A New Definition of Order

The first time you see the University of Texas – Austin’s AIM traffic management simulator in action, you can’t believe it would work. It shows the intersection of two 12 lane, heavily trafficked roads. There are no traffic lights, no stop signs, none of the traffic control systems we’re familiar with. Yet, traffic zips through with an efficiency that’s astounding. It appears to be total chaos, but no cars have to wait more than a few seconds to get through the intersection and there’s nary a collision in site. Not even a minor fender bender.

Oh, one more thing. The model depends on there being no humans to screw things up. All the vehicles are driverless. In fact, if just one of the vehicles had a human behind the wheel, the whole system would slow dramatically. The probability of an accident would also soar.

The thing about the simulation is that there is no order – or, at least – there is no order that is apparent to the human eye. The programmers at the U of T seem to recognize this with a tongue in cheek nod to our need for rationality. This particular video clip is called “insanity.” There are other simulation videos available at the project’s website, including ones where humans drive cars at intersections controlled by stoplights. These seem much saner and controlled. They’re also much less efficient. And likely more dangerous. No simulation that includes a human factor comes even close to matching the efficiency of the 100% autonomous option.

The AIM simulation is complex, but it isn’t complicated. It’s actually quite simple. As cars approach the intersection, they signal to a central “manager” if they want to turn or go straight ahead. The manager predicts whether the vehicles path will intersect another vehicle’s predicted path. If it does, it delays the vehicle slightly until the path is clear. That’s it.

The complexity comes in trying to coordinate hundreds of these paths at any given moment. The advantage the automated solution has is that it is in communication with all the vehicles. What appears chaotic to us is actually highly connected and coordinated. It’s fluid and organic. It has a lot in common with things like beehives, ant colonies and even the rhythms of our own bodies. It may not be orderly in our rational sense, but it is natural.

Humans don’t deal very well with complexity. We can’t keep track of more than a dozen or so variables at any one time. We categorize and “chunk” data into easily managed sets that don’t overwhelm our working memory. We always try to simplify things down by imposing order. We use heuristics when things get too complex. We make gut calls and guesses. Most of the time, it works pretty well, but this system gets bogged down quickly. If we pulled the family SUV into the intersection shown in the AIM simulation, we’d probably jam on the brakes and have a minor mental meltdown as driverless cars zipped by us.

Artificial intelligence, on the other hand, loves complexity. It can juggle amounts of disparate data that humans could never dream of managing. This is not to say that computers are more powerful than humans. It’s just that they’re better at different things. It’s referred to as Moravec’s Paradox: It’s relatively easy to program a computer to do what a human finds hard, but it’s really difficult to get it to do what humans find easy. Tracking the trajectories and coordinating the flow of hundreds of autonomous cars would fall into the first category. Understanding emotions would fall into the second category.

This matters because, increasingly, technology is creating a world that is more dynamic, fluid and organic. Order, from our human perspective, will yield to efficiency. And the fact is that – in data rich environments – machines will be much better at this than humans.   Just like our perspectives on driving, our notions of order and efficiency will have to change.


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