Spreading insights instantaneously, worldwide

Another 2035 scene. You’ve just been picked up by a self-driving car that covers part of your trajectory to the AI conference. It’s raining heavily and while the car picks up speed on the highway, it suddenly has to swerve to avoid a tree branch that has blown onto the road. The vehicles next to and behind your car have to break and there is a short moment of chaos. A near miss. Very rare, but possible. Overnight, the data of the vehicles involved are analyzed and an update is sent to all cars worldwide on how to handle this situation in the future. By then you’ve long reached your destination – unaware of how your journey influenced and even improved the driving behavior of all cars worldwide.

Humans can change their mind, adapt their behavior to new circumstances and new learning. So can intelligent agents such as cars. And because the world of 2035 is tightly interconnected, the new knowledge can be spread to all agents almost simultaneously. So there’s no risk of colliding with a car that’s running on last year’s intelligence.

As was to be expected with such pervasive technology, there are technical and ethical caveats. One is the issue of explainable AI: if a critical system takes a decision, we humans should be able to track down its reasoning, to understand why the system did what it did. Another issue is that machine learning is only as good the data it is fed. Therefore, technologists are continuously on the lookout for biases that may pop up in the behavior of smart systems. Or biases that are added with malicious intent. Examples are recognition or profiling on the basis of ethnicity or gender, or seeing as global what in effect are only local customs or behaviors, or even just temporary, commercial hypes. And last there’s the concern that people should always remain free in their choice to contribute or retract personal data, or to act upon the suggestions of AI systems.

Global but individualized

Of course, in 2035 your shoes and clothes are made to fit to perfection. When you need a new pair, your local shoe factory consults your digital twin, derives all possible parameters and produces a pair of shoes that’s unique in the world – costing no more than you used to pay for the average Joe’s size 11 shoes which always left your left ankle hurting. But there’s more: you just bought and attached a sport’s sensor that’s now breaking in. Give it a few more hours with you, learning the very intimate relation between your blood pressure, heart beat, temperature and many more… and it will have become part of you, a sensor that matches up with no other person in the world but you.

The industry is no longer making a small range of average products. Instead, they are able to make separate, individual products for everyone. Like the good old cobbler used to do. Individual but at the cost and speed of mass-manufacturing. And some products even keep on changing and learning after you bought them. It’s machine learning but no longer trained at the manufacturer’s with labeled input, but on your body with unlabeled data.