Michael Levin: belief

Transcript context

…You’ve got to tell me more about this behavior that is observable, that is unrelated to the explicitly stated goal of a particular algorithm. So you looked at a simple algorithm of sorting. Can you explain what was done? Sure. First, just the goal of this study: there are two things that people generally assume. One is that we have a pretty good intuition about what kind of systems are going to have competencies. So from observing biologicals, we’re not terribly surprised when biology does interesting things. Everybody always says, “Well, it’s biology, you know, of course it does all this cool stuff.” And yeah, but do we have these machines? And the whole point of having machines and algorithms and so on is they do exactly what you tell them to do, right? And people feel pretty strongly that that’s a binary distinction, and that’s what we can carve up the world in that way. So I wanted to do two things. I wanted to first of all explore that and hopefully break the assumption that we’re good at seeing this, because I think we’re not. And I think it’s extremely important that we understand very soon that we need to get much better at knowing when to expect these things. And the other thing I wanted to do was to find out, you know, mostly people assume that you need a lot of complexity for this. So when somebody says, “Well, the capabilities of my mind are not properly encompassed by the rules of biochemistry,” everybody’s like, “Yeah, that makes sense.” Where, you know, you’re very complex and okay, your mind does things that you couldn’t… You didn’t see that coming from the rules of biochemistry, right? Like, we know that. So mostly people think that has to do with complexity, and what I would like to find out as part of understanding what kind of interfaces give rise to what kind of ingressions, is it really about complexity? How much complexity do you actually need? Is there some threshold after which this happens? Is it really specific materials? Is it biologicals? Is it something about evolution? Like, what is it about these kinds of things that allows this surprise, right? Allows this idea that we are more than the sum of our parts. And I had a strong intuition that none of those things are actually required, that this is… This kind of magic, so to speak, seeps into pretty much everything. And so to look at that, I wanted also to have an example that had significant shock value. Because the thing with biology is there’s always more mechanism to be discovered, right? Like, there’s infinite depth of what the materials are doing. You know, somebody will always say, “Well, there’s a mechanism for that, you just haven’t found it yet.” So I wanted an example that was simple, transparent, so you could see all the stuff. There was nowhere to hide. I wanted it to be deterministic, because I don’t want it to be something around unpredictability or stochasticity, and I want it to be something familiar to people, minimal. There was nowhere to hide. I wanted it to be deterministic, because I don’t want it to be something around unpredictability or stochasticity, and I want it to be something familiar to people, minimal. And I wanted to use it as a model system for honing our abilities to take a new system and looking at it with fresh eyes, and that’s because these sorting algorithms have been studied for over 60 years. We all think we know what they do and what their properties are. The algorithm itself is just a few lines of code, you know? You can see exactly what’s there, it’s deterministic. So that’s why. That’s why, right? I wanted the most shock value out of a system like that, if we were to find anything, and to use it as an example of taking something minimal and seeing what can be gotten out of it. So I’ll describe two interesting things about it, and then we have lots of other work coming in the next year about even simpler systems. I mean, it’s actually crazy. So the very first thing is this: the standard sorting… so let’s say bubble sort, right? And all these sorting algorithms, you know, what you’re starting out with is an array of jumbled up digits, okay, so integers. It’s an array of mixed up integers, and what the algorithm is designed to do is to eventually arrange them all into order, and what it does, generally, is compare some pieces of that array and based on which one is larger than which, it swaps them around. And you can imagine that if you just keep doing that and you just keep comparing and swapping, then eventually you can get all the digits in the same order. So, the first thing I decided to do, and this is the work of my student Kaining Zhang and then Adam Goldstein on this paper, this goes back to our original discussion about putting a barrier between it and its goals. And the first thing I said, “Okay, how do we put a barrier in?” Well, how about this? The traditional algorithm assumes that the hardware is working correctly. So if you have a seven and then a five, and you tell them to swap, the lines that swap the five and the seven, and then you go on, you never check. Did it swap? Because you assume that it’s reliable hardware, okay? So what we decided to do was to break one of the digits so that it doesn’t move. When you tell it to move, it doesn’t move. We don’t change the algorithm. That’s really key. We do not put anything new in the algorithm that says, “What do you do if the damn thing didn’t move?” Okay? Just run it exactly the same way. What happens? Turns out, something very interesting happens. It still works, so it still sorts it, but it eventually sorts it by moving all the stuff around the broken number, okay? And that makes sense, but here’s something interesting. Suppose we, suppose we plot, at any given moment, we plot the degree of sortedness of the string as a function of time.…

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