If we are sharing how we
apply a technology but with different definitions and understandings of what it
actually is, we are not effectively communicating or collaborating. In
actuality, we create more problems going forward.
Artificial intelligence
applies to compute systems designed to perform tasks usually reserved for human
intelligence using logic, if-then rules, and decision trees. Artificial
Intelligence recognizes patterns from vast amounts of quality data providing
insights, predicting outcomes, and making complex decisions.
Machine learning development
is a subset of AI that utilizes advanced statistical techniques to enable
computing systems to improve at tasks with experience over time. Chatbots like
Amazon’s Alexa and Apple’s Siri improve every year thanks to constant use by
consumers coupled with the machine learning that takes place in the background.
Deep learning is a subset of
machine learning that uses advanced algorithms to enable an AI system to train
itself to perform tasks by exposing multilayered neural networks to vast amounts
of data. It then uses what it learns to recognize new patterns contained in the
data. We can say Learning can be human-supervised learning, unsupervised
learning, and/or reinforcement learning like Google used with DeepMind to learn
how to beat humans at the game Go.
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