The advancement of computer technology has allowed for many sci-tech miracles to occur in the past 70 years, and yet it still seems as if we’ve hit a plateau. As I’ve explained in the post on Yuli’s Law, this is a fallacy— the only reason why an illusion of stagnation appears is because computing power is too weak to accomplish the goals of long-time challenges. That, or we have already accomplished said goals a long time ago.
The perfect example of this can be seen with personal computing devices, including PCs, laptops, smartphones— and calculators.
The necessary computing power to run a decent college-ready calculator has long been achieved, and miniaturization has allowed calculators to be sold for pennies. There is no major quantum leap between calculators and early computer programs.
Calculating the trajectory of a rocket requires far less computing power than some might think, and this is because of the task required: guiding an object using simple algorithms. A second grader could conceivably create a program that guides a bottle rocket in a particular direction.
This is still a step up from purely mechanical systems that give the illusion of programming, but there are obvious limits.
I’ll explain these limits by using a particular example, an example that is the focus of this post: a domestic robot. Particularly, a Roomba.
An analog domestic robot has no digital programming, so it is beholden to its mechanics. If it is designed to move in a particular direction, it will never move in another direction. In essence, it’s exactly like a wind-up toy.
I will wind up this robot and set it off to clean my floors. Thirty seconds later, it makes a left turn. After it makes this left turn, it will move for twenty seconds before making another left turn. And so on and so forth until it returns to its original spot or runs out of energy.
There are many problems with this. For one, if the Roomba runs into an obstacle, it will not move around it. It will make no attempt to avoid it a second time through. It only moves along a preset path, a path you can perfectly predict the moment you set it off. There is a way to get around this— by adding sensors. Little triggers that will force a turn early if it hits an object.
Let’s bring in a digitally programmed Roomba, something akin to a robot you could have gotten in 2005. Despite having a digital computer for a brain, it seems to act absolutely no different from the mechanical Roomba. It still gets around by bumping into things. Even though the mechanical Roomba could have been created by someone in Ancient Greece, yours doesn’t seem any more impressive on a practical level.
Thus, the robot seems to be more novel than practical. And that’s the perception of Roombas today— cat taxis that clean your floor as a bonus rather than a legitimate domestic robot.
Yet this is no longer a fair perception as the creators of the Roomba, iRobot, have added much-needed intelligence to their machines. This has only been possible thanks to increases in computing power allowing for the proper algorithms to run in real-time.
For example, a 2017-era Roomba 980 can actually “see” in advance when it’s about to run into something and avoid it. It can also remember where it’s been, recognize certain objects, among other things (though Neato’s been able to do this for a long time). Much more impressive, though still not quite what we’re looking for.
What’s going on? Why are robots so weak in an age of reusable space rockets, terabyte smartphones, and popular drone ownership?
We need that last big push. We need computers to be able to understand 3D space.
Imagine a Roomba 2000 from the year 2025. It’s connected to the Cloud and it utilizes the latest in artificial intelligence in order to do a better job than any of its predecessors. I set it down, and the first thing it begins doing is mapping out my home. It recognizes any obstacle as well as any stain— that means if it detects dog poop, it’ll either avoid it or switch to a different suction to pick it up. Once it has mapped my house, it is able to get a good feel for where things are and should be. Of course, I could also send it a picture of another room, and it will still be able to get a feel for what it will need to do even if it’s never roamed around inside before.
The same thing applies to other domestic robots such as robotic lawn mowers— you’d rather have a lawn mower that knows when to stop cutting, whether that means because it’s moving over a new terrain or because it’s approaching your child’s Slip n’ Slide. Without the ability to comprehend 3D space or remember where it’s been or where it needs to go, it’ll be stuck operating within a pre-set invisible fence.
Over all of this, there’s the promise of bipedal and wheeled humanoid robots working in the home. After all, homes are designed around the needs of humans, so it makes sense to design tools modeled after humans. But the same rules apply— no comprehension of 3D space, no dice.
In fact, a universal utility robot like a future model of Atlas or ASIMO will require greater advancements than specialized utility robots like a Roomba or Neato. They must be capable of utilizing tools, including tools they may never have used before. They must be capable of depth perception— a robot that makes the motions of mopping a floor is only useful when you make sure the floor isn’t too closer or far away, but a robot that genuinely knows how to mop is universally useful. They must be capable of understanding natural language so you can give them orders. They must be flexible, in that they can come across new and unknown situations and react to them accordingly. A mechanical robot would come across a small obstacle, fall over, and continue moving its legs. A proper universal utility robot will avoid the obstacle entirely, or at least pick itself up and know to avoid the obstacle and things like it. These are all amazingly difficult problems to overcome at our current technological level.
All these things and more require further improvements in computing power. Improvements were are, indeed, still seeing.