large amounts of matrix multiplication and convolution.  CPUs are highly programmable and not as specialized in what

Artificial Intelligence and Machine Learning: What’s the Difference?

By Dick Weisinger

Artificial Intelligence is the imitation of intelligent human behavior by a machine or software algorithm.  “Applied AI” is specific to a particular task like managing finances. “General AI” is less specific and describes a system that can mimic or perform