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Speaker 1: Hey, welcome to Stuff to Blow Your Mind. My name

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Speaker 1: is Robert Lamb and I'm Joe McCormick, and it's Saturday.

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Speaker 1: What does that mean, Robert, What means it's time to

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Speaker 1: open the vault. It looks like it's already open, so

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Speaker 1: let's get in there. There's a lot of P in here.

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Speaker 1: There's also a lot of MP in here. Perhaps you

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Speaker 1: can explain this to everybody. Smooth transition, Robert, very smooth.

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Speaker 1: I like it. Uh. This is the episode on the

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Speaker 1: P versus MP problem, a classic fascinating vexing problem in

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Speaker 1: logic and math and computer science and uh. And in

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Speaker 1: this episode we tackle the question of what it actually

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Speaker 1: means to solve a problem. This originally aired on April twelve,

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Speaker 1: and we're bringing it back for you now, so we

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Speaker 1: hope you enjoy our exploration of the P versus MP problem.

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Speaker 1: Welcome to Stuff to Blow your Mind from how Stuff

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Speaker 1: Works dot com. Hey, welcome to Sceptable in your Mind.

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Speaker 1: My name is Robert Lamb and I'm Joe McCormick. And

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Speaker 1: today we're gonna be taking a look at the issue

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Speaker 1: in computer science. Uh. Funny enough, I'd say that's not

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Speaker 1: one of the science, as we dip into very frequently

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Speaker 1: on this podcast. Yes, and I mean, really, we should

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Speaker 1: probably just remind everyone to stick with us, trust us

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Speaker 1: on this one. Uh, don't be scared off by the

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Speaker 1: computer science thing. Don't be scared off by the P

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Speaker 1: versus MP thing. It's it's it's all gonna make a

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Speaker 1: type of sense at the end, hopefully. But I'm wondering

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Speaker 1: if maybe we should dip into computer science more often

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Speaker 1: because or at least wherever we can find a way

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Speaker 1: to make the contents of it reasonably concrete, because, let's

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Speaker 1: be honest, as we've discovered in researching this episode, it's

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Speaker 1: very abstract, very difficult, and a lot of times hard

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Speaker 1: to come up with ways of explaining that makes sense

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Speaker 1: just just talking about it without visual aids or watching

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Speaker 1: programs Exit Cute as an example. Yeah, it's definitely one

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Speaker 1: of those topics that it's a swimming pool of a

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Speaker 1: topic in which there is no gradual deepening from the

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Speaker 1: kitty area to the deep end. It's just shallow, and

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Speaker 1: at times it feels too shallow and then you're immediately

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Speaker 1: out of your depth. Yeah, but if you're thinking of

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Speaker 1: computer science, really does that fit with the show hold

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Speaker 1: on for a second, because I think it does. Um.

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Speaker 1: Computer science to me is a fascinating subject. Uh, and

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Speaker 1: it's not just limited to how computers work. So my

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Speaker 1: advice is, when you think about the idea of computer science,

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Speaker 1: forget the computer sitting in front of you. That's not

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Speaker 1: all it is. Computer science is really something more akin

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Speaker 1: to the philosophy of logic, understanding the the underlying sorcery

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Speaker 1: of how logic and math work in the universe we inhabit,

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Speaker 1: and especially the science of how problems are solved. And

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Speaker 1: certainly when you start bringing math into the equation here

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Speaker 1: as well, I mean you're talking we're talking about the

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Speaker 1: essentially the very fabric of the universe. We're talking about

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Speaker 1: either the fabric of the universe, either the way the

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Speaker 1: universe works, or this perfect creation that humans have come

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Speaker 1: up with that so accurately describes how the universe works,

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Speaker 1: and that is pretty mind blowing territory. Well, either way

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Speaker 1: you look at it, there is something mystical about about

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Speaker 1: the math that we walk on every day that you know,

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Speaker 1: that makes up the fabric and the logic math, the

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Speaker 1: math beneath our feet. You know, if you go with

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Speaker 1: the max Tech mark idea of the mathematical universe. Some

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Speaker 1: people don't like this idea because, like I don't understand

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Speaker 1: what that means, but at least it's a very intriguing idea.

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Speaker 1: I think his ideas that the underlying basis of all

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Speaker 1: reality not just as described by math, but is math,

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Speaker 1: and the universe is a mathematical object. But we're gonna

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Speaker 1: get back to this idea of problem solving because today

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Speaker 1: we want to focus on algorithms and on the inherent

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Speaker 1: logic of problem solving in our universe, with some attention

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Speaker 1: to a special example of one really interesting outstanding problem

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Speaker 1: in computer science, and that's the P versus n P issue.

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Speaker 1: If you've never heard of this before, don't worry. We'll

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Speaker 1: explain what the terms mean in in a simplified manner.

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Speaker 1: And uh I at this point also do want to

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Speaker 1: give a shout out to our listener Jim in New Jersey,

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Speaker 1: who has been encouraging me over email to tackle this

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Speaker 1: issue for a while despite all the challenges, and has

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Speaker 1: also sent some really helpful, uh really helpful guides and

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Speaker 1: explainers on some stuff. He he learned about this when

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Speaker 1: he was in graduate school. Yeah, indeed, and UH and

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Speaker 1: I think this is great too, because this episode is

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Speaker 1: coming on the heels of first of all, the Wicked

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Speaker 1: Problems episode that came out a few weeks ago, as

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Speaker 1: well as the more recent Cargo Cults episode which in

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Speaker 1: which we discuss some outside context problems a little bit

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Speaker 1: as well. So it's it's perfectly fitting that we would

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Speaker 1: discuss another problem. Well, what does it mean inherently to

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Speaker 1: solve a problem? If you get into the theory of

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Speaker 1: problem solving? What what what does this process look like? Well,

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Speaker 1: when it comes down just to the basics open and

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Speaker 1: this also kind of gets into the whole Wicked Problems

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Speaker 1: area of like, what's what's missing when you don't have, um,

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Speaker 1: you know, everything you need to solve a problem for

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Speaker 1: for a real problem, you have to be able to,

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Speaker 1: of course, do to find what the problem is. A

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Speaker 1: lot of a lot of attempts fail right there. Yeah,

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Speaker 1: you've got to You've got to be able to say

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Speaker 1: this is the thing you know and then you and

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Speaker 1: then you have to be able to measure your success

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Speaker 1: and check the solution. So you essentially have to be

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Speaker 1: able to say, hey, this is wrong because of X.

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Speaker 1: And then if you then figure out what x is

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Speaker 1: and see if the equation balances out. Um, it sounds

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Speaker 1: pretty simple. But like I said, as we discussed in

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Speaker 1: Wicked problems, that's uh, it can be very difficult to do,

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Speaker 1: especially in you know, the very complex social situations when

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Speaker 1: you're dealing with with certainly some of the larger problems

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Speaker 1: that we're going to talk about here, or even if

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Speaker 1: you want to go into the simplest level, well, I mean,

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Speaker 1: depending on what you would call simple. In fact, what

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Speaker 1: we're going to be getting into today is directly referred

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Speaker 1: to as complexity theory. Uh. So maybe it's not so simple,

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Speaker 1: but at least simple in terms of not involving uh

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Speaker 1: phenomena in the real world, but just math, just math

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Speaker 1: and logic and and true versus untrue and UH and algorithms.

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Speaker 1: So I think it's time to pull back the curtain

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Speaker 1: a little bit and reveal some of the deep weirdness

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Speaker 1: of the nature of algorithms and problem solving in our universe.

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Speaker 1: So let's look at this P versus MP problem. This

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Speaker 1: is something that comes from two of the great minds

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Speaker 1: of the twentieth century, Kurt Girdle and John von Neuman,

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Speaker 1: and in nineteen fifty six, Kurt Girdle, who's a mathematician

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Speaker 1: and logician, wrote a letter to John von Neuman which

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Speaker 1: kicked off this quest to solve one of the biggest

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Speaker 1: questions in computer science, the P versus n P issue. Now,

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Speaker 1: who were these guys, but both were titans of the

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Speaker 1: twentieth century in terms of math, logic, and computers. Well.

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Speaker 1: Godal is probably most famous for his first incompleteness theorem,

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Speaker 1: and this states that any adequate um axiomatizeable theory that means,

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Speaker 1: a theory that's based on self evident but unprovable proofs,

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Speaker 1: is incomplete or inconsistent. Yeah. Girdle's whole incompleteness theorem set. Yeah,

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Speaker 1: A couple of his incompleteness theorems essentially amount to the

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Speaker 1: idea that any mathematical system that makes sense will have

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Speaker 1: some statements that are true yet impossible to prove. It's

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Speaker 1: sort of the idea that you can't ever know everything

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Speaker 1: about a self consistent system. Yeah, and the the The

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Speaker 1: implication here, according to theoretical physicist and mathematician Freeman Dyson,

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Speaker 1: who has is also quite a giant in the field,

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Speaker 1: is that mathematics is inexhaustible, that no matter how many

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Speaker 1: problems we solve, will inevitably encounter more unsolvable problems within

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Speaker 1: the existing rules. I take comfort in that measure of futility. Yeah,

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Speaker 1: but there's also John von Neuman, the recipient of the letter.

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Speaker 1: And von Neuman. I don't know what you've heard about him,

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Speaker 1: but I'd say he's often considered one of the most

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Speaker 1: intelligent people who ever lived that we know about at least,

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Speaker 1: and so maybe we call him a mathematician and a physicist,

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Speaker 1: but he made contributions to numerous fields. He was a

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Speaker 1: modern Da Vinci kind of you know, a polymath, and

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Speaker 1: and that includes computer science, for example, the von Neuman

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Speaker 1: architecture in the history of computer design, which is basically

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Speaker 1: it's a way of controlling the interaction between processing operations

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Speaker 1: the CPU and the memory of a computer. And this

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Speaker 1: letter in nineteen fifty six from Girdle to von Neumann

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Speaker 1: started this process of looking into the question of whether

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Speaker 1: P does or does not equal in P. Now, like

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Speaker 1: I said, we're about to explain what all the terms

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Speaker 1: here mean. But I do want to note at the

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Speaker 1: outset of this explanation that you know, on this show

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Speaker 1: we always try to do our best to present our

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Speaker 1: subjects accurately, but then at the same time be understandable

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Speaker 1: to the average person. And this, this P versus in

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Speaker 1: P issue in complexity theory is probably the most difficult

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Speaker 1: and abstract subject I've ever tried to cover on a podcast.

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Speaker 1: So we'll have to do our best to explain the

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Speaker 1: issue and its implications without losing you in asphyxiating clouds

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Speaker 1: of abstraction. Yeah, I mean, basically, the House of Works

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Speaker 1: UH mission overall is to demystify your science and UH

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Speaker 1: topics like this can be a bit difficult because you

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Speaker 1: don't want to through the explanation just mystify it even

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Speaker 1: more for the average listener exactly right. So this is

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Speaker 1: necessarily going to involve a lot of simplified versions of principles.

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Speaker 1: We won't be able to go down uh and explore

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Speaker 1: all of the complex details behind these principles, but we

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Speaker 1: hope that you computer scientists and mathematicians out there will

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Speaker 1: not be too scandalized or think we're doing violence to

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Speaker 1: your subject. Uh. Anyway, here we go. So we we

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Speaker 1: got to start with the concept of algorithms. What what

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Speaker 1: is an algorithm? Well, I'd say an algorithm is a

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Speaker 1: self contained list of instructions to solve a problem. You've

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Speaker 1: got a goal, and then you make us step by

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Speaker 1: step list of things to do that gets you to

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Speaker 1: the goal. A common example within a computer program would

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Speaker 1: be a subroutine designed to sort a list of things.

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Speaker 1: That's an algorithm. Yeah, and you know, algorithms are something

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Speaker 1: we encounter on a just on a daily basis, especially online.

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Speaker 1: I mean Facebook, Google. Both of these depend on ever

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Speaker 1: changing algorithms to decide what you see and don't see

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Speaker 1: on your feeds and on your search results. Yeah, and

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Speaker 1: I think that's a great example of how complex algorithms

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Speaker 1: can get. You've got the simple sorting algorithm on one hand,

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Speaker 1: and then you've got the stuff that decides whether you

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Speaker 1: only see political articles you agree with or whether you

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Speaker 1: sometimes see stuff that's going to make you mad. So,

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Speaker 1: when you're designing algorithms in in a computer science arena,

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Speaker 1: or really to solve any problem, but we're mostly gonna

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Speaker 1: be talking about computer programs, you compare how much time

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Speaker 1: it takes to solve a problem with an algorithm, given

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Speaker 1: the scope of a problem. So this is usually expressed

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Speaker 1: in terms of inputs versus time. So I want to

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Speaker 1: give a quick example with sorting. Like I said, you say,

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Speaker 1: you're given a spreadsheet that includes a list of all

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Speaker 1: the James Bond movies that exist currently in a random order,

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Speaker 1: and you've got to write a computer program that sorts

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Speaker 1: all of those lists of James Bond movie titles into

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Speaker 1: a list in the order they came out. How would

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Speaker 1: you do that? Now, there are a lot of ways

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Speaker 1: you actually could approach the problem, and that they don't

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Speaker 1: all take the same amount of time. Some are much

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Speaker 1: more efficient than others. Here's one example. You could create

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Speaker 1: an algorithm that goes like this. Step one, rearrange the

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Speaker 1: entire list at random. Step two, check each movie in

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Speaker 1: the list to see if it came out before the

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Speaker 1: next movie in the list. If the answer is yes,

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Speaker 1: all the way down the line, then the list is

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Speaker 1: sorted correctly and you're done. If not, start over and

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Speaker 1: rearrange it entirely randomly. Now, given enough time and a

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Speaker 1: small enough data set, this algorithm will if eventually finished

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Speaker 1: by blind luck. It is just brute force burning through

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Speaker 1: computer resources wastefully in order to eventually solve the problem

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Speaker 1: by blind block. But there are also much more efficient

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Speaker 1: ways you could go about it. For example, you could

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Speaker 1: go down the list comparing each movie to the next,

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Speaker 1: and if the second movie came out before the first,

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Speaker 1: you switch their order on the list and then go

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Speaker 1: on like that. Uh, and then you do that until

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Speaker 1: the list is sorted. But some problems are inherently a

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Speaker 1: lot harder than others, and there aren't any algorithmic shortcuts

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Speaker 1: like that that we know about. We don't know of

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Speaker 1: any easy way to solve them. The only thing we

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Speaker 1: know how to do is do that stupid brute force

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Speaker 1: method where you just wastefully burned through computer resources until

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Speaker 1: it's solved by time and force. And in fact, I'd

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Speaker 1: like to make a comparison here in the you know,

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Speaker 1: the efficient algorithm versus brute force methods, to what you

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Speaker 1: might see in animals in the wild using intelligence to

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Speaker 1: solve a problem. So like, if you're hunting another animal,

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Speaker 1: you could use a brute force method of just running

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Speaker 1: after the animal until it is tired or until your

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Speaker 1: muscles have allowed you to catch it, and then killing

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Speaker 1: it with the strength of your muscles. That's sort of

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Speaker 1: the brute force method. Or you could set a trap,

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Speaker 1: or you could build a weapon that these are shortcuts

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Speaker 1: that make the process of hunting a lot more efficient.

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Speaker 1: Now here's where we get to our main terms in

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Speaker 1: this discussion, P and n P. P is going to

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Speaker 1: stand for polynomial time and n P stands for nondeterministic

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Speaker 1: polynomial time. You don't really need to remember that for

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Speaker 1: the purpose of this discussion, because we're gonna make it

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Speaker 1: a lot simpler. Yeah, I mean, this is one of

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Speaker 1: the problems with the topic, is that like just the

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Speaker 1: word just this the the the basic idea here of

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Speaker 1: P and MP there, it's so dry and unrelatable. But

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Speaker 1: allow us to explain. Yeah, okay, So the real difference

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Speaker 1: has to do with um processes of solving problems on

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Speaker 1: a determinus dick Turing machine, which is equivalent to the

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Speaker 1: kind of computer you'd be using right now, you know,

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Speaker 1: any device you have, versus a hypothetical nondeterministic machine, which

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Speaker 1: in theory you could say works by magically guessing the

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Speaker 1: answers to questions and then just checking to see if

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Speaker 1: the magic guess is correct. But, like we said, we

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Speaker 1: don't want to get too bogged down in all those details.

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Speaker 1: So here's the simplified version. P is a set of

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Speaker 1: all problems that can be solved by an algorithm quickly

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Speaker 1: or easily or efficiently. This is the easy. The list

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Speaker 1: of easy problems in computer science. N P, on the

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Speaker 1: other hand, stands for answers that can be easily checked

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Speaker 1: by a computer once you have them, but they can't

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Speaker 1: necessarily be solved easily. Yeah. So it's difficult to place

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Speaker 1: this in a non mathematical context. But one way I

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Speaker 1: like to think about this is in terms of written reviews,

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Speaker 1: for for albums, for for for musical albums. Um, because

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Speaker 1: a good a good review, a good music review is

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Speaker 1: is difficult to write, uh, and hard to find and

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Speaker 1: hard to find. Yeah, Like in my own experience, you

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Speaker 1: often find I mean, I I write many reviews of

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Speaker 1: stuff of stuff from time to time, and that alone

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Speaker 1: is challenging enough for me. But but yeah, when you

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Speaker 1: even when you're looking out of the major publications, uh, yeah,

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Speaker 1: it's hard to find one that feels just right. It's Uh.

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Speaker 1: The average reader, though, can can swiftly judge to what

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Speaker 1: extent they agree with the author, obviously, to what extent

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Speaker 1: the author is just blowing smoke. We've all read those

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Speaker 1: music reviews where you get the sense that the the

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Speaker 1: author is really using the music as an excuse to

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Speaker 1: sort of write his or her own poetry. Uh, they're

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Speaker 1: on the page as opposed actually describing what the music

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Speaker 1: is like. But but the reader knows. So the reader

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Speaker 1: can can can look at the material and either believe

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Speaker 1: them or you don't. Either you buy into their opinion

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Speaker 1: or you don't. Yeah, so you could. You don't have

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Speaker 1: the algorithm internally to efficiently right this piece of writing yourself,

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Speaker 1: but you know it when you see it exactly. Yeah,

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Speaker 1: it's kind of like pornography in that sens right. You

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Speaker 1: might not be able to define clearly the difference between

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Speaker 1: art and pornography, but you know it when you see it.

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Speaker 1: Once you have that answer certificate there, you can check

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Speaker 1: and by golly, it checks out. And I like that

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Speaker 1: because it also gives a whole new meaning to the

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Speaker 1: P and to the MP and H. Now, so there

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Speaker 1: are a couple of other terms that matter there. There's

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Speaker 1: MP hard, and this means that are problems that are

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Speaker 1: as hard as any other MP problem essentially. And then

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Speaker 1: there's also MP complete, which is a big issue in

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Speaker 1: this arena, and this is problems that are m P

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Speaker 1: and n P hard so you you can check the

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Speaker 1: answer once you have it in in a reasonable amount

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Speaker 1: of time, and their MP hard. Now, the interesting thing

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Speaker 1: about MP complete problems is that it has been proved

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Speaker 1: in the literature that if you have an algorithm that

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Speaker 1: and efficiently solve one MP complete problem, it can be

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Speaker 1: transposed to solve all of them. These problems reduced to

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Speaker 1: each other. So if you if you can solve one

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Speaker 1: MP complete problem in a reasonable amount of time, you

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Speaker 1: have found the master key. And this in the universe

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Speaker 1: kind of shrinks in response to this. So a classic

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Speaker 1: example of an MP problem is the prime factorization problem

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Speaker 1: that we use in encryption on the Internet. Again, we

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Speaker 1: don't want you to get lost too much here, so

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Speaker 1: here's the simple version with smaller numbers than usual. Let's

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Speaker 1: say I just throw out a random number, and let's

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Speaker 1: say it's a number of I don't know what's a

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Speaker 1: good one skulls in a pile. So let's say I

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Speaker 1: give you a number. Let's say there are seven hundred

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Speaker 1: and twenty one thousand, four hundred twenty one skulls in

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Speaker 1: a pile. It is now I tell you this number

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Speaker 1: is the product of two prime numbers of skulls in

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Speaker 1: a pile, but I don't tell you what they are. Now,

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Speaker 1: how could you figure out what those two prime numbers

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Speaker 1: of skulls in a pile are? For a computer with

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Speaker 1: numbers this small, this wouldn't be all that big a deal.

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Speaker 1: But we're we're gonna have to extrapolate too much bigger numbers.

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Speaker 1: But for you, this would be really annoying to figure out, right,

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Speaker 1: because there's no simple efficient way to do it. You'd

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Speaker 1: pretty much have to get out a huge list of

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Speaker 1: all the prime numbers between zero and seven thousand, four

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Speaker 1: hundred one and start trying multiplying them together to see

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Speaker 1: if they give you the right answer. Yeah, and in

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Speaker 1: this situation, I imagine it's like the opening scenes of Terminator,

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Speaker 1: and I'm probably already pretty distracted by the Pyramids of

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Speaker 1: Bone and the Hunter Killer, the Hunter Killers exactly, I'm

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Speaker 1: not gonna have time for all this prime number nuns. Now,

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Speaker 1: since we've solved p equals in p at this point,

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Speaker 1: they don't have rubber skin anymore. They figured out how

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Speaker 1: to how to get through the problem to make the

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Speaker 1: bio or suits. So yeah, you're you're in a real

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Speaker 1: rush here. But anyway, back to the problem. Yeah, there's

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Speaker 1: just no fast, simple, easy way to solve this. And

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Speaker 1: if you use numbers large enough, this type of problem

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Speaker 1: is excruciatingly slow, even for computers to conquer by brute force.

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Speaker 1: But let's say I told you the two prime numbers

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Speaker 1: are seven hundred and fifty seven and nine hundred and

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Speaker 1: fifty three piles skulls in a pile. It would be

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Speaker 1: trivially easy for you to check and see if that's

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Speaker 1: the correct solution. You just have to multiply them and

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Speaker 1: see if you get the right answer, and that takes

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Speaker 1: almost no time at all. And in fact, I'd really

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Speaker 1: only need to tell you one of the numbers because

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Speaker 1: you already know what they're supposed to multiply too, so

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00:19:43,400 --> 00:19:46,199
Speaker 1: you could just divide that by the one number. So

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Speaker 1: here's an example. This is a problem that if you're

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Speaker 1: going to try to solve it starting with no information,

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Speaker 1: it's just going to take you ages. It's going to

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Speaker 1: be impossible. But if you already know a selected answer

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Speaker 1: to test, you can I can see if that answer

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Speaker 1: is right. Yeah, I mean this brings to mind. Uh,

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Speaker 1: it's a bit like trying to crack a four number

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Speaker 1: code on a simple combination lock, right, And I'm talking

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Speaker 1: about a human doing this, not a computer. So it

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Speaker 1: would take me as a human quite a while to

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Speaker 1: test out all ten thousand possible solutions, but no time

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Speaker 1: at all to check a solution that someone else had

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Speaker 1: provided me. So you know, I'd be there all day

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Speaker 1: just putting in each one of those ten thousand solutions.

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Speaker 1: But but I can easily put in you know, uh,

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Speaker 1: thirty three sixty six and see if that is correct.

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Speaker 1: You found a really interesting request for help on this subject,

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Speaker 1: and you, oh, yeah, yeah, there was because I wanted

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Speaker 1: to check to make sure that my math was right

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Speaker 1: on how many possible combinations. I was pretty sure it

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Speaker 1: was the ten by ten by ten by ten thing.

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Speaker 1: But I I did one of those searchers to just

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Speaker 1: see people asking math questions online, and I found one where, um,

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Speaker 1: this user apparently had a combination lock or it maybe

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Speaker 1: it was like a contract actor's box, you know, like

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Speaker 1: at a house we have a real estate agent. Yeah,

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Speaker 1: and she says it's like a thirty dollar box. So

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00:21:06,440 --> 00:21:08,480
Speaker 1: she didn't want to just have it, you know, cut

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Speaker 1: into and ruin it in order to get the keys out.

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00:21:12,040 --> 00:21:14,159
Speaker 1: She wanted to, but she didn't know what the combo was,

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Speaker 1: so she wanted to just enter all the possible combinations

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Speaker 1: to get it. And here's the here's the caveat though

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Speaker 1: she knows that no number was used, uh more than once,

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Speaker 1: and that cut it down significantly, just just more than

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Speaker 1: five thousand. Yeah, just was like five thousand forty or something. Yeah.

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Speaker 1: And and the person who supply the answer on this forum,

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Speaker 1: they included a list of all of them for her convenience.

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00:21:39,320 --> 00:21:41,720
Speaker 1: So um, I and I don't know how that came out.

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00:21:41,760 --> 00:21:45,200
Speaker 1: I wonder if she then took that list and just painstakingly, uh,

399
00:21:45,560 --> 00:21:47,719
Speaker 1: spent the time to try each one out, or if

400
00:21:47,720 --> 00:21:52,000
Speaker 1: she decided, you know, actually that inputting all those numbers

401
00:21:52,080 --> 00:21:54,280
Speaker 1: is not worth the thirty dollars that I would save

402
00:21:54,359 --> 00:21:57,080
Speaker 1: by keeping the box intact. That sounds like a fun saturday,

403
00:21:57,160 --> 00:21:59,520
Speaker 1: you know, on the porch in front of the box

404
00:21:59,560 --> 00:22:10,600
Speaker 1: with the case a beer. Hopefully it's nice weather. But anyway,

405
00:22:10,840 --> 00:22:14,680
Speaker 1: so yeah, this is problems that can potentially be brutally

406
00:22:14,720 --> 00:22:17,320
Speaker 1: hard to solve, but they're easy to check once you

407
00:22:17,359 --> 00:22:20,840
Speaker 1: have a certificate of the answer. Another classic and often

408
00:22:20,880 --> 00:22:25,800
Speaker 1: cited example of an MP complete problem is the traveling

409
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Speaker 1: salesman problem. Now I think something is exciting because now

410
00:22:29,240 --> 00:22:32,000
Speaker 1: we have something with more of an anthropomorphic name. It

411
00:22:32,040 --> 00:22:34,920
Speaker 1: seems to imply a scenario. It's like the Infinity Hotel. Well,

412
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Speaker 1: I want to change it up and I want to

413
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Speaker 1: call I want to call this the nationwide infection problem.

414
00:22:40,280 --> 00:22:42,600
Speaker 1: So imagine that you are the vanguard of an alien

415
00:22:42,640 --> 00:22:45,679
Speaker 1: species that has come to Earth and you want to

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Speaker 1: land in a country, you say the United States, and

417
00:22:49,200 --> 00:22:53,800
Speaker 1: infect at least one person in every township and municipality

418
00:22:54,160 --> 00:22:58,280
Speaker 1: in this country with one of your larvae. But you've

419
00:22:58,320 --> 00:23:00,600
Speaker 1: got limited time to do it. A k. You know,

420
00:23:00,680 --> 00:23:04,040
Speaker 1: no Dilly dalian around. So what you're looking for is

421
00:23:04,200 --> 00:23:06,600
Speaker 1: a route that you can plan out on your alien

422
00:23:06,680 --> 00:23:09,960
Speaker 1: equivalent of Google Maps directions or or you know, your

423
00:23:09,960 --> 00:23:12,760
Speaker 1: Apple Directions device that will tell you how to go

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00:23:12,800 --> 00:23:16,280
Speaker 1: to every single city and township in the country, only

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00:23:16,359 --> 00:23:21,960
Speaker 1: one time each in the shortest route possible. Okay, that's

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Speaker 1: not easy. If you just had four or five cities,

427
00:23:25,000 --> 00:23:27,120
Speaker 1: this wouldn't be such a big deal for a computer

428
00:23:27,200 --> 00:23:31,679
Speaker 1: to figure out. Once you start adding hundreds or thousands

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00:23:31,760 --> 00:23:34,600
Speaker 1: of cities, how is it going to figure this out?

430
00:23:35,000 --> 00:23:37,439
Speaker 1: The only way we know of is back to brute force.

431
00:23:37,920 --> 00:23:40,520
Speaker 1: It could try one method, so well, you go to

432
00:23:40,680 --> 00:23:43,240
Speaker 1: city A, and then city B, and then city C

433
00:23:43,520 --> 00:23:45,320
Speaker 1: and then D and go all the way around the

434
00:23:45,359 --> 00:23:48,199
Speaker 1: country and see how long that takes. And then it

435
00:23:48,240 --> 00:23:51,679
Speaker 1: could try again with first city B and then A,

436
00:23:52,080 --> 00:23:54,760
Speaker 1: and then the same from there and then so you

437
00:23:54,800 --> 00:23:59,520
Speaker 1: end up getting these exponentially multiplying combinations there. It is

438
00:23:59,560 --> 00:24:02,760
Speaker 1: just going to take massive amounts of time and computing

439
00:24:02,800 --> 00:24:07,280
Speaker 1: power to figure out what is actually the shortest trip. Now,

440
00:24:07,440 --> 00:24:10,160
Speaker 1: you might already see an issue with including this problem

441
00:24:10,160 --> 00:24:12,760
Speaker 1: within in P and actually reade an interesting blog post

442
00:24:12,840 --> 00:24:17,679
Speaker 1: by somebody writing for for IBM about how, under certain

443
00:24:17,720 --> 00:24:21,120
Speaker 1: conditions this problem actually isn't in P depending on how

444
00:24:21,160 --> 00:24:23,959
Speaker 1: you define what you're checking for. Like, if you're just

445
00:24:24,040 --> 00:24:26,560
Speaker 1: looking to check that a given route is a correct

446
00:24:26,600 --> 00:24:30,720
Speaker 1: solution it visits every city only once, uh, then it

447
00:24:30,840 --> 00:24:33,040
Speaker 1: is easy to check. You can check that very quickly.

448
00:24:33,400 --> 00:24:37,880
Speaker 1: This comes down to accurately defining the problem right exactly. Now,

449
00:24:37,920 --> 00:24:40,280
Speaker 1: if you're looking to check that it visits every city

450
00:24:40,320 --> 00:24:43,600
Speaker 1: only once under a certain mileage, that's also easy to check.

451
00:24:43,640 --> 00:24:46,439
Speaker 1: You just see that it visited every city once and

452
00:24:46,440 --> 00:24:48,879
Speaker 1: see how long it took. But if you're looking to

453
00:24:48,960 --> 00:24:51,840
Speaker 1: verify whether a solution is in fact the shortest of

454
00:24:51,880 --> 00:24:55,840
Speaker 1: all possible routes, that's not easy to check because you'd

455
00:24:55,880 --> 00:24:58,560
Speaker 1: still have you'd essentially have to do the entire brute

456
00:24:58,560 --> 00:25:00,800
Speaker 1: force method that way and compare or it to every

457
00:25:00,840 --> 00:25:04,840
Speaker 1: other possibility. All possible routes have to be to have

458
00:25:04,920 --> 00:25:07,320
Speaker 1: to be considered. So if if that's what you're going for,

459
00:25:07,440 --> 00:25:09,800
Speaker 1: it's not hard to solve, easy to check, It's hard

460
00:25:09,840 --> 00:25:12,760
Speaker 1: to solve and hard to check. But here's the big

461
00:25:12,800 --> 00:25:15,359
Speaker 1: problem with the with the P versus n P issue.

462
00:25:15,720 --> 00:25:19,719
Speaker 1: We know that P problems are a subset of MP problems,

463
00:25:20,480 --> 00:25:24,280
Speaker 1: But what if the subset is actually the same as

464
00:25:24,359 --> 00:25:28,639
Speaker 1: the set, Meaning what if all NP problems are actually

465
00:25:28,920 --> 00:25:32,840
Speaker 1: P problems? Meaning what if all problems where we can

466
00:25:32,920 --> 00:25:37,399
Speaker 1: check the answer are actually problems where we can solve

467
00:25:37,440 --> 00:25:40,960
Speaker 1: them efficiently. We just haven't figured out how to solve

468
00:25:41,000 --> 00:25:43,960
Speaker 1: them efficiently yet, Okay, or we haven't developed the here

469
00:25:44,400 --> 00:25:47,600
Speaker 1: the machines they can do it. Yeah, yeah, So is

470
00:25:47,680 --> 00:25:51,439
Speaker 1: that possible? And that's actually probably the single biggest open

471
00:25:51,560 --> 00:25:55,680
Speaker 1: question in computer science today? Is P equal or not

472
00:25:55,960 --> 00:25:58,399
Speaker 1: equal to n P? Are they or are they not

473
00:25:58,600 --> 00:26:04,439
Speaker 1: equivalent sets. Now, the obvious answer is no, right, that

474
00:26:04,800 --> 00:26:07,399
Speaker 1: seems intuitive, That seems that that seems to be the

475
00:26:07,440 --> 00:26:10,640
Speaker 1: answer that that feels most in keeping with our our

476
00:26:10,800 --> 00:26:13,800
Speaker 1: understanding of like the limits of human ability, the limits

477
00:26:13,800 --> 00:26:16,399
Speaker 1: of human knowledge, and just sort of the fabric of

478
00:26:16,400 --> 00:26:19,560
Speaker 1: our universe. Yeah, and so most computer scientists and mathematicians,

479
00:26:19,600 --> 00:26:22,840
Speaker 1: I think agree that the more likely answer to this

480
00:26:22,920 --> 00:26:26,399
Speaker 1: unsolved question is that P does not equal MP. I

481
00:26:26,440 --> 00:26:28,960
Speaker 1: found one pole that was taken. It was more than

482
00:26:28,960 --> 00:26:31,280
Speaker 1: ten years ago. I don't know if things have changed

483
00:26:31,359 --> 00:26:34,320
Speaker 1: much since then. But in two thousand two, the University

484
00:26:34,320 --> 00:26:37,480
Speaker 1: of Maryland computer scientist William I. Gas Arc did a

485
00:26:37,480 --> 00:26:41,119
Speaker 1: poll of colleagues in complexity theory and UH. And he

486
00:26:41,160 --> 00:26:44,080
Speaker 1: found the results. And so he found out of this

487
00:26:44,160 --> 00:26:47,280
Speaker 1: poll of colleagues, sixty one of his colleagues thought that

488
00:26:47,359 --> 00:26:51,960
Speaker 1: P did not equal MP. Nine thought that P did

489
00:26:52,119 --> 00:26:54,760
Speaker 1: equal MP. And some of those that said that said

490
00:26:54,800 --> 00:26:58,359
Speaker 1: they they said that basically just to be contrarian or

491
00:26:58,359 --> 00:27:03,040
Speaker 1: to continue encourage people to research the possibility. UH. And

492
00:27:03,080 --> 00:27:06,880
Speaker 1: then several other colleagues either offered no opinion or offered

493
00:27:07,040 --> 00:27:11,119
Speaker 1: UH sort of complex answers that weren't yes. Or no.

494
00:27:12,280 --> 00:27:14,439
Speaker 1: But so you can obviously see that the opinion that

495
00:27:14,560 --> 00:27:17,600
Speaker 1: P does equal MP, or the prediction that that will

496
00:27:17,640 --> 00:27:22,480
Speaker 1: be what's eventually proved, is the minority. It's not what

497
00:27:22,520 --> 00:27:26,440
Speaker 1: we would tend to think is the more likely possibility. Okay,

498
00:27:26,600 --> 00:27:32,159
Speaker 1: it's the more outsider consideration here. So let's assume for

499
00:27:32,200 --> 00:27:34,360
Speaker 1: a second that that is the case that one day

500
00:27:34,560 --> 00:27:38,679
Speaker 1: some amazing mathematician or computer scientists somebody comes along and

501
00:27:38,720 --> 00:27:41,200
Speaker 1: they figure out a way to prove that P does

502
00:27:41,280 --> 00:27:44,520
Speaker 1: not equal in P. Proof Proofs like this happen in uh,

503
00:27:44,600 --> 00:27:46,520
Speaker 1: in math and computer science all the time. You might

504
00:27:46,520 --> 00:27:48,720
Speaker 1: be wondering, how could that be proved, But people figure

505
00:27:48,720 --> 00:27:53,440
Speaker 1: out ways to demonstrate logically that something is true like this.

506
00:27:53,520 --> 00:27:56,840
Speaker 1: So let's say it's demonstrated that P does not equal MP.

507
00:27:57,400 --> 00:28:01,000
Speaker 1: What are the implications. Well, mostly, I'd say not much changes.

508
00:28:01,880 --> 00:28:04,199
Speaker 1: This is sort of the obvious conclusion. It's the one

509
00:28:04,240 --> 00:28:07,320
Speaker 1: that wouldn't surprise us. In other words, all of our

510
00:28:07,359 --> 00:28:12,200
Speaker 1: brute force problems remain brute force problems. UH. But if

511
00:28:12,240 --> 00:28:14,119
Speaker 1: this is the case, it would still be useful to know.

512
00:28:14,440 --> 00:28:16,880
Speaker 1: It would be useful that people wouldn't start floating into

513
00:28:16,920 --> 00:28:19,359
Speaker 1: the sky. The great old ones wouldn't come back. It

514
00:28:19,400 --> 00:28:22,600
Speaker 1: would just business is usual for most people. Yeah, so

515
00:28:23,200 --> 00:28:25,160
Speaker 1: we we'd have a proof, so people could stop trying

516
00:28:25,200 --> 00:28:27,159
Speaker 1: to solve it, and we'd be able to use the

517
00:28:27,200 --> 00:28:29,479
Speaker 1: fact that P is not equal to MP as an

518
00:28:29,480 --> 00:28:34,040
Speaker 1: assumption for other work in mathematics and computer science. So

519
00:28:34,080 --> 00:28:37,480
Speaker 1: we just move on with our lives basically essentially. But

520
00:28:37,600 --> 00:28:40,880
Speaker 1: here's where things get interesting. What would the implications be

521
00:28:41,120 --> 00:28:46,000
Speaker 1: if P does equal m P. Well, right off the

522
00:28:46,000 --> 00:28:47,640
Speaker 1: top of my head, of of course, what comes to

523
00:28:47,680 --> 00:28:50,120
Speaker 1: mind is the the the use of encryption that we've

524
00:28:50,160 --> 00:28:53,400
Speaker 1: already talked about, Like that's really like our most everyday

525
00:28:53,800 --> 00:28:58,520
Speaker 1: interaction with with the idea of of of P and MP. Yeah,

526
00:28:58,560 --> 00:29:00,600
Speaker 1: I mean, why is it not easy for me to

527
00:29:00,760 --> 00:29:04,000
Speaker 1: get into those photos you have on your phone, whatever

528
00:29:04,040 --> 00:29:07,280
Speaker 1: they are. Well, it's because it's because we use these

529
00:29:07,360 --> 00:29:12,680
Speaker 1: encryption methods. And almost all current encryption methods would be

530
00:29:13,040 --> 00:29:16,800
Speaker 1: subject to UH. They just depend on the fact that

531
00:29:16,840 --> 00:29:20,440
Speaker 1: you don't have, you know, tons of supercomputers and time

532
00:29:21,000 --> 00:29:24,800
Speaker 1: to sit around trying to brute force crack into people's junk.

533
00:29:25,600 --> 00:29:28,520
Speaker 1: But if you were able to reduce those problems to

534
00:29:28,960 --> 00:29:32,480
Speaker 1: UH to essentially easily solvable problems, problems that could be

535
00:29:32,480 --> 00:29:37,680
Speaker 1: solved in regular polynomial time. The P class then suddenly, yeah,

536
00:29:37,760 --> 00:29:40,560
Speaker 1: by by encryption essentially. I mean, we can't know for

537
00:29:40,600 --> 00:29:43,400
Speaker 1: sure exactly how big a deal this, uh, this would

538
00:29:43,400 --> 00:29:46,160
Speaker 1: be in terms of applied sciences and technology, but the

539
00:29:46,280 --> 00:29:50,640
Speaker 1: likely implication, uh seems to be that any job currently

540
00:29:50,720 --> 00:29:54,640
Speaker 1: hindered by the limits of brute force computation would be revolutionized.

541
00:29:54,920 --> 00:29:58,200
Speaker 1: So yeah, there's the there's the prime factorization issue that

542
00:29:58,200 --> 00:30:01,800
Speaker 1: that feeds into encryption. And the informal way of summing

543
00:30:01,800 --> 00:30:03,840
Speaker 1: this up is that you know, if, if if our

544
00:30:03,880 --> 00:30:07,800
Speaker 1: present methods of encryption and data security are like a

545
00:30:07,880 --> 00:30:11,920
Speaker 1: like a plastic diary lock on our information, every hacker

546
00:30:11,960 --> 00:30:14,280
Speaker 1: on Earth might have access to a pair of fourteen

547
00:30:14,280 --> 00:30:17,200
Speaker 1: inch bolt cutters if P equals n P. On the

548
00:30:17,240 --> 00:30:19,240
Speaker 1: other hand, and this would be a positive, it could

549
00:30:19,280 --> 00:30:22,520
Speaker 1: also mean that research projects that rely on brute force

550
00:30:22,640 --> 00:30:26,960
Speaker 1: computation could also potentially see huge leaps forward. For example,

551
00:30:27,000 --> 00:30:29,840
Speaker 1: one one that I saw, I've seen mentioned, and I've

552
00:30:29,840 --> 00:30:33,120
Speaker 1: read about before is protein folding simulation. Have you ever

553
00:30:33,120 --> 00:30:36,320
Speaker 1: read about this? Uh, yeah, a little bit. Um. I

554
00:30:36,360 --> 00:30:40,120
Speaker 1: think I tend to a discussion on on the topic

555
00:30:40,200 --> 00:30:43,320
Speaker 1: a few years back. Yeah, so this research, it involves

556
00:30:43,480 --> 00:30:47,920
Speaker 1: going through permutations of of different ways of folding proteins

557
00:30:48,080 --> 00:30:51,960
Speaker 1: in a computer simulation. And this research could help cure

558
00:30:52,040 --> 00:30:54,880
Speaker 1: diseases and treat medical conditions if we learned the right

559
00:30:54,920 --> 00:30:58,040
Speaker 1: things about the behavior of how protein molecules fold up

560
00:30:58,040 --> 00:31:01,440
Speaker 1: on one another and behave in the body. But simulating

561
00:31:01,480 --> 00:31:05,440
Speaker 1: all of these folding permutations is a brute force computing project.

562
00:31:05,840 --> 00:31:08,840
Speaker 1: So if this actually reduced to a p problem, we

563
00:31:08,920 --> 00:31:12,160
Speaker 1: might be able to hasten research that saves lives, maybe

564
00:31:12,160 --> 00:31:15,720
Speaker 1: cure cancer. Who knows. Okay, so we're already we're looking

565
00:31:15,760 --> 00:31:17,960
Speaker 1: at a world where maybe we have to go back

566
00:31:17,960 --> 00:31:21,880
Speaker 1: to using just normal mail instead of email. But on

567
00:31:21,920 --> 00:31:25,320
Speaker 1: the other hand, maybe we cure cancer. Or if people

568
00:31:25,400 --> 00:31:28,480
Speaker 1: i mean digital security maybe could still be a thing.

569
00:31:28,520 --> 00:31:30,959
Speaker 1: If people just come up with another method, we just

570
00:31:31,040 --> 00:31:35,000
Speaker 1: our current methods would would possibly become obsolete. Yeah, and

571
00:31:35,040 --> 00:31:38,239
Speaker 1: then the new method will be sealed envelope under your

572
00:31:38,280 --> 00:31:41,360
Speaker 1: back or a chest buried in your backyard. Yeah, that's

573
00:31:41,360 --> 00:31:44,320
Speaker 1: where you have to keep all You'd have to physically

574
00:31:44,400 --> 00:31:47,240
Speaker 1: meet up with everybody to trade information with an agree

575
00:31:47,280 --> 00:31:49,960
Speaker 1: on a password in person, you know, that would be interesting,

576
00:31:50,520 --> 00:31:56,200
Speaker 1: like trying to imagine a a digital civilization that suddenly

577
00:31:56,240 --> 00:31:59,600
Speaker 1: has to become a non digital civilization, but wants to

578
00:31:59,680 --> 00:32:03,200
Speaker 1: keep everything uh operating more or less as it did

579
00:32:03,200 --> 00:32:05,520
Speaker 1: when it moves digital. You know, like they still want

580
00:32:05,560 --> 00:32:10,520
Speaker 1: to use tender uh, but they can no longer use

581
00:32:10,640 --> 00:32:13,040
Speaker 1: a true digital version of it. What does that even

582
00:32:13,080 --> 00:32:17,000
Speaker 1: consist of? Oh wow, yeah, that's fascinating. But of course

583
00:32:17,080 --> 00:32:21,320
Speaker 1: the changes wouldn't just be in applied sciences and technology.

584
00:32:21,360 --> 00:32:24,920
Speaker 1: One of the interesting things about this is multiple experts

585
00:32:24,920 --> 00:32:27,920
Speaker 1: have commented that if we live in a P equals

586
00:32:27,960 --> 00:32:32,840
Speaker 1: in P universe, we have been sorely mistaken about what

587
00:32:33,000 --> 00:32:36,840
Speaker 1: reality is like. If this is the universe we inhabit,

588
00:32:36,880 --> 00:32:39,800
Speaker 1: in fact, it is quite different than we thought. And

589
00:32:40,080 --> 00:32:43,680
Speaker 1: one one thing I want to quote is by Scott Aaronson,

590
00:32:43,760 --> 00:32:48,040
Speaker 1: who offers this as quote a physical philosophical argument against

591
00:32:48,160 --> 00:32:51,400
Speaker 1: P equals in P, and he says, quote, if P

592
00:32:51,520 --> 00:32:54,440
Speaker 1: equals in P, then the world would be a profoundly

593
00:32:54,600 --> 00:32:57,840
Speaker 1: different place than we usually assume it to be. There

594
00:32:57,880 --> 00:33:01,959
Speaker 1: would be no special value for creative leaps, no fundamental

595
00:33:02,040 --> 00:33:06,000
Speaker 1: gap between solving a problem and recognizing the solution once

596
00:33:06,040 --> 00:33:10,840
Speaker 1: it's found everyone who could appreciate a symphony would be Mozart,

597
00:33:11,360 --> 00:33:14,560
Speaker 1: everyone who could follow a step by step argument would

598
00:33:14,640 --> 00:33:18,960
Speaker 1: be Gauss. Everyone who could recognize a good investment strategy

599
00:33:19,320 --> 00:33:22,160
Speaker 1: would be Warren Buffett. It's possible to put the point

600
00:33:22,160 --> 00:33:25,360
Speaker 1: in Darwinian terms. If this is the sort of universe

601
00:33:25,400 --> 00:33:29,160
Speaker 1: we inhabited, why wouldn't we already have evolved to take

602
00:33:29,200 --> 00:33:34,640
Speaker 1: advantage of it. That's a really interesting point. But at

603
00:33:34,640 --> 00:33:36,920
Speaker 1: the same time, so he's framing it in terms of it.

604
00:33:37,040 --> 00:33:39,240
Speaker 1: This is one among a list of arguments he gives

605
00:33:39,280 --> 00:33:43,400
Speaker 1: that P probably does not equal to. This seems to

606
00:33:43,400 --> 00:33:47,360
Speaker 1: be very much an argument in favor of their, of

607
00:33:47,400 --> 00:33:50,600
Speaker 1: their being no equality are right. But you could also

608
00:33:50,680 --> 00:33:53,240
Speaker 1: look at that look at it as an interesting comment

609
00:33:53,360 --> 00:33:56,280
Speaker 1: on how different the world would be from how we

610
00:33:56,360 --> 00:33:59,400
Speaker 1: assume it is if this were in fact the case.

611
00:33:59,760 --> 00:34:02,240
Speaker 1: And it's interesting to note that we shouldn't assume that

612
00:34:02,320 --> 00:34:04,480
Speaker 1: just because it doesn't feel like we live in the

613
00:34:04,560 --> 00:34:07,240
Speaker 1: P equals in P world, that P equals MP is

614
00:34:07,320 --> 00:34:11,239
Speaker 1: necessarily false. Our our intuitions about what's possible in the

615
00:34:11,280 --> 00:34:13,880
Speaker 1: math and problem solving space have turned out to be

616
00:34:14,120 --> 00:34:17,960
Speaker 1: very wrong in the past, and sometimes long standing problems

617
00:34:17,960 --> 00:34:21,040
Speaker 1: in math and computer science are solved by uh, solved.

618
00:34:21,040 --> 00:34:25,080
Speaker 1: They're solved or proved in ways that just seem extremely peculiar,

619
00:34:25,280 --> 00:34:28,719
Speaker 1: Yet you can't deny the result. I can't help but

620
00:34:28,800 --> 00:34:32,000
Speaker 1: circle back around to the earlier opinion that we mentioned

621
00:34:32,120 --> 00:34:37,640
Speaker 1: was attributed to Freeman Dyson that mathematics is inexhaustible. If

622
00:34:37,719 --> 00:34:42,919
Speaker 1: P equals MP, then the universe. Is the universe really inexhaustible? Yeah?

623
00:34:43,120 --> 00:34:45,680
Speaker 1: And uh? And if P equals MP, does that mean

624
00:34:45,719 --> 00:34:49,520
Speaker 1: that there is in a sense a universal algorithm out there? Uh?

625
00:34:49,800 --> 00:34:52,239
Speaker 1: I mean there there is a theory of everything within

626
00:34:52,280 --> 00:34:55,920
Speaker 1: our mathematical universes, as mass Max tech Mark argues in

627
00:34:55,960 --> 00:34:58,839
Speaker 1: mathematical universe theory that we mentioned earlier. Because the tech

628
00:34:58,880 --> 00:35:01,120
Speaker 1: Mark even go so far is to predict that a

629
00:35:01,200 --> 00:35:04,959
Speaker 1: mathematical proof for a theory of everything could eventually fit

630
00:35:05,000 --> 00:35:07,359
Speaker 1: on a T shirt. Well, I would kind of like

631
00:35:07,480 --> 00:35:11,439
Speaker 1: to the universe. Yeah, I mean that's an interesting thought

632
00:35:11,520 --> 00:35:14,000
Speaker 1: on its own. And tech Marks theories I I, as

633
00:35:14,040 --> 00:35:16,319
Speaker 1: I think I said earlier in this episode, I find

634
00:35:16,360 --> 00:35:19,319
Speaker 1: them very interesting, even if I'm not qualified enough to

635
00:35:19,400 --> 00:35:22,839
Speaker 1: know whether they're really rigorous physics. I read that book,

636
00:35:23,160 --> 00:35:27,360
Speaker 1: are our mathematical universe, and I found it amazingly stimulating.

637
00:35:27,400 --> 00:35:30,759
Speaker 1: You know, he talks about different levels of of multiverse

638
00:35:30,840 --> 00:35:35,400
Speaker 1: realities and what they each imply. Uh, and he gives

639
00:35:35,480 --> 00:35:37,600
Speaker 1: a very, at least to the lay person, a very

640
00:35:37,640 --> 00:35:42,239
Speaker 1: reasonable sounding explanation of how these are natural conclusions from

641
00:35:42,239 --> 00:35:45,040
Speaker 1: what we know about physics. But I do want to

642
00:35:45,160 --> 00:35:46,879
Speaker 1: use what you said as a sort of jumping off

643
00:35:46,920 --> 00:35:50,560
Speaker 1: point to take a broader view about the algorithmic nature

644
00:35:50,760 --> 00:35:53,719
Speaker 1: of reality. But first we're going to take a quick

645
00:35:53,719 --> 00:36:00,840
Speaker 1: break to hear from the sponsor of this episode. Everybody

646
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Speaker 1: making that awesome website. So whatever the solution to P

670
00:37:12,880 --> 00:37:15,239
Speaker 1: equals and P turns out to be, I think one

671
00:37:15,280 --> 00:37:19,000
Speaker 1: thing that's very interesting about it is just the idea

672
00:37:19,160 --> 00:37:23,640
Speaker 1: that this problem in computer science runs under the skin

673
00:37:23,800 --> 00:37:27,320
Speaker 1: of everything that exists. You know. It's it's not something

674
00:37:27,320 --> 00:37:31,000
Speaker 1: that people just made up. This is talking about a

675
00:37:31,080 --> 00:37:33,880
Speaker 1: fact about the universe that would be a fact about

676
00:37:33,880 --> 00:37:37,839
Speaker 1: the universe whether we were here to discuss it or not. Yeah,

677
00:37:37,880 --> 00:37:40,920
Speaker 1: I mean there's a certain amount of it's difficult kind

678
00:37:40,920 --> 00:37:43,880
Speaker 1: of to get to get outside of the mirror language

679
00:37:44,320 --> 00:37:47,000
Speaker 1: of the situation when we're talking about problem solving, because

680
00:37:47,000 --> 00:37:49,520
Speaker 1: we can't help but think about a human mind trying

681
00:37:49,520 --> 00:37:53,120
Speaker 1: to solve a problem. But in a sense, problem solving

682
00:37:53,520 --> 00:37:56,120
Speaker 1: takes place not only the human level, it takes place

683
00:37:56,160 --> 00:37:59,400
Speaker 1: that at at at at the animal level, as you know,

684
00:37:59,440 --> 00:38:02,760
Speaker 1: as this particular entity is trying to navigate a world

685
00:38:02,800 --> 00:38:06,200
Speaker 1: of fixed and moving objects, generally to acquire some gold,

686
00:38:06,280 --> 00:38:09,480
Speaker 1: to acquire food or or a mate. Uh, you could

687
00:38:09,520 --> 00:38:12,440
Speaker 1: even you could probably even extrapolated as far to say

688
00:38:12,640 --> 00:38:15,960
Speaker 1: to say that that an object obeying gravity is kind

689
00:38:16,000 --> 00:38:21,120
Speaker 1: of engaging in a sort of non mental problem solving

690
00:38:22,040 --> 00:38:24,759
Speaker 1: in a way, in in a limited way. Um, I

691
00:38:24,800 --> 00:38:26,640
Speaker 1: guess what I'm just trying to drive home here is

692
00:38:26,680 --> 00:38:29,680
Speaker 1: that when we continue to talk about problem solving here,

693
00:38:30,080 --> 00:38:32,400
Speaker 1: it's like trying not to think about it as much

694
00:38:33,280 --> 00:38:36,759
Speaker 1: within in the human realm, because it is we're about

695
00:38:36,800 --> 00:38:39,480
Speaker 1: to see it gets well outside of it, remove the

696
00:38:39,560 --> 00:38:44,239
Speaker 1: consciousness from it, and retain only the teleology exactly that

697
00:38:44,400 --> 00:38:47,040
Speaker 1: there there are there are steps towards a purpose, but

698
00:38:47,160 --> 00:38:49,520
Speaker 1: you don't have to know what the purpose is, and

699
00:38:49,600 --> 00:38:53,000
Speaker 1: you don't even have to realize you're taking steps right now.

700
00:38:53,040 --> 00:38:56,200
Speaker 1: A great example of this is the slime mold. So

701
00:38:56,239 --> 00:38:59,480
Speaker 1: slime molds don't have brains. They consists of a single

702
00:38:59,560 --> 00:39:03,000
Speaker 1: cell containing millions of nuclei and they form a network

703
00:39:03,040 --> 00:39:07,480
Speaker 1: of protoplasmic tubes to creep toward a food source along

704
00:39:07,520 --> 00:39:11,359
Speaker 1: the shortest path. That's essential here. Uh. It sends out

705
00:39:11,400 --> 00:39:15,000
Speaker 1: limbs to find food and when it finds us food source,

706
00:39:15,160 --> 00:39:18,319
Speaker 1: it spreads over, it secretes the digestive enzymes and has

707
00:39:18,360 --> 00:39:21,839
Speaker 1: its meal. When it doesn't, yeah, it's pretty it's pretty great.

708
00:39:22,160 --> 00:39:25,840
Speaker 1: Essentially the blob right, and when it doesn't find food,

709
00:39:25,960 --> 00:39:28,799
Speaker 1: then the limb you know, dies in retreats back. So

710
00:39:28,880 --> 00:39:32,160
Speaker 1: in this it creates a network for transporting nutrients and

711
00:39:32,280 --> 00:39:37,200
Speaker 1: chemicals for inter cellular communication. And that the method allows

712
00:39:37,239 --> 00:39:39,840
Speaker 1: them to perform such feats and this is generally and

713
00:39:40,160 --> 00:39:43,680
Speaker 1: this in lab environments. Uh, such feats as solving a maze,

714
00:39:43,760 --> 00:39:45,600
Speaker 1: like a straight up maze that you would put a

715
00:39:45,600 --> 00:39:49,520
Speaker 1: mouse in, as well as when presented with a miniaturized

716
00:39:49,560 --> 00:39:53,160
Speaker 1: earth environment, Uh, they can they can recreate some of

717
00:39:53,200 --> 00:39:56,040
Speaker 1: the great trade routes um of the world, of some

718
00:39:56,120 --> 00:40:00,920
Speaker 1: of the great highway systems. They can model cancer growth. Um,

719
00:40:02,200 --> 00:40:04,360
Speaker 1: let me go to a little detail about the silk

720
00:40:04,480 --> 00:40:07,239
Speaker 1: road thing, and this is this gets into exactly what

721
00:40:07,280 --> 00:40:11,080
Speaker 1: we're talking earlier about an algorithm attempting to to plot

722
00:40:11,120 --> 00:40:13,680
Speaker 1: a course, right and having to hit all those stuffs

723
00:40:13,680 --> 00:40:17,600
Speaker 1: the salesman problem that we're discussing earlier. Okay, So back

724
00:40:17,640 --> 00:40:23,640
Speaker 1: into computer scientist Andre Adamantski from the University of the

725
00:40:23,680 --> 00:40:26,680
Speaker 1: West of England. He took a globe. Okay, and you

726
00:40:26,680 --> 00:40:28,319
Speaker 1: can do this at home probably, I guess, if you

727
00:40:28,320 --> 00:40:31,680
Speaker 1: have access to the materials. He took a globe and

728
00:40:31,680 --> 00:40:34,040
Speaker 1: he coated it with auger all right, this, of course

729
00:40:34,120 --> 00:40:36,040
Speaker 1: is the stuff in a petri dish that you know

730
00:40:36,120 --> 00:40:40,600
Speaker 1: bacteria grows up. Yeah. Uh and then he um. And

731
00:40:40,640 --> 00:40:44,120
Speaker 1: then what he did is he removed uh, the augur

732
00:40:44,239 --> 00:40:46,560
Speaker 1: from the areas over the ocean, so it's just covering

733
00:40:46,560 --> 00:40:49,000
Speaker 1: the continents and the land at this point. And then

734
00:40:49,040 --> 00:40:52,160
Speaker 1: he placed oat flakes at the locations of twenty four

735
00:40:52,239 --> 00:40:55,560
Speaker 1: different major cities on the globe, so that's a food source. Okay.

736
00:40:56,200 --> 00:40:58,680
Speaker 1: Then he introduced the slime mold. Alright. He did this

737
00:40:58,760 --> 00:41:02,080
Speaker 1: thirty different times. In each time the slime mold conquered

738
00:41:02,120 --> 00:41:06,320
Speaker 1: the world in a slightly different way, establishing trade routes

739
00:41:06,600 --> 00:41:10,640
Speaker 1: between the various oats cities. Yeah, and it's uh there

740
00:41:10,640 --> 00:41:14,040
Speaker 1: their pictures out there. This is pretty remarkable, uh because

741
00:41:14,040 --> 00:41:16,200
Speaker 1: it it maybe a little bit scary because you see

742
00:41:16,360 --> 00:41:18,840
Speaker 1: these ten drils just spreading out all over the world.

743
00:41:19,719 --> 00:41:23,359
Speaker 1: It managed to plan out engineering projects that, of course

744
00:41:23,440 --> 00:41:26,440
Speaker 1: humans can only dream of right now, like Transatlantic bridges.

745
00:41:26,520 --> 00:41:29,040
Speaker 1: Obviously we're not going to do that. It wasn't, I didn't.

746
00:41:29,080 --> 00:41:30,840
Speaker 1: I don't think it was ever able to really conquer

747
00:41:30,880 --> 00:41:34,399
Speaker 1: the Pacific. Pacific was just too too uh too great

748
00:41:34,400 --> 00:41:38,360
Speaker 1: of a distance without Augur there for it to grow on. Uh.

749
00:41:38,480 --> 00:41:42,200
Speaker 1: But but it also managed to recreate the Silk Road

750
00:41:43,080 --> 00:41:46,840
Speaker 1: as well as the modern Asian Highway network, which consists

751
00:41:46,840 --> 00:41:49,520
Speaker 1: of about the eighty seven thousand miles of roads running

752
00:41:49,520 --> 00:41:53,239
Speaker 1: between thirty two countries. So it exactly it provides a

753
00:41:53,320 --> 00:41:57,360
Speaker 1: great example of uh, not a problem solving intelligence, but

754
00:41:57,400 --> 00:42:02,800
Speaker 1: an algorithmic problem solving organic system. Of course, this I

755
00:42:02,840 --> 00:42:05,880
Speaker 1: do think raises the specter of the question how do

756
00:42:05,920 --> 00:42:10,400
Speaker 1: you tell the difference between an algorithm and intelligence? Unless

757
00:42:10,400 --> 00:42:13,360
Speaker 1: you want to be anthropomorphic and say, well, intelligence is

758
00:42:13,400 --> 00:42:16,520
Speaker 1: consciousness and the ability to love and yeah, and then

759
00:42:16,560 --> 00:42:19,719
Speaker 1: we start gazing down that of this right, Yeah, yeah,

760
00:42:19,760 --> 00:42:21,800
Speaker 1: we get back to some of the problems we've discussed

761
00:42:22,320 --> 00:42:25,120
Speaker 1: in relation to AI in the past. How do you

762
00:42:25,160 --> 00:42:28,160
Speaker 1: know when you created it? If you can't really say

763
00:42:28,200 --> 00:42:30,919
Speaker 1: what it is, it becomes this this problem. We can't

764
00:42:30,920 --> 00:42:33,040
Speaker 1: even define the problem, So how can we come up

765
00:42:33,040 --> 00:42:35,120
Speaker 1: with the solution or check the solution? Yeah, And it's

766
00:42:35,120 --> 00:42:38,840
Speaker 1: funny that we were talking earlier about about sets that

767
00:42:38,960 --> 00:42:42,080
Speaker 1: sort of recursively consume one another. Is one set within

768
00:42:42,120 --> 00:42:44,920
Speaker 1: another set, but then the first set is the second

769
00:42:44,920 --> 00:42:48,440
Speaker 1: set is also in the first set. Now, we just

770
00:42:48,800 --> 00:42:52,000
Speaker 1: gave an example of how nature can be like an algorithm.

771
00:42:52,040 --> 00:42:55,400
Speaker 1: But you can also say that plenty of algorithms and

772
00:42:55,400 --> 00:43:00,000
Speaker 1: computer science have been essentially derived from nature. Yeah. Um,

773
00:43:00,160 --> 00:43:02,839
Speaker 1: And there's a great example of this with ants. So

774
00:43:03,640 --> 00:43:06,040
Speaker 1: ant colonies. We've discussed ance here in the podcast before

775
00:43:06,040 --> 00:43:07,600
Speaker 1: and I'm sure we'll cover them again in the future.

776
00:43:07,920 --> 00:43:10,440
Speaker 1: You know that they're complex societies, and we see plenty

777
00:43:10,440 --> 00:43:14,400
Speaker 1: of examples in which colonies accomplished complex tasks that exceed

778
00:43:14,480 --> 00:43:17,319
Speaker 1: the individual capacities of a single ant. Of course, so

779
00:43:17,400 --> 00:43:20,880
Speaker 1: they worked together and they're able to solve problems, and

780
00:43:20,920 --> 00:43:24,800
Speaker 1: this is mound mind. Yeah, exactly, it's the the the

781
00:43:24,600 --> 00:43:29,600
Speaker 1: the the emergent intelligence of the group, the swarm intelligence. Um.

782
00:43:29,640 --> 00:43:32,560
Speaker 1: This is a particular note to computer programming as though,

783
00:43:32,600 --> 00:43:35,839
Speaker 1: as we see how they're self organizing capacities and distributed

784
00:43:36,080 --> 00:43:42,040
Speaker 1: organization enable them to solve difficult optimization and distributed control problems. Okay,

785
00:43:42,080 --> 00:43:46,680
Speaker 1: So back in a Stanford study looked at how harvester

786
00:43:46,880 --> 00:43:51,399
Speaker 1: ants determine how many foragers to send out. Of course,

787
00:43:51,400 --> 00:43:54,560
Speaker 1: so they're sending out a rating party, right, Uh, which

788
00:43:54,600 --> 00:43:57,120
Speaker 1: is you know, more complex than than than you might think,

789
00:43:58,080 --> 00:44:00,359
Speaker 1: because you get down to the basic you know, how

790
00:44:00,360 --> 00:44:03,960
Speaker 1: many do you send out? You know, what's the way time? Uh?

791
00:44:04,000 --> 00:44:06,120
Speaker 1: And then they compared this to the manner in which

792
00:44:06,160 --> 00:44:10,480
Speaker 1: a search engine brings back search results. So specifically, yeah, specifically,

793
00:44:10,520 --> 00:44:15,279
Speaker 1: we're talking about transmission control protocol or TCP, which, if

794
00:44:15,320 --> 00:44:17,919
Speaker 1: you're like me, that's mostly something that you run into

795
00:44:17,960 --> 00:44:21,320
Speaker 1: when you have to adjust something on your your internet

796
00:44:21,600 --> 00:44:33,120
Speaker 1: situation at home. Yeah, but in anyway, it's a it's

797
00:44:33,200 --> 00:44:36,880
Speaker 1: essentially an algorithm that manages data congestion on the Internet.

798
00:44:37,680 --> 00:44:41,400
Speaker 1: So they they compared the two, right, and they combined

799
00:44:41,480 --> 00:44:46,160
Speaker 1: the two and they created the anternet. This is so

800
00:44:46,239 --> 00:44:50,520
Speaker 1: that's that's Internet, except instead of you have an ant UM.

801
00:44:50,560 --> 00:44:55,160
Speaker 1: So it's a TCP influenced algorithm that accurately matched ant

802
00:44:55,200 --> 00:44:57,720
Speaker 1: behavior in the experiment. So it's an example of ants

803
00:44:58,080 --> 00:45:03,560
Speaker 1: reaching the same place as our computer programming UM. So basically,

804
00:45:03,600 --> 00:45:07,719
Speaker 1: they created a TCP program that accurately predicted how the

805
00:45:07,760 --> 00:45:11,360
Speaker 1: ants were going to act. Yeah, we've definitely talked on

806
00:45:11,440 --> 00:45:13,719
Speaker 1: the other podcasts that I do with Jonathan Strickland and

807
00:45:13,800 --> 00:45:17,200
Speaker 1: Lauren Vogelbaum Forward Thinking. We talk about biomimetic robotics a

808
00:45:17,239 --> 00:45:21,480
Speaker 1: lot about ways that robots can be inspired by the

809
00:45:21,520 --> 00:45:25,360
Speaker 1: ways that animals move, especially in mobile robotics. You know,

810
00:45:25,360 --> 00:45:27,480
Speaker 1: how do you get a swarm of things to behave

811
00:45:27,560 --> 00:45:30,920
Speaker 1: as a group correctly? And I'd imagine that this would

812
00:45:30,920 --> 00:45:34,040
Speaker 1: be a very good example of how to control them. Yeah,

813
00:45:34,080 --> 00:45:37,759
Speaker 1: you see swarm organisms used in various AI programs. I

814
00:45:37,760 --> 00:45:41,560
Speaker 1: believe here Georgia Tech in Atlanta, they've used bees a lot.

815
00:45:41,760 --> 00:45:45,040
Speaker 1: Oh yeah, yeah. And more recently, a two thousand fourteen

816
00:45:45,080 --> 00:45:48,239
Speaker 1: study from Zurix Institute of Pharmaceutical Science has explored the

817
00:45:48,280 --> 00:45:51,839
Speaker 1: possibility of using ant algorithms to search for new composite

818
00:45:51,880 --> 00:45:55,120
Speaker 1: agents in the development of new pharmaceuticals. And this gets

819
00:45:55,400 --> 00:45:58,160
Speaker 1: down to the whole situation discussing earlier about do you

820
00:45:58,200 --> 00:46:01,920
Speaker 1: do you take a brute uh ranked approach to finding

821
00:46:01,960 --> 00:46:05,239
Speaker 1: out which connections amid all these possible connections are the

822
00:46:05,320 --> 00:46:09,600
Speaker 1: ideal ones to then test and explore. Um. So they've

823
00:46:09,600 --> 00:46:12,240
Speaker 1: looked into the possibility of using an ant based algorithm

824
00:46:12,440 --> 00:46:16,040
Speaker 1: to find though to do essentially, you know, magically guess

825
00:46:16,480 --> 00:46:21,240
Speaker 1: those uh those those the composite agents that that deserve

826
00:46:21,360 --> 00:46:24,520
Speaker 1: further examination. Well, you know, one thing these examples in

827
00:46:24,640 --> 00:46:27,959
Speaker 1: nature make me think about is an idea that really

828
00:46:28,000 --> 00:46:31,080
Speaker 1: intrigues me, and that's the question of whether evolution by

829
00:46:31,200 --> 00:46:36,759
Speaker 1: natural selection can itself be best interpreted as an algorithm,

830
00:46:36,800 --> 00:46:39,200
Speaker 1: because think about it, it's it's kind of an iterate

831
00:46:39,280 --> 00:46:42,600
Speaker 1: and test style algorithmic procedure. Let me give you a

832
00:46:42,640 --> 00:46:47,080
Speaker 1: list of steps. Step one, randomly introduce a change that

833
00:46:47,080 --> 00:46:49,640
Speaker 1: would be like a mutant allile into the genome. You

834
00:46:49,880 --> 00:46:53,719
Speaker 1: very one gene from the existing model, so you've random that.

835
00:46:53,719 --> 00:46:56,480
Speaker 1: That's step one. And then step two test against the

836
00:46:56,480 --> 00:46:59,960
Speaker 1: baseline performance rate of the standard allile in the environment

837
00:47:00,200 --> 00:47:03,320
Speaker 1: or you know, the individual steps here would be attempt

838
00:47:03,400 --> 00:47:07,920
Speaker 1: to copy. If copying succeeds, go to one or return

839
00:47:08,000 --> 00:47:13,000
Speaker 1: to the first step. If copying fails, return void. It's

840
00:47:13,000 --> 00:47:15,399
Speaker 1: almost like a computer program. Yeah, I think I think

841
00:47:15,520 --> 00:47:17,239
Speaker 1: there's a very strong case to be made there. I mean,

842
00:47:18,200 --> 00:47:21,920
Speaker 1: earlier I made that I said that gravity an object

843
00:47:21,920 --> 00:47:23,600
Speaker 1: of baying gravity and is it is in a way

844
00:47:24,080 --> 00:47:28,040
Speaker 1: kind of obeying a certain algorithm. It's kind of problem solving.

845
00:47:28,120 --> 00:47:31,120
Speaker 1: So this I think this FITSI bill as well. Yeah,

846
00:47:31,160 --> 00:47:33,279
Speaker 1: and I certainly didn't come up with this idea. I know,

847
00:47:33,320 --> 00:47:35,960
Speaker 1: I've read about it in the philosopher Daniel Dennett, who

848
00:47:36,200 --> 00:47:39,000
Speaker 1: advocated this point of view in this nine six book.

849
00:47:39,080 --> 00:47:42,120
Speaker 1: Darwin's dangerous idea was which was a lot about the

850
00:47:42,160 --> 00:47:46,960
Speaker 1: implications of evolution beyond just explaining the diversity of species

851
00:47:47,000 --> 00:47:50,360
Speaker 1: on Earth. Evolution is a sort of principle that extends

852
00:47:50,400 --> 00:47:56,120
Speaker 1: even beyond biology. But this universal driving force that that

853
00:47:56,320 --> 00:48:00,360
Speaker 1: drives design through the design space, and the way he

854
00:48:00,360 --> 00:48:04,040
Speaker 1: would explain it. But uh so, of course, not everybody

855
00:48:04,040 --> 00:48:07,160
Speaker 1: agrees with that interpretation, thinks that an algorithm is a

856
00:48:07,200 --> 00:48:09,760
Speaker 1: good way of thinking about evolution. But I've got another

857
00:48:09,880 --> 00:48:13,040
Speaker 1: follow up question that's kind of interesting to me, if

858
00:48:13,080 --> 00:48:16,840
Speaker 1: evolution is an algorithm, is it an efficient algorithm or

859
00:48:16,880 --> 00:48:20,880
Speaker 1: a brute force algorithm? Oh, now that's a good question.

860
00:48:20,960 --> 00:48:22,640
Speaker 1: I mean it seems to me kind of like it

861
00:48:22,640 --> 00:48:25,520
Speaker 1: would be a brute force algorithm, right, because you're you're

862
00:48:25,560 --> 00:48:29,399
Speaker 1: trying any number, it's you know, just brute force combinatrix.

863
00:48:29,480 --> 00:48:32,840
Speaker 1: You're trying something. Here's a pair of genes, here's an alleal.

864
00:48:33,280 --> 00:48:35,960
Speaker 1: Does that work? Now? Okay, throw in the trash. It

865
00:48:36,000 --> 00:48:39,120
Speaker 1: seems very wasteful. Yeah, here's a lizard with spots, here's

866
00:48:39,160 --> 00:48:41,800
Speaker 1: one without spots. Which one gets eaten? Which one continues?

867
00:48:41,880 --> 00:48:44,560
Speaker 1: That sounds like kind of a brute brute force You're

868
00:48:44,560 --> 00:48:47,000
Speaker 1: just like throwing out all the produce possible prototypes and

869
00:48:47,120 --> 00:48:48,960
Speaker 1: uh and seeing what then you see what happens. And

870
00:48:49,000 --> 00:48:51,280
Speaker 1: I think this would actually be one way of framing

871
00:48:51,360 --> 00:48:55,880
Speaker 1: the difference between the standard scientific materialist view of evolution,

872
00:48:55,920 --> 00:48:58,640
Speaker 1: which I think would probably be is best I can tell.

873
00:48:58,680 --> 00:49:01,200
Speaker 1: I bet that'd be the brute force method. And then

874
00:49:01,280 --> 00:49:04,560
Speaker 1: some types of believers in intelligent design. Right, So if

875
00:49:04,600 --> 00:49:08,279
Speaker 1: you are somebody who believes um, you you accept the

876
00:49:08,320 --> 00:49:11,840
Speaker 1: evidence for evolution and common descent, but you simply believe

877
00:49:11,920 --> 00:49:14,560
Speaker 1: the process was guided in one way or another. You

878
00:49:14,600 --> 00:49:17,200
Speaker 1: think aliens or a god or some other you know,

879
00:49:17,320 --> 00:49:23,080
Speaker 1: powerful supernatural or or other worldly technological force interfered with evolution,

880
00:49:23,160 --> 00:49:27,400
Speaker 1: maybe reached in to cause specific mutations to the terrestrial

881
00:49:27,520 --> 00:49:30,680
Speaker 1: genes space at key points. That sounds like that would

882
00:49:30,680 --> 00:49:37,080
Speaker 1: be optimizing the algorithm, right, like somebody's introducing artificial efficiency.

883
00:49:37,280 --> 00:49:39,759
Speaker 1: But then again, i'd i'd be interested in hearing from

884
00:49:39,880 --> 00:49:43,240
Speaker 1: the evolutionary biologists and geneticists sat there in the audience

885
00:49:43,280 --> 00:49:45,480
Speaker 1: on this one. Like, if you accept the idea that

886
00:49:46,120 --> 00:49:49,640
Speaker 1: natural selection is like an algorithm, is it a brute

887
00:49:49,680 --> 00:49:53,600
Speaker 1: force algorithm unless you go to the intelligent design hypothesis?

888
00:49:53,719 --> 00:49:57,400
Speaker 1: Or are there other ways of thinking of the algorithm

889
00:49:57,440 --> 00:50:02,239
Speaker 1: as in some way optimized by material cir substances. And

890
00:50:02,440 --> 00:50:06,319
Speaker 1: since you did mention god or the gods here would

891
00:50:06,400 --> 00:50:09,640
Speaker 1: the god in this scenario that Okay, so this is

892
00:50:09,640 --> 00:50:13,000
Speaker 1: a force coming from outside our universe, then perhaps in

893
00:50:13,040 --> 00:50:18,680
Speaker 1: this scenario our world is is a P does not

894
00:50:18,840 --> 00:50:22,200
Speaker 1: equal in P universe, but the realm of the gods

895
00:50:22,480 --> 00:50:25,840
Speaker 1: is a P equals in P universe. Uh Well, I

896
00:50:25,840 --> 00:50:29,400
Speaker 1: mean that's an interesting way of putting it, because they

897
00:50:29,560 --> 00:50:31,879
Speaker 1: can they can knock it out right there, Gods, they're

898
00:50:31,920 --> 00:50:35,480
Speaker 1: basically limitless. Well, it's infinite possibility, I mean, the whole

899
00:50:36,719 --> 00:50:40,240
Speaker 1: the realm of mathematics and logic is. In many ways,

900
00:50:40,680 --> 00:50:43,760
Speaker 1: it often signals to me the sort of underlying hint

901
00:50:43,800 --> 00:50:48,640
Speaker 1: of infinite possibilities but also infinite constraints. At the same time.

902
00:50:49,280 --> 00:50:53,080
Speaker 1: Mathematics is both infinite power and ultimate helplessness. You know,

903
00:50:53,200 --> 00:50:56,759
Speaker 1: it's the power to accomplish anything and the inevitability of

904
00:50:56,760 --> 00:51:01,399
Speaker 1: being thwarted and destroyed by processes beyond your control. Uh,

905
00:51:01,880 --> 00:51:05,480
Speaker 1: it just sort of makes everything good and bad possible. Well,

906
00:51:05,480 --> 00:51:07,480
Speaker 1: it sounds like you're talking about the gods again. It

907
00:51:07,520 --> 00:51:10,160
Speaker 1: could be yeah, all right, So there you have it.

908
00:51:10,320 --> 00:51:14,319
Speaker 1: P m P P versus m P P equals mp

909
00:51:14,480 --> 00:51:19,160
Speaker 1: P does not equal mp. Hopefully at this point you

910
00:51:19,200 --> 00:51:20,719
Speaker 1: have if you if you didn't know what any of

911
00:51:20,719 --> 00:51:24,120
Speaker 1: this stuff was about beforehand, you have a much better

912
00:51:24,160 --> 00:51:26,840
Speaker 1: grasp on the idea. You can at least realize why.

913
00:51:26,920 --> 00:51:30,520
Speaker 1: It is a topic that people continue to discuss and

914
00:51:30,600 --> 00:51:32,640
Speaker 1: even argue about. But if you want to get into

915
00:51:32,680 --> 00:51:36,040
Speaker 1: the actual details of of it, there are plenty of

916
00:51:36,080 --> 00:51:38,279
Speaker 1: good resources out there on the internet. If you are

917
00:51:38,320 --> 00:51:41,320
Speaker 1: a math and computer science and logic inclined to person

918
00:51:41,360 --> 00:51:43,640
Speaker 1: who has a good abstract mind for that kind of thing.

919
00:51:44,239 --> 00:51:46,200
Speaker 1: But either way, I do want to remind you to

920
00:51:46,400 --> 00:51:50,360
Speaker 1: always think about the algorithmic nature of the ground beneath

921
00:51:50,400 --> 00:51:53,120
Speaker 1: your feet and the laws that govern the way the

922
00:51:53,160 --> 00:51:58,319
Speaker 1: way everything around you works, the logic of reality. Is

923
00:51:58,360 --> 00:52:02,839
Speaker 1: there a problem solving process inherent to everything that's going

924
00:52:02,880 --> 00:52:05,160
Speaker 1: on around you all the time? I don't know. It's

925
00:52:05,200 --> 00:52:08,200
Speaker 1: it's a strange specter of an idea to keep behind

926
00:52:08,200 --> 00:52:10,759
Speaker 1: your head at all times. Yeah. Again, like when a

927
00:52:11,120 --> 00:52:14,200
Speaker 1: when like a walnut falls out of a tree, there

928
00:52:14,200 --> 00:52:16,400
Speaker 1: are certainly there's an there's an algorithm at play right

929
00:52:16,440 --> 00:52:18,680
Speaker 1: as to how exactly it's going to make it to

930
00:52:18,719 --> 00:52:21,120
Speaker 1: the ground, which branches is going to hit? I guess

931
00:52:21,120 --> 00:52:23,919
Speaker 1: that depends on your perspective. Is there is there a goal,

932
00:52:24,000 --> 00:52:29,680
Speaker 1: there is something happening or did something just happen? I

933
00:52:29,719 --> 00:52:32,719
Speaker 1: don't know, man, it's pretty far out. Well, let's not

934
00:52:32,719 --> 00:52:35,040
Speaker 1: get too much into weird stone or territory. Here at

935
00:52:35,040 --> 00:52:37,560
Speaker 1: the end, I do want to say, right here at

936
00:52:37,600 --> 00:52:40,839
Speaker 1: the end, if you are somebody who's involved in mathematics

937
00:52:40,920 --> 00:52:43,920
Speaker 1: or computer science and uh, and you would like to

938
00:52:43,960 --> 00:52:46,960
Speaker 1: write in to tell us about one of the one

939
00:52:47,000 --> 00:52:49,319
Speaker 1: of the more detailed or complex aspects of this that

940
00:52:49,360 --> 00:52:51,200
Speaker 1: we didn't get to. Like we said, we we gave

941
00:52:51,239 --> 00:52:54,400
Speaker 1: you the very simple version. Please right in and we'd

942
00:52:54,400 --> 00:52:57,279
Speaker 1: love to share your thoughts with the rest of you guys. Yeah,

943
00:52:57,280 --> 00:52:59,360
Speaker 1: and I'd also love to hear from anyone you know

944
00:52:59,360 --> 00:53:02,520
Speaker 1: who's read, uh some science fiction that definitely weighs in

945
00:53:02,600 --> 00:53:04,879
Speaker 1: on this. Um. You know, I was trying to think

946
00:53:04,880 --> 00:53:09,439
Speaker 1: of specific examples from N. N. Banks culture books because

947
00:53:09,480 --> 00:53:12,520
Speaker 1: the cour they have these minds, these ais that are

948
00:53:12,760 --> 00:53:15,960
Speaker 1: incredibly powerful, But for the life of me, I can't remember, uh,

949
00:53:16,000 --> 00:53:20,279
Speaker 1: exactly where they they weigh in in terms of the

950
00:53:20,440 --> 00:53:23,960
Speaker 1: or we're indeed, um Banks is universe there weighs in

951
00:53:24,160 --> 00:53:28,759
Speaker 1: on the on the the P and P spectrum. Hey.

952
00:53:28,800 --> 00:53:30,960
Speaker 1: But in the meantime, you want to check out this

953
00:53:31,040 --> 00:53:33,799
Speaker 1: and other pieces of content, head on over to stuff

954
00:53:33,800 --> 00:53:35,759
Speaker 1: to Blow your Mind dot com. That's the mothership. That's

955
00:53:35,760 --> 00:53:37,520
Speaker 1: where we have all the articles. That's where we have

956
00:53:37,600 --> 00:53:40,239
Speaker 1: blog posts, we have links out to social media, we

957
00:53:40,280 --> 00:53:43,520
Speaker 1: have some videos, uh, be sure to go there. Hey,

958
00:53:43,520 --> 00:53:48,000
Speaker 1: and if you listen to us on what iTunes, Spotify,

959
00:53:48,120 --> 00:53:52,960
Speaker 1: Google Play, however, you get your podcasts um it's possible

960
00:53:53,080 --> 00:53:55,319
Speaker 1: to do, so leave us a nice review there, give

961
00:53:55,360 --> 00:53:58,919
Speaker 1: us a little boost in the algorithm that ultimately uh

962
00:53:59,200 --> 00:54:02,200
Speaker 1: determines our eight. You can tweak that at that hour,

963
00:54:02,280 --> 00:54:04,280
Speaker 1: and you can reach out as a as an outside

964
00:54:04,280 --> 00:54:07,400
Speaker 1: force like a god, and shift things in our favor.

965
00:54:07,480 --> 00:54:10,640
Speaker 1: So invite you to do stuff. And as always, if

966
00:54:10,640 --> 00:54:12,120
Speaker 1: you'd like to get in touch with us, and we

967
00:54:12,200 --> 00:54:14,359
Speaker 1: really hope you do, you can email us and blow

968
00:54:14,400 --> 00:54:26,520
Speaker 1: the mind at how stuff works dot com for more

969
00:54:26,560 --> 00:54:28,839
Speaker 1: on this and thousands of other topics. Is it how

970
00:54:28,920 --> 00:54:52,680
Speaker 1: stuff works dot com.