How does an AI “learn”? Machine Learning builds models using these three approaches:
– Supervised learning when an algorithm learns from example data and target responses. This data might include numeric values or string labels such as classes or tags. Later, when posed with new examples, ML can predict the correct response.
– Unsupervised learning. ML learns from examples without any associated response. Thus, the algorithm determines the data patterns on its own.
– Reinforcement Learning. ML is trained to make specific decisions from the environment. In this way, the machine captures the best possible knowledge to make accurate decisions.
Then how does AI use that learning? AI applies those models to the business decisions at hand: buy vs. sell, schedule now vs. defer, deplete stores vs. restock, approve or deny, etc. If the AI can match the scenario to an appropriate model then it employs the model to make a recommendation or a decision.
This video is one of an eleven part series on AI in business by consultant Dan Hermes of Lexicon Systems interviewed by Jen Stirrup. This series is part of a larger series called RD Perspectives which includes interviews with Microsoft Regional Directors (RD) about topics of interest. Ms. Stirrup is also a Microsoft Regional Director (RD).