Can a machine take us by surprise ? This is the central argument of Alan Turing ’ s 1950 ’ s seminal work , “ Computing Machinery and Intelligence .” Can a human take another human by surprise ? This is one of the central ‘ arguments ’ at the center of the performance of magic . What can an investigation on the structure of machine learning teach us about the form of magic and about the experience of surprise ?
Let ’ s begin at the beginning , of sorts . In the 1840 ’ s , Ada Lovelace came to prominence as a mathematician and is thought of as the first computer programmer after creating a series of operatioans for Charles Babbage ’ s “ Computing Engine ,” which is typically thought of as the first automatic digital computer . Lovelace posited that due to humans building and programming such computing machines , it was not possible for them to take us by surprise .
Nearly a century later , famed mathematician Alan Turing argued the opposite . He states that : “ Machines take me by surprise with great frequency . This is largely because I do not do sufficient calculation to decide what to expect them to do , or rather because , although I do a calculation , I do it in a hurried , slipshod fashion , taking risks . Perhaps I say to myself , ‘ I suppose the voltage here ought to be the same as there : anyway , let ’ s assume it is . Naturally I am often wrong , and the result is a surprise for me , for by the time the experiment is done these assumptions have been forgotten . These admissions lay me open to lectures on the subject of my vicious ways , but do not throw any doubt on my credibility when I testify to the surprises I experience .”
If a machine can take a human by surprise , how does this influence our definitions of surprise ? More on this later .
Turing created one of the most famous thought experiments of our age as a way to tease out ideas about if machines could possess ‘ intelligence ’ and , if so , how to distinguish human intelligence from that exhibited by a machine : The Turing Test .
He expanded this to propose that : “ Suppose that we have a person , a machine , and an interrogator . The interrogator is in a room separated from the other person and the machine . The object of the game is for the interrogator to determine which of the other two is the person , and which is the machine . [ This is done via the interrogator asking a series of questions to each anonymous entity .] The object of the machine is to try to cause the interrogator to mistakenly conclude that the machine is the person ; the object of the person is to try to help the interrogator to correctly identify the machine .”
Turing ’ s test was inspired by a Victorian-era parlor game , the Imitation Game , which possessed a similar framework , but was instead composed of a man , woman , and interrogator . Here , it was the job of the interrogator to distinguish the man from the woman based on asking a series of questions to decipher gender as defined by the stereotypes of the time .
As a lifelong magician , I have often wondered as to why certain pieces of magic are more surprising to audiences than others . Why do some effects , such as a cut and restored rope , seem to provide , in lieu of a surprise , a feeling of a rational completion , whereas others provide a borderline level of shock , such as the transformation of a ball into a dove ?
I began deeply thinking about the experience of surprise in 2019 after finding Thomas Griffith ’ s 2014 paper “ Revealing ontological commitments by magic .” Here , he poses that “ Considering the appeal of different magical transformations exposes some systematic asymmetries . For example , it is more interesting to transform a vase into a rose than a rose into a vase . An experiment in which people judged how interesting they found different magic tricks showed that these asymmetries reflect the direction
MAGIC AND SCIENCE
INTERBLEND , AND ONE LEADS TO THE FRINGE OF THE OTHER .”
- SAM H . SHARPE