Coined in 1956 by Dartmouth Assistant Professor John McCarthy, ‘Artificial Intelligence (AI) is a general term that refers to hardware or software that exhibits behavior that appears intelligent.
Basic ‘AI’ has existed for decades, via rules-based programs that deliver rudimentary displays of ‘intelligence’ in specific contexts. Progress, however, has been limited — because algorithms to tackle many real-world problems are too complex for people to program by hand.
Machine learning (ML) is a subset of AI. All machine learning is AI, but not all AI is machine learning. Interest in ‘AI’ today reflects enthusiasm for machine learning, where advances are rapid and significant.
The goal of most machine learning is to develop a prediction engine for a particular use case. Machine learning algorithms learn through training.
Some of the most effective machine learning algorithms beyond deep learning include:
Deep learning involves using an artificial ‘neural network’ — a collection of ‘neurons’ connected together.
An artificial neuron has one or more inputs. It performs a mathematical calculation based on these to deliver an output. The input-output function can vary. A neuron may be:
Example applications of AI include the following; there are many more.
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