Although there is often lots of hype surrounding Artificial Intelligence (AI), once we strip away the marketing fluff, what is revealed is a rapidly developing technology that is already changing our lives. But to fully appreciate its potential, we need to understand what it is and what it is not!

Defining “intelligence” is tricky, but key attributes include logic, reasoning, conceptualization, self-awareness, learning, emotional knowledge, planning, creativity, abstract thinking, and problem solving. From here we move onto the ideas of self, of sentience, and of being.

Artificial Intelligence is therefore a machine which possesses one or many of these characteristics. However, no matter how you define it, one of AI’s central aspects learning.

For a machine to demonstrate any kind of intelligence it must be able to learn. When most technology companies talk about AI, they are in fact talking about Machine Learning (ML) — the ability for machines to learn from past experiences to change the outcome of future decisions.

Stanford University defines machine learning as “the science of getting computers to act without being explicitly programmed.” The science of getting computers to act without being explicitly programmed In this context, past experiences are datasets of existing examples which can be used as training platforms. These datasets are varied and can be large, depending on the area of application. For example, a machine learning algorithm can be fed a large set of images about dogs, with the goal of teaching the machine to recognize different dog breeds. Read more from…

thumbnail courtesy of