Workplace 2025: Machine learning is about to free millions of minds – here’s how to manage it
One of the many things that make human beings so amazing is our ability to learn.
It means we develop as we go along, so we’re constantly doing more complex and exciting things.
We have always shared our capacity for learning with the rest of the natural world.
But recently we’ve also been sharing our learning ability with machines.
This – the advent of machine learning – is a real turning point in our history.
Machines no longer need to be instructed or controlled by all of us, all the time. This frees people to do other things.
In concrete terms, machine learning actually trains computer systems by feeding them data about things that you want to be automated.
And we have more data than ever thanks to the emerging internet of things. This is one of the big reasons for machine learning’s recent rise.
It can cause difficulty if you are trying to automate something and no data for it exists, however.
What do you do when you don’t have any data for machines to learn from?
And how can you maneuver machine learning through the potential data issues thrown up by GDPR?
These are questions we are still puzzling over.
The true definition of machine learning
Whilst machine learning is becoming a buzzword, there is some confusion over what the term refers to exactly – particularly in relation to artificial intelligence (AI).
The two do overlap, so it’s understandable why people conflate them.
To clarify: AI is a discipline of computer science, which is a huge field.
It revolves around the general concept of machines being able to carry out tasks in an ‘intelligent’ way.
Machine learning is an application of AI that enables machines to learn for themselves without being explicitly programmed for it, based on the data you feed them.
In short: machine learning is a form of AI, but AI is much broader than machine learning.
Some people have a problem with the term ‘artificial intelligence’.
Truthfully I don’t think it’s an absolutely correct term, either.
The certain thing about artificial intelligence is that it is artificial – not that it is intelligent.
It can’t create and imagine in a way that a human could. Not today, or any time soon.
For this reason, some people advocate renaming AI so that it is called augmented intelligence instead.
This would reflect the fact that it is there to serve the need of people who do the human, intelligent part.
It’s an addition to our intelligence that does some of the procedural heavy lifting.
But in computer science terms this debate it practically ancient – it goes back to Alan Turing in 1950, who decided that asking ‘can machines think?’ was a silly question.
Using machine learning in the workplace
Now that we’ve clarified what machine learning is, let’s examine how it will fit into the future workplace.
Great complexity is created by a large workplace environment.
Machines are on all of the time (or most of the time), so keeping the software up and running is a challenge. And it’s expensive.
Automation can cut through this very nicely. It can help to maintain a complex environment at a low cost.
This is potentially the most trivial advantage of machine learning in the workplace.
The more exciting aspects involve human behavior.
If you observe yourself in a workplace environment, you will notice that you behave in a very predictable way.
What we do tends to be the same in terms of usage – we tend to click in the same place, or watch the same part of the screen, or type with the same rhythm.
So if we can use some embedded technology that learns with you, this will make your life easier.
It will learn your preferences to improve your workplace experience without you even noticing.
Finally, machine learning can also inspire creativity.
It takes away repetitive, routine work by doing it for you.
This frees up more human time to be creative, which is a uniquely human ability.
For this reason, I predict the kind of jobs that we can expect to see in the future will be heavily creative.
Workplace is a state of mind
The final point I would like to make touches the distant future of the workplace.
Machine learning will automatically improve the workplace for us as it learns more about it.
So the next generation of workplace systems will not even involve our conscious choice.
We could be looking at the appearance of the deviceless workplace before we know it.
Machine learning could generate an automatic recommendation to connect to a virtual workplace environment with a direct hook–up that’s capable of reading our brain waves.
There are lots of attempts and experiments moving in that direction already.
It might be like having wearables on the inside so that you’ll be constantly, universally connected.
This means we’ll be able to get to the workplace by just thinking about it.
It won’t be an actual place anymore – you’ll be at work when you think that way.
This shouldn’t be frightening – it’s the future.
And we’ll get there through machine learning.