I heard the word ‘machine’ used as a verb the other day.
The scene was yet another panel discussion about the value of sport at yet another marketing conference.
This is not an exact reproduction of the conversation, but it captures the gist:
“They can’t sell the Six Nations title. What’s it worth, do you think?”
“Dunno, but I’m sure we could machine it.”
I love ‘machine it’.
It’s such a revealing phrase, one that says much more about the speaker than it does the subject matter.
The dash for simple answers
Since sport started pulling in the serious brand cash, sponsors and rights holders have danced around the age-old question of value.
Finding the definitive answer has been the holy grail since Mark McCormack and Arnold Palmer shook hands.
As in every business sphere, some of this work has been automated: sponsors and rights holders are using machines of varying quality and complexity, from simple Excel dashboards to data-devouring supercomputers.
But the real power of ‘machine it’ lies in the promise of something more. It is about our fundamental quest for simple answers to complex questions.
‘Machine it’ allows us to glide across questions of cause and effect by devolving the donkey work of marketing attribution to an elegant equation, providing a beguilingly simple number that even a chief exec can understand.
“Alexa, what’s the Six Nations worth?”
“The answer is 79”
“No problem. Is there anything else I can help you with today?”
Think of all that intellectual energy devoted to doggedly pursuing a single measurement framework for sports sponsorship, only for the robots to get there first, with a contraption that sounds like the bastard love child of IBM Watson and Lesa Ukman.
The shadow of Frankenstein
In 2011, IBM’s supercomputer beat human contestants on the quiz show Jeopardy, live on American TV.
Since then, Watson has been used as shorthand for the debate about AI, automation and the future of work, a centuries-long meme that has at its centre the concept of an all-knowing machine, a man-made system that attains sentience.
This was the plot of Frankenstein, written by Mary Shelley almost exactly 200 years ago, and versions of the story have driven science fiction since the word ‘robot’ first appeared in Czech literature in the 1920s, meaning ‘labourer’ or ‘serf’.
This backdrop colours how we view the progress of AI and machine learning.
In short, we veer between the two extremes of rabid fear and unlimited expectation.
On the one hand, the imagined potential of AI is that it will solve our problems, both big (world hunger) and small (the value of a WTA title deal).
This utopian view is balanced with the corresponding disappointment that robots still wrestle with the shortcomings of Frankenstein’s mate: the absence of nuanced judgement, creativity, intuition and instinct – AKA the human bit.
This false dichotomy betrays a fundamental misunderstanding of what computers are.
This was best explained on the day after the famous IBM stunt, when the philosopher John Searle wrote a piece in the Wall Street Journal entitled, ‘Watson Doesn’t Know It Won on Jeopardy’:
‘Watson did not understand the questions,’ he wrote, ‘nor its answers, nor that some of its answers were right and some wrong, nor that it was playing a game, nor that it won – because it doesn’t understand anything.’
IBM’s computer was not and could not have been designed to understand, said Searle. ‘Rather, it was designed to simulate understanding, to act as if it understood. It is an evasion to say, as some commentators have put it, that computer understanding is different from human understanding. Literally speaking, there is no such thing as computer understanding. There is only simulation. Computers recognise symbols and are very fast. But symbols are not meanings.’
So the box in the corner is a fast idiot and humans are not about to go out of fashion. It’s probably a sign of the times, but I find this point oddly reassuring.
Richard Gillis is author of The Captain Myth: The Ryder Cup and Sport’s Great Leadership Delusion, published by Bloomsbury in the UK and US.