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AI and ML: Project Success Often Defined by Data Quality
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
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?
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?
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…
Machine Learning: Lack of Reproducibility Threatens Credibility
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…