Updating to a flexible machine
Want a head start at the future labor market? Learn to collaborate with a flexible machine.
Sometimes it feels like the most common interaction I have with my apps and devices is about updating - like our sound system at home for example. All too often when I open my Sonos app, automatically pops up a message stating: “Hi, here is a new version of me, want to update?”. But having done this a few times I know what a hassle it can be (especially if not all of our speakers are connected) and I want to listen to music now, not fiddle with the app.
The same goes for almost all of my connected devices. I don’t want to wait while the device has some internal dialog with the provider. I want to browse apps on my phone, look at the latest photos on Instagram or play the games on my console.
At the same time, most of these updates usually bring something good. They fix issues, add features and sometimes even give me a new user interface. This is good, it reminds me that the companies providing me with these apps and services care about my experience, and constantly work on making the apps, services and devices better (even if not all updates are to my liking, or I fail to notice the changes they made).
But where is this constant improvement of our connected devices heading? My Sonos system, my phone or my PS4 is one thing. But what about tomorrow? How will my connected coffeemaker behave, the connected thermostat in my connected home, my connected car? “Good morning, Rebecka, nice that you want to drive me today. By the way, I have updated during the night and now have this new user interface. Want to see my new functions?”.
But it is not only pushed updates that are going to make our devices and services constantly change. Using machine learning and other AI technologies, machines around us will become increasingly flexible. They will have the potential to learn and evolve depending on the data we provide.
In comparison to the machines that have been emerging since the 19th century, we can see a huge difference. Traditional machines do what they were designed or constructed to do. Spinning threads, transporting goods, making coffee, etc. If you wanted new functionality, you had to physically upgrade the machine or simply build or buy a new one. Nevertheless, the old inflexible machines came with the perk that you only had to learn once how to operate it. Once you knew how, it would not change. Whether the machine was located on the production line or in your kitchen at home.
With the more flexible machines of tomorrow this is no longer the case. Constantly updating, learning and flexible machines will demand that YOU become more flexible too, and learn new ways to collaborate with them. This will be especially clear in the way we collaborate with machines at work.
Already today AI systems support different professions at work, from medical doctors to cyber security experts. But so far, as long as you have a slightly complex job, the machines are only going to take over some (not all!) tasks and activities. However, now part of your new job assignment will be to find the best possible way to work with them. And as they learn new things you also will need to keep up, and find new ways to harvest the best out of any system.
At Consumer & IndustryLab, a research area within Ericsson Research, we just published a report focusing on how AI is going to impact work life and the labor market. The need for constant learning and education, due to flexible machines, is one of the key findings from the report. And we predict that people who excel at collaborating with machines will have a head start at the future labor market.
This is not only a message to people looking for jobs in the near future. It is also an important insight for all employers out there. The most attractive companies or organizations to work for in the future are likely going to be the ones that provide the best development and competence building plans to their employees.
Still, no matter how much training and education I will get, there is one other thing that I hope for in the future. As the machines become increasingly flexible and learn from us, I hope that they learn how to get their updates in a less intrusive manner, making my interaction smoother than today. And as they learn new things, perhaps they can teach me a thing or two, without forcing me to pause my activity? What a symbiotic future.
Discover more insights by checking out the full Ericsson Consumer & IndustryLab Creative Machines report, and also take a look at the “eternal newbie” trend from ConsumerLab’s 10 Hot Consumer trends 2018.
Both Ericsson and Rebecka take learning on the job very seriously. So come this fall, Rebecka is going to start her PhD studies on Responsible Machine Intelligence, to further explore how we should work with Machine Intelligence in the future.