Precision is not pedantry; clarity is not cynicism
Photo credit: Vadim Bogulov
The Nomenclature Committee of the Association of Computing Machinery might not sound very exciting. However, it got to decide the words that we use to describe computers, and words matter: naming is a powerful act. When the computing pioneer, Grace Hopper, chaired the committee in the 1950s, she steered them to avoid ‘words of the magic brain class’, and to use terms such as ‘storage’ instead of ‘memory’, and ‘processing’ instead of ‘thinking’.
This direction was needed in the 1950s. Computers were new, and to most people they seemed like magic. Even though the computation they performed was complex - since the early days, computers had been used for hard mathematical problems such as code breaking and navigation - they did far less than the computers we have today. Today, it would seem strange to describe a machine that was limited to mathematical operations (no speech, no graphics, no sound) as thinking. Yet, in those early days, it was astonishing that computers could compute at all: that they could do work previously reserved for the human brain and mind. It is unsurprising that they were described with breathless excitement.
Since the 1950s, computing power has increased exponentially, and that exponential growth in power has changed the world. Multiple waves of innovation have followed the path from shocking, to surprising, to useful, to ubiquitous (usually passing through disappointment on the way). What hasn’t changed though, is the language used to describe the frontier.
Most seasoned computing professionals have been through this cycle many times. They may be the people at the back of the room when the sales person is standing at the front, flanked by slides and videos, casually using phrases such as ‘revolutionary’, ‘next generation, ‘game changing’ - and, in the current wave of AI innovation, many words of the magic brain class: ‘thinking’, ‘reasoning’ and ‘learning’. Some AI sales pitches boil down to something like, ‘my magic brain is better than their magic brain’. This is when the person at the back of the room rolls their eyes, and starts to ask awkward questions about performance, reliability, integration, cost, stability and so on - and kills the mood of excitement. Don’t they get it? others are thinking. This is the start of something big!
It is easy to mistake the questions of those seasoned professionals for pedantry and cynicism, just as it might seem that Grace Hopper was sucking the excitement out of the early days of computing by insisting on precise terms. Seeking details and certainty feels like trying to see how the magic trick is done, or looking for the man behind the curtain. Can’t they just enjoy the show?
In my experience, computing professionals rarely seek precision and clarity because of pedantry and cynicism. Rather, they are driven by practicality and curiosity. They are frustrated by high level, magic brain sales pitches, because those sales pitches don’t tell them how things work - or, rather, they present them in terms which makes their own brains scream, it doesn’t work that way! They want to know how things work, not so that they can dismiss or disregard them, but so that they can figure out how to use them effectively. After all, when the sales pitch is over, the case is made and the orders are signed, they are the ones that are going to have to build the solution.
Grace Hopper aimed to bring clarity to the field of computing so that it would be accessible to everybody. She worked on high level languages such as COBOL in order to bring computers out of the lab, and out of the field of mathematics, and into the realm of everyday language. The computing professionals who seek clarity and precision today on topics such as AI are trying to do the same: to turn mystifying magic boxes into tools which we understand and can put to work.
If you spot a person at the back of the sales pitch who is huffing and puffing and rolling their eyes, or asking awkward questions, don’t dismiss them as a cynic or a sceptic. Try following their chain of curiosity. It might reveal how the magic trick is done - and teach us how to do the magic.
