A Short History of Machine Learning -- Every Manager Should Read

A Short History of Machine Learning -- Every Manager Should Read

It’s all well and good to ask if androids dream of electric sheep, but science fact has evolved to a point where it’s beginning to coincide with science fiction. No, we don’t have autonomous androids struggling with existential crises — yet — but we are getting ever closer to what people tend to call “artificial intelligence.”

A Short History of Machine Learning -- Every Manager Should Read

Machine Learning is a sub-set of artificial intelligence where computer algorithms are used to autonomously learn from data and information. In machine learning computers don’t have to be explicitly programmed but can change and improve their algorithms by themselves.

Today, machine learning algorithms enable computers to communicate with humans, autonomously drive cars, write and publish sport match reports, and find terrorist suspects. I firmly believe machine learning will severely impact most industries and the jobs within them, which is why every manager should have at least some grasp of what machine learning is and how it is evolving.

In this post I offer a quick trip through time to examine the origins of machine learning as well as the most recent milestones.

1950 — Alan Turing creates the “Turing Test” to determine if a computer has real intelligence. To pass the test, a computer must be able to fool a human into believing it is also human.

1952 — Arthur Samuel wrote the first computer learning programme, The programme was the game of checkers, and the IBM computer improved at the game the more it played, studying which moves made up winning strategies and incorporating those moves into its programme.

1957 — Frank Rosenblatt designed the first neural network for computers (the perceptron), which simulate the thought processes of the human brain.

1967 — The “nearest neighbour” algorithm was written, allowing computers to begin using very basic pattern recognition. This could be used to map a route for travelling salesmen, starting at a random city but ensuring they visit all cities during a short tour.

1979 — Students at Stanford University invent the “Stanford Cart” which can navigate obstacles in a room on its own.

1981 — Gerald Dejong introduces the concept of Explanation Based Learning (EBL), in which a computer analyses training data and creates a general rule it can follow by discarding unimportant data.

1985 — Terry Sejnowski invents NetTalk, which learns to pronounce words the same way a baby does.

1990s — Work on machine learning shifts from a knowledge-driven approach to a data-driven approach. Scientists begin creating programmes  for computers to analyse large amounts of data and draw conclusions — or “learn” — from the results.

1997 — IBM’s Deep Blue beats the world champion at chess.

2006 — Geoffrey Hinton coins the term “deep learning” to explain new algorithms that let computers “see” and distinguish objects and text in images and videos.

2010 — The Microsoft Kinect can track 20 human features at a rate of 30 times per second, allowing people to interact with the computer via movements and gestures.

2011 — IBM’s Watson beats its human competitors at Jeopardy.

2011 — Google Brain is developed, and its deep neural network can learn to discover and categorise objects much the way a cat does.

2012 – Google’s X Lab develops a machine learning algorithm that is able to autonomously browse YouTube videos to identify the videos that contain cats.

2014 – Facebook develops DeepFace, a software algorithm that is able to recognise or verify individuals on photos to the same level as humans can.

2015 – Amazon launches its own machine learning platform.

2015 – Microsoft creates the Distributed Machine Learning Toolkit, which enables the efficient distribution of machine learning problems across multiple computers.

2015 – Over 3,000 AI and Robotics researchers, endorsed by Stephen Hawking, Elon Musk and Steve Wozniak (among many others), sign an open letter warning of the danger of autonomous weapons which select and engage targets without human intervention.

2016 – Google’s artificial intelligence algorithm beats a professional player at the Chinese board game Go, which is considered the world’s most complex board game and is many times harder than chess. The AlphaGo algorithm developed by Google DeepMind managed to win five games out of five in the Go competition.

So are we drawing closer to artificial intelligence? Some scientists believe that’s actually the wrong question.

They believe a computer will never “think” in the way that a human brain does, and that comparing the computational analysis and algorithms of a computer to the machinations of the human mind is like comparing apples and oranges.

Regardless, computers’ abilities to see, understand, and interact with the world around them is growing at a remarkable rate. And as the quantities of data we produce continue to grow exponentially, so will our computers’ ability to process and analyse — and learn from — that data grow and expand.


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Written by

Bernard Marr

Bernard Marr is a bestselling author, keynote speaker, and advisor to companies and governments. He has worked with and advised many of the world's best-known organisations. LinkedIn has recently ranked Bernard as one of the top 10 Business Influencers in the world (in fact, No 5 - just behind Bill Gates and Richard Branson). He writes on the topics of intelligent business performance for various publications including Forbes, HuffPost, and LinkedIn Pulse. His blogs and SlideShare presentation have millions of readers.

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