Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking eithan everyone, and welcome to Forward Thinking, the
Speaker 1: podcast that looks at the future and says there's a
Speaker 1: place for us somewhere, a place for us. I'm Jonathan
Speaker 1: Strickland and I'm Joe McCormick. And are our good friend
Speaker 1: Lauren is still battling bacteria. Uh, possibly with the sword.
Speaker 1: Strange coincidence. Yeah, it's almost as if we just continued
Speaker 1: recording on the same day as the other episode. There's
Speaker 1: a peak behind the curt most But anyway, we still
Speaker 1: want to talk a little more about bacteria and uh.
Speaker 1: And in the last podcast, we kind of talked about bacteria,
Speaker 1: about how they how we how we've used bacteria to
Speaker 1: create stuff, whether it's drugs or chemicals of some sort,
Speaker 1: or even possibly harnessing bacteria to form nano wires or
Speaker 1: even to do physical work on a very tiny scale.
Speaker 1: But we wanted to talk about a different application for
Speaker 1: bacteria in this podcast. Yeah, we wondered, have you ever
Speaker 1: considered the fact that the bacteria that gave you food
Speaker 1: poisoning when you were on vacation um and kept you
Speaker 1: inside for a week might be smarter than you are,
Speaker 1: or at least better at math. No, I have not.
Speaker 1: I thought that I was dumb forgetting it, but I
Speaker 1: didn't think that the bacteria itself was smarter. Well that's
Speaker 1: what you get when you eat the seafood special at
Speaker 1: a gas station. Yeah. Yeah, never again, well maybe one
Speaker 1: more time. But yeah, we wanted to talk about using
Speaker 1: bacteria to to do computations and the fact that a
Speaker 1: bacterial computer is in fact a possibility for very specific
Speaker 1: types of problems. People are probably going like, wait, wait,
Speaker 1: wait what Yeah, no, you heard us right, like, using
Speaker 1: bacteria to solve computational problems. It's real. We're not talking
Speaker 1: about you need to clean your keyboard, although that's also true. Gross. Yeah,
Speaker 1: this is also not just crazy future is speculation. We're
Speaker 1: talking about stuff that's already been done in the lab.
Speaker 1: So so, Jonathan, how what what are we talking about here?
Speaker 1: What's involved? We're mostly talking about NP class problems. So
Speaker 1: these are problems that are classically very difficult for computers
Speaker 1: to solve. NP problems tend to have lots of variables,
Speaker 1: and they tend to have a lot of potential uh
Speaker 1: pathways that you can take and Classically, the way a
Speaker 1: computer eliminates possibilities is it goes through every single permutation
Speaker 1: and checks to find out whether or not it's a
Speaker 1: valid response, which means that it has to go through
Speaker 1: them sequentially and one at a time, one at a time. So,
Speaker 1: for example, the one of the problems we can talk
Speaker 1: about is the Hamiltonian path problem. Uh, there's also the
Speaker 1: burnt pancake problem, which do you want to talk about
Speaker 1: that one? First, let's start with an example. What is
Speaker 1: the first explore and what the problems are? Okay, so
Speaker 1: the burnt pancake problem, Well, it starts with the pancake problem.
Speaker 1: And this is a sorting problem in computation. Um. And
Speaker 1: so a lot of what computer programmers do is come
Speaker 1: up with ways of arranging data in a certain way,
Speaker 1: like from smallest to largest. Um. So the pancake problem
Speaker 1: tells you to imagine this. Okay, So you're you're like
Speaker 1: a waiter running around in a restaurant and you've got
Speaker 1: a stack of X number of pancakes in your hand. Um,
Speaker 1: that's not sanitary. It should be on a plate. Well, okay,
Speaker 1: imagine it's on a plate, but you're you're holding the
Speaker 1: bottom of the plate with one hand. Um, and while
Speaker 1: you're running around the restaurant. Um, you want to arrange
Speaker 1: the pancakes so that the largest pancake is on the
Speaker 1: bottom and the smallest pancake is on the top, thus
Speaker 1: ensuring pancakes stability. Right, very important. Right, you can't put
Speaker 1: the tallest pancake on the top it say it was
Speaker 1: just the topple over. It's madness. Uh so how do
Speaker 1: you arrange it? Well, you've only got one free hand,
Speaker 1: so you can't go sticking inserting all over. You can
Speaker 1: only flip stacks of pancakes. So imagine you've got a
Speaker 1: stack of pancakes and you can stick a spatula in
Speaker 1: anywhere along the stack and flip everything that's above the
Speaker 1: stat the spatula over. So yeah, so you would want
Speaker 1: to try and find the way of doing this with
Speaker 1: the fewest number of flaps. What's the fewest number of
Speaker 1: times you can stick the spatula in and turn over
Speaker 1: everything on top of it to get the stacks sorted
Speaker 1: from largest to smallest. See my solution to this problem
Speaker 1: is to order waffles. But you could have some waffles
Speaker 1: bigger than another. Oh well, probably not actually, because the
Speaker 1: waffle press has a standard size, whereas the pancakes that
Speaker 1: they spread out according to the poor exactly anyway. So
Speaker 1: that so that's smart. That's a more symmetrical order. Okay, well, um,
Speaker 1: so so you're sorting like that. So that's one type
Speaker 1: of hard problem. And what you want to do when
Speaker 1: you've got a pancake sorting algorithm is to figure out
Speaker 1: the least number of flips, like we said to get
Speaker 1: the stack in order. The burnt pancake problem adds another complication.
Speaker 1: It says that each pancake is burned on one side,
Speaker 1: got you, and so you want to get the stack
Speaker 1: in order, but you also want all of the burned
Speaker 1: sides facing down, got youa so you, So it may
Speaker 1: require extra flips because some of the when you when
Speaker 1: you arrange stuff, it may turn out that the one
Speaker 1: that would have been in the right position is it's
Speaker 1: from based on size, it's perfectly fine, but because the
Speaker 1: burnt side is up, you have to flip that. But
Speaker 1: that means that everything on top of it also gets flipped.
Speaker 1: So then you have to figure out how many more
Speaker 1: flips do you do to get all ordered largest on
Speaker 1: the bottom and burnt sides down. If you just stop
Speaker 1: and think about this for a second, this is a
Speaker 1: really hard problem to solve. Yeah, it's not something that
Speaker 1: is you know, you just look at Especially it gets
Speaker 1: harder the more pancakes you add, right, And that's that's
Speaker 1: another thing that we can talk about that a lot
Speaker 1: of these NP problems fall into this category where they
Speaker 1: get uh much more complex when you start to add
Speaker 1: extra variables in they get what would is it exponentially
Speaker 1: more complex? I would need to look into it to
Speaker 1: make absolutely sure that exponentially is in fact the correct term.
Speaker 1: I would say at least geometrically more complex. In other words,
Speaker 1: that it's it's increasing at a very um predictable rate,
Speaker 1: because exponentially would mean that it's increasing ten times over
Speaker 1: each time or what. Okay, yeah, we'll settle that later. Yes,
Speaker 1: but anyway, it does get far more complex. And once
Speaker 1: you get to a certain level of complexity and you're
Speaker 1: using a classic computer to try and solve this, remember
Speaker 1: that classic computer is going through every single variation of
Speaker 1: the solution of that problem, so it has to wait
Speaker 1: until it's finished with one before it can begin the
Speaker 1: next one. If you have a multi core processor, then
Speaker 1: it can divide that up among the various cores and
Speaker 1: try and solve the problem more quickly by each core
Speaker 1: we're working on a different variation of that, but even
Speaker 1: then you're still only reducing the overall time by a
Speaker 1: tiny amount compared to how you know, this could take
Speaker 1: decades or centuries to solve, depending on how complex the
Speaker 1: problem is. If you're using a classical computer UM and
Speaker 1: the other NP problem, we're going to talk about the
Speaker 1: Hamiltonian path problem. This is one you guys may have
Speaker 1: heard about. The idea is that you've got these different
Speaker 1: nodes within a system, or you can give it as
Speaker 1: cities UM. Sometimes this is a variation of this is
Speaker 1: called the traveling salesman problem. The traveling salesman problem is
Speaker 1: a little bit harder because it involves calculating a minimum distance. Right.
Speaker 1: This is a little different in that the ideas that
Speaker 1: you've got Let's let's say you've got five cities and
Speaker 1: you want to visit all five cities, and the cities
Speaker 1: have roadways, but not every city is connected to every
Speaker 1: other city, right, So some cities are connected to maybe
Speaker 1: just one other city. Other cities are connected to two
Speaker 1: or three, and you have to figure out which way
Speaker 1: you can take where you visit each city once and
Speaker 1: only once, but you do visit all the cities. So again,
Speaker 1: a classic computer would have to sit there and go
Speaker 1: through each variation and validate or throw out the results
Speaker 1: based upon what happened. So depending on how how many
Speaker 1: cities are there, that can take a really long time
Speaker 1: because there's so many different options. So instead of trying
Speaker 1: each thing, instead of having one smart computer try each
Speaker 1: option one at a time. Is there something? Is there
Speaker 1: something that can do lots of different variations all at
Speaker 1: the same time. There are a couple of things, and
Speaker 1: one of them, of course, is what we're talking about. Bacteria. Uh.
Speaker 1: It turns out that if you are using bacteria and
Speaker 1: you encode d n A so that it essentially simulates
Speaker 1: whatever the problem is. Uh, And then you kind of
Speaker 1: sounds crazy, but you're kind of shaking the bacteria altogether.
Speaker 1: What what results is the answer to the problem. So
Speaker 1: both of the problems we've just described, the burnt pancake
Speaker 1: problem and the Hamiltonian path problem, believe it or not,
Speaker 1: have been solved by bacteria. Shut your mouth, yes, okay,
Speaker 1: bacterial computers what some people call uh back toputing. I
Speaker 1: wish I had not heard that, But alright, we're bacterial puting,
Speaker 1: or you're not making it any better pupingpingputing, coputing, okay,
Speaker 1: okay um. Bacterial computers, literally colonies of bacteria have been
Speaker 1: made to solve these problems. Well, how on earth could
Speaker 1: they do that? They're they're able to do this in
Speaker 1: parallel as opposed to sequentially. So the classical computer is
Speaker 1: trying to solve the problem sequentially, like we said, it's
Speaker 1: going through all but bacteria can actually examine all of
Speaker 1: these kind of at once. And it's interesting. They researchers
Speaker 1: had for the Hamiltonian problem. What they did was they
Speaker 1: only used three cities, which dramatically reduces the complexity. But
Speaker 1: it's a proof of concept and it's and they showed
Speaker 1: that it's a sound proof of concept. What they did
Speaker 1: was they modified the DNA of E. Coli. Bacteria are
Speaker 1: good old buddies who make you feel not so good
Speaker 1: after you've had that lukewarm hot dog. Uh. But they
Speaker 1: modified the DNA and they ended up shuffling this DNA
Speaker 1: together and the bacteria ended up producing the correct answer.
Speaker 1: The back to the DNA was to give the bacteria
Speaker 1: command to either glow red or green. And then uh,
Speaker 1: they put the bacteria in different combinations that represented the
Speaker 1: various um options for traveling through this this these nodes,
Speaker 1: and the one that was the actual answer ended up
Speaker 1: turning yellow. The bacteria turned yellow. And so in a
Speaker 1: way you're thinking, all right, well, you're just throwing all
Speaker 1: the options into variations and then you shake it up
Speaker 1: and see what comes out. And that's actually true. You know,
Speaker 1: you're using all these but it is processing in parallel.
Speaker 1: But but it's useful. I mean, it can solve the problem.
Speaker 1: It's like even if the bacteria don't know what they're doing.
Speaker 1: Of course, one might point out that the silicon and
Speaker 1: your computer chip doesn't know what it's doing. Yeah, and
Speaker 1: often I don't know what I'm doing, so I don't
Speaker 1: see where the But so the bacteria don't know what
Speaker 1: they're doing, they don't understand that they're solving the problem.
Speaker 1: But it just happens to be that we can set
Speaker 1: it so that the ones that do solve the problem
Speaker 1: make it clear. Yeah, exactly. So what we can do
Speaker 1: is you create the bacteria with all the different variations
Speaker 1: that you need to address whatever the problem is. You
Speaker 1: do all the different combinations that those bacteria can be in,
Speaker 1: in other words, like the variations, and then you you
Speaker 1: take a look and see the results and whatever. Whatever
Speaker 1: the right answer is is going to be evident based
Speaker 1: upon whatever you've told the bacteria to do. When it
Speaker 1: when it's the result you need. So the what I
Speaker 1: was reading, if I understand correctly, the way they ascertained
Speaker 1: the success of computing the burnt pancake problem in bacteria
Speaker 1: was they I think they introduced an antibiotic into the
Speaker 1: bacterial colonies and basically they had worked it so that
Speaker 1: bacteria that solved the problem correctly would end up with
Speaker 1: a resistance to the antibiotics, so those bacteria would survive
Speaker 1: in all the rest. Ye off, Yeah, yeah, that's and
Speaker 1: that that's a very quick way of taking a look
Speaker 1: and saying, all right, here's your answer, because it has
Speaker 1: to be. Uh, that's it's kind of an interesting approach.
Speaker 1: The other the other big method I've heard of for
Speaker 1: tackling these sort of problems, besides using something like a
Speaker 1: graphics processing unit which has lots and lots of different
Speaker 1: parallel processors in it. Uh, they that would still take
Speaker 1: a long time to solve. A lot of these NP
Speaker 1: problems um depending upon how complex they were. But quantum
Speaker 1: computers are very take a very similar approach. But that's
Speaker 1: a whole other episode. It's a different episode. But just
Speaker 1: really quickly, a quantum computer, you know, a classic computer
Speaker 1: uses bits which are either a zero or a one,
Speaker 1: so that that binary digit um, that's what bit stands for. Well,
Speaker 1: quantum computers use cubits qu b I t s, and
Speaker 1: these are bits that exist in superposition, meaning that they
Speaker 1: not just a zero or one, they are both a
Speaker 1: zero and the one and technically everything in between. And theoretically,
Speaker 1: if you had enough cubits together, you could process these
Speaker 1: problems in parallel, where each cubit is either a zero
Speaker 1: or a one or both or whatever, and you process
Speaker 1: all the all the possibilities all at once, assuming that
Speaker 1: you have enough cubits to tackle whatever the big problem is.
Speaker 1: So you would have to first create enough cubits to
Speaker 1: be able to do this. If you did, then you
Speaker 1: could solve the problem in parallel. We're really good at
Speaker 1: manipulating cubits, aren't we not. Unfortunately, no, no, because if
Speaker 1: you it's very it's a very um delicate system. Anything
Speaker 1: time you're you're talking about quantum effects. You are talking
Speaker 1: about a very delicate system that can very simply break down.
Speaker 1: And if it broke down, your quantum computer would essentially
Speaker 1: become a classical computer. Do we do we interfere with
Speaker 1: the state of the cubit whenever we look at the cubit?
Speaker 1: I mean, that's the problem, right. And then also I
Speaker 1: should point out that the answers you get from a
Speaker 1: quantum computer are probabilistic. They are not definitive. So you
Speaker 1: would get essentially a level of like a percentage of
Speaker 1: how accurate is this answer? Like is it it's there's
Speaker 1: an eight percent chance this is the answer kind of
Speaker 1: thing like you're never gonna get response where you're going
Speaker 1: to know for sure that you've got the right answer.
Speaker 1: That's again grossly under stating what a quantum computer does.
Speaker 1: But you know, for the purposes of this discussion, I
Speaker 1: thought it was interesting just because again it was one
Speaker 1: of those things where as I was reading about these
Speaker 1: bacterial computers, I thought, well, that's kind of similar to
Speaker 1: what a quantum computer does. It's just doing it with
Speaker 1: d n A and doing it in a you know,
Speaker 1: not in a quantum like a weird quantum effect kind
Speaker 1: of way. Ps right to us and tell us to
Speaker 1: do an episode on quantum computers and we can convince
Speaker 1: our bosses to do it. Yeah, we need enough of
Speaker 1: you guys to say I really want to know about
Speaker 1: this quantum computer. Also we can talk about quantum encryption,
Speaker 1: which is wicked awesome. But yeah, you need to write
Speaker 1: inn until So Yeah, so we have solved problems with bacteria. Yes,
Speaker 1: what what else do we need to do to make
Speaker 1: a bacterial computer. Well, you've got to be able to
Speaker 1: go beyond just the prototype stage and make sure you have.
Speaker 1: You have to. It's all a question of scale, right,
Speaker 1: So the scale is an issue. And also you know
Speaker 1: you got to feed your computer as it turns out. Well,
Speaker 1: maybe here's how I should rephrase it. Are the ways
Speaker 1: that we know we can use bacteria in uh, or
Speaker 1: at least we think we will be able to use
Speaker 1: bacteria in in computing in the nearer future. In a
Speaker 1: in an indirect way, there are bacteria that can produce
Speaker 1: essentially magnetic material magnetite, right, magnetite, it's the most magnetic
Speaker 1: natural mineral in them yep. And there are there's bacteria
Speaker 1: that that consumes iron and creates magnetite, and so you know,
Speaker 1: if you've ever wondered about how how a hard drive works,
Speaker 1: a classic hard drive, those are magnetic drives. They are
Speaker 1: storing information magnetically. That's also why if you've ever worked
Speaker 1: with any kind of computer or magnetic storage, you know
Speaker 1: you're not supposed to bring powerful magnets anywhere close to
Speaker 1: that because you could accidentally wipe whatever it is you're
Speaker 1: working with. Um. Well, there there are bacteria that produce magnetite,
Speaker 1: and there's a possibility that we could use these to
Speaker 1: create new types of hard drives. Because one of the
Speaker 1: challenges we're encountering is designing uh computer components that are
Speaker 1: on an increasingly smaller scale. It's hard to get to
Speaker 1: a level of precision once we get down below a
Speaker 1: certain level, and this might give us a chance to
Speaker 1: create even more precise means of constructing something like hard drives.
Speaker 1: So it wouldn't be a full computer, it would be
Speaker 1: a computer component. But that's an actual possibility that we
Speaker 1: may see within the next few years. That's pretty cool, now,
Speaker 1: that's pretty awesome. The idea of harnessing bacteria to create
Speaker 1: things that that will increase our own ability to to
Speaker 1: store information and access information. That's interesting if you've listened
Speaker 1: to our last episode that that isn't the only thing,
Speaker 1: uh that bacteria can manufacture. Now we can create the
Speaker 1: like wires I mean, and the wires thing. To me,
Speaker 1: the wires thing is really interesting because the idea of
Speaker 1: being able to create wires that can exist within an
Speaker 1: organism means that you have a lot of options when
Speaker 1: it comes to cyborg implants. And as we've already covered
Speaker 1: in a previous episode of Forward Thinking, I'm an aspiring cyborg.
Speaker 1: So that'll just pave my pathway to robotic uh superiority
Speaker 1: that much more quickly. So you know, stay on. That
Speaker 1: gets a crazy look in your eye. I pretty much
Speaker 1: always have a crazy look at my eye, but I
Speaker 1: have a have a specifically crazy look at my eye here.
Speaker 1: I'm I'm mostly imagining that scene in Terminator where you
Speaker 1: see the robot on the mound of human skulls. That's
Speaker 1: you know, that's my happy They end up there. They
Speaker 1: don't necessarily start there, but I find a way to
Speaker 1: find a way. Yeah, how does the the naked in
Speaker 1: high school dream turned to well, standing on the skulls?
Speaker 1: Clothes don't don't transfer over when you go through the
Speaker 1: time point. That's correct. So okay, speaking of robots, let's
Speaker 1: do that. I had a kind of crazy idea. Um
Speaker 1: and and here we're going to utter speculation. But in
Speaker 1: the last podcast, we talked about how bacteria can be
Speaker 1: used to generate mechanical motion. Right, so you can, um,
Speaker 1: you can regulate the amount of oxygen and fluid um
Speaker 1: with bacteria suspended in it to get the bacteria to
Speaker 1: push micro gears. They're actually they're turning gears. They can
Speaker 1: operate tiny machinery. Okay, so you've got mechanical motion. We
Speaker 1: just talked about computing. Those two things put together are
Speaker 1: the building blocks of something we would call a robot. Yeah. Now,
Speaker 1: if you're talking about a bacterial robot, uh, this is
Speaker 1: not a nanobot. It's going to be larger than the nanoscale,
Speaker 1: but it would still would be made of bacteria. Yeah,
Speaker 1: you can have a in theory if we were able
Speaker 1: to harness this sort of power, then yeah, I guess
Speaker 1: I imagine. So, I mean you would still need uh,
Speaker 1: some means of processing that information because right now, what
Speaker 1: we're talking about with the computers is mostly you know,
Speaker 1: put all the dice in a in a in a
Speaker 1: bucket and shake um. And when you remove the bucket
Speaker 1: and you reveal what the dice have rolled. Because of
Speaker 1: the way that this system works, only the dice that
Speaker 1: are necessary to answer the question show up and you
Speaker 1: get the answer. Going from that to some sort of
Speaker 1: computer that can actually process information in a meaningful way
Speaker 1: and give commands, that's a big jump. It's not to
Speaker 1: say that we won't make it so. And also, like
Speaker 1: we've said before, if we're if we're talking about building
Speaker 1: something at a very tiny scale, it it behooves us
Speaker 1: to look at nature because they're already these natural organisms
Speaker 1: that are on this tiny, tiny scale that do very well.
Speaker 1: So if we can emulate that or manipulate them in
Speaker 1: some way, then we can create things on that scale
Speaker 1: much more quickly than we would if we just build
Speaker 1: from the ground up, so to speak. So could we
Speaker 1: have a bacterial robot? I don't see why not. If
Speaker 1: we did, what would it be good for an autonomous
Speaker 1: bacterial machine? I would imagine that that would have medical uses.
Speaker 1: I mean something that could especially if you had any
Speaker 1: way of guiding it in within a larger organism than
Speaker 1: you could end up using that to either administer medicine
Speaker 1: or do even you know, think about cellular level surgery,
Speaker 1: you know, I mean talking about surgery on the cellular
Speaker 1: level where you're actually repairing injured or damaged or or
Speaker 1: diseased tissue with something that has a level of precision
Speaker 1: far greater than even the sharpest scalpel. So that's a
Speaker 1: potential use for it. Whether we actually ever see at
Speaker 1: I don't know. I'm a I'm a little skeptical. It
Speaker 1: sounds like it's a little science fiction e to me.
Speaker 1: But then I'm sure if we had talked to people,
Speaker 1: you know, fifty years ago, they would say that the
Speaker 1: stuff that we take for granted now is the stuff
Speaker 1: of science fiction. It would never come to pass the Internet,
Speaker 1: the Internet, having the Internet in your in your hand,
Speaker 1: having things like tell Us surgery. I mean, we already
Speaker 1: have tell usurgery where people can can perform surgeries from
Speaker 1: across the world. Well, it seems clear that if there
Speaker 1: ever will be a bacterial robot, it's a long way off.
Speaker 1: But another thing that seemed possible to me is a
Speaker 1: use for a bacterial robot is um So how do
Speaker 1: you get your other bacterial machines to do their job.
Speaker 1: I mean, if we have like microfluidic machines that are
Speaker 1: controlled by bacteria, it could be useful to have autonomous
Speaker 1: bacterial machines that know what they're doing via the computing power. Yeah.
Speaker 1: I would imagine you would mostly have to have some
Speaker 1: bacteria owen sentives in there, like some bacterial coffee, uh,
Speaker 1: like a good bacterial life work balance, you know, some
Speaker 1: maybe you could work from outside the organism three days
Speaker 1: all the week, a bacterial vacation plan. Yeah, yeah, every
Speaker 1: now and then your holidays in Jonathan Strickland's lower intestine. Yeah, well,
Speaker 1: boy do they But to seriously answer your question, that's
Speaker 1: that's a good that's an excellent question. And honestly, it's
Speaker 1: one of those things where again, like I can kind
Speaker 1: of see where the individual pieces would need to come
Speaker 1: in from. I have no idea what the execution would
Speaker 1: actually be, but it's one of those things where I'm
Speaker 1: sure there are people who are working on that, and
Speaker 1: they're also you know, keep in mind, we've got people
Speaker 1: working on on manipulating and altering bacteria from multiple disciplines,
Speaker 1: so we're going to see advances from many different disciplines
Speaker 1: all kind of combined together in the future to h
Speaker 1: to create stuff that we can only dream about right now,
Speaker 1: or some of us can't even dream about it. It It
Speaker 1: may be beyond even the crazy stuff we're talking about here.
Speaker 1: So that's the coolest thing I think about this kind
Speaker 1: of research is that it will lead us to discoveries
Speaker 1: that you know, we can only speculate on, and I
Speaker 1: doubt we'll even get close to the cool stuff that
Speaker 1: comes down the line. This world is so weird to us,
Speaker 1: the very very small, Yeah, and also outside of the
Speaker 1: way we think that I always say, like, there's certain
Speaker 1: worlds that are largely a mystery to us, the very
Speaker 1: tiny and the extremely large, like the cosmological scale. But there.
Speaker 1: But the thing, the thing that makes them really different
Speaker 1: in my mind is on the galactic scale, there are
Speaker 1: no machines there. Well, yeah, not that we know. I mean,
Speaker 1: God would be terrifying. We could be living in a
Speaker 1: computer simulation, in which case there could be galaxy sized
Speaker 1: cells of bacteria. Yeah, um, but as far as we
Speaker 1: know at the at the galactic level, there are no machines.
Speaker 1: At the micro level, there are tons of machines. There's
Speaker 1: there's a machine for almost everything, and if there and
Speaker 1: if there isn't one, we can manipulate it to do it. Yeah. Yeah,
Speaker 1: it's pretty awesome. So uh yeah, well, we'll have to
Speaker 1: see how will harness bacteria in the future and what
Speaker 1: kind of interesting applications we can have. We've really just
Speaker 1: scratched the surface here, and there are a lot of
Speaker 1: incredibly smart people who are really pushing the boundaries of
Speaker 1: what we thought was possible even as little as five
Speaker 1: years ago. I think if you had taught to some
Speaker 1: of the researchers five years ago about some of the
Speaker 1: stuff that's going on now, they would have thought you
Speaker 1: were crazy. So it'll be interesting to see how this
Speaker 1: goes in the future. Guys, if you have any suggestions
Speaker 1: for future topics of forward thinking, please get in touch
Speaker 1: with us. Let's know, like, let's say you want to
Speaker 1: know about quantum computers and you think I hope they
Speaker 1: get around to that. It will help if you're write
Speaker 1: in or if there's any other topic you want, let's
Speaker 1: know our email addresses FW thinking at discovery dot com
Speaker 1: or go to FW thinking dot com. That's the website
Speaker 1: where we have all of our videos, the podcast, the
Speaker 1: blogs and all of the links to our social media.
Speaker 1: So if you want to get in touch with us
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