How are AI, machine learning and deep learning different?
We are witnessing just the beginning of the artificial intelligence (AI) era.
The computer program AlphaGo defeated the world’s top player in the complex Chinese board game of Go for the last time in May 2017. The program had run out of human competition. Instead, its developers designed AlphaGo Zero to simply play against itself without the aid of any historical game data. AlphaGo Zero taught itself how to beat all versions of AlphaGo in 40 days.
People have been playing Go for millennia. And yet all the human wisdom accrued during those countless hours of competition across the continents and throughout history turned out to be no rival to an AI program with 40 days to itself.
And AI’s footprint is not limited to board games. Its imprint can be seen in countless industries and professions, from finance, to medicine, to accounting. JPMorgan’s COIN program performed 360,000 hours of finance-related work in a few seconds. An AI program at the University of Nottingham can now predict strokes and heart attacks more accurately than doctors.
The threat artificial intelligence poses to white-collar jobs is obvious. But before embarking on an elaborate discussion about whether and how AI will put human investment managers out of business, we first need to define what AI, machine learning, and deep learning are all about.
At a basic level, AI is a branch of computer science that, to paraphrase Bill Gates’s mission when starting Microsoft Research, seeks to build computers that can see, hear, and understand humans.
Alan Turing’s work to make thinking machines, summarised in “Computer Machinery and Intelligence,” was a major milestone in AI’s history. In that 1950 paper, Turing asked, “Can machines communicate in natural language in a manner indistinguishable from that of a human being?” This is the essence of the famous Turing Test, which has become a key benchmark for generations of AI researchers in evaluating the power of their programmes.
For our purposes, the term AI applies to programmes that simulate human cognitive abilities as well as those that process and apply the information captured. Natural language processing (NLP) and speech and image recognition applications are examples of AI. NLP seeks to understand written language texts.
