Computers outpaced human performance in chess a long time ago. In 1997, the computer called Deep Blue beat then-world champion Garry Kasparov in a game, seemingly shutting the door on that matter; however, the story doesn’t end there. What we’ve found in subsequent years is that we’re better than computers at chess, when we work with other computers.
In a series of tests, amateur chess players who played alongside computers were able to beat not only grandmasters (elite chess champions), but also computers, and even grandmasters who used less powerful computers. This hints at incredible possibilities for the future. Not only does this indicate that we will always have a place working alongside computers, but it proves that when we work with computers, we will be better than we’ve ever been before.
What does this mean for the workforce today? It’s commonly agreed upon, when looking at Artificial Intelligence (AI)and the modern workforce, that there are four main types of work:
1. Routine Manual
2. Routine Cognitive
3. Non-routine Manual
4. Non-routine Cognitive
Manual and Cognitive just about speak for themselves, but what about Routine? Routine work is essentially anything that happens repetitively, or in a pattern. For example, factory assembly work is routine: the assembly line requires the same jobs be done in the same section, over and over again.
On the contrary, plumbing is an example of non-routine manual work. It’s finnicky, fiddly, and vastly different from job to job, day to day. This is the major reason that so many factories are becoming automated, or roboticised, while something like plumbing, is not.
Artificial Intelligence is really, really good at understanding and repeating patterns. This is why computers are so good at chess.
Researchers suggest that routine work is the work that is most likely to be automated, and it will be the fastest to be automated, too.
So aside from just listing all the different jobs that fall into each category, how can we know what kind of work needs humans, and not computers? I like to think of it in the opposite way to how we approach AI: when we look at automating work, we ask, what do humans need computers for? Instead, we can ask - what do computers need humans for? The answer, to perhaps over-simplify it: formulating questions.
Computers are great at storing and sorting enormous stores of information. This is why we don’t need to be able to hold as many facts and figures in our heads these days – how many phone numbers have you committed to memory today, compared to even 15 years ago? Computers can do it better, and more conveniently, which frees our mental space for more important, abstract thinking. But, without humans to formulate the questions and set the parameters, no program would know what information to seek out in the first place. For example, the Google search engine might hold an incomprehensible amount of information, but it doesn’t do its job until you search for something.
Only we can decide what it is we’re looking for, and when we find it, we can make use of those findings, in a way that computers cannot. This is non-patterned thinking, and it is the exact intersection of human and computer workers: it’s where we make meaning.
What is making meaning, then? Making meaning is mobilising knowledge. Knowledge is essentially useless, a waste of time and mental space, if it’s immobile. That is to say, if it does nothing for us.
The uses for knowledge, or the places we can take our knowledge, are of course many, and varied, and can wander into the abstract. Knowing what the green light in The Great Gatsby represents, for example, seems immobile at first: it applies to one specific book.
But when we learn about the green light in order to learn about metaphor, and symbols, suddenly that knowledge can go a lot further, a lot wider. That’s because we can make meaning with it.
For example, I’m using it as a metaphor right now: for learning, for making meaning. I’ve mobilised my knowledge about learning, to share it with you, in the hope that you will be able to mobilise it for yourself and your daughters, in whatever ways are most useful.
This need for knowledge that moves will only increase as time goes on, and as technology automates more and more of our work. The key is not to face this upcoming challenge with fear, but with excitement, for the opportunities it may bring. Because that is how we will best position ourselves to work with AI: to be better than we were before.
This week at school
We recently completed our senior school autumn fixtures of basketball, soccer and touch football. Approximately 260 participated this season; participation has increased (five years ago, 185 senior school students played an autumn sport) and our teams are becoming more competitive. Congratulations to the Intermediate Year 9/10 soccer team and to the 9A touch team, both undefeated premiers. We acknowledge students, coaching staff, coordinators and supporters for all of your combined efforts over the season.
• Sarah-Jane Clarke (1991) who was recognised yesterday in the Queen’s Birthday Honours List for her longstanding charity work and contributions to the fashion industry, alongside business partner, Heidi Middleton. Sarah-Jane and Heidi are the founders of fashion label Sass & Bide.
Best wishes to:
• Junior School students for the Andrews Cup Metro Meet for netball on Thursday
• All attending our latest Living Room talk with Karen Young on Thursday evening
• Year 3 students for their Planetarium excursion
• Senior School students and staff for the Interhouse Athletics Carnival at Ambi on Friday