The example that comes to mind here is tumble-and-travel chemotaxis.
For those not familiar with it, it’s how e coli (and many other bacteria) get to places where the chemical environment favors them. From an algorythmic perspective, it senses the current pleasantness of the chemical environment (more food, less poison) as a scalar, compares that pleasantness to its general happiness level (also a scalar), is more likely to go straight if the former is higher and more likely to tumble if the latter is, and updates its happiness in the direction of the pleasantness. The overall effect is that it goes straight when things are getting better and randomly turns when they’re getting worse, which does a passable job of going toward food and away from danger. The environment consists of its location and an entire map, but its memory is a single scalar.
I’m not sure what you’re saying about systems like this. That they exist? Of course. This one is well studied. That they outperform model-based systems? Certainly if you include the energy cost of building and running a more complex system. Probably not if you don’t, though I can’t prove it.
Or are you claiming that this sort of system can solve arbitrarily complex problems? Maybe, but you’ll need to do more than assert that.
Or are you claiming that this sort of system can solve arbitrarily complex problems?
You mean, that I have a solution to strong AI? No, not at all. Just the italicised claim, in opposition to the idea that anything that succeeds at funnelling reality through a desired path must be using a model.
The example that comes to mind here is tumble-and-travel chemotaxis.
For those not familiar with it, it’s how e coli (and many other bacteria) get to places where the chemical environment favors them. From an algorythmic perspective, it senses the current pleasantness of the chemical environment (more food, less poison) as a scalar, compares that pleasantness to its general happiness level (also a scalar), is more likely to go straight if the former is higher and more likely to tumble if the latter is, and updates its happiness in the direction of the pleasantness. The overall effect is that it goes straight when things are getting better and randomly turns when they’re getting worse, which does a passable job of going toward food and away from danger. The environment consists of its location and an entire map, but its memory is a single scalar.
I’m not sure what you’re saying about systems like this. That they exist? Of course. This one is well studied. That they outperform model-based systems? Certainly if you include the energy cost of building and running a more complex system. Probably not if you don’t, though I can’t prove it.
Or are you claiming that this sort of system can solve arbitrarily complex problems? Maybe, but you’ll need to do more than assert that.
You mean, that I have a solution to strong AI? No, not at all. Just the italicised claim, in opposition to the idea that anything that succeeds at funnelling reality through a desired path must be using a model.