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

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Speaker 1: works dot com. Hey, welcome to Spectable 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 going to be taking a look at 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 sciences we dip into very frequently on

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

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

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

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Speaker 1: science thing, don't be scared off by the P versus

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Speaker 1: NP thing. It's it's it's all gonna make a type

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

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

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

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

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Speaker 1: as we've discovered in researching this episode, it's very abstract,

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

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Speaker 1: up with ways of explaining that makes sense just just

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Speaker 1: talking about it without visual aids or watching programs execute

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Speaker 1: as an example. Yeah, it's definitely one of those topics

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Speaker 1: that it's a swimming pool of a topic in which

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Speaker 1: there is no gradual deepening from the kitty area to

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Speaker 1: the deep end. It's just shallow, and at times it

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Speaker 1: feels too shallow, and then you're immediately out of your depth. Yeah.

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Speaker 1: But if you're thinking, kind of computer science, really does

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Speaker 1: that fit with the show? Hold on for a second,

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Speaker 1: because I think it does. Um. Computer science to me

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Speaker 1: is a fascinating subject. Uh, and it's not just limited

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Speaker 1: to how computers work. So my advice is, when you

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Speaker 1: think about the idea of computer science, forget the computer

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Speaker 1: sitting in front of you. That's not all it is.

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Speaker 1: Computer science is really something more akin to the philosophy

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Speaker 1: of logic, understanding the the underlying sorcery of how logic

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Speaker 1: and math work in the universe we inhabit, and especially

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Speaker 1: the science of how problems are solved, and certainly when

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Speaker 1: you start bringing math into the equation here as well,

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Speaker 1: I mean you're talking we're talking about the essentially the

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

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

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Speaker 1: or this perfect creation that humans have come up with

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

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Speaker 1: is pretty mind blowing territory. Well, either way you look

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Speaker 1: at it, there is something mystical about about the math

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

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Speaker 1: makes up the fabric and the logic math beneath 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 teg Mark idea of the mathematical universe that

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

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Speaker 1: understand what that means. But at least it's a very

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Speaker 1: intriguing idea. I think it. His idea is that the

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Speaker 1: underlying basis of all reality not just as described by math,

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Speaker 1: but is math, and the universe is a mathematical object.

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Speaker 1: But we're gonna get back to this idea of problem

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Speaker 1: solving because today we want to focus on algori rhythms

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Speaker 1: and on the inherent logic of problem solving in our universe,

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Speaker 1: with some attention to a special example of one really

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Speaker 1: interesting outstanding problem in computer science, and that's the P

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Speaker 1: versus n P issue. If you've never heard of this before,

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Speaker 1: don't worry. We'll explain what the terms mean in a

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Speaker 1: simplified manner. And uh I at this point also do

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Speaker 1: want to give a shout out to our listener Jim

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Speaker 1: in New Jersey, who has been encouraging me over email

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Speaker 1: to tackle this issue for a while despite all the challenges,

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

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Speaker 1: guides and explainers on some stuff he he learned about

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

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Speaker 1: uh and I think this is great too because this

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Speaker 1: episode is coming on the heels of, first of all,

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Speaker 1: the Wicked Problems episode that came out of a few

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Speaker 1: weeks ago, as well as the more recent Cargo Cults

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Speaker 1: episode which in which we discuss outside context problems a

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Speaker 1: little bit as well. So it's it's perfectly fitting that

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Speaker 1: we would discuss another problem. Well, well, what does it

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Speaker 1: mean inherently to solve a problem? If you get into

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Speaker 1: the theory of problem solving? What what what does this

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Speaker 1: process look like? Well, when it comes down just to

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Speaker 1: the basics open and this also kind of gets into

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Speaker 1: the whole Wicked Problems area of like what's what's missing

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Speaker 1: when you don't have, um, you know, everything you need

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Speaker 1: to solve a problem for for a real problem, you

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Speaker 1: have to be able to course do to find what

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Speaker 1: the problem is a lot of a lot of attempts

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Speaker 1: fail right there. Yeah, you've got to you've got to

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Speaker 1: be able to say this is the thing you know,

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Speaker 1: and then you and then you have to be able

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Speaker 1: to measure your success and check the solution. So you

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Speaker 1: essentially have to be able to say, hey, this is

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Speaker 1: wrong because of X, and then if you then figure

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Speaker 1: out what X is and see if the equation balances out. Um,

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Speaker 1: it sounds pretty simple. But like I said, as we

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Speaker 1: discussed in Wicked Problems, that's uh, it can be very

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Speaker 1: difficult to do, especially in you know, the very complex

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Speaker 1: social situations when you're dealing with with certainly some of

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Speaker 1: the larger problems that we're going to talk about here,

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Speaker 1: or even if you want to go into the simplest level, well,

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

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

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Speaker 1: referred to as complexity theory. Uh. So maybe it's not

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Speaker 1: so simple, but at least simple in terms of not

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

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Speaker 1: just math and logic and and true versus untrue and

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Speaker 1: UH and algorithms. So I think it's time to pull

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Speaker 1: back the curtain a little bit and reveal some of

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Speaker 1: the deep weirdness of the nature of algorithms and problem

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Speaker 1: solving in our universe. So let's look at this P

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Speaker 1: versus MP problem. This is something that comes from two

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Speaker 1: of the great minds of the twentieth century, Kurt Girdle

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Speaker 1: and John von Neuman. And in nineteen fifty six, Kurt Girdle,

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Speaker 1: who's a mathematician and logician, wrote a letter to John

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Speaker 1: von Neuman which kicked off this quest to solve one

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Speaker 1: of the biggest questions in computer science, the P versus

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Speaker 1: n P issue. Now, who were these guys, but both

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Speaker 1: were heightens of the twentieth century in terms of math, logic,

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Speaker 1: and computers. Well. Godal is probably most famous for his

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Speaker 1: first incompleteness theorem, and this states that any adequate um

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Speaker 1: axiomatizeable theory that means, a theory that's based on self

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Speaker 1: evident but unprovable proofs, is incomplete or inconsistent. Yeah, Girdle's

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Speaker 1: whole incompleteness theorem set. He had a couple of his

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Speaker 1: incompleteness theorems essentially amount to the idea that any mathematical

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Speaker 1: system that makes sense will have some statements that are

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Speaker 1: true yet impossible to prove. It's sort of the idea

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Speaker 1: that you can't ever know everything about a self consistent system. Yeah,

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Speaker 1: And the the the implication here, according to theoretical physicist

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Speaker 1: and mathematician Freeman Dyson, who has is also quite a

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Speaker 1: giant in the field, is that mathematics is inexhaustible, that

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Speaker 1: no matter how many problems we solve, will inevitably encounter

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Speaker 1: more unsolvable problems within the existing rules. I take comfort

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Speaker 1: in that measure a few tility. Yeah, but there's also

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Speaker 1: John von Neuman, the recipient of the letter. And von Neuman,

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Speaker 1: I don't know what you've heard about him, but I'd

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Speaker 1: say he's often considered one of the most intelligent people

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

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Speaker 1: 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 so,

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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 Neuman

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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 the 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 INP

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

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Speaker 1: 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 that the house stuff

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Speaker 1: works 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. Anyway, here we go. So we we've got

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

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

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

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

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Speaker 1: list of things to do that gets you to the goal.

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Speaker 1: A common example within a computer program would be a

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Speaker 1: subroutine designed to sort a list of things. That's an algorithm. Yeah,

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Speaker 1: and you know, algorithms are something we encounter on a

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Speaker 1: just on a daily basis, especially online. I mean Facebook, Google.

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Speaker 1: Both of these depend on ever changing algorithms to decide

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Speaker 1: what you see and don't see on your feeds and

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Speaker 1: on your search results. Yeah, and I think that's a

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Speaker 1: great example of how complex algorithms can get. You've got

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Speaker 1: the simple sorting algorithm on one hand, and then you've

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Speaker 1: got the stuff that decides whether you only see political

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Speaker 1: articles you agree with, or whether you sometimes see stuff

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Speaker 1: that's going to make you mad. So when you're designing

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Speaker 1: algorithms in in a computer science arena, or really to

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Speaker 1: solve any problem, but we're mostly gonna be talking about

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Speaker 1: computer programs. You compare how much time it takes to

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Speaker 1: solve a problem with an algorithm given the scope of

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Speaker 1: a problem. So this is usually expressed in terms of

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Speaker 1: inputs versus time. So I want to give a quick

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Speaker 1: example with sorting. Like I said, you say, you're given

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Speaker 1: a spreadsheet that includes a list of all the James

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

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

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

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

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

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

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

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

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

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

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

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

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Speaker 1: the way down the line, then the list is sorted correctly,

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Speaker 1: and you're done. If not, start over and rearrange it

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

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Speaker 1: data set, this algorithm will eventually finish by blind luck.

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Speaker 1: Is just brute force burning through computer resources wastefully in

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Speaker 1: order to eventually solve the problem by blind block. But

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Speaker 1: there are also much more efficient ways you could go

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Speaker 1: about it. For example, you could go down the list

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Speaker 1: comparing each movie to the next, and if the second

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Speaker 1: movie came out before the first, you switch their order

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Speaker 1: on the list, and then go on like that. Uh,

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Speaker 1: and then you do that until the list is sorted.

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Speaker 1: But some problems are inherently a lot harder than others,

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Speaker 1: and there aren't any algorithmic shortcuts like that that we

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Speaker 1: know about. We don't know of any easy way to

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Speaker 1: solve them. The only thing we know how to do

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Speaker 1: is do that stupid brute force method where you just

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Speaker 1: wastefully burned through computer resources until it's solved by time

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Speaker 1: and force. And in fact, I'd like to make a

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Speaker 1: comparison here in the you know, the efficient algorithm versus

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Speaker 1: brute force methods to what you might see in animals

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Speaker 1: in the wild using intelligence to solve a problem. So,

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Speaker 1: like if you're hunting another animal, you could use a

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Speaker 1: brute force method of just running after the animal until

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Speaker 1: it is tired or until your muscles have allowed you

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Speaker 1: to catch it, and then killing it with the strength

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Speaker 1: of your muscles. That's sort of the brute force method.

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Speaker 1: Or you could set a trap, or you could build

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Speaker 1: a weapon that these are shortcuts that make the process

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Speaker 1: of hunting a lot more efficient. Now here's where we

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Speaker 1: get to our main terms in this discussion. P and

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Speaker 1: n P. P is going to stand for polynomial time,

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Speaker 1: and in P stands for nondeterministic polynomial time. You don't

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Speaker 1: really need to remember that for the purpose of this discussion,

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Speaker 1: because we're gonna make it a lot simpler. Yeah, I mean,

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Speaker 1: this is one of the problems with the topic is

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Speaker 1: that like just the word this, this the the the

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Speaker 1: basic idea here of P and n P. There, it's

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Speaker 1: so dry and unrelated, doble, But allow us to explain. Yeah, okay,

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Speaker 1: so the real difference has to do with um processes

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Speaker 1: of solving problems on a deterministic Turing machine, which is

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Speaker 1: equivalent to the kind of computer you'd be using right now.

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

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

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

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

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Speaker 1: we don't want to get too bogged down in all

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Speaker 1: those details. So here's the simplified version. P is a

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Speaker 1: set of all problems that can be solved by an

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Speaker 1: algorithm quickly or easily or efficiently. This is the easy

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

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Speaker 1: on the other hand, stands for answers that can be

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Speaker 1: easily checked by a computer once you have them, but

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Speaker 1: they can't necessarily be solved easily. Yeah, so it's difficult

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Speaker 1: to place this in a non mathematical context. But one

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Speaker 1: way I like to think about this is in terms

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Speaker 1: of written reviews for for albums, for you know, for

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Speaker 1: for musical albums, um. Because a good a good review,

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Speaker 1: a good music review is difficult to write, uh, and

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Speaker 1: hard to find and hard to find. Yeah, like in

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Speaker 1: my own experience you often find I mean, I I

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Speaker 1: write many reviews of stuff of stuff from time to time,

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Speaker 1: and that alone is challenging enough for me but but yeah,

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

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Speaker 1: uh yeah, it's hard to find one that feels just right.

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Speaker 1: It's uh. The average reader, though, can can swiftly judge

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

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Speaker 1: what extent the author is just blowing smoke. We've all

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

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

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Speaker 1: excuse to sort of write his or her own poetry.

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Speaker 1: Uh there on the page episodes actually just describing what

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

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Speaker 1: the reader can can can look at the material and

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Speaker 1: you either believe in or you don't. Either you buy

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Speaker 1: into their opinion or you don't. Yeah, so you could.

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Speaker 1: You don't have the algorithm internally to efficiently right this

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Speaker 1: piece of writing yourself, but you know it when you

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Speaker 1: see it exactly. Yeah. It's kind of like pornography, and

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Speaker 1: that's it. It's right. You might not be able to

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Speaker 1: define clearly the difference between art and pornography, but you

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Speaker 1: know it when you see it. Once you have that

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Speaker 1: answer certificate there, you can check and by golly, it

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Speaker 1: checks out. And I like that because it also gives

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Speaker 1: a whole new meaning to the P and to the NP.

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Speaker 1: And now, so there are a couple of other terms

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Speaker 1: that matter. There's MP hard, and this means that are

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Speaker 1: problems that are as hard as any other MP problem essentially.

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Speaker 1: And then there's also MP complete, which is a big

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Speaker 1: issue in this arena, and this is problems that are

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Speaker 1: m P and m P hard. So you you can

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Speaker 1: check the answer once you have it in in a

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Speaker 1: reasonable amount of time, and they're in P hard. Now,

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Speaker 1: the interesting thing about MP complete problems is that it

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Speaker 1: has been proved in the literature that if you have

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Speaker 1: an algorithm that can efficiently solve one MP complete problem,

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Speaker 1: it can be transposed to solve all of them. These

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Speaker 1: problems reduced to each other. So if you if you

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Speaker 1: can solve one MP complete problem and a reasonable amount

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Speaker 1: of time, you have found the master key. And this

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Speaker 1: in the universe kind of shrinks. Yeah, in response to this,

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Speaker 1: So a classic example of an MP problem is the

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

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

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

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

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

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

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

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

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Speaker 1: in a pile. It's a lot skulls, it is. Now,

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Speaker 1: I tell you this number is the product of two

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Speaker 1: prime numbers of skulls in a pile, but I don't

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Speaker 1: tell you what they are. Now, how could you figure

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Speaker 1: out what those two prime numbers of skulls in a

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Speaker 1: pile are? For a computer with numbers this small, this

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Speaker 1: wouldn't be all that big a deal. But we're we're

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Speaker 1: gonna have to extrapolate too much bigger numbers. But for you,

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Speaker 1: this would be really annoying to figure out, right, Oh yes,

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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 how

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

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Speaker 1: since we've solved the 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 org 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 it takes

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Speaker 1: almost no time at all. And in fact, I 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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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 ages. It's going to be impossible.

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Speaker 1: But if you already know a selected answer to test,

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Speaker 1: you can check and see if that answer is right. Yeah,

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Speaker 1: I mean this brings to mind. Uh it's a bit

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Speaker 1: like trying to crack a four number code on a

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Speaker 1: simple combination lock right, And I'm talking about a human

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Speaker 1: doing this, not a computer. So it would take me,

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Speaker 1: as a human quite a while to test out all

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Speaker 1: ten thousand possible solutions, but no time at all to

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Speaker 1: check a solution that someone else had provided me. So

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Speaker 1: you know, I'd be there all day just putting in

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Speaker 1: each one of those ten thousand solutions. But but I

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00:19:35,520 --> 00:19:39,199
Speaker 1: can easily put in, you know, uh, thirty three sixty

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Speaker 1: six and see if that is correct. You found a

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00:19:41,440 --> 00:19:44,800
Speaker 1: really interesting request for help on this subject in you

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Speaker 1: Oh yeah, yeah, there was because I wanted to check

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Speaker 1: to make sure that my math was right on how

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

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Speaker 1: ten by ten by ten by ten thing. But I

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Speaker 1: I did one of those searchers to just see people

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Speaker 1: 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 contractor's box, you know, like at

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Speaker 1: a house we had real estate agent. Yeah, and she

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Speaker 1: says it's like a thirty dollar box. So she didn't

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Speaker 1: want to just have it, you know, cut into and

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

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Speaker 1: 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

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

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Speaker 1: than five thousand, yeah, just like or something. Yeah. And

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

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Speaker 1: they included a list of all of them for her convenience, so, um,

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Speaker 1: and I don't know how that came out. I wonder

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Speaker 1: if she then took that list and just painstakingly, uh

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Speaker 1: spent the time to try each one out, or if

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00:20:55,400 --> 00:20:59,639
Speaker 1: she decided, you know, actually that inputting all those numbers

388
00:20:59,720 --> 00:21:01,920
Speaker 1: is not worth the thirty dollars that I would save

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00:21:02,040 --> 00:21:04,760
Speaker 1: by keeping the box attacked. That sounds like a fun Saturday,

390
00:21:04,840 --> 00:21:07,199
Speaker 1: you know, on the porch in front of the box

391
00:21:07,240 --> 00:21:11,640
Speaker 1: with a case of beer. Hopefully it's nice weather. But anyway,

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00:21:11,840 --> 00:21:15,680
Speaker 1: So yeah, this is problems that can potentially be brutally

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00:21:15,760 --> 00:21:18,320
Speaker 1: hard to solve, but they're easy to check once you

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00:21:18,359 --> 00:21:21,840
Speaker 1: have a certificate of the answer. Another classic and often

395
00:21:21,920 --> 00:21:26,840
Speaker 1: cited example of an MP complete problem is the traveling

396
00:21:26,920 --> 00:21:30,240
Speaker 1: salesman problem. Now I think something is exciting because now

397
00:21:30,280 --> 00:21:34,280
Speaker 1: we have something with more of an anthropomorphic name smply scenario.

398
00:21:34,359 --> 00:21:36,639
Speaker 1: It's like the Infinity Hotel. Well, I want to change

399
00:21:36,640 --> 00:21:38,080
Speaker 1: it up, and I want to call I want to

400
00:21:38,119 --> 00:21:42,159
Speaker 1: call this the nationwide infection problem. So imagine that you

401
00:21:42,200 --> 00:21:45,240
Speaker 1: are the vanguard of an alien species that has come

402
00:21:45,280 --> 00:21:48,080
Speaker 1: to Earth, and you want to land in a country,

403
00:21:48,200 --> 00:21:52,280
Speaker 1: say the United States, and infect at least one person

404
00:21:52,400 --> 00:21:56,960
Speaker 1: in every township and municipality in this country with one

405
00:21:57,200 --> 00:22:01,360
Speaker 1: of your larvae. But you've got limited time to do it. Okay,

406
00:22:01,400 --> 00:22:04,399
Speaker 1: you know, no dilly dalian around. So what you're looking

407
00:22:04,440 --> 00:22:07,040
Speaker 1: for is a route that you can plan out on

408
00:22:07,119 --> 00:22:10,760
Speaker 1: your alien equivalent of Google Maps directions or or you know,

409
00:22:10,840 --> 00:22:13,639
Speaker 1: your Apple Directions device that will tell you how to

410
00:22:13,680 --> 00:22:16,800
Speaker 1: go to every single city and township in the country

411
00:22:16,920 --> 00:22:22,360
Speaker 1: only one time each in the shortest route possible. Okay,

412
00:22:22,680 --> 00:22:26,000
Speaker 1: that's not easy. If you just had four or five cities,

413
00:22:26,000 --> 00:22:28,120
Speaker 1: this wouldn't be such a big deal for a computer

414
00:22:28,200 --> 00:22:32,760
Speaker 1: to figure out. Once you start adding hundreds or thousands

415
00:22:32,760 --> 00:22:35,639
Speaker 1: of cities, how is it going to figure this out?

416
00:22:36,000 --> 00:22:38,480
Speaker 1: The only way we know of is back to brute force.

417
00:22:38,960 --> 00:22:41,520
Speaker 1: It could try one method, so well, you go to

418
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Speaker 1: city A, and then city B, and then city C

419
00:22:44,520 --> 00:22:46,359
Speaker 1: and then D and go all the way around the

420
00:22:46,359 --> 00:22:49,240
Speaker 1: country and see how long that takes. And then it

421
00:22:49,280 --> 00:22:52,679
Speaker 1: could try again with first city B and then A,

422
00:22:53,080 --> 00:22:55,760
Speaker 1: and then the same from there and then so you

423
00:22:55,840 --> 00:23:00,560
Speaker 1: end up getting these exponentially multiplying combinations there. It is

424
00:23:00,600 --> 00:23:03,800
Speaker 1: just going to take massive amounts of time and computing

425
00:23:03,840 --> 00:23:08,280
Speaker 1: power to figure out what is actually the shortest trip. Now,

426
00:23:08,480 --> 00:23:11,160
Speaker 1: you might already see an issue with including this problem

427
00:23:11,200 --> 00:23:13,800
Speaker 1: within in P and actually reade an interesting blog post

428
00:23:13,880 --> 00:23:18,639
Speaker 1: by somebody writing for for IBM about how, under certain

429
00:23:18,720 --> 00:23:22,120
Speaker 1: conditions this problem actually isn't in P depending on how

430
00:23:22,200 --> 00:23:25,000
Speaker 1: you define what you're checking for, Like if you're just

431
00:23:25,040 --> 00:23:28,120
Speaker 1: looking to check that a given route is a correct solution,

432
00:23:28,520 --> 00:23:31,960
Speaker 1: it visits every city only once. Uh, then it is

433
00:23:32,000 --> 00:23:34,359
Speaker 1: easy to check. You can check that very quickly. All right,

434
00:23:34,440 --> 00:23:38,880
Speaker 1: this comes down to accurately defining the problem right exactly now.

435
00:23:38,920 --> 00:23:41,280
Speaker 1: If you're looking to check that it visits every city

436
00:23:41,359 --> 00:23:44,720
Speaker 1: only once under a certain mileage, that's also easy to checking.

437
00:23:44,880 --> 00:23:47,680
Speaker 1: Just see that it visited every city once and see

438
00:23:47,680 --> 00:23:50,320
Speaker 1: how long it took. But if you're looking to verify

439
00:23:50,359 --> 00:23:53,160
Speaker 1: whether a solution is in fact the shortest of all

440
00:23:53,200 --> 00:23:57,120
Speaker 1: possible routes, that's not easy to check because you'd still

441
00:23:57,200 --> 00:23:59,760
Speaker 1: have you'd essentially have to do the entire brute force

442
00:23:59,800 --> 00:24:02,880
Speaker 1: meth that that way and compare it to every other possibility.

443
00:24:03,119 --> 00:24:06,560
Speaker 1: All possible routes have to be have to be considered.

444
00:24:06,720 --> 00:24:08,840
Speaker 1: So if if that's what you're going for, it's not

445
00:24:08,960 --> 00:24:11,280
Speaker 1: hard to solve, easy to check. It's hard to solve

446
00:24:11,320 --> 00:24:14,479
Speaker 1: and hard to check. But here's the big problem with

447
00:24:14,600 --> 00:24:17,040
Speaker 1: the with the P versus n P issue. We know

448
00:24:17,160 --> 00:24:21,560
Speaker 1: that P problems are a subset of MP problems, But

449
00:24:21,720 --> 00:24:25,840
Speaker 1: what if the subset is actually the same as the set,

450
00:24:26,400 --> 00:24:31,000
Speaker 1: Meaning what if all NP problems are actually P problems?

451
00:24:31,680 --> 00:24:34,320
Speaker 1: Meaning what if all problems where we can check the

452
00:24:34,359 --> 00:24:39,240
Speaker 1: answer are actually problems where we can solve them efficiently.

453
00:24:39,640 --> 00:24:43,440
Speaker 1: We just haven't figured out how to solve them efficiently yet, Okay,

454
00:24:43,560 --> 00:24:46,399
Speaker 1: Or we haven't developed the here the machines that can

455
00:24:46,440 --> 00:24:50,040
Speaker 1: do it. Yeah. Yeah, so is that possible? And that's

456
00:24:50,080 --> 00:24:54,879
Speaker 1: actually probably the single biggest open question in computer science today.

457
00:24:54,920 --> 00:24:58,440
Speaker 1: Is P equal or not equal to n P? Are

458
00:24:58,480 --> 00:25:02,280
Speaker 1: they or are they not? Equival valence sets? Now, the

459
00:25:02,359 --> 00:25:07,479
Speaker 1: obvious answer is no, Right, that seems intuitive, That seems

460
00:25:07,600 --> 00:25:10,000
Speaker 1: that that seems to be the answer. That that feels

461
00:25:10,040 --> 00:25:13,679
Speaker 1: most in keeping with our our understanding of the limits

462
00:25:13,680 --> 00:25:16,199
Speaker 1: of human ability, the limits of human knowledge, and just

463
00:25:16,240 --> 00:25:18,600
Speaker 1: sort of the fabric of our universe. Yeah, and so

464
00:25:18,720 --> 00:25:22,280
Speaker 1: most computer scientists and mathematicians, I think agree that the

465
00:25:22,359 --> 00:25:25,280
Speaker 1: more likely answer to this unsolved question is that P

466
00:25:25,640 --> 00:25:29,399
Speaker 1: does not equal MP. I found one pole that was taken.

467
00:25:29,400 --> 00:25:30,960
Speaker 1: It was more than ten years ago. I don't know

468
00:25:31,000 --> 00:25:33,480
Speaker 1: if things have changed much since then. But in two

469
00:25:33,480 --> 00:25:37,439
Speaker 1: thousand two, the University of Maryland computer scientist William I.

470
00:25:37,600 --> 00:25:40,920
Speaker 1: Guess Arc did a poll of colleagues in complexity theory

471
00:25:41,400 --> 00:25:44,000
Speaker 1: and UH and he found the results. And so he

472
00:25:44,040 --> 00:25:47,280
Speaker 1: found out of this poll of colleagues, sixty one of

473
00:25:47,359 --> 00:25:51,639
Speaker 1: his colleagues thought that P did not equal MP. Nine

474
00:25:51,880 --> 00:25:54,760
Speaker 1: thought that P did equal MP, And some of those

475
00:25:54,840 --> 00:25:58,040
Speaker 1: that said that said they they said that basically just

476
00:25:58,119 --> 00:26:02,120
Speaker 1: to be contrarian or to continue encouraging people to research

477
00:26:02,200 --> 00:26:06,399
Speaker 1: the possibility. UH. And then several other colleagues either offered

478
00:26:06,440 --> 00:26:10,800
Speaker 1: no opinion or offered UH sort of complex answers that

479
00:26:10,880 --> 00:26:14,680
Speaker 1: weren't yes or no. But so you can obviously see

480
00:26:14,680 --> 00:26:17,479
Speaker 1: that the opinion that P does equal MP, or the

481
00:26:17,480 --> 00:26:22,280
Speaker 1: prediction that that will be what's eventually proved, is the minority.

482
00:26:22,800 --> 00:26:25,040
Speaker 1: It's not what we would tend to think is the

483
00:26:25,080 --> 00:26:31,240
Speaker 1: more likely possibility. Okay, it's the more outsider consideration here.

484
00:26:31,600 --> 00:26:34,320
Speaker 1: So it let's assume for a second that that is

485
00:26:34,400 --> 00:26:37,800
Speaker 1: the case that one day some amazing mathematician or computer

486
00:26:37,840 --> 00:26:40,640
Speaker 1: scientists somebody comes along and they figure out a way

487
00:26:40,680 --> 00:26:43,520
Speaker 1: to prove that P does not equal in P. Proof

488
00:26:43,560 --> 00:26:46,479
Speaker 1: Proofs like this happen in UH, in math and computer

489
00:26:46,520 --> 00:26:48,400
Speaker 1: science all the time. You might be wondering how could

490
00:26:48,400 --> 00:26:51,080
Speaker 1: that be proved, But people figure out ways to demonstrate

491
00:26:51,880 --> 00:26:55,200
Speaker 1: logically that something is true like this. So let's say

492
00:26:55,200 --> 00:26:58,719
Speaker 1: it's demonstrated that P does not equal MP. What are

493
00:26:58,760 --> 00:27:03,040
Speaker 1: the implications. Well, mostly I'd say not much changes. This

494
00:27:03,119 --> 00:27:05,359
Speaker 1: is sort of the obvious conclusion. It's the one that

495
00:27:05,400 --> 00:27:08,720
Speaker 1: wouldn't surprise us. In other words, all of our brute

496
00:27:08,760 --> 00:27:13,400
Speaker 1: force problems remain brute force problems. Uh. But if this

497
00:27:13,480 --> 00:27:15,159
Speaker 1: is the case, it would still be useful to know.

498
00:27:15,440 --> 00:27:17,920
Speaker 1: It would be useful to people. Wouldn't start floating into

499
00:27:17,960 --> 00:27:20,359
Speaker 1: the sky, the great old ones, wouldn't come back, It

500
00:27:20,400 --> 00:27:23,280
Speaker 1: would just the business is usual for most people. Yeah,

501
00:27:23,440 --> 00:27:25,920
Speaker 1: So we we'd have a proof so people can stop

502
00:27:25,960 --> 00:27:28,040
Speaker 1: trying to solve it, and we'd be able to use

503
00:27:28,119 --> 00:27:30,359
Speaker 1: the fact that P is not equal to MP as

504
00:27:30,359 --> 00:27:34,600
Speaker 1: an assumption for other work in mathematics and computer science.

505
00:27:34,920 --> 00:27:37,760
Speaker 1: So we just move on with our lives basically essentially.

506
00:27:38,440 --> 00:27:41,800
Speaker 1: But here's where things get interesting. What would the implications

507
00:27:41,840 --> 00:27:47,000
Speaker 1: be if P does equal MP. Well, right off the

508
00:27:47,040 --> 00:27:48,679
Speaker 1: top of my head, of of course, what comes to

509
00:27:48,720 --> 00:27:51,160
Speaker 1: mind is the the the use of encryption that we've

510
00:27:51,160 --> 00:27:54,399
Speaker 1: already talked about. Like that's really like our most everyday

511
00:27:54,800 --> 00:27:59,520
Speaker 1: interaction with with the idea of of of P and NP. Yeah,

512
00:27:59,560 --> 00:28:01,600
Speaker 1: I mean, why is it not easy for me to

513
00:28:01,800 --> 00:28:05,040
Speaker 1: get into those photos you have on your phone? Whatever

514
00:28:05,080 --> 00:28:08,280
Speaker 1: they are? Well, it's because it's because we use these

515
00:28:08,400 --> 00:28:13,720
Speaker 1: encryption methods, and almost all current encryption methods would be

516
00:28:14,040 --> 00:28:17,800
Speaker 1: subject to UH. They just depend on the fact that

517
00:28:17,880 --> 00:28:21,439
Speaker 1: you don't have, you know, tons of supercomputers and time

518
00:28:22,000 --> 00:28:25,800
Speaker 1: to sit around trying to brute force crack into people's junk.

519
00:28:26,600 --> 00:28:29,520
Speaker 1: But if you were able to reduce those problems to

520
00:28:30,000 --> 00:28:33,480
Speaker 1: UH to essentially easily solvable problems, problems that could be

521
00:28:33,480 --> 00:28:38,640
Speaker 1: solved in regular polynomial time, the P class then suddenly, yeah,

522
00:28:38,760 --> 00:28:41,560
Speaker 1: by by encryption essentially, I mean, we can't know for

523
00:28:41,640 --> 00:28:44,400
Speaker 1: sure exactly how big a deal this uh this would

524
00:28:44,440 --> 00:28:47,160
Speaker 1: be in terms of applied sciences and technology, but the

525
00:28:47,320 --> 00:28:51,640
Speaker 1: likely implication UH seems to be that any job currently

526
00:28:51,720 --> 00:28:55,680
Speaker 1: hindered by the limits of brute force computation would be revolutionized.

527
00:28:55,920 --> 00:28:59,240
Speaker 1: So yeah, there's the there's the prime factorization issue that

528
00:28:59,240 --> 00:29:02,800
Speaker 1: that feeds into encryption. And the informal way of summing

529
00:29:02,800 --> 00:29:04,880
Speaker 1: this up is that, you know, if if if our

530
00:29:04,920 --> 00:29:08,800
Speaker 1: present methods of encryption and data security are like a

531
00:29:08,920 --> 00:29:12,959
Speaker 1: like a plastic diary lock on our information, every hacker

532
00:29:13,000 --> 00:29:15,280
Speaker 1: on Earth might have access to a pair of fourteen

533
00:29:15,320 --> 00:29:18,200
Speaker 1: inch bolt cutters of the equals in p on the

534
00:29:18,240 --> 00:29:20,280
Speaker 1: other hand, and this would be a positive. It could

535
00:29:20,280 --> 00:29:23,560
Speaker 1: also mean that research projects that rely on brute force

536
00:29:23,640 --> 00:29:27,960
Speaker 1: computation could also potentially see huge leaps forward. For example,

537
00:29:28,000 --> 00:29:30,840
Speaker 1: one one that I saw, I've seen mentioned, and I've

538
00:29:30,840 --> 00:29:34,120
Speaker 1: read about before is protein folding simulation. Have you ever

539
00:29:34,160 --> 00:29:37,360
Speaker 1: read about this? Uh? Yeah, a little bit. Um. I

540
00:29:37,360 --> 00:29:41,320
Speaker 1: think I tended a discussion on on the topic a

541
00:29:41,360 --> 00:29:44,800
Speaker 1: few years back. Yeah. So this research, it involves going

542
00:29:44,840 --> 00:29:49,200
Speaker 1: through permutations of of different ways of folding proteins in

543
00:29:49,400 --> 00:29:53,600
Speaker 1: a computer simulation. And this research could help cure diseases

544
00:29:53,640 --> 00:29:56,160
Speaker 1: and treat medical conditions if we learned the right things

545
00:29:56,160 --> 00:29:59,240
Speaker 1: about the behavior of how protein molecules fold up on

546
00:29:59,240 --> 00:30:02,440
Speaker 1: one another and be have in the body. But simulating

547
00:30:02,480 --> 00:30:06,480
Speaker 1: all of these folding permutations is a brute force computing project.

548
00:30:06,840 --> 00:30:09,880
Speaker 1: So if this actually reduced to a p problem, we

549
00:30:09,960 --> 00:30:13,160
Speaker 1: might be able to hasten research that saves lives, maybe

550
00:30:13,160 --> 00:30:16,760
Speaker 1: cure cancer. Who knows. Okay, so we're already we're looking

551
00:30:16,760 --> 00:30:18,960
Speaker 1: at a world where maybe we have to go back

552
00:30:19,000 --> 00:30:22,920
Speaker 1: to using just normal mail instead of email. But on

553
00:30:22,960 --> 00:30:26,000
Speaker 1: the other hand, and maybe we cure cantor or if

554
00:30:26,040 --> 00:30:29,480
Speaker 1: people I mean digital security, maybe could still be a thing.

555
00:30:29,560 --> 00:30:32,000
Speaker 1: If people just come up with another method, we just

556
00:30:32,040 --> 00:30:36,040
Speaker 1: our current methods would would possibly become obsolete. Yeah, and

557
00:30:36,040 --> 00:30:39,280
Speaker 1: then the new method will be sealed envelope under your

558
00:30:39,280 --> 00:30:42,400
Speaker 1: back or a chest buried in your backyard. Yeah, that's

559
00:30:42,400 --> 00:30:45,320
Speaker 1: where you have to keep all You'd have to physically

560
00:30:45,400 --> 00:30:48,280
Speaker 1: meet up with everybody to trade information with an agree

561
00:30:48,280 --> 00:30:51,000
Speaker 1: on a password in person. You know. That would be interesting.

562
00:30:51,520 --> 00:30:57,200
Speaker 1: Like trying to imagine a a digital civilization that suddenly

563
00:30:57,240 --> 00:31:00,640
Speaker 1: has to become a non digital civilization, but wants to

564
00:31:00,760 --> 00:31:04,240
Speaker 1: keep everything uh operating more or less as it did

565
00:31:04,240 --> 00:31:06,520
Speaker 1: when it lives digital, you know, like they still want

566
00:31:06,560 --> 00:31:11,520
Speaker 1: to use tender uh, but they can no longer use

567
00:31:11,640 --> 00:31:14,040
Speaker 1: a true digital version of it. What does that even

568
00:31:14,080 --> 00:31:18,040
Speaker 1: consist of? Oh wow, Yeah, that's fascinating. But of course

569
00:31:18,080 --> 00:31:22,360
Speaker 1: the changes wouldn't just be in applied sciences and technology.

570
00:31:22,400 --> 00:31:25,920
Speaker 1: One of the interesting things about this is multiple experts

571
00:31:25,960 --> 00:31:28,960
Speaker 1: have commented that if we live in a P equals

572
00:31:28,960 --> 00:31:33,880
Speaker 1: in P universe, we have been sorely mistaken about what

573
00:31:34,040 --> 00:31:37,840
Speaker 1: reality is like. If this is the universe we inhabit,

574
00:31:37,920 --> 00:31:40,800
Speaker 1: in fact, it is quite different than we thought. And

575
00:31:41,120 --> 00:31:44,720
Speaker 1: one one thing I want to quote is by Scott Aaronson,

576
00:31:44,800 --> 00:31:49,080
Speaker 1: who offers this as quote a physical philosophical argument against

577
00:31:49,200 --> 00:31:52,440
Speaker 1: P equals in P, and he says, quote, if P

578
00:31:52,560 --> 00:31:55,440
Speaker 1: equals in P, then the world would be a profoundly

579
00:31:55,600 --> 00:31:58,880
Speaker 1: different place than we usually assume it to be. There

580
00:31:58,920 --> 00:32:03,000
Speaker 1: would be no special value for creative leaps, no fundamental

581
00:32:03,040 --> 00:32:07,040
Speaker 1: gap between solving a problem and recognizing the solution once

582
00:32:07,080 --> 00:32:11,840
Speaker 1: it's found. Everyone who could appreciate a symphony would be Mozart,

583
00:32:12,360 --> 00:32:15,600
Speaker 1: Everyone who could follow a step by step argument would

584
00:32:15,640 --> 00:32:19,960
Speaker 1: be Gauss. Everyone who could recognize a good investment strategy

585
00:32:20,320 --> 00:32:23,120
Speaker 1: would be Warren Buffett. It's possible to put the point

586
00:32:23,200 --> 00:32:26,360
Speaker 1: in Darwinian terms. If this is the sort of universe

587
00:32:26,400 --> 00:32:30,200
Speaker 1: we inhabited, why wouldn't we already have evolved to take

588
00:32:30,200 --> 00:32:35,600
Speaker 1: advantage of it. That's a really interesting point. But at

589
00:32:35,680 --> 00:32:37,959
Speaker 1: the same time, so he's framing it in terms of it.

590
00:32:38,040 --> 00:32:40,280
Speaker 1: This is one among a list of arguments he gives

591
00:32:40,280 --> 00:32:43,800
Speaker 1: that P probably does not equal and seems to be

592
00:32:43,960 --> 00:32:47,240
Speaker 1: This seems to be very much an argument in favor

593
00:32:47,520 --> 00:32:51,000
Speaker 1: of their, of their being no equality here, right, But

594
00:32:51,040 --> 00:32:53,120
Speaker 1: you could also look at that look at it as

595
00:32:53,160 --> 00:32:56,240
Speaker 1: an interesting comment on how different the world would be

596
00:32:56,640 --> 00:32:59,600
Speaker 1: from how we assume it is if this were in

597
00:32:59,640 --> 00:33:02,680
Speaker 1: fact case. And it's interesting to note that we shouldn't

598
00:33:02,680 --> 00:33:05,360
Speaker 1: assume that just because it doesn't feel like we live

599
00:33:05,360 --> 00:33:07,880
Speaker 1: in a P equals in P world, that P equals

600
00:33:07,960 --> 00:33:12,000
Speaker 1: np is necessarily false. Our our intuitions about what's possible

601
00:33:12,040 --> 00:33:14,560
Speaker 1: in the math and problem solving space have turned out

602
00:33:14,560 --> 00:33:18,000
Speaker 1: to be very wrong in the past, and sometimes long

603
00:33:18,040 --> 00:33:21,240
Speaker 1: standing problems in math and computer science are solved by

604
00:33:21,560 --> 00:33:24,080
Speaker 1: UH solved. They're solved or proved in ways that just

605
00:33:24,120 --> 00:33:29,240
Speaker 1: seem extremely peculiar. Yet you can't deny the result. I

606
00:33:29,240 --> 00:33:32,479
Speaker 1: can't help but circle back around to the earlier opinion

607
00:33:32,520 --> 00:33:36,680
Speaker 1: that we mentioned is attributed to Freeman Dyson that mathematics

608
00:33:36,840 --> 00:33:40,800
Speaker 1: is inexhaustible. If P equals MP in the universe, is

609
00:33:40,840 --> 00:33:46,000
Speaker 1: the universe really inexhaustible? And uh? And if P equals mp,

610
00:33:46,200 --> 00:33:47,640
Speaker 1: does that mean that there is, in a sense, that

611
00:33:47,680 --> 00:33:51,560
Speaker 1: a universal algorithm out there? Uh? I mean there there

612
00:33:51,640 --> 00:33:55,200
Speaker 1: is a theory of everything within our mathematical universes, as

613
00:33:55,280 --> 00:33:58,240
Speaker 1: math max tech Mark argues in mathematical universe theory that

614
00:33:58,240 --> 00:34:00,680
Speaker 1: we mentioned earlier. Because the tech Mark even go so

615
00:34:00,720 --> 00:34:04,040
Speaker 1: far as to predict that a mathematical proof for a

616
00:34:04,120 --> 00:34:07,520
Speaker 1: theory of everything could eventually fit on a T shirt. Well,

617
00:34:07,560 --> 00:34:09,319
Speaker 1: I would kind of like to do that the kind

618
00:34:09,320 --> 00:34:12,600
Speaker 1: of universe. Yeah, I mean, that's an interesting thought on

619
00:34:12,640 --> 00:34:15,239
Speaker 1: its own, and tech marks theories I I, as I

620
00:34:15,280 --> 00:34:17,520
Speaker 1: think I said earlier in this episode, I find them

621
00:34:17,640 --> 00:34:20,560
Speaker 1: very interesting, even if I'm not qualified enough to know

622
00:34:20,640 --> 00:34:24,319
Speaker 1: whether they're really rigorous physics. I read that book Our

623
00:34:24,400 --> 00:34:28,600
Speaker 1: Our Mathematical Universe, and I found it amazingly stimulating. You know.

624
00:34:28,640 --> 00:34:32,759
Speaker 1: He talks about different levels of of multiverse realities and

625
00:34:33,200 --> 00:34:37,000
Speaker 1: what they each imply. Uh, And he gives a very,

626
00:34:37,120 --> 00:34:39,760
Speaker 1: at least to the lay person, a very reasonable sounding

627
00:34:40,400 --> 00:34:43,520
Speaker 1: explanation of how these are natural conclusions from what we

628
00:34:43,560 --> 00:34:46,440
Speaker 1: know about physics. But I do want to use what

629
00:34:46,520 --> 00:34:48,319
Speaker 1: you said as a sort of jumping off point to

630
00:34:48,360 --> 00:34:52,960
Speaker 1: take a broader view about the algorithmic nature of reality.

631
00:34:53,000 --> 00:34:55,160
Speaker 1: But first we're going to take a quick break to

632
00:34:55,200 --> 00:35:02,040
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633
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636
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637
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639
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640
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Speaker 1: out and make one of these things. We don't have

641
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Speaker 1: the money to throw at some sort of big fancy,

642
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643
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644
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645
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646
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Speaker 1: that professional looking website. Yeah, it'll look great and it

647
00:35:38,640 --> 00:35:41,760
Speaker 1: won't be scary. They take away all of the gears

648
00:35:41,800 --> 00:35:44,440
Speaker 1: and the creepiness of designing a website. They make it

649
00:35:44,520 --> 00:35:47,600
Speaker 1: super intuitive, super easy. You have what you need and

650
00:35:47,640 --> 00:35:49,960
Speaker 1: you can make it yourself in no time. And hey,

651
00:35:49,960 --> 00:35:52,239
Speaker 1: if you want to use our offer code, you can

652
00:35:52,320 --> 00:35:55,600
Speaker 1: go to squarespace dot com and enter in mind blown

653
00:35:55,680 --> 00:35:58,640
Speaker 1: to get twenty off your first purchase and a free

654
00:35:58,680 --> 00:36:00,759
Speaker 1: domain when you sign up today. So go check out

655
00:36:00,800 --> 00:36:05,520
Speaker 1: squarespace dot com special code mind blown and get started

656
00:36:05,600 --> 00:36:13,759
Speaker 1: making that awesome website. So whatever the solution to P

657
00:36:13,880 --> 00:36:16,279
Speaker 1: equals and P turns out to be. I think one

658
00:36:16,320 --> 00:36:20,000
Speaker 1: thing that's very interesting about it is just the idea

659
00:36:20,160 --> 00:36:24,680
Speaker 1: that this problem in computer science runs under the skin

660
00:36:24,800 --> 00:36:28,320
Speaker 1: of everything that exists. You know, It's it's not something

661
00:36:28,360 --> 00:36:32,000
Speaker 1: that people just made up. This is talking about a

662
00:36:32,080 --> 00:36:34,880
Speaker 1: fact about the universe. That would be a fact about

663
00:36:34,920 --> 00:36:38,879
Speaker 1: the universe whether we were here to discuss it or not. Yeah,

664
00:36:38,880 --> 00:36:41,960
Speaker 1: I mean there's a certain amount of it's difficult, kind

665
00:36:41,960 --> 00:36:44,920
Speaker 1: of to get to get outside of the mirror language

666
00:36:45,360 --> 00:36:48,000
Speaker 1: of the situation when we're talking about problem solving, because

667
00:36:48,000 --> 00:36:50,520
Speaker 1: we can't help but think about a human mind trying

668
00:36:50,560 --> 00:36:54,160
Speaker 1: to solve a problem. But in a sense, problem solving

669
00:36:54,560 --> 00:36:57,160
Speaker 1: takes place not only the human level, it takes place

670
00:36:57,160 --> 00:37:00,160
Speaker 1: that at at at at the animal level, at as

671
00:37:00,239 --> 00:37:03,280
Speaker 1: you know, as this particular entity is trying to navigate

672
00:37:03,320 --> 00:37:06,680
Speaker 1: a world of fixed and moving objects, generally to acquire

673
00:37:06,760 --> 00:37:09,879
Speaker 1: some goal, to acquire food or or a mate. Uh.

674
00:37:10,239 --> 00:37:12,960
Speaker 1: You could even you could probably even extrapolate it as

675
00:37:12,960 --> 00:37:15,920
Speaker 1: far to say to say that that an object obeying

676
00:37:15,960 --> 00:37:19,320
Speaker 1: gravity is kind of engaging in a sort of non

677
00:37:19,680 --> 00:37:25,520
Speaker 1: mental problem solving in a way, in in a limited way. Uh,

678
00:37:25,719 --> 00:37:27,680
Speaker 1: I guess I'm just trying to drive home here, is

679
00:37:27,719 --> 00:37:30,680
Speaker 1: that when we continue to talk about problem solving here,

680
00:37:31,120 --> 00:37:33,440
Speaker 1: it's like trying not to think about it as much

681
00:37:34,280 --> 00:37:37,799
Speaker 1: within in the human realm, because it is we're about

682
00:37:37,800 --> 00:37:40,520
Speaker 1: to see it gets well outside of it, remove the

683
00:37:40,560 --> 00:37:45,560
Speaker 1: consciousness from it, retain only the teleology exactly that there

684
00:37:45,719 --> 00:37:48,279
Speaker 1: there are, there are steps towards a purpose, but you

685
00:37:48,360 --> 00:37:50,680
Speaker 1: don't have to know what the purpose is, and you

686
00:37:50,719 --> 00:37:54,000
Speaker 1: don't even have to realize you're taking steps right now.

687
00:37:54,040 --> 00:37:57,200
Speaker 1: A great example of this is the slime mold. So

688
00:37:57,280 --> 00:38:00,520
Speaker 1: slime molds don't have brains. They consist of a single

689
00:38:00,560 --> 00:38:04,000
Speaker 1: cell containing millions of nuclei and they form a network

690
00:38:04,080 --> 00:38:08,520
Speaker 1: of protoplasmic tubes to creep toward a food source along

691
00:38:08,560 --> 00:38:12,160
Speaker 1: the shortest path. And that's essential here. Uh. It sends

692
00:38:12,200 --> 00:38:16,000
Speaker 1: out limbs to find food, and when it finds food source,

693
00:38:16,200 --> 00:38:19,359
Speaker 1: it spreads over, It secretes too digestive enzymes, and has

694
00:38:19,400 --> 00:38:23,480
Speaker 1: its meal. When it doesn't, yeah, it's pretty it's pretty great. Essentially,

695
00:38:23,520 --> 00:38:26,840
Speaker 1: have a blob, right and when it doesn't find food,

696
00:38:27,000 --> 00:38:29,839
Speaker 1: then the limb you know, dies in retreats back. So

697
00:38:29,920 --> 00:38:33,920
Speaker 1: in this it creates a network for transporting nutrients and chemicals,

698
00:38:34,480 --> 00:38:38,480
Speaker 1: for inter cellular communication and the method allows them to

699
00:38:38,520 --> 00:38:41,320
Speaker 1: perform such feats and this is generally and this is

700
00:38:41,320 --> 00:38:44,640
Speaker 1: in lab environments. Uh, such feats as solving a maze,

701
00:38:44,760 --> 00:38:46,600
Speaker 1: like a straight up maze that you would put a

702
00:38:46,640 --> 00:38:50,560
Speaker 1: mouse in, as well as when presented with a miniaturized

703
00:38:50,600 --> 00:38:54,200
Speaker 1: earth environment. Uh, they can they can recreate some of

704
00:38:54,200 --> 00:38:57,279
Speaker 1: the great trade routes of the world, some of the

705
00:38:57,320 --> 00:39:03,359
Speaker 1: great highway systems. They can model cancer growth. Um. Let

706
00:39:03,360 --> 00:39:06,080
Speaker 1: me go to a little detail about the silk road thing,

707
00:39:06,360 --> 00:39:08,719
Speaker 1: and this is this gets into exactly what we're talking

708
00:39:08,719 --> 00:39:12,560
Speaker 1: earlier about an algorithm attempting to to plot a course

709
00:39:13,120 --> 00:39:14,759
Speaker 1: right and and having to hit all those stuffs the

710
00:39:14,800 --> 00:39:19,200
Speaker 1: salesman problem that we're discussing earlier. Okay, so back into

711
00:39:19,719 --> 00:39:24,960
Speaker 1: computer scientist Andre Adamanski from the University of the West

712
00:39:24,960 --> 00:39:27,839
Speaker 1: of England. He took a globe. Okay, and you can

713
00:39:27,840 --> 00:39:29,360
Speaker 1: do this at home probably, I guess if you have

714
00:39:29,440 --> 00:39:32,799
Speaker 1: access to the materials. He took a globe and he

715
00:39:32,840 --> 00:39:35,200
Speaker 1: coated it with auger. All right, this, of course is

716
00:39:35,200 --> 00:39:37,080
Speaker 1: the stuff in a peat free dish that you know

717
00:39:37,120 --> 00:39:41,319
Speaker 1: bacteria grows up eat. Yeah. Uh and then he um.

718
00:39:41,560 --> 00:39:44,560
Speaker 1: And then what he did is he removed uh, the

719
00:39:44,840 --> 00:39:47,239
Speaker 1: auger from the areas over the ocean, so it's just

720
00:39:47,280 --> 00:39:49,880
Speaker 1: covering the continents and the land at this point. And

721
00:39:49,920 --> 00:39:52,920
Speaker 1: then he placed oat flakes at the locations of twenty

722
00:39:52,960 --> 00:39:55,560
Speaker 1: four different major cities on the globe, so that's a

723
00:39:55,600 --> 00:39:59,280
Speaker 1: food source. Okay. Then he introduced the slime mold. Alright.

724
00:39:59,320 --> 00:40:01,399
Speaker 1: He did this third any different times, and each time

725
00:40:01,680 --> 00:40:05,279
Speaker 1: the slime mold conquered the world in a slightly different way,

726
00:40:05,440 --> 00:40:10,880
Speaker 1: establishing trade routes between the various oats cities. Yeah, and

727
00:40:10,920 --> 00:40:13,839
Speaker 1: it's the picture their pictures out there. This is pretty remarkable,

728
00:40:14,280 --> 00:40:16,879
Speaker 1: uh because it and maybe a little bit scary because

729
00:40:16,880 --> 00:40:19,399
Speaker 1: you see these ten drils just spreading out all over

730
00:40:19,440 --> 00:40:24,000
Speaker 1: the world. It managed to plan out engineering projects that,

731
00:40:24,080 --> 00:40:26,040
Speaker 1: of course humans can only dream of right now, like

732
00:40:26,080 --> 00:40:29,440
Speaker 1: Transatlantic bridges. Obviously we're not going to do that. It

733
00:40:29,480 --> 00:40:31,160
Speaker 1: wasn't I don't. I don't think it was ever able

734
00:40:31,160 --> 00:40:33,319
Speaker 1: to really conquer the Pacific, be Pacific was just too

735
00:40:33,920 --> 00:40:37,600
Speaker 1: too uh too great of a distance without agur there

736
00:40:37,600 --> 00:40:40,960
Speaker 1: for it to grow on. Uh. But but it also

737
00:40:41,200 --> 00:40:45,400
Speaker 1: managed to recreate the Silk Road as well as the

738
00:40:45,480 --> 00:40:48,680
Speaker 1: modern Asian highway network, which consists of about the eighty

739
00:40:48,719 --> 00:40:51,960
Speaker 1: seven thousand miles of roads running between thirty two countries.

740
00:40:52,680 --> 00:40:56,359
Speaker 1: So it exactly it provides a great example of uh,

741
00:40:56,640 --> 00:41:00,719
Speaker 1: not a problem solving intelligence, but an algorithmic problem solving

742
00:41:01,280 --> 00:41:04,960
Speaker 1: organic system. Of course, this, I do think raises the

743
00:41:05,000 --> 00:41:07,800
Speaker 1: specter of the question how do you tell the difference

744
00:41:07,840 --> 00:41:11,840
Speaker 1: between an algorithm and intelligence unless you want to be

745
00:41:11,880 --> 00:41:16,000
Speaker 1: anthropomorphic and say, well, intelligence is consciousness and the ability

746
00:41:16,040 --> 00:41:18,480
Speaker 1: to love and yeah, and then we start gazing down

747
00:41:18,480 --> 00:41:21,279
Speaker 1: that of this right, Yeah, you know, we get back

748
00:41:21,280 --> 00:41:23,880
Speaker 1: that to some of the problems we've discussed in relation

749
00:41:23,920 --> 00:41:26,719
Speaker 1: to AI in the past. How do you know when

750
00:41:26,760 --> 00:41:29,680
Speaker 1: you created it? If you can't really say what it is,

751
00:41:29,920 --> 00:41:32,840
Speaker 1: it becomes this this problem. We can't even define the problem,

752
00:41:32,920 --> 00:41:34,719
Speaker 1: so how can we come up with the solution or

753
00:41:34,800 --> 00:41:37,200
Speaker 1: check the solution? Yeah, and it's funny that we were

754
00:41:37,200 --> 00:41:41,560
Speaker 1: talking earlier about about sets that sort of recursively consume

755
00:41:41,680 --> 00:41:44,040
Speaker 1: one another. Is one set within another set, but then

756
00:41:44,080 --> 00:41:46,719
Speaker 1: the first set is the second set is also in

757
00:41:46,760 --> 00:41:50,799
Speaker 1: the first set. Now we just gave an example of

758
00:41:50,840 --> 00:41:53,520
Speaker 1: how nature can be like an algorithm. But you can

759
00:41:53,600 --> 00:41:57,279
Speaker 1: also say that plenty of algorithms and computer science have

760
00:41:57,360 --> 00:42:01,920
Speaker 1: been essentially derived from nature. Um and yah, there's a

761
00:42:01,920 --> 00:42:05,520
Speaker 1: great example of this with ants. So ant colonies we've

762
00:42:05,520 --> 00:42:07,440
Speaker 1: discussed adnce here in the podcast before and I'm sure

763
00:42:07,440 --> 00:42:09,160
Speaker 1: we'll cover them again in the future. You know that

764
00:42:09,239 --> 00:42:12,160
Speaker 1: they're complex societies, and we see plenty of examples in

765
00:42:12,160 --> 00:42:16,880
Speaker 1: which colonies accomplished complex tasks that exceed the individual capacities

766
00:42:16,880 --> 00:42:19,160
Speaker 1: of a single ant. Of course, so they worked together

767
00:42:19,760 --> 00:42:23,960
Speaker 1: and they're able to solve problems. And this is mound mind. Yeah, exactly,

768
00:42:24,000 --> 00:42:28,200
Speaker 1: it's the the the the the emergent intelligence of the group,

769
00:42:28,440 --> 00:42:31,880
Speaker 1: the swarm intelligence. Um. This is a particular note to

770
00:42:31,920 --> 00:42:34,720
Speaker 1: computer programming as though, as we see how they're self

771
00:42:34,800 --> 00:42:39,480
Speaker 1: organizing capacities and distributed organization enable them to solve difficult

772
00:42:39,520 --> 00:42:44,719
Speaker 1: optimization and distributed control problems. Okay, So back in a

773
00:42:44,880 --> 00:42:49,600
Speaker 1: Stanford study looked at how harvester ants determine how many

774
00:42:49,680 --> 00:42:53,120
Speaker 1: foragers to send out. Of course, so they're sending out

775
00:42:53,120 --> 00:42:56,120
Speaker 1: a rating party, right, uh, which is you know, more

776
00:42:56,160 --> 00:42:59,759
Speaker 1: complex than than than you might think, because you get

777
00:42:59,800 --> 00:43:01,719
Speaker 1: down to the basic you know, how many do you

778
00:43:01,760 --> 00:43:05,120
Speaker 1: send out? You know, what's the way time? Uh? And

779
00:43:05,120 --> 00:43:07,279
Speaker 1: then they compared this to the manner in which a

780
00:43:07,360 --> 00:43:11,560
Speaker 1: search engine brings back search results. So specifically, yeah, specifically,

781
00:43:11,560 --> 00:43:16,279
Speaker 1: we're talking about transmission control Protocol or TCP, which, if

782
00:43:16,320 --> 00:43:18,960
Speaker 1: you're like me, that's mostly something that you run into

783
00:43:18,960 --> 00:43:22,320
Speaker 1: when you have to adjust something on your your Internet

784
00:43:22,640 --> 00:43:27,759
Speaker 1: situation at home. Yeah, but but in anyway, it's a

785
00:43:27,800 --> 00:43:31,680
Speaker 1: it's essentially an algorithm that manages data congestion on the Internet.

786
00:43:32,480 --> 00:43:36,200
Speaker 1: So they they compared the two, right, and they combined

787
00:43:36,280 --> 00:43:42,000
Speaker 1: the two, and they created the anternet. So that's that's Internet,

788
00:43:42,000 --> 00:43:45,440
Speaker 1: except instead of you, you have an ant um. So

789
00:43:45,480 --> 00:43:50,440
Speaker 1: it's a TCP influenced algorithm that accurately matched ant behavior

790
00:43:50,480 --> 00:43:53,280
Speaker 1: in the experiment. So it's an example of ants reaching

791
00:43:53,320 --> 00:43:58,360
Speaker 1: the same place as our computer programming UM. So basically

792
00:43:58,400 --> 00:44:02,399
Speaker 1: they created a tc PEE program that accurately predicted how

793
00:44:02,440 --> 00:44:05,840
Speaker 1: the ants were going to act. Yeah, we've definitely talked

794
00:44:06,080 --> 00:44:08,480
Speaker 1: on the other podcasts that I do with Jonathan Strickland

795
00:44:08,480 --> 00:44:11,840
Speaker 1: and Lauren Vogelbaum Forward Thinking. We talk about biommetic robotics

796
00:44:11,920 --> 00:44:16,120
Speaker 1: a lot about ways that robots can be inspired by

797
00:44:16,160 --> 00:44:20,160
Speaker 1: the ways that animals move, especially in mobile robotics. You know,

798
00:44:20,160 --> 00:44:22,280
Speaker 1: how do you get a swarm of things to behave

799
00:44:22,360 --> 00:44:25,720
Speaker 1: as a group correctly, and I'd imagine that this would

800
00:44:25,719 --> 00:44:28,879
Speaker 1: be a very good example of how to control them. Yeah,

801
00:44:28,880 --> 00:44:32,560
Speaker 1: you see swarm organisms used in various AI programs. I

802
00:44:32,560 --> 00:44:36,359
Speaker 1: believe here Georgia Tech in Atlanta, they've used bees a lot.

803
00:44:36,560 --> 00:44:39,840
Speaker 1: Oh yeah, yeah. And more recently, a two thousand fourteen

804
00:44:39,880 --> 00:44:43,640
Speaker 1: study from Zero's Institute of Pharmaceutical Sciences explore the possibility

805
00:44:43,640 --> 00:44:47,120
Speaker 1: of using ant algorithms to search for new composite agents

806
00:44:47,120 --> 00:44:50,439
Speaker 1: in the development of new pharmaceuticals. And this gets down

807
00:44:50,440 --> 00:44:52,840
Speaker 1: to the whole in a situation discussing earlier about do

808
00:44:52,880 --> 00:44:56,080
Speaker 1: you do you take a brute uh strength approach to

809
00:44:56,280 --> 00:44:59,920
Speaker 1: finding out which connections amid all these possible connections are

810
00:44:59,920 --> 00:45:04,040
Speaker 1: the ideal ones to then test and explore. Um So

811
00:45:04,160 --> 00:45:06,520
Speaker 1: they've looked into the possibility of using an ant based

812
00:45:06,560 --> 00:45:10,440
Speaker 1: algorithm to find though to do essentially, you know, magically

813
00:45:10,520 --> 00:45:16,080
Speaker 1: guess those uh, those those composite agents that that deserve

814
00:45:16,160 --> 00:45:19,319
Speaker 1: further examination. Well, you know, one thing these examples in

815
00:45:19,440 --> 00:45:22,759
Speaker 1: nature make me think about is an idea that really

816
00:45:22,800 --> 00:45:25,880
Speaker 1: intrigues me, and that's the question of whether evolution by

817
00:45:26,000 --> 00:45:31,560
Speaker 1: natural selection can itself be best interpreted as an algorithm,

818
00:45:31,600 --> 00:45:34,000
Speaker 1: Because think about it. It's it's kind of an iterate

819
00:45:34,080 --> 00:45:37,400
Speaker 1: and test style algorithmic procedure. Let me give you a

820
00:45:37,440 --> 00:45:41,880
Speaker 1: list of steps. Step one, randomly introduce a change that

821
00:45:41,880 --> 00:45:44,440
Speaker 1: would be like a mutant allele into the genome. You

822
00:45:44,680 --> 00:45:48,400
Speaker 1: very one gene from the existing model, so you've random that.

823
00:45:48,520 --> 00:45:51,840
Speaker 1: That's step one. Then step two test against the baseline

824
00:45:51,840 --> 00:45:55,160
Speaker 1: performance rate of the standard allile in the environment or

825
00:45:55,200 --> 00:45:58,680
Speaker 1: you know. The individual steps here would be attempt to copy.

826
00:45:59,640 --> 00:46:03,000
Speaker 1: If being succeeds, go to one or return to the

827
00:46:03,000 --> 00:46:08,239
Speaker 1: first step. If copying fails, return void. It's almost like

828
00:46:08,239 --> 00:46:10,560
Speaker 1: a computer program. Yeah, I think I think there's a

829
00:46:10,640 --> 00:46:13,480
Speaker 1: very strong case to be made there. I mean earlier

830
00:46:13,560 --> 00:46:16,759
Speaker 1: I made that I said that gravity an object of

831
00:46:16,800 --> 00:46:19,120
Speaker 1: band gravity and is it is in a way kind

832
00:46:19,120 --> 00:46:22,840
Speaker 1: of obeying a certain algorithm. It's kind of problem solving.

833
00:46:22,880 --> 00:46:25,919
Speaker 1: So this I think this fits Bill as well. Yeah,

834
00:46:25,960 --> 00:46:28,080
Speaker 1: and I certainly didn't come up with this idea. I know,

835
00:46:28,120 --> 00:46:30,759
Speaker 1: I've read about it in the philosopher Daniel Dennett, who

836
00:46:31,000 --> 00:46:34,360
Speaker 1: advocated this point of view in this nine book. Darwin's

837
00:46:34,440 --> 00:46:37,800
Speaker 1: dangerous idea was which was a lot about the implications

838
00:46:37,800 --> 00:46:42,200
Speaker 1: of evolution beyond just explaining the diversity of species on Earth.

839
00:46:42,719 --> 00:46:47,160
Speaker 1: Evolution is a sort of principle that extends even beyond biology.

840
00:46:47,280 --> 00:46:52,759
Speaker 1: But this universal driving force that that drives design through

841
00:46:52,840 --> 00:46:55,880
Speaker 1: the design space and the way he would explain it.

842
00:46:56,360 --> 00:46:59,440
Speaker 1: But uh so, of course, not everybody agrees with that

843
00:46:59,480 --> 00:47:02,319
Speaker 1: interpretation ation thinks that an algorithm is a good way

844
00:47:02,320 --> 00:47:05,120
Speaker 1: of thinking about evolution. But I've got another follow up

845
00:47:05,200 --> 00:47:08,600
Speaker 1: question that's kind of interesting to me. If evolution is

846
00:47:08,640 --> 00:47:12,080
Speaker 1: an algorithm, is it an efficient algorithm or a brute

847
00:47:12,120 --> 00:47:16,080
Speaker 1: force algorithm? Now that's a good question. I mean, it

848
00:47:16,160 --> 00:47:18,000
Speaker 1: seems to me kind of like it would be a

849
00:47:18,040 --> 00:47:21,400
Speaker 1: brute force algorithm, right, because you're you're trying any number,

850
00:47:21,480 --> 00:47:25,360
Speaker 1: it's you know, just brute force combinatrix. You're trying something.

851
00:47:25,640 --> 00:47:29,320
Speaker 1: Here's a pair of genes, here's an alleal. Does that work? Now? Okay,

852
00:47:29,320 --> 00:47:32,439
Speaker 1: throw in the trash. It seems very wasteful. Yeah, here's

853
00:47:32,440 --> 00:47:35,160
Speaker 1: a lizard with spots, here's one without spots, which one

854
00:47:35,160 --> 00:47:37,560
Speaker 1: gets eaten, which one continues. That sounds like kind of

855
00:47:37,600 --> 00:47:40,120
Speaker 1: a brute brute force You're just like throwing out all

856
00:47:40,160 --> 00:47:42,759
Speaker 1: the produce possible prototypes and uh and seeing what the

857
00:47:42,760 --> 00:47:44,399
Speaker 1: thing you see what happens, and I think this would

858
00:47:44,400 --> 00:47:47,400
Speaker 1: actually be one way of framing the difference between the

859
00:47:47,480 --> 00:47:51,439
Speaker 1: standard scientific materialist view of evolution, which I think would

860
00:47:51,520 --> 00:47:53,960
Speaker 1: probably be is best I can tell. I bet that'd

861
00:47:53,960 --> 00:47:56,680
Speaker 1: be the brute force method, and then some types of

862
00:47:56,719 --> 00:48:00,120
Speaker 1: believers in intelligent design. Right, So if you are are

863
00:48:00,160 --> 00:48:03,640
Speaker 1: somebody who believes um, you you accept the evidence for

864
00:48:03,680 --> 00:48:07,360
Speaker 1: evolution and common descent, but you simply believe the process

865
00:48:07,520 --> 00:48:10,279
Speaker 1: was guided in one way or another. You think aliens

866
00:48:10,480 --> 00:48:13,440
Speaker 1: or a god or some other you know, powerful supernatural

867
00:48:13,680 --> 00:48:18,879
Speaker 1: or otherworldly technological force interfered with evolution, maybe reached in

868
00:48:19,320 --> 00:48:23,239
Speaker 1: to cause specific mutations to the terrestrial gene space at

869
00:48:23,320 --> 00:48:28,000
Speaker 1: key points. That sounds like that would be optimizing the algorithm, right,

870
00:48:28,080 --> 00:48:33,000
Speaker 1: like somebody's introducing artificial efficiency. Yeah, but then again, i'd

871
00:48:33,040 --> 00:48:36,360
Speaker 1: i'd be interested in hearing from the evolutionary biologists and

872
00:48:36,400 --> 00:48:38,680
Speaker 1: geneticists sat there in the audience on this one. Like,

873
00:48:38,719 --> 00:48:42,520
Speaker 1: if you accept the idea that natural selection is like

874
00:48:42,560 --> 00:48:46,120
Speaker 1: an algorithm, is it a brute force algorithm unless you

875
00:48:46,160 --> 00:48:49,640
Speaker 1: go to the intelligent design hypothesis or are there other

876
00:48:49,680 --> 00:48:53,040
Speaker 1: ways of thinking of the algorithm as in some way

877
00:48:53,080 --> 00:48:58,840
Speaker 1: optimized by material circumstances. And since you did mention God

878
00:48:58,960 --> 00:49:03,359
Speaker 1: or the gods, here would the God in this scenario

879
00:49:03,480 --> 00:49:06,000
Speaker 1: that Okay, so this is a force coming from outside

880
00:49:06,040 --> 00:49:12,040
Speaker 1: our universe. Then perhaps in this scenario our world is

881
00:49:12,040 --> 00:49:15,560
Speaker 1: is a ped is not equal in the universe, but

882
00:49:15,719 --> 00:49:18,800
Speaker 1: the realm of the gods is a P equals in

883
00:49:18,920 --> 00:49:22,920
Speaker 1: p universe. Huh. Well, I mean that's an interesting way

884
00:49:22,920 --> 00:49:25,640
Speaker 1: of putting it. Like they can they can knock it

885
00:49:25,680 --> 00:49:29,239
Speaker 1: out right there, Gods, they're basically limitless. Well, it's infinite possibility,

886
00:49:29,280 --> 00:49:33,840
Speaker 1: I mean, the whole the realm of mathematics and logic is.

887
00:49:34,200 --> 00:49:37,359
Speaker 1: In many ways, it often signals to me the sort

888
00:49:37,360 --> 00:49:42,200
Speaker 1: of underlying hint of infinite possibilities but also infinite constraints.

889
00:49:42,640 --> 00:49:46,200
Speaker 1: At the same time. Mathematics is both infinite power and

890
00:49:46,320 --> 00:49:49,960
Speaker 1: ultimate helplessness. You know. It's the power to accomplish anything

891
00:49:50,040 --> 00:49:54,120
Speaker 1: and the inevitability of being thwarted and destroyed by processes

892
00:49:54,160 --> 00:49:58,120
Speaker 1: beyond your control. Uh. It just sort of makes everything

893
00:49:58,160 --> 00:50:01,040
Speaker 1: good and bad possible. But it sounds like you're talking

894
00:50:01,040 --> 00:50:04,080
Speaker 1: about the gods again. It could be Yeah, all right,

895
00:50:04,320 --> 00:50:07,920
Speaker 1: So there you have at p m p P versus

896
00:50:08,040 --> 00:50:10,480
Speaker 1: m P P equals m p P does not equal MP.

897
00:50:10,960 --> 00:50:14,719
Speaker 1: Hopefully at this point you have if you if you

898
00:50:14,760 --> 00:50:17,520
Speaker 1: didn't know what any of this stuff was about beforehand,

899
00:50:17,800 --> 00:50:20,000
Speaker 1: you have a much better graph on the idea. You

900
00:50:20,000 --> 00:50:23,040
Speaker 1: can at least realize why it is a topic that

901
00:50:23,200 --> 00:50:26,719
Speaker 1: people continue to discuss and even argue about. But if

902
00:50:26,719 --> 00:50:30,040
Speaker 1: you want to get into the actual details of of it,

903
00:50:30,080 --> 00:50:32,200
Speaker 1: there are plenty of good resources out there on the

904
00:50:32,200 --> 00:50:34,719
Speaker 1: internet if you are a math and computer science and

905
00:50:34,800 --> 00:50:37,759
Speaker 1: logic inclined person who has a good abstract mind for

906
00:50:37,800 --> 00:50:40,239
Speaker 1: that kind of thing. But either way, I do want

907
00:50:40,239 --> 00:50:43,800
Speaker 1: to remind you to always think about the algorithmic nature

908
00:50:43,920 --> 00:50:46,400
Speaker 1: of the ground beneath your feet, and the laws that

909
00:50:46,480 --> 00:50:49,920
Speaker 1: govern the way the way everything around you works, the

910
00:50:50,440 --> 00:50:55,840
Speaker 1: logic of reality. Uh, Is there a problem solving process

911
00:50:55,920 --> 00:50:58,959
Speaker 1: inherent to everything that's going on around you all the time?

912
00:50:59,200 --> 00:51:01,839
Speaker 1: I don't know. It's it's a strange specter of an

913
00:51:01,840 --> 00:51:05,120
Speaker 1: idea to keep behind your head at all times. Yeah. Again,

914
00:51:05,200 --> 00:51:07,560
Speaker 1: like when a when like a walnut falls out of

915
00:51:07,560 --> 00:51:10,560
Speaker 1: a tree, there are certain there's an there's an algorithm

916
00:51:10,600 --> 00:51:13,040
Speaker 1: at play right as to how exactly it's going to

917
00:51:13,120 --> 00:51:15,160
Speaker 1: make it to the ground, which branches is going to hit.

918
00:51:15,560 --> 00:51:18,160
Speaker 1: I guess that depends on your perspective. Is there is

919
00:51:18,200 --> 00:51:22,040
Speaker 1: there a goal, there is something happening, or did something

920
00:51:22,080 --> 00:51:27,120
Speaker 1: just happen. I don't know, man, it's pretty far out. Well,

921
00:51:27,160 --> 00:51:29,680
Speaker 1: let's not get too much into weird stone or territory here.

922
00:51:29,920 --> 00:51:32,680
Speaker 1: And I do want to say right here at the end,

923
00:51:32,920 --> 00:51:36,240
Speaker 1: if you are somebody who's involved in mathematics or computer

924
00:51:36,320 --> 00:51:39,279
Speaker 1: science and uh and you would like to write in

925
00:51:39,480 --> 00:51:41,960
Speaker 1: to tell us about one of the one of the

926
00:51:41,960 --> 00:51:44,520
Speaker 1: more detailed or complex aspects of this that we didn't

927
00:51:44,520 --> 00:51:46,239
Speaker 1: get to. Like we said, we we gave you the

928
00:51:46,360 --> 00:51:49,560
Speaker 1: very simple version. Please right in and we'd love to

929
00:51:49,760 --> 00:51:52,080
Speaker 1: share your thoughts with the rest of you guys. Yeah,

930
00:51:52,080 --> 00:51:54,160
Speaker 1: and I'd also love to hear from anyone you know

931
00:51:54,200 --> 00:51:57,319
Speaker 1: who's read some some science fiction that definitely weighs in

932
00:51:57,400 --> 00:51:59,640
Speaker 1: on this. Um. You know, I was trying to think

933
00:51:59,680 --> 00:52:04,239
Speaker 1: of sific examples from N. N. Banks culture books, because

934
00:52:04,280 --> 00:52:07,360
Speaker 1: the courent they have these minds, these aies that are

935
00:52:07,560 --> 00:52:10,239
Speaker 1: incredibly powerful. But for the life of me, I can't remember,

936
00:52:10,400 --> 00:52:15,000
Speaker 1: uh exactly where they they weigh in in terms of

937
00:52:15,040 --> 00:52:18,600
Speaker 1: the air We're indeed, um Banks is universe, their wigs

938
00:52:18,680 --> 00:52:23,560
Speaker 1: in on the on the P and P spectrum. Hey,

939
00:52:23,600 --> 00:52:25,759
Speaker 1: but in the meantime, you want to check out this

940
00:52:25,840 --> 00:52:28,600
Speaker 1: and other pieces of content, head on over to stuff

941
00:52:28,600 --> 00:52:30,560
Speaker 1: to Blow your mind dot com. That's the mothership. That's

942
00:52:30,560 --> 00:52:32,359
Speaker 1: where we have all the articles. That's where we have

943
00:52:32,400 --> 00:52:35,040
Speaker 1: blog posts, we have links out to social media, we

944
00:52:35,080 --> 00:52:38,319
Speaker 1: have some videos. Uh, be sure to go there. Hey,

945
00:52:38,320 --> 00:52:42,799
Speaker 1: and if you listen to us on what iTunes, Spotify,

946
00:52:42,920 --> 00:52:47,200
Speaker 1: Google Play, however you get your podcast, um it, it's

947
00:52:47,239 --> 00:52:49,879
Speaker 1: possible to do. So, leave us a nice review there,

948
00:52:49,960 --> 00:52:53,279
Speaker 1: give us a little boost in the algorithm that ultimately,

949
00:52:53,520 --> 00:52:56,719
Speaker 1: uh determines our faith. You can tweet that at that

950
00:52:56,760 --> 00:52:58,319
Speaker 1: out of of and you can reach out as an

951
00:52:58,320 --> 00:53:01,640
Speaker 1: as an outside force like God and shift things in

952
00:53:01,680 --> 00:53:04,680
Speaker 1: our favor. So I invite you to do so. And

953
00:53:04,760 --> 00:53:06,680
Speaker 1: as always, if you'd like to get in touch with us,

954
00:53:06,680 --> 00:53:08,759
Speaker 1: and we really hope you do, you can email us

955
00:53:08,760 --> 00:53:20,960
Speaker 1: and blow the mind at how stuff works dot com

956
00:53:20,960 --> 00:53:23,440
Speaker 1: for more onness and thousands of other topics. Is it

957
00:53:23,520 --> 00:53:47,520
Speaker 1: how stuff works dot com