Before machine learning (ML) and artificial intelligence (AI) joined the industry-speak, these terms were unknown jargon. Many predicted we will have superintelligent robots today at the first-ever AI conference (Dartmouth conference)y. But like flying cars, the “intelligence” is nowhere to be seen. I like Stanislaw Ulam
‘s take on it.
What is it that you see when you see? You see an object as a key, a man in a car as a passenger, some sheets of paper as a book. It is this word ‘as’ that must be mathematically formalised, on par with connectives “and”, “or”, “implies”, and “not”. …Until you do that, you will not get very far with your AI problem.
But anyway. The terms are here; they’re not going anywhere. So, what do they mean in the modern context? David Robinson proposes a wonderful distinction between the three ideas. Data science produces insights. Machine learning produces predictions. Artificial intelligence produces actions.
Data science involves understanding the data to gain insight. It involves a human decision maker looking at topic like statistical inference, data visualisation and experimental design. Machine learning is a field of prediction: if I know X, what can I say about Y? Finally, artificial intelligence is far less trivial to explain. That is how AlphaGo can defeat the best Go player. It is notoriously hard to understand but it works.
If nothing, jump on for David’s humour.
(Meme for today’s newsletter was cross-sourced from his blog.)