Music from our material world

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Physics world.

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Hello, and welcome to the Physics World Stories

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podcast.

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I'm Andrew Glester, and this is the

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sound of a spider's web. It's the result

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of some of the work of professor Marcus

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Buhler from MIT.

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Markus and his team have been working on

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sonifying

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the proteins and amino acids that are the

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building blocks of all life on Earth.

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That might sound like an interesting science art

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project but where it becomes really interesting and

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really scientifically interesting is when they translate music

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and these sounds

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back in the other direction.

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Later in the podcast, we'll hear the sound

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of the COVID nineteen virus and discover how

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this research might even help reveal the inner

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workings

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of our brains.

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Here's Marcus Buehler. I'm a professor at MIT

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in Cambridge, Massachusetts, United States. Now trying to

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get a different perspective in in hearing how,

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physical structure structures can, can sound like. But

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how did this come about? What made you

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go down this road? Yeah. For many years,

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we've been exploring,

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similarities

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between material structure and other types of languages,

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one of them being music. And the, you

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know, the way we got to this essentially

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is we,

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we my lab has long been interested in

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studying how

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materials drive their function. And so, you know,

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I'm thinking about,

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the strength of a piece of material or

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the toughness

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or its color or whatever property you're interested

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in and and and usually some physical phenomena.

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And, you know, we we model these,

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properties by understanding how building blocks in a

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material interact. So in a material, you have

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atoms and molecules and,

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grains or whatever material you're looking at, so

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different building blocks. And the the way they

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interact in in different ways, at different scales

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and levels

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controls the the properties that we're we're trying

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to understand. And and that process is very

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similar to what we see in in in

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in other types of languages, like, well, human

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language,

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but also music where we have building blocks

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like sine waves, waveforms,

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which are are assembled in a, you know,

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in a basically in different ways, different frequencies

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or different instruments, different,

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melodies or chords and and an orchestra potentially.

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And so you have a very similar way

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by which these systems are built. And I've

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been fascinated with understanding

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how how different are the systems. Are there

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similarities

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between them?

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Can we learn from one and the other

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or vice versa? And can we exchange information

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between them? And so that's been something we've

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been studying for a long time in my

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lab and well, mainly theoretically in the beginning,

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and then we have some experimental

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analysis as well, computational and including in the

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last, you know, couple years, we've become very

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involved in,

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using this paradigm of translating matter into sound

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and and matter and also sound into matter,

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as a way of doing research and asking

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questions of, how can we generate new types

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of musical structures and forms, new instruments,

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and can we design new materials for music

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as well? I I imagine if, if people

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listening or anything like me, it's not something

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that they would ever have thought of. Right?

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My wife's

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a, a violinist. She's played with the Royal

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Liverpool Philharmonic Orchestra for a very long time.

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I've sat in many a concert, and never

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have I thought, I wonder if we could

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change this

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music into materials.

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Well, so, you know, it comes down to

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when you think about a,

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you know, very literally, if you if you

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you describe a material, you know, based on

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mathematical equations. Right? You you say, here's

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an atom and a lattice, and this is

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the space group, and this is how it's

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arranged, and, you know, here's the size of

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it and and all these things. So we

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use mathematics as a language to describe material,

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and then we can, you know, we can

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make it basically in the lab or we

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can, you know, synthesize it. And so in

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a way, you were using language already to

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describe phenomena in nature

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and matter, and we design new materials through

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this process. And so, you know, music is

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just another way to think about a language.

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And we,

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can ask a question if you if you

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hear something that somebody has created. Well, what

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would it actually what would it look like

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or feel like if it were to be

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material? And and that's it's a challenging thing

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to do, obviously. So we have to figure

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out what are the mechanisms to do this

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translation and make it happen. But but in

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principle, it's sort of in that way. And

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it's it's interesting when you, yeah, you go

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to a concert and you listen to that

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and then you say, well, you know, it

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kinda has an effect on your on your

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on your mind or your body. You enjoy

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it or you, you know, you have certain

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emotions from it.

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And and the question is, you know, these

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feel like real material effects, you know, they

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they affect you. But,

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so we have been thinking, you know, one

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step beyond this if if you could actually

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materialize what you hear. Now how would it

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you know, does that look like or touch?

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How would it feel like if you were

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to hold a piece of Beethoven in your

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hand or buck in your hand, you know,

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whatever it would be like. And and so

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so that's,

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more generally the idea behind that, and then

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it really comes back to this,

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way we think about materials as a as

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a in a very fundamental mathematical sense in

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in that it's it's really a collection of

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of of objects, building blocks we call them

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that are interacting. And and that's that framework

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when you go to that level of abstraction,

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you can apply this framework to to anything,

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you know, including materials and including music and

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and love other things. And so now you

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can you can translate, you know, sort of

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a mapping of of something that's in a

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in a very abstract space where you can,

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you know, make some operations

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work that are

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seemingly impossible or weird, but they can actually

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be made in you know, that can be

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done in in that mathematical way. And and

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and that's what we that's what we can

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do. A few years ago now, some of

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the Physics World team visited Marcus and his

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team in their lab at MIT.

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From Physics World, here's Tushna commissariat.

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My Physics World colleague, Sarah Tush, and I,

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we had a really great day visiting Marcus's

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lab. This was in March of twenty nineteen

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because we were already in Boston for the

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APS March meeting, and Marcus very kindly organized

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a day for us even though he couldn't

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make it there, but we got to go

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and see his lab and number of others.

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And I remember what really stuck out to

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me was,

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hearing the stories,

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about their their spider silk project and and

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and what they were working on on on

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scanning a spider web.

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And we got to go, to the lab

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and see

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what work they were doing on that then.

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And we were shown around by Francisco

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Martinez

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and Zhao Quinn,

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who were then part of Marcus's group.

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And, you know, they they took us to

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the lab where they first had,

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the spiders build a web

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in what was basically,

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a sort of,

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a a a cube, but that was like

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a jungle gym for the spiders. And they

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were hoping the spiders would build their webs,

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in this hollow cubic

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structure,

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But, spiders don't tend to like labs very

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much,

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and they don't tend to linger in in

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hollow cubes if you tell them to.

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So they had to first figure out how

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to make sure that the spiders didn't all

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run away and escape, And that involved putting,

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these these hollow cubes on on some kind

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of surface

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and then suspending that within a big tray

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of water, like a moat for the spiders.

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And they had some sort of, trouble with

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that. They had to make it, they realized

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that the spiders could jump. So they had

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to make the moat big enough and make

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sure they were in the middle of the

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room so the spiders can leap away.

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And then they had all sorts of problems

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making sure to keeping the spiders alive,

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to be able to feed them because spiders

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don't like dead flies. They like live flies.

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So they needed special kinds of flies that

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would still fly, but wouldn't fly too far

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away so the spiders could get them. And

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they were joking about how,

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they had these special slightly mutated flies from

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the biology department that wouldn't fly away too

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quickly.

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Occasionally, these spiders would escape anyway. And so

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they have these sort of mutant fly eating

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spiders, probably nestling somewhere in the bowels of

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MIT as we speak.

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But after all of that, they did work

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out and they got the environment comfortable enough

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for the spiders to build these beautiful webs.

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And it kind of just looks like a

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really big cobweb, almost a bit bit scary

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when you look at it.

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But then they showed us their laser scanning

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technology,

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for how the laser runs through it and

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how they build up these transfer sections of

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the lab and seeing

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what its geometry looks like. And and that

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was just amazing watching even just the scanning

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happening.

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And from that scanning, they then built these

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amazing,

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maps of what this this this spider silk

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and the spider structure looked like. And, of

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course, all of that led to this unbelievable

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art project,

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called spiders canvas.

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That's this sort of performance piece that had

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music and sound and projections of this spider

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web. And we were really lucky that it

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was being displayed at MIT, the art project,

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while we were visiting. So not only did

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we get to go and see the very,

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sort of

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building blocks of it all in the lab,

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we got to see this beautiful amazing final

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project where it's the size of a room

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and you're walking around within the bowels of

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this spider web. And it was really something

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quite amazing to see, and that was only

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a small portion of what Markus's lab does.

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Here's Markus Buhler again.

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We we thought about lots of different ways

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by which we can,

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you know, go. So create you mentioned the

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violin, which is a beautiful instrument. It's been

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around for a long time.

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And, you know, we've always been thinking, well,

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what if you heard had different types of,

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you know, ways of of creating sounds which

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might not be a macroscopic string. Right?

271
00:10:06,870 --> 00:10:08,889
And, of course, there have been wild

272
00:10:09,509 --> 00:10:11,829
musical creations. I think John Cage worked on

273
00:10:11,829 --> 00:10:13,429
things like this where you you have all

274
00:10:13,429 --> 00:10:15,829
sorts of weird ways to to write music

275
00:10:15,829 --> 00:10:17,449
and and use sounds of,

276
00:10:18,230 --> 00:10:20,514
sources that are unconventional. But sort of in

277
00:10:20,514 --> 00:10:22,355
that vein of thinking, you know, we thought,

278
00:10:22,355 --> 00:10:23,955
well, you know, what if we were to

279
00:10:23,955 --> 00:10:26,195
look at some, you know, things you cannot

280
00:10:26,195 --> 00:10:28,274
actually hear, I mean, with your ears and,

281
00:10:28,274 --> 00:10:30,514
you know, like a spider web. And and

282
00:10:30,514 --> 00:10:32,674
in a spider web, we basically use this

283
00:10:32,674 --> 00:10:34,355
web, and we we thought, well, how can

284
00:10:34,355 --> 00:10:36,500
we listen to that? I mean, it's almost

285
00:10:36,500 --> 00:10:38,820
like a like a harp. Right? It's it

286
00:10:38,820 --> 00:10:40,419
has a lot of different filaments and and

287
00:10:40,419 --> 00:10:42,259
strings, so it's very close to the idea

288
00:10:42,259 --> 00:10:44,500
of a of a conventional musical instrument. But

289
00:10:44,500 --> 00:10:46,360
but, yeah, you can't really hear it without

290
00:10:46,419 --> 00:10:48,899
mathematical processing. You need to do something to

291
00:10:48,899 --> 00:10:51,434
make those frequencies audible, and that's something we

292
00:10:51,434 --> 00:10:53,274
can do. But yeah. So now you can

293
00:10:53,274 --> 00:10:54,955
think about the spider web as a as

294
00:10:54,955 --> 00:10:56,955
a harp. We can pluck the strings. You

295
00:10:56,955 --> 00:10:59,355
can listen to the whole orchestra of strings

296
00:10:59,355 --> 00:11:01,355
vibrating. You can see how the wind affects

297
00:11:01,355 --> 00:11:03,595
it or whatever your or tension, you know,

298
00:11:03,595 --> 00:11:05,190
we've we've we've pulled it. We've broken the

299
00:11:05,190 --> 00:11:07,590
spider web essentially and increased tension. Now you're

300
00:11:07,990 --> 00:11:09,529
you kind of hear this,

301
00:11:10,550 --> 00:11:13,929
the these these these these web filaments vibrating,

302
00:11:13,990 --> 00:11:16,070
which which aren't tuned. Right? And that's the

303
00:11:16,070 --> 00:11:17,590
unique thing about that. It's sort of you

304
00:11:17,590 --> 00:11:19,670
know, I mentioned earlier, it's observing nature. You

305
00:11:19,670 --> 00:11:22,065
know, you kinda hear how nature sounds like.

306
00:11:22,524 --> 00:11:24,845
And this that that's a very, very foundational

307
00:11:24,845 --> 00:11:26,044
way to think about it. You don't wanna

308
00:11:26,044 --> 00:11:27,485
use it as a microscope, you know, and

309
00:11:27,485 --> 00:11:29,325
how how does spider web sound like? And

310
00:11:29,325 --> 00:11:31,725
it's not created by humans. It's not created

311
00:11:31,725 --> 00:11:32,764
with the intention to,

312
00:11:33,659 --> 00:11:35,419
listen to it necessarily. But, of course, the

313
00:11:35,419 --> 00:11:37,740
spider lives in this vibrational universe where she,

314
00:11:37,740 --> 00:11:38,480
you know, will

315
00:11:38,860 --> 00:11:41,419
sense vibrations, very keen sense vibrations to orient

316
00:11:41,419 --> 00:11:42,799
itself in the spider web.

317
00:11:43,339 --> 00:11:44,700
So we have a sort of a connection

318
00:11:44,700 --> 00:11:47,019
there, and then, yeah, we we can we

319
00:11:47,019 --> 00:11:49,555
can listen. It sounds like it is something

320
00:11:49,555 --> 00:11:50,375
we haven't heard,

321
00:11:50,995 --> 00:11:53,394
but something that is quite quite interesting actually

322
00:11:53,394 --> 00:11:54,675
when you dive a little bit deeper. And

323
00:11:54,675 --> 00:11:56,055
now and then we worked with,

324
00:11:57,075 --> 00:11:58,455
a bunch of other people at MIT,

325
00:11:59,955 --> 00:12:03,019
to, to actually create performances with that. And,

326
00:12:04,120 --> 00:12:07,559
Evan Sapurin and Christine Southworth were two leading

327
00:12:07,559 --> 00:12:08,059
this,

328
00:12:08,600 --> 00:12:11,240
together with my student, Isabelle Hsu, who who

329
00:12:11,240 --> 00:12:12,840
played the spider web. And so basically, they

330
00:12:12,840 --> 00:12:14,759
created an orchestra on stage, you know. And

331
00:12:14,759 --> 00:12:15,580
I think when

332
00:12:16,085 --> 00:12:18,085
the physics world team was at MIT, I

333
00:12:18,085 --> 00:12:19,705
think they saw a version of that,

334
00:12:20,085 --> 00:12:21,924
either the whole thing or maybe a part

335
00:12:21,924 --> 00:12:23,785
of the installation that they made. But,

336
00:12:24,245 --> 00:12:25,764
yeah. So so that was the, you know,

337
00:12:25,764 --> 00:12:28,320
the the idea of of using the, basically,

338
00:12:28,320 --> 00:12:30,080
the knowledge of the physical the spider web

339
00:12:30,080 --> 00:12:32,340
that we have developed in the vibrational spectrum

340
00:12:32,800 --> 00:12:34,800
to then create a a human playable instrument

341
00:12:34,800 --> 00:12:36,639
and then create a performance actually that you

342
00:12:36,639 --> 00:12:38,000
can you can attend. Just like you would

343
00:12:38,000 --> 00:12:39,519
go to a concert, you could now go

344
00:12:39,519 --> 00:12:41,845
to that spider web, concert end. And it's

345
00:12:41,924 --> 00:12:44,264
and it sounds weird, and and you might

346
00:12:44,325 --> 00:12:45,304
play some of those,

347
00:12:45,924 --> 00:12:47,845
recordings of that, and it sounds weird because

348
00:12:47,845 --> 00:12:50,024
it doesn't sound like anything that we conventionally

349
00:12:50,164 --> 00:12:51,845
listen to. You know? It's a spider web.

350
00:12:51,845 --> 00:12:52,745
It's not a violin.

351
00:12:53,205 --> 00:12:54,644
But that's what makes it interesting. Right? And

352
00:12:54,644 --> 00:12:56,404
I think I'm particularly interested in this in

353
00:12:56,404 --> 00:12:58,879
this question if you you take that spider

354
00:12:58,879 --> 00:13:01,360
web or that that protein or whatever source

355
00:13:01,360 --> 00:13:03,299
you have now, quantum phenomena.

356
00:13:03,839 --> 00:13:05,600
What if you wanna you as a human

357
00:13:05,600 --> 00:13:07,519
interact with that manner? You wanna make it

358
00:13:07,679 --> 00:13:09,279
you wanna, you know, kind of massage that

359
00:13:09,279 --> 00:13:11,534
into something that sounds more appealing maybe, or

360
00:13:11,774 --> 00:13:14,254
you you play that with a human violin,

361
00:13:14,254 --> 00:13:16,034
or you you play that with a conventional

362
00:13:16,574 --> 00:13:18,355
you sing to it or you, you know,

363
00:13:18,574 --> 00:13:19,934
you begin to interact with it from the

364
00:13:19,934 --> 00:13:22,815
human perspective. And that's something that we I

365
00:13:22,815 --> 00:13:25,269
think we we're we're very excited about right

366
00:13:25,269 --> 00:13:26,790
now is that we have a way of

367
00:13:26,790 --> 00:13:29,509
translation, right, between the the different scales and

368
00:13:29,509 --> 00:13:32,309
different paradigms, different species where we can now

369
00:13:32,309 --> 00:13:34,090
listen to the way the spider web

370
00:13:34,710 --> 00:13:37,269
is like for the spider, and we can

371
00:13:37,269 --> 00:13:38,950
interact with that as a human. We have

372
00:13:38,950 --> 00:13:41,325
thoughts. We can, you know, we can interact

373
00:13:41,325 --> 00:13:43,165
with that web and and create new sounds,

374
00:13:43,165 --> 00:13:45,725
vibrations. And perhaps that, you know, there's a

375
00:13:45,725 --> 00:13:47,804
way to communicate now through all these different

376
00:13:47,804 --> 00:13:50,044
modalities that that other organisms use. You know,

377
00:13:50,044 --> 00:13:51,884
we use a particular way of of interacting

378
00:13:51,884 --> 00:13:53,700
with our environment, which is the modality of

379
00:13:53,860 --> 00:13:56,600
of sense and, and smell and vision and

380
00:13:56,980 --> 00:13:57,720
and hearing.

381
00:13:58,580 --> 00:14:00,179
But but, of course, yeah, a spider is

382
00:14:00,179 --> 00:14:02,740
gonna have very different ways of mod very

383
00:14:02,740 --> 00:14:04,820
different modalities of interacting with the environment. They

384
00:14:04,820 --> 00:14:07,379
have different kinds of sensors, basically. Yeah. And

385
00:14:07,379 --> 00:14:09,605
and and and so that translation,

386
00:14:09,985 --> 00:14:11,504
you know, is there's a lot of really

387
00:14:11,504 --> 00:14:14,785
interesting physics involved in mathematics and numerical tools.

388
00:14:14,785 --> 00:14:16,705
A lot of this comes down to, deep

389
00:14:16,705 --> 00:14:18,705
learning methods that we use to to actually

390
00:14:18,705 --> 00:14:21,700
make this translation happen. So it's a complex

391
00:14:21,700 --> 00:14:24,580
problem to translate between human languages, but it's

392
00:14:24,580 --> 00:14:26,419
even more interesting, I would argue, or or

393
00:14:26,419 --> 00:14:27,879
or equally interesting perhaps,

394
00:14:28,500 --> 00:14:30,820
when you begin to translate between languages and

395
00:14:30,820 --> 00:14:31,879
expressions between

396
00:14:32,580 --> 00:14:34,899
materials and spiders and humans and all sorts

397
00:14:34,899 --> 00:14:35,879
of different representations.

398
00:14:36,259 --> 00:14:38,274
Yeah. Earlier on, I was having a phone

399
00:14:38,274 --> 00:14:40,115
call with somebody, and I was just gazing

400
00:14:40,115 --> 00:14:41,794
out of the window. There's actually a spider's

401
00:14:41,794 --> 00:14:44,774
web on the window, and a fly

402
00:14:45,154 --> 00:14:47,794
hit the web. The the web vibrated. The

403
00:14:47,794 --> 00:14:49,554
spider came down, picked up the fly, took

404
00:14:49,554 --> 00:14:52,250
it back, ate it. That's a process that's

405
00:14:52,250 --> 00:14:54,889
happening. There's vibrations. Those vibrations you're talking about,

406
00:14:54,889 --> 00:14:57,450
is that the literal vibration of the web

407
00:14:57,450 --> 00:14:59,690
as the spider this fly hits it and

408
00:14:59,690 --> 00:15:00,830
then the spider moves?

409
00:15:01,370 --> 00:15:03,149
Yeah. Exactly. Yeah. Exactly. So,

410
00:15:03,475 --> 00:15:05,875
yeah, spiders and many other animals and and

411
00:15:05,875 --> 00:15:07,794
and humans are not any different now. We

412
00:15:07,794 --> 00:15:09,554
we use vibrations as a way of understanding

413
00:15:09,554 --> 00:15:11,495
the world around us and, you know, we

414
00:15:11,794 --> 00:15:14,195
we have ears and eyes, but, you know,

415
00:15:14,195 --> 00:15:16,195
spiders have very keen vibrational senses. And so

416
00:15:16,195 --> 00:15:17,554
you're right. You know, they when when a

417
00:15:17,554 --> 00:15:19,440
spider hits the web oh, sorry. A fly

418
00:15:19,440 --> 00:15:20,080
hits the web,

419
00:15:20,799 --> 00:15:22,240
they will not only be able to notice

420
00:15:22,240 --> 00:15:23,279
that there is a fly, but they can

421
00:15:23,279 --> 00:15:25,039
actually localize it. It's like a radar, if

422
00:15:25,039 --> 00:15:26,959
you wish. And and that's the unique thing.

423
00:15:26,959 --> 00:15:28,559
You know? Now they have all these sensors

424
00:15:28,559 --> 00:15:30,959
on their, on their bodies, and they can,

425
00:15:30,959 --> 00:15:32,580
through processing these signals,

426
00:15:33,075 --> 00:15:35,394
understand where the prey is located and they

427
00:15:35,394 --> 00:15:37,634
can catch it. Spiders have their poor eyesight.

428
00:15:37,634 --> 00:15:39,095
You know, most species say,

429
00:15:39,475 --> 00:15:40,995
they see very little. It's not like they

430
00:15:40,995 --> 00:15:42,835
they watch the fly fly in there and

431
00:15:42,835 --> 00:15:44,434
they catch it. No. They actually use vibration

432
00:15:44,434 --> 00:15:44,934
signals.

433
00:15:45,394 --> 00:15:47,075
And it's not limited to the fly flying

434
00:15:47,075 --> 00:15:49,350
in. It's, you know, literally, you know, anything.

435
00:15:49,409 --> 00:15:51,350
You know, they they find other spiders.

436
00:15:52,370 --> 00:15:54,610
They find fats in the maman. You know,

437
00:15:54,610 --> 00:15:58,230
people believe that ants can sense, earthquakes happening

438
00:15:58,370 --> 00:16:00,129
because they have very keen senses. So there's

439
00:16:00,129 --> 00:16:00,870
a lot of

440
00:16:01,274 --> 00:16:01,774
different

441
00:16:02,154 --> 00:16:04,574
species that have these modalities of of contextual,

442
00:16:05,514 --> 00:16:06,014
processing,

443
00:16:06,315 --> 00:16:08,714
which allow them to make a decision about

444
00:16:08,714 --> 00:16:10,654
what they're gonna do or what's gonna happen,

445
00:16:11,194 --> 00:16:13,754
just by listening to these these these periodic

446
00:16:13,754 --> 00:16:15,995
changes of of energy, essentially what which what

447
00:16:15,995 --> 00:16:17,410
it what it is. So yeah. And and

448
00:16:17,410 --> 00:16:19,490
in the in the sonification of the webs,

449
00:16:19,490 --> 00:16:21,169
that's what we we use it literally. You

450
00:16:21,169 --> 00:16:22,529
know, we basically say, well, here's a web,

451
00:16:22,529 --> 00:16:24,610
and let's see how it vibrates. And and

452
00:16:24,610 --> 00:16:25,809
that's sort of the the physics of that

453
00:16:25,809 --> 00:16:27,190
is well known. It's a macroscopic

454
00:16:27,970 --> 00:16:30,345
thing. It's it's nothing, you know, fancy there.

455
00:16:30,904 --> 00:16:32,504
The the the challenges were a lot in

456
00:16:32,504 --> 00:16:34,345
the in actually creating the models. And once

457
00:16:34,345 --> 00:16:36,504
you, you wanna make that work, you have

458
00:16:36,504 --> 00:16:38,584
to create a computer model essentially that that

459
00:16:38,904 --> 00:16:40,504
where the spider web lives in. So that's

460
00:16:40,504 --> 00:16:41,865
something we spend a lot of time on

461
00:16:41,865 --> 00:16:43,304
and figuring out how do we how do

462
00:16:43,304 --> 00:16:44,940
we create something like this? How do we

463
00:16:45,179 --> 00:16:47,279
make it so that you can interact with

464
00:16:47,500 --> 00:16:49,580
as a human on stage. Yeah. So that's

465
00:16:49,580 --> 00:16:51,100
something we did, like I mentioned earlier, with

466
00:16:51,100 --> 00:16:53,580
the team, at MIT, the music department. They

467
00:16:53,580 --> 00:16:55,840
have lots of experience in how to build,

468
00:16:56,299 --> 00:16:58,214
performances and and and and how do you

469
00:16:58,214 --> 00:16:59,495
make that in real time and so on.

470
00:16:59,495 --> 00:17:00,934
And so, you know, we we hooked up

471
00:17:00,934 --> 00:17:01,595
our model

472
00:17:01,975 --> 00:17:04,454
with these virtual reality environments so you can

473
00:17:04,454 --> 00:17:06,295
become it kind of becomes an instrument. Yeah.

474
00:17:06,295 --> 00:17:08,375
So but that is, I think the challenge

475
00:17:08,375 --> 00:17:09,355
there is and,

476
00:17:10,130 --> 00:17:11,650
you know, how do you how do you

477
00:17:11,650 --> 00:17:13,970
build this web into the into the computer?

478
00:17:13,970 --> 00:17:15,410
How do you scan it? And how do

479
00:17:15,410 --> 00:17:17,009
you get all the notes and the connections?

480
00:17:17,009 --> 00:17:18,150
So you probably saw,

481
00:17:18,610 --> 00:17:20,930
all web in your window, I'm guessing, but

482
00:17:20,930 --> 00:17:22,450
the the webs we've been looking at are

483
00:17:22,450 --> 00:17:25,605
are three-dimensional web structures, which we we study

484
00:17:25,605 --> 00:17:28,005
with in collaboration with Thomas Saracino, who's an

485
00:17:28,005 --> 00:17:30,404
artist in Berlin who has a huge collection

486
00:17:30,404 --> 00:17:31,945
of three d webs. And he,

487
00:17:32,325 --> 00:17:35,065
actually introduced us to the three-dimensional webs. And,

488
00:17:35,525 --> 00:17:37,704
that was the whole beginning of this avenue

489
00:17:37,765 --> 00:17:38,424
of studying

490
00:17:39,039 --> 00:17:41,299
spider webs in three d and and digitizing

491
00:17:41,359 --> 00:17:43,440
them. And and we have worked with Thomas

492
00:17:43,440 --> 00:17:45,220
and others for many years in

493
00:17:45,519 --> 00:17:47,519
in various ways to make that to explore

494
00:17:47,519 --> 00:17:49,599
that that aspect. There's this spider web thing.

495
00:17:49,599 --> 00:17:52,025
There's the vibrations going on, but then there's

496
00:17:52,025 --> 00:17:52,525
proteins

497
00:17:52,904 --> 00:17:55,404
and vibrations and amino acids and vibrations.

498
00:17:55,704 --> 00:17:57,784
Well, so when you when you, look at

499
00:17:57,784 --> 00:18:00,184
the world, you know, at every level scale,

500
00:18:00,744 --> 00:18:02,825
you you can see that the spider web

501
00:18:02,825 --> 00:18:05,065
you mentioned, you it doesn't always vibrate, at

502
00:18:05,065 --> 00:18:07,319
least for eyes. It's sort of static. But

503
00:18:07,480 --> 00:18:08,759
but if you were to go inside the

504
00:18:08,759 --> 00:18:10,279
spider web and you look, you know, what's

505
00:18:10,279 --> 00:18:12,299
the molecular makeup and how do the molecules,

506
00:18:12,759 --> 00:18:14,119
well, look like? And if you were to

507
00:18:14,119 --> 00:18:16,440
take a movie of the molecules, they they

508
00:18:16,440 --> 00:18:19,159
actually are gonna be wiggling around all the

509
00:18:19,159 --> 00:18:21,019
time. They they're not sitting there.

510
00:18:21,414 --> 00:18:22,934
Like, if you open up and say, you

511
00:18:22,934 --> 00:18:25,095
open a textbook, chemistry or physics textbook, you're

512
00:18:25,095 --> 00:18:28,775
gonna see these beautiful pictures of this molecule,

513
00:18:28,775 --> 00:18:31,015
this water molecule, or the protein, and and

514
00:18:31,015 --> 00:18:32,154
it looks like it's just

515
00:18:32,855 --> 00:18:34,855
one thing. It's one structure, but it's actually

516
00:18:34,855 --> 00:18:37,470
not like this. It's, it's a continuously changing

517
00:18:37,470 --> 00:18:38,450
shape. And,

518
00:18:38,990 --> 00:18:40,130
so molecules

519
00:18:40,750 --> 00:18:42,610
like proteins and others are

520
00:18:43,070 --> 00:18:45,710
continuously moving and vibrating. That's the nature of

521
00:18:45,710 --> 00:18:47,924
how they how they look like. And and

522
00:18:47,924 --> 00:18:50,245
then and so the the concept for that

523
00:18:50,245 --> 00:18:52,325
was essentially to we don't even need to

524
00:18:52,325 --> 00:18:53,684
excite it. Like in a spider web, you

525
00:18:53,684 --> 00:18:54,884
have to sort of pluck it, you know,

526
00:18:54,884 --> 00:18:57,205
to hear it. But at the molecular level,

527
00:18:57,205 --> 00:18:59,365
you you hear that that noise or music

528
00:18:59,365 --> 00:19:00,965
or whatever you wanna call it all the

529
00:19:00,965 --> 00:19:03,125
time just by observing it. And that's because

530
00:19:03,125 --> 00:19:05,500
the the energy in the room basically on

531
00:19:05,500 --> 00:19:06,000
outside

532
00:19:06,460 --> 00:19:09,019
is enough to to perturb the bonds in

533
00:19:09,019 --> 00:19:09,600
the molecule

534
00:19:10,140 --> 00:19:11,900
in such a way that they they vibrate.

535
00:19:11,900 --> 00:19:14,299
Now this is continuous exchange of of kinetic

536
00:19:14,299 --> 00:19:15,519
energy, which is temperature

537
00:19:16,024 --> 00:19:18,585
and the deformation of a structure. And that's

538
00:19:18,585 --> 00:19:19,724
what gives a characteristic

539
00:19:20,505 --> 00:19:22,265
sound, if you wish, to everything in the

540
00:19:22,265 --> 00:19:24,265
world. You know, there's a sound of a

541
00:19:24,265 --> 00:19:26,585
of a protein, there's a sound of water

542
00:19:26,585 --> 00:19:28,825
molecules and sound of everything. And and and

543
00:19:28,825 --> 00:19:29,644
if you can,

544
00:19:30,620 --> 00:19:33,100
you know, systematically explore that, you you have

545
00:19:33,100 --> 00:19:35,259
a a whole orchestra, you know, at your

546
00:19:35,259 --> 00:19:37,580
hands quite easily because there's a lot of

547
00:19:37,580 --> 00:19:39,180
two different kind of chemical structures in the

548
00:19:39,180 --> 00:19:41,740
world. They vibrate, but not at a frequency

549
00:19:41,740 --> 00:19:43,075
we can hear them. Right? And are you

550
00:19:43,075 --> 00:19:46,295
literally just translating that vibration into an audible

551
00:19:46,434 --> 00:19:49,715
vibration? Exactly. So, like, good good point. When

552
00:19:49,715 --> 00:19:51,154
you when you were if you were to

553
00:19:51,154 --> 00:19:52,835
look at this, or try to hear it,

554
00:19:52,835 --> 00:19:54,195
I mean, our ears are not made to

555
00:19:54,195 --> 00:19:55,715
hear that. And and and the same with

556
00:19:55,715 --> 00:19:57,630
the spider web. You know, our our the

557
00:19:57,869 --> 00:19:59,230
frequency is gonna be way too long. We

558
00:19:59,230 --> 00:20:00,309
won't be able to hear that as a

559
00:20:00,309 --> 00:20:01,710
as a sound, right. So we have to

560
00:20:01,710 --> 00:20:03,009
do something there to

561
00:20:03,309 --> 00:20:04,829
bring it. If you if we wanna listen

562
00:20:04,829 --> 00:20:06,109
to it as humans, we're gonna have to

563
00:20:06,109 --> 00:20:08,670
transpose those sounds. And that's something that is

564
00:20:08,670 --> 00:20:09,869
done all the time, you know, when you

565
00:20:09,869 --> 00:20:12,210
have, you know, basically somebody,

566
00:20:12,934 --> 00:20:13,674
a vocalist

567
00:20:14,214 --> 00:20:14,714
performing.

568
00:20:15,015 --> 00:20:17,355
Usually what they do is they would transpose

569
00:20:17,494 --> 00:20:19,654
the song or the melody to a range

570
00:20:19,654 --> 00:20:21,654
that they can sing. And and it doesn't

571
00:20:21,654 --> 00:20:23,575
change the musical meaning. Like, so you can

572
00:20:23,575 --> 00:20:24,075
play

573
00:20:24,470 --> 00:20:26,730
four at least at, you know, different frequency

574
00:20:26,869 --> 00:20:28,869
ranges as long as you keep the ratios

575
00:20:28,869 --> 00:20:31,509
of frequency identical. You will always recognize this

576
00:20:31,509 --> 00:20:33,750
is four at least. This is some whatever

577
00:20:33,750 --> 00:20:34,250
song,

578
00:20:34,950 --> 00:20:36,390
or melody. And and so that,

579
00:20:36,994 --> 00:20:38,674
concept is what we use. So we use

580
00:20:38,674 --> 00:20:39,414
the transpositional

581
00:20:39,875 --> 00:20:40,375
equivalence,

582
00:20:41,875 --> 00:20:43,715
formulas to do that, and and we can

583
00:20:43,715 --> 00:20:46,195
then transpose frequencies that are way too high

584
00:20:46,195 --> 00:20:47,715
of us to hear into a range that

585
00:20:47,715 --> 00:20:50,275
we can understand as humans, but it retains

586
00:20:50,275 --> 00:20:52,535
the musical information or the structural information

587
00:20:52,960 --> 00:20:54,960
the way it would actually sound like if

588
00:20:54,960 --> 00:20:55,759
we were to be able to hear it.

589
00:20:55,759 --> 00:20:57,380
And that's what we can do with algorithms,

590
00:20:57,440 --> 00:20:59,119
computer algorithms, who can who can make that

591
00:20:59,119 --> 00:21:00,799
happen. So now we can we can listen

592
00:21:00,799 --> 00:21:01,860
to it without ears.

593
00:21:10,865 --> 00:21:13,825
Deep learning usually comes when we wanna understand

594
00:21:13,825 --> 00:21:14,865
the language. So this,

595
00:21:15,904 --> 00:21:16,644
the transposition

596
00:21:17,025 --> 00:21:17,525
is

597
00:21:17,849 --> 00:21:18,589
fairly simple. It's

598
00:21:18,890 --> 00:21:20,809
like a mapping. It's a dictionary. You you

599
00:21:20,890 --> 00:21:23,869
or mathematic operation where you scale frequencies. But,

600
00:21:24,410 --> 00:21:26,009
but the deep learning comes in when you

601
00:21:26,009 --> 00:21:27,529
when you think about well, so now you

602
00:21:27,529 --> 00:21:29,289
have these these notes being played by the

603
00:21:29,289 --> 00:21:30,890
spider or you have the notes being played

604
00:21:30,890 --> 00:21:33,115
by the molecules, like the DNA codes, you

605
00:21:33,115 --> 00:21:33,855
know, kind of a,

606
00:21:34,794 --> 00:21:36,954
a wave encoding different amino acids, which each

607
00:21:36,954 --> 00:21:38,095
have a unique tone.

608
00:21:38,554 --> 00:21:40,474
So now you have a pattern. And the

609
00:21:40,474 --> 00:21:42,734
question then becomes it's like translating,

610
00:21:43,194 --> 00:21:45,375
you know, English to French or

611
00:21:46,140 --> 00:21:48,400
whatever language to one another. And

612
00:21:48,779 --> 00:21:50,880
you have a a code that would be

613
00:21:50,940 --> 00:21:51,440
readable

614
00:21:51,820 --> 00:21:53,580
to the cell, understanding how to make the

615
00:21:53,580 --> 00:21:55,820
protein and this sort of you can you

616
00:21:55,820 --> 00:21:57,339
can look at it or you can listen

617
00:21:57,339 --> 00:21:58,000
to it.

618
00:21:58,335 --> 00:22:00,255
But it's essentially is a is a pattern

619
00:22:00,255 --> 00:22:03,234
of of notes or melodies or structures. And

620
00:22:03,454 --> 00:22:04,894
the the deep learning comes in usually when

621
00:22:04,894 --> 00:22:06,335
we wanna understand that and mind that. You

622
00:22:06,335 --> 00:22:08,174
know, let's say, well, what if we can

623
00:22:08,174 --> 00:22:10,734
we understand what this code would mean as

624
00:22:10,734 --> 00:22:12,740
a protein? You know, if we take music

625
00:22:12,740 --> 00:22:14,419
from Bach, for example, that's worked on a

626
00:22:14,419 --> 00:22:15,240
recent paper,

627
00:22:16,019 --> 00:22:18,659
we have these patterns we can develop by

628
00:22:18,659 --> 00:22:21,059
by listening essentially to the music and then

629
00:22:21,059 --> 00:22:22,200
ask the question, well,

630
00:22:22,579 --> 00:22:24,659
what kind of protein pattern would this refer

631
00:22:24,659 --> 00:22:26,505
to? We can make that mapping because each

632
00:22:26,664 --> 00:22:28,424
amino acid has a unique note. So we

633
00:22:28,424 --> 00:22:30,265
can we can with an algorithm, we can

634
00:22:30,265 --> 00:22:31,164
simply simply

635
00:22:31,625 --> 00:22:33,944
align them. It's like a dictionary. You know?

636
00:22:33,944 --> 00:22:35,164
You say, well, this is,

637
00:22:35,944 --> 00:22:38,184
there's some organization in there. But then the

638
00:22:38,184 --> 00:22:39,944
the deep learning comes in to us saying,

639
00:22:39,944 --> 00:22:41,849
what does this mean? It's it's sort of

640
00:22:41,849 --> 00:22:43,309
saying if you if you translate,

641
00:22:43,769 --> 00:22:46,009
you know, English to French, you you have

642
00:22:46,009 --> 00:22:48,250
a way of mapping the words from English

643
00:22:48,250 --> 00:22:50,650
to French, but you don't know what the

644
00:22:50,650 --> 00:22:52,650
sentence actually means, you know, in a different

645
00:22:52,650 --> 00:22:53,150
language.

646
00:22:54,490 --> 00:22:56,089
And and that that is really what the

647
00:22:56,089 --> 00:22:57,744
deep learning can do. So it's not just

648
00:22:57,744 --> 00:22:59,984
the building blocks. It's how the building blocks

649
00:22:59,984 --> 00:23:01,684
form a meaning and significance,

650
00:23:02,625 --> 00:23:04,865
when they interact. And and this is what

651
00:23:04,865 --> 00:23:06,865
the deep learning does very well. And so

652
00:23:06,865 --> 00:23:09,265
we can take sequence of notes and we

653
00:23:09,265 --> 00:23:12,509
can predict what protein would that actually be

654
00:23:12,589 --> 00:23:13,970
if if it were protein.

655
00:23:19,710 --> 00:23:21,230
You take a piece of music. You could

656
00:23:21,230 --> 00:23:22,829
take any piece of music. And then using

657
00:23:22,829 --> 00:23:24,130
this this process

658
00:23:24,430 --> 00:23:27,265
from which you create music from the amino

659
00:23:27,265 --> 00:23:28,565
acids to the proteins.

660
00:23:29,184 --> 00:23:31,664
You you can revert that back again, and

661
00:23:31,664 --> 00:23:34,224
sometimes that might be a protein that already

662
00:23:34,224 --> 00:23:36,325
exists, and sometimes it is the protein.

663
00:23:36,704 --> 00:23:39,585
Exactly. Exactly. Yeah. Sometimes we exactly. So there

664
00:23:39,585 --> 00:23:41,184
could be a new protein altogether. It could

665
00:23:41,184 --> 00:23:43,400
be something that would exist. You can make

666
00:23:43,400 --> 00:23:43,799
it,

667
00:23:44,359 --> 00:23:46,599
but nature hasn't found it yet or it

668
00:23:46,599 --> 00:23:48,380
could be it could be proteins that,

669
00:23:48,920 --> 00:23:50,119
and we find all sorts of in the

670
00:23:50,119 --> 00:23:50,619
spectrum.

671
00:23:50,920 --> 00:23:53,640
Some proteins are are found that are have

672
00:23:53,640 --> 00:23:55,240
been found in a similar way by nature

673
00:23:55,240 --> 00:23:57,794
through evolution, and some proteins are are totally

674
00:23:57,794 --> 00:23:59,794
new. And they're kind of found through this

675
00:23:59,794 --> 00:24:02,835
other evolutionary mechanism, which is human imagination, which

676
00:24:02,835 --> 00:24:03,335
is,

677
00:24:04,674 --> 00:24:06,595
we're also a product of the evolutionary process,

678
00:24:06,595 --> 00:24:09,335
but we we we create these interesting sequences,

679
00:24:09,394 --> 00:24:10,615
which we call music.

680
00:24:11,190 --> 00:24:13,750
But, yeah, they actually have other significances in

681
00:24:13,750 --> 00:24:15,130
other context. And that's,

682
00:24:15,669 --> 00:24:17,909
that's really the basis to this what excites

683
00:24:17,909 --> 00:24:19,829
me really about this is that we can

684
00:24:19,829 --> 00:24:20,329
make

685
00:24:20,630 --> 00:24:22,069
discoveries. It's always like,

686
00:24:22,549 --> 00:24:24,809
you know, in science, we we always wanna

687
00:24:25,065 --> 00:24:27,065
have new microscopes, a new way to understand

688
00:24:27,065 --> 00:24:28,585
the world and get new data. And this

689
00:24:28,585 --> 00:24:29,784
is another way to do that. You know,

690
00:24:29,784 --> 00:24:32,024
we cannot say, well, we have data out

691
00:24:32,024 --> 00:24:32,845
there already,

692
00:24:33,384 --> 00:24:35,144
which is essentially I would I would argue

693
00:24:35,144 --> 00:24:37,065
that when what we create as humans,

694
00:24:37,384 --> 00:24:40,099
through creation of of anything really, but particularly

695
00:24:40,160 --> 00:24:42,640
art, of course, or music as an example

696
00:24:42,640 --> 00:24:43,140
here,

697
00:24:43,920 --> 00:24:45,440
you know, is an expression of ourselves. It

698
00:24:45,440 --> 00:24:47,599
comes from us. It's it's something that that

699
00:24:47,599 --> 00:24:48,099
is

700
00:24:48,400 --> 00:24:50,960
something that we create to project something that's

701
00:24:50,960 --> 00:24:52,480
within us to the world, you know, and

702
00:24:52,480 --> 00:24:53,220
it's in a,

703
00:24:53,785 --> 00:24:55,144
you know, it could be very literal, like,

704
00:24:55,144 --> 00:24:57,184
in literature. It could be story with experience.

705
00:24:57,184 --> 00:24:58,365
It could be autobiography.

706
00:24:59,144 --> 00:25:00,744
In music, it's a lot more abstract, which

707
00:25:00,744 --> 00:25:02,265
I think is why it's so interesting. It's

708
00:25:02,265 --> 00:25:04,984
it's not something that there's there are some

709
00:25:04,984 --> 00:25:07,305
more algorithmic musical compositions, of course, but a

710
00:25:07,305 --> 00:25:09,730
lot of times, it's an inspiration or an

711
00:25:09,730 --> 00:25:12,529
idea or or maybe mathematical rules even, but

712
00:25:12,529 --> 00:25:14,930
they're projected, you know, kind of processed through

713
00:25:14,930 --> 00:25:16,849
the brain. And and and and so we

714
00:25:16,849 --> 00:25:17,750
believe that

715
00:25:18,049 --> 00:25:20,130
in this process by which we create these

716
00:25:20,130 --> 00:25:21,670
notes and sequences and patterns,

717
00:25:23,105 --> 00:25:23,684
we inadvertently

718
00:25:23,984 --> 00:25:26,625
project some information about the way our brain

719
00:25:26,625 --> 00:25:28,704
and our body works into that structure that's

720
00:25:28,704 --> 00:25:30,865
created. That's the hypothesis we're trying to work

721
00:25:30,865 --> 00:25:33,264
from. And if that is the case, then

722
00:25:33,264 --> 00:25:36,085
by analyzing these human created structures,

723
00:25:36,529 --> 00:25:37,970
we might be able to learn something about

724
00:25:37,970 --> 00:25:39,649
how our brain works or how our body

725
00:25:39,649 --> 00:25:42,609
works or how our consciousness maybe looks like.

726
00:25:42,609 --> 00:25:44,130
And and this is what we're trying to

727
00:25:44,130 --> 00:25:45,589
understand through deep learning.

728
00:25:46,130 --> 00:25:47,809
Okay. And how do you test that? So

729
00:25:47,809 --> 00:25:50,049
there's this always question of universality. You know?

730
00:25:50,049 --> 00:25:50,950
Can you find

731
00:25:51,355 --> 00:25:53,914
a pattern that you can it's about normalizing

732
00:25:53,914 --> 00:25:55,674
the patterns. So you're sort of saying,

733
00:25:56,315 --> 00:25:59,054
if one civilization has made an invention

734
00:25:59,595 --> 00:26:01,674
and the civilization let's say it's alien, you

735
00:26:01,674 --> 00:26:03,595
know, alien worlds and, you know, have they

736
00:26:03,595 --> 00:26:05,700
invented the wheel kind of like this. Right?

737
00:26:06,259 --> 00:26:08,019
If we find the wheel being invented in

738
00:26:08,019 --> 00:26:10,980
basically a hundred different planets, different civilizations that

739
00:26:10,980 --> 00:26:11,960
have never communicated,

740
00:26:12,500 --> 00:26:14,019
that seems to be the wheel is a

741
00:26:14,019 --> 00:26:16,179
universal thing that, you know, is very powerful.

742
00:26:16,179 --> 00:26:17,700
Maybe that's true. So that's kind of how

743
00:26:17,700 --> 00:26:19,380
we approach this, you know, sort of asking

744
00:26:19,380 --> 00:26:19,960
the question,

745
00:26:20,345 --> 00:26:23,065
are the patterns that appear in proteins on

746
00:26:23,065 --> 00:26:23,565
DNA,

747
00:26:24,105 --> 00:26:26,345
are there patterns and other patterns that are

748
00:26:26,345 --> 00:26:28,045
universal that are often found

749
00:26:28,424 --> 00:26:30,345
in music as well? You know, are these

750
00:26:30,345 --> 00:26:30,845
structures

751
00:26:31,305 --> 00:26:32,525
of similar?

752
00:26:33,349 --> 00:26:34,630
And if that's the case, there seems to

753
00:26:34,630 --> 00:26:36,390
be some universality. That's one thing that we

754
00:26:36,390 --> 00:26:37,670
can test that pretty easily. We can just

755
00:26:37,670 --> 00:26:39,109
compare the patterns. And like I said, we

756
00:26:39,109 --> 00:26:41,130
have found that there are some similarities there.

757
00:26:41,670 --> 00:26:42,809
The other thing could be,

758
00:26:43,269 --> 00:26:45,670
you know, if, a pattern or or something

759
00:26:45,670 --> 00:26:47,670
discovered in one domain, is it useful for

760
00:26:47,670 --> 00:26:49,865
the other domain? Right? Can we make a

761
00:26:49,865 --> 00:26:51,784
protein and actually is it gonna have a

762
00:26:51,784 --> 00:26:53,704
function that might be useful or not useful?

763
00:26:53,704 --> 00:26:55,244
Just doesn't have any function.

764
00:26:55,944 --> 00:26:57,784
Is there a protein that Bach has created

765
00:26:57,784 --> 00:26:58,284
or

766
00:26:58,585 --> 00:27:00,125
whatever music has created,

767
00:27:00,914 --> 00:27:02,769
and and what is it gonna do? I

768
00:27:02,769 --> 00:27:04,609
mean, is it a protein that maybe has

769
00:27:04,609 --> 00:27:07,170
absolutely no read, no no positive or reasonable

770
00:27:07,170 --> 00:27:09,490
function? It's it's just protein that maybe does

771
00:27:09,490 --> 00:27:11,029
something that nature doesn't need.

772
00:27:11,330 --> 00:27:13,009
That's a that's another test. You know? Is

773
00:27:13,009 --> 00:27:14,454
there something that we can create as a

774
00:27:14,454 --> 00:27:16,774
functionality? It doesn't just exist, but it has

775
00:27:16,774 --> 00:27:18,394
a function on top of that. And,

776
00:27:18,774 --> 00:27:20,694
and that that would be another test if

777
00:27:20,694 --> 00:27:22,554
if there's sort of this this cross cutting,

778
00:27:23,335 --> 00:27:25,095
discussion. And then the other one would be

779
00:27:25,095 --> 00:27:26,934
if you if you would analyze and this

780
00:27:26,934 --> 00:27:29,250
is something that that maybe isn't possible today

781
00:27:29,470 --> 00:27:31,230
yet, but if you were to take the

782
00:27:31,230 --> 00:27:32,369
whole body of knowledge

783
00:27:32,990 --> 00:27:35,390
of of the creation of music and if

784
00:27:35,390 --> 00:27:36,990
you were to say, you know, can or

785
00:27:36,990 --> 00:27:39,390
other other expressions of humans, can we mine

786
00:27:39,390 --> 00:27:40,829
that data? You know, what do we discover?

787
00:27:40,829 --> 00:27:42,109
Are there are there kind of hidden patterns

788
00:27:42,109 --> 00:27:43,744
in this? You know, like like, we have

789
00:27:43,744 --> 00:27:45,744
a lot of data from in physics, you

790
00:27:45,744 --> 00:27:47,825
know, that we we analyze and then we

791
00:27:47,825 --> 00:27:49,825
say, oh, wait. Here's a theory that explains

792
00:27:49,825 --> 00:27:51,904
this. Right? There's a there's actually a pattern

793
00:27:51,904 --> 00:27:53,984
in that data, and and it's because the

794
00:27:53,984 --> 00:27:56,224
mass affects the outcome in that particular way

795
00:27:56,224 --> 00:27:58,029
or the the geometry of this of the

796
00:27:58,029 --> 00:27:59,710
shape, you know, and this goes through through

797
00:27:59,710 --> 00:28:00,769
the history of science.

798
00:28:01,230 --> 00:28:02,830
So the question could be we have this

799
00:28:02,830 --> 00:28:03,330
data.

800
00:28:03,789 --> 00:28:05,070
We can mine the data. And this is

801
00:28:05,070 --> 00:28:06,750
where I think most likely we're not gonna

802
00:28:06,750 --> 00:28:08,109
be able to mine that with the human

803
00:28:08,109 --> 00:28:10,289
brain because our your brain can only create

804
00:28:10,590 --> 00:28:12,404
it's hard enough to create one piece of

805
00:28:12,404 --> 00:28:14,884
music. Now we're looking at billions and billions

806
00:28:14,884 --> 00:28:17,525
of creations for many different humans, but computers

807
00:28:17,525 --> 00:28:19,044
can't do that. And and so the question

808
00:28:19,044 --> 00:28:20,265
would be, you know, can we,

809
00:28:20,724 --> 00:28:21,224
find

810
00:28:21,605 --> 00:28:25,109
interesting patterns in that, that maybe we cannot

811
00:28:25,109 --> 00:28:26,730
explain or maybe they explain,

812
00:28:27,109 --> 00:28:28,169
phenomena that

813
00:28:28,470 --> 00:28:30,629
we we haven't understood yet. You know, here's

814
00:28:30,629 --> 00:28:32,389
a here's a way these building blocks come

815
00:28:32,389 --> 00:28:34,309
together. And and it's it's really about the

816
00:28:34,309 --> 00:28:35,909
building blocks. You know, we you think about

817
00:28:35,909 --> 00:28:36,345
the,

818
00:28:36,744 --> 00:28:39,224
the the emergence of function of a physical

819
00:28:39,224 --> 00:28:41,464
object. It's it's it's really it's it doesn't

820
00:28:41,464 --> 00:28:43,065
actually matter what the building blocks are. It's

821
00:28:43,065 --> 00:28:44,664
it's how they relate with one another. Right?

822
00:28:44,664 --> 00:28:47,005
So music, for example, literature or music particulars,

823
00:28:47,704 --> 00:28:49,384
sits there, and we enjoy it. But

824
00:28:50,039 --> 00:28:51,960
and we have, of course, found mathematical rules

825
00:28:51,960 --> 00:28:53,640
behind it, and there's a lot of, you

826
00:28:53,640 --> 00:28:56,360
know, fear theory around this. But there's also

827
00:28:56,360 --> 00:28:58,539
a a fairly big question of how

828
00:28:59,160 --> 00:29:00,840
why was it created? You know, how does

829
00:29:00,840 --> 00:29:02,940
it come about? And and and that's fascinating

830
00:29:03,000 --> 00:29:04,759
to me. And I I feel there is

831
00:29:05,794 --> 00:29:07,875
we we believe, we hypothesize, there's a lot

832
00:29:07,875 --> 00:29:11,255
more in that data than we understand today.

833
00:29:11,315 --> 00:29:13,875
Would you foresee us possibly deciphering that in

834
00:29:13,875 --> 00:29:15,634
your lifetime? Yes. Yeah.

835
00:29:16,115 --> 00:29:18,035
Yeah. Because we we we have really made

836
00:29:18,035 --> 00:29:19,714
it. Now just look back ten years ago,

837
00:29:19,714 --> 00:29:20,934
we when we started

838
00:29:22,019 --> 00:29:23,799
this investigation, it began by,

839
00:29:24,180 --> 00:29:25,880
like I mentioned earlier, observation,

840
00:29:26,900 --> 00:29:28,980
and then we developed mathematical theories for it,

841
00:29:28,980 --> 00:29:31,720
and and it was toy problems, basically. Now

842
00:29:31,779 --> 00:29:34,420
we we can treat much more realistic systems

843
00:29:34,420 --> 00:29:36,019
using computer simulations. And,

844
00:29:36,805 --> 00:29:38,484
I think we're at a point now where

845
00:29:38,484 --> 00:29:40,845
the, you know, the intelligence, if you wish,

846
00:29:40,845 --> 00:29:43,845
from, computers is approaching a level where they

847
00:29:43,845 --> 00:29:44,664
can understand,

848
00:29:45,205 --> 00:29:47,684
really complex relationships in in all sorts of

849
00:29:47,684 --> 00:29:49,065
different systems, in particular,

850
00:29:50,399 --> 00:29:53,119
methods from what's called natural language processing, which

851
00:29:53,119 --> 00:29:55,539
is basically the the the toolkit that allows,

852
00:29:56,399 --> 00:29:57,379
computers to,

853
00:29:58,000 --> 00:29:58,500
express

854
00:29:58,960 --> 00:30:01,359
ideas and concepts or learn concepts like like

855
00:30:01,359 --> 00:30:02,579
humans do. And,

856
00:30:03,295 --> 00:30:05,855
and and it's all around the idea of

857
00:30:05,855 --> 00:30:08,255
of discrete building blocks, which is really we

858
00:30:08,255 --> 00:30:10,015
we know the world is filled around that

859
00:30:10,015 --> 00:30:11,055
idea. You know, we're not,

860
00:30:12,015 --> 00:30:14,575
necessarily a continuum of energy. We have discrete

861
00:30:14,575 --> 00:30:17,055
states of energy. And I think when we

862
00:30:17,055 --> 00:30:18,095
we expand on that,

863
00:30:18,950 --> 00:30:20,549
we're gonna arrive at a a sort of

864
00:30:20,549 --> 00:30:23,589
description where the physics of matter is is

865
00:30:23,589 --> 00:30:26,309
a language, which we we have with words

866
00:30:26,309 --> 00:30:27,849
that have multiple states and relationships.

867
00:30:28,950 --> 00:30:30,309
So yeah. So I think we're getting to

868
00:30:30,309 --> 00:30:33,174
the point where computers are smart enough or,

869
00:30:33,174 --> 00:30:35,255
you know, good enough for models that are

870
00:30:35,255 --> 00:30:37,355
deep enough to to capture these relationships.

871
00:30:37,735 --> 00:30:38,934
And, you know, there's still a way to

872
00:30:38,934 --> 00:30:40,235
go, but I yeah. Absolutely.

873
00:30:40,615 --> 00:30:42,215
I think we can mine that and and

874
00:30:42,215 --> 00:30:44,615
understand relationships much more better. I mean, we

875
00:30:44,615 --> 00:30:46,394
have already shown that we can mine

876
00:30:47,269 --> 00:30:49,029
existing music and make proteins from it and

877
00:30:49,029 --> 00:30:50,009
the other way around.

878
00:30:51,029 --> 00:30:53,669
And now we could take much more data

879
00:30:53,669 --> 00:30:56,149
and try to discover much more complex relationships.

880
00:30:56,149 --> 00:30:58,629
So in principle, that's already possible. Now Johan

881
00:30:58,629 --> 00:31:00,730
Sebastian Bach is, of course, no stranger

882
00:31:01,035 --> 00:31:03,755
to where science and music collide. And his

883
00:31:03,755 --> 00:31:05,835
music is, of course, included on the Voyager

884
00:31:05,835 --> 00:31:06,335
spacecraft.

885
00:31:06,795 --> 00:31:09,755
The idea being, with the Voyager's record, that

886
00:31:09,755 --> 00:31:11,535
should an alien species

887
00:31:12,075 --> 00:31:13,615
find the Voyager spacecraft,

888
00:31:13,994 --> 00:31:15,434
then they'd be able to decipher the instructions

889
00:31:15,434 --> 00:31:16,255
on board and play the

890
00:31:16,919 --> 00:31:20,039
on board and play the record with the

891
00:31:20,039 --> 00:31:22,440
sounds of Earth. There's a story that when

892
00:31:22,440 --> 00:31:24,279
they were choosing the sounds and the music

893
00:31:24,279 --> 00:31:26,940
to go on there, when somebody suggested Bach,

894
00:31:27,000 --> 00:31:28,779
someone, perhaps Carl Sagan,

895
00:31:29,079 --> 00:31:31,785
said, well, that would just be showing off.

896
00:31:32,085 --> 00:31:34,325
I wondered why Marcus chose it for this

897
00:31:34,325 --> 00:31:34,825
research.

898
00:31:35,285 --> 00:31:36,964
If you think about this from a science

899
00:31:36,964 --> 00:31:37,464
perspective,

900
00:31:38,085 --> 00:31:39,605
you wanna pick something that,

901
00:31:40,884 --> 00:31:43,365
has well, one way to start with this

902
00:31:43,365 --> 00:31:44,984
would be to pick something with structure.

903
00:31:45,519 --> 00:31:46,900
And and Bach, of course, has,

904
00:31:47,599 --> 00:31:49,440
in fact, the real quick variation piece that

905
00:31:49,440 --> 00:31:50,420
we worked with

906
00:31:51,039 --> 00:31:53,920
is a really a mathematical composition where, you

907
00:31:53,920 --> 00:31:56,480
know, he basically started with the beginning aria

908
00:31:56,480 --> 00:31:58,259
and he said, well, let's let's,

909
00:31:58,615 --> 00:32:00,934
you know, evolve this piece into many different

910
00:32:00,934 --> 00:32:02,075
directions and use

911
00:32:02,855 --> 00:32:06,394
mathematics to some degree, but also creativity, imagination,

912
00:32:06,694 --> 00:32:08,534
and and the, you know, the the secret

913
00:32:08,534 --> 00:32:10,294
sauce, I guess, by which he was able

914
00:32:10,294 --> 00:32:10,694
to,

915
00:32:11,174 --> 00:32:13,119
manipulate notes and add new notes and and

916
00:32:13,119 --> 00:32:14,799
patterns and rhythms and so on to it.

917
00:32:14,799 --> 00:32:16,740
But it is a very structured way, and

918
00:32:17,119 --> 00:32:18,819
and we began to explore,

919
00:32:19,119 --> 00:32:20,819
you know, how that mathematical

920
00:32:21,279 --> 00:32:21,779
description

921
00:32:22,480 --> 00:32:23,839
mixed with a little bit of core a

922
00:32:23,839 --> 00:32:25,599
lot of creativity, maybe you could say, but

923
00:32:25,599 --> 00:32:27,599
with creativity, you know, would would perhaps relate

924
00:32:27,599 --> 00:32:28,454
to a physical structure

925
00:32:29,015 --> 00:32:29,914
protein. And,

926
00:32:30,454 --> 00:32:32,054
but it can be done for other since

927
00:32:32,054 --> 00:32:33,355
then, we've looked at other

928
00:32:33,815 --> 00:32:34,875
types of compositions.

929
00:32:35,255 --> 00:32:36,954
And, and so there's definitely

930
00:32:37,974 --> 00:32:39,654
lots to discover for sure, but that's how

931
00:32:39,654 --> 00:32:41,654
we began with that type of music is

932
00:32:41,654 --> 00:32:44,100
really that it's a it's a very structured

933
00:32:44,100 --> 00:32:46,980
organization like DNA is essentially. Okay. So if

934
00:32:46,980 --> 00:32:47,559
I chose,

935
00:32:48,820 --> 00:32:51,240
napalm death, it wouldn't work quite as well?

936
00:32:52,100 --> 00:32:53,559
Well, probably would actually.

937
00:32:54,855 --> 00:32:55,355
Because,

938
00:32:55,774 --> 00:32:56,855
you know, a lot of it I I

939
00:32:56,855 --> 00:32:58,875
would I would say, you know, in Balaji,

940
00:32:59,015 --> 00:33:01,734
you you actually find that there are very

941
00:33:01,734 --> 00:33:03,815
few notes. You know? Very few a lot

942
00:33:03,815 --> 00:33:05,815
of the ideas are repeated all over. So,

943
00:33:05,815 --> 00:33:07,015
I mean, if you look something like, you

944
00:33:07,015 --> 00:33:08,634
know, punk rock, it would be

945
00:33:09,099 --> 00:33:10,059
you don't have a,

946
00:33:10,940 --> 00:33:13,179
very simple ideas of this, you know, that

947
00:33:13,179 --> 00:33:15,179
are repeated. Yeah. That's common in nature. So

948
00:33:15,179 --> 00:33:18,000
you would probably find maybe some amyloid proteins

949
00:33:18,059 --> 00:33:20,619
or some some beta sheet proteins, which are

950
00:33:20,619 --> 00:33:23,154
basically, like, for example, is a structure that's

951
00:33:23,154 --> 00:33:26,195
very interesting material device, but it's a repeat

952
00:33:26,195 --> 00:33:28,434
of the same sequence over and over again.

953
00:33:28,434 --> 00:33:30,515
And so yeah. You know? Yes. You would

954
00:33:30,515 --> 00:33:32,595
find and that's that's the interesting the sweet

955
00:33:32,595 --> 00:33:34,195
spot is actually when you look at something

956
00:33:34,195 --> 00:33:35,414
that's very simple.

957
00:33:36,160 --> 00:33:38,000
You're gonna probably find something like this there

958
00:33:38,000 --> 00:33:39,460
as well. But if it's more complex,

959
00:33:40,000 --> 00:33:40,500
the

960
00:33:41,039 --> 00:33:42,799
the ordering might not be as obvious. So

961
00:33:42,799 --> 00:33:44,160
if it's a very simple structure, we can

962
00:33:44,160 --> 00:33:45,759
actually recognize it as humans. We can look

963
00:33:45,759 --> 00:33:46,720
at the data and then see, oh, yeah.

964
00:33:46,720 --> 00:33:48,835
Here's a pattern. In the more complex,

965
00:33:49,455 --> 00:33:51,295
systems like Bach, I mean, it's not as

966
00:33:51,295 --> 00:33:52,815
easy to see. You know, you need,

967
00:33:53,375 --> 00:33:56,115
you need more algorithmic power to really understand

968
00:33:56,174 --> 00:33:58,015
the relationships and how that protein would look

969
00:33:58,015 --> 00:33:58,755
like. But

970
00:33:59,214 --> 00:34:01,134
I I think that's an interesting question of

971
00:34:01,134 --> 00:34:04,240
how well different styles of music and and

972
00:34:04,539 --> 00:34:06,000
and their, you know, completely,

973
00:34:07,339 --> 00:34:09,659
you know, random evolutions of notes and then

974
00:34:09,659 --> 00:34:10,800
and and unstructured

975
00:34:11,099 --> 00:34:12,079
music or,

976
00:34:13,420 --> 00:34:14,800
you know, in in improvisations

977
00:34:15,420 --> 00:34:17,445
and variations come in, and it's

978
00:34:17,925 --> 00:34:19,765
it's very interesting. And has a lot of

979
00:34:19,765 --> 00:34:21,785
analogies to biology, of course. You know, biology

980
00:34:22,485 --> 00:34:24,244
essentially is the base. If you just look

981
00:34:24,244 --> 00:34:27,364
at evolution, it's making species that are about

982
00:34:27,364 --> 00:34:29,525
the same, and then suddenly there's a mutation.

983
00:34:29,525 --> 00:34:30,824
Right? And so you create,

984
00:34:31,550 --> 00:34:33,869
attempts to vary the structure a little bit,

985
00:34:33,869 --> 00:34:35,730
and that creates really interesting

986
00:34:36,030 --> 00:34:38,989
variations which sometimes lead to new species and

987
00:34:38,989 --> 00:34:41,230
versions of the species, and they might they're

988
00:34:41,230 --> 00:34:43,309
much better, and so they can survive longer,

989
00:34:43,309 --> 00:34:44,670
and they they take, you know, like, the

990
00:34:44,670 --> 00:34:46,429
virus. A great example is, you know, you

991
00:34:46,429 --> 00:34:46,965
have a

992
00:34:47,445 --> 00:34:50,025
new variant, which is much more infectious.

993
00:34:50,485 --> 00:34:52,565
Well, it takes over. Right? And and so

994
00:34:52,565 --> 00:34:54,164
so that's the idea kind of fit with

995
00:34:54,164 --> 00:34:55,364
music as well. You have,

996
00:34:56,724 --> 00:34:57,925
you know, if you just look at the

997
00:34:57,925 --> 00:34:59,925
mechanics, we we call it the mechanics of

998
00:34:59,925 --> 00:35:01,539
music. Essentially, you look at this and say,

999
00:35:01,539 --> 00:35:03,139
well, how was it created? Well, there's an

1000
00:35:03,139 --> 00:35:04,440
idea. But if you were

1001
00:35:04,739 --> 00:35:07,000
to, you know, pull together a million ideas,

1002
00:35:07,059 --> 00:35:09,539
they're all different, nobody would probably enjoy that.

1003
00:35:09,539 --> 00:35:10,900
You know? So what the way what we

1004
00:35:10,900 --> 00:35:13,139
enjoy as music, and, you know, we look

1005
00:35:13,139 --> 00:35:15,514
at some rock music and you

1006
00:35:15,815 --> 00:35:17,974
know, and and even Beethoven or Bach and,

1007
00:35:17,974 --> 00:35:19,654
you know, there's repetition in there. There's there's

1008
00:35:19,654 --> 00:35:22,135
a there's there's a concept about repetition and

1009
00:35:22,135 --> 00:35:23,114
variation, and

1010
00:35:23,494 --> 00:35:25,894
and it's much more complex in classical music,

1011
00:35:25,894 --> 00:35:27,514
of course. We have a lot more variation

1012
00:35:27,734 --> 00:35:29,974
and complex arrangements of structure, which is why

1013
00:35:29,974 --> 00:35:31,869
we were so interested in this. But in

1014
00:35:31,869 --> 00:35:33,969
in any music, you have that that balance,

1015
00:35:34,190 --> 00:35:36,110
and and I think humans respond to that

1016
00:35:36,110 --> 00:35:37,869
so well because it's really how we're made.

1017
00:35:37,869 --> 00:35:39,489
You know, our bodies are made from

1018
00:35:39,869 --> 00:35:41,949
same DNA, the same cells with little bit

1019
00:35:41,949 --> 00:35:44,030
of variation. You know, skins skin cells are

1020
00:35:44,030 --> 00:35:45,789
a little bit different than our bone cells,

1021
00:35:45,789 --> 00:35:48,375
and they make proteins that have some similarities,

1022
00:35:48,375 --> 00:35:50,295
but they're also differences. And and I think

1023
00:35:50,295 --> 00:35:52,855
music is just a reflection of that structure

1024
00:35:52,855 --> 00:35:54,135
in the body, which is what we talked

1025
00:35:54,135 --> 00:35:54,795
about earlier.

1026
00:35:55,175 --> 00:35:57,575
What can we discover from music? Well, it's

1027
00:35:57,575 --> 00:35:59,469
the fact that we we have all these

1028
00:35:59,469 --> 00:36:01,469
similarities on the surface. We can just see

1029
00:36:01,469 --> 00:36:01,969
them,

1030
00:36:02,590 --> 00:36:04,349
and, you know, what else is in there

1031
00:36:04,349 --> 00:36:05,950
that we can't see, you know, because we

1032
00:36:05,950 --> 00:36:07,950
we have a very hard time looking at

1033
00:36:07,950 --> 00:36:10,910
individual molecules and understanding how molecules interact in

1034
00:36:10,910 --> 00:36:11,570
our body.

1035
00:36:11,964 --> 00:36:13,484
But music might be a way for us

1036
00:36:13,484 --> 00:36:14,284
to dis

1037
00:36:14,764 --> 00:36:16,944
disentangle these relationships more clearly.

1038
00:36:26,070 --> 00:36:28,390
If this sounds familiar to you, then you

1039
00:36:28,390 --> 00:36:30,410
may be one of the 1,000,000

1040
00:36:30,550 --> 00:36:32,489
people who streamed it so far

1041
00:36:32,789 --> 00:36:35,750
on Marcus's SoundCloud account because this is the

1042
00:36:35,750 --> 00:36:36,809
viral hit

1043
00:36:37,110 --> 00:36:38,890
that Marcus had, sonifying

1044
00:36:39,244 --> 00:36:40,144
the coronavirus

1045
00:36:40,525 --> 00:36:41,025
spike

1046
00:36:49,164 --> 00:36:51,565
protein. Yeah. So as an example, how the

1047
00:36:51,724 --> 00:36:54,640
this concept of looking at music or sound

1048
00:36:54,640 --> 00:36:58,079
actually led to scientific discoveries where we, we

1049
00:36:58,079 --> 00:37:00,239
essentially did that when COVID began, we,

1050
00:37:00,800 --> 00:37:02,800
and, and there's a lot of different musical

1051
00:37:02,800 --> 00:37:05,039
pieces on that I created on, on, on

1052
00:37:05,039 --> 00:37:07,699
the COVID pathogen itself, spike protein, which

1053
00:37:08,175 --> 00:37:11,135
essentially I needed to explore that new protein,

1054
00:37:11,135 --> 00:37:12,574
which was suddenly very famous.

1055
00:37:13,055 --> 00:37:15,135
And you know, before that even, in fact,

1056
00:37:15,135 --> 00:37:16,735
just a few months before that I worked

1057
00:37:16,735 --> 00:37:18,815
with a different kind of virus and I

1058
00:37:18,815 --> 00:37:20,969
made music from that. So sort of a

1059
00:37:21,449 --> 00:37:22,569
very obvious thing for us to do at

1060
00:37:22,569 --> 00:37:23,389
the time, but

1061
00:37:23,690 --> 00:37:25,530
so we're listening to these and then we,

1062
00:37:25,769 --> 00:37:26,829
discovered that well,

1063
00:37:27,210 --> 00:37:29,289
you know, these variants were coming around and

1064
00:37:29,289 --> 00:37:30,670
we began to see

1065
00:37:30,969 --> 00:37:33,449
all these different variants, which are slightly different,

1066
00:37:33,449 --> 00:37:35,769
but almost the same as the original wild

1067
00:37:35,769 --> 00:37:36,269
type.

1068
00:37:36,664 --> 00:37:37,484
And so

1069
00:37:37,864 --> 00:37:39,625
we heard them and we saw that, we

1070
00:37:39,625 --> 00:37:41,244
heard that basically they sound

1071
00:37:41,704 --> 00:37:43,724
similar, but a little bit different. And

1072
00:37:44,025 --> 00:37:45,945
we then began to explore that scientifically. This

1073
00:37:45,945 --> 00:37:47,464
is where the science comes from, back to

1074
00:37:47,464 --> 00:37:48,864
science now and said, well, if they sound

1075
00:37:48,864 --> 00:37:51,065
a little bit different, can we characterize the

1076
00:37:51,065 --> 00:37:53,510
way they sound different quantitatively? Can we write

1077
00:37:53,510 --> 00:37:56,150
down equations and, you know, put on some

1078
00:37:56,150 --> 00:37:58,710
numbers basically in quick correlations? And we basically

1079
00:37:58,710 --> 00:37:59,690
discovered that

1080
00:38:00,070 --> 00:38:02,889
among the, the different types of coronavirus species,

1081
00:38:02,949 --> 00:38:03,690
if you wish,

1082
00:38:03,989 --> 00:38:05,824
MERS, SARS, COVID-nineteen,

1083
00:38:07,724 --> 00:38:09,105
and the variants from that,

1084
00:38:09,964 --> 00:38:12,764
they kind of fall on different classes of

1085
00:38:12,764 --> 00:38:13,905
types of infectiousness

1086
00:38:14,204 --> 00:38:17,005
and lethality. Violence is different for the different

1087
00:38:17,005 --> 00:38:19,105
types of COVID or coronavirus

1088
00:38:19,405 --> 00:38:22,650
diseases. And they correlated actually with the way

1089
00:38:22,650 --> 00:38:25,289
we could quantify these vibrations. And so we

1090
00:38:25,289 --> 00:38:26,829
created a model that would predict

1091
00:38:27,210 --> 00:38:29,070
by just looking or listening,

1092
00:38:30,170 --> 00:38:31,469
to how the molecule

1093
00:38:31,769 --> 00:38:33,550
changes shape over time, which is

1094
00:38:34,170 --> 00:38:35,309
the vibrational spectrum,

1095
00:38:35,715 --> 00:38:38,375
how that can actually predict the outcome

1096
00:38:38,675 --> 00:38:41,414
at the epidemiological scale, which means

1097
00:38:41,715 --> 00:38:42,535
the virus

1098
00:38:43,075 --> 00:38:45,494
becomes more lethal, less lethal and so on.

1099
00:38:45,795 --> 00:38:48,114
And it's an outcome, how you can begin

1100
00:38:48,114 --> 00:38:51,280
to ask scientific questions based on hypothesis you're

1101
00:38:51,280 --> 00:38:54,239
developing by by using a human imagination. Are

1102
00:38:54,239 --> 00:38:55,760
you listening to it and you say, hey,

1103
00:38:55,760 --> 00:38:58,079
here's a direction. Let's explore that. And then

1104
00:38:58,079 --> 00:38:59,139
we could, you know,

1105
00:38:59,519 --> 00:39:00,880
pin down the data. So in in the

1106
00:39:00,960 --> 00:39:02,420
in that study, we didn't

1107
00:39:02,954 --> 00:39:05,434
use human subjects to investigate how it sounds

1108
00:39:05,434 --> 00:39:07,515
like. We actually, of course, used the we

1109
00:39:07,515 --> 00:39:09,275
created the data. We never listened, but we

1110
00:39:09,275 --> 00:39:11,275
don't necessarily listen to it anymore. At that

1111
00:39:11,275 --> 00:39:13,775
time, we we calculate the vibration of spectrum.

1112
00:39:13,914 --> 00:39:16,859
We use mathematics to write down the different

1113
00:39:16,859 --> 00:39:19,099
spectra and then prepare them. But but the

1114
00:39:19,099 --> 00:39:20,639
idea originally came from

1115
00:39:21,019 --> 00:39:23,500
the the human the human subject listening to

1116
00:39:23,500 --> 00:39:25,440
the music and having that inspiration.

1117
00:39:33,565 --> 00:39:35,484
When you look at the coronavirus and everyone

1118
00:39:35,484 --> 00:39:37,085
has seen these pictures, and I think we're

1119
00:39:37,085 --> 00:39:38,925
all tired of seeing them probably, but they

1120
00:39:38,925 --> 00:39:40,444
all they kinda look like a it looks

1121
00:39:40,444 --> 00:39:41,964
like these spike proteins coming out of the

1122
00:39:41,964 --> 00:39:43,889
virus, and they they look like

1123
00:39:44,449 --> 00:39:46,849
physical structures, which they are. But what's not

1124
00:39:46,849 --> 00:39:48,789
captured in these pictures is that,

1125
00:39:49,569 --> 00:39:50,949
this virus spike,

1126
00:39:51,329 --> 00:39:53,730
is is actually continuously moving. It's like it's

1127
00:39:53,730 --> 00:39:55,569
vibrating like a guitar string. You know? It's

1128
00:39:55,650 --> 00:39:57,089
and that's like I said earlier, it's from

1129
00:39:57,089 --> 00:39:59,349
the thermal motion. It's basically the way the

1130
00:39:59,494 --> 00:40:01,255
the temperature in the room is high enough

1131
00:40:01,255 --> 00:40:04,375
for outside even, to to basically perturb the

1132
00:40:04,375 --> 00:40:06,474
structure, the shape. And so this protein is,

1133
00:40:07,414 --> 00:40:09,655
not a single structure. It's it's a collection

1134
00:40:09,655 --> 00:40:11,655
of many different structures that change over time,

1135
00:40:11,655 --> 00:40:12,155
and

1136
00:40:12,549 --> 00:40:14,389
that defines the spectrum. Just like in a

1137
00:40:14,389 --> 00:40:16,389
guitar string or a violin, you you you

1138
00:40:16,389 --> 00:40:17,989
plug it and you can actually draw the

1139
00:40:17,989 --> 00:40:20,230
physical vibrations and you control the different modes,

1140
00:40:20,230 --> 00:40:22,069
and that can be done for molecules as

1141
00:40:22,069 --> 00:40:24,389
well. And so this, you know, gives rise

1142
00:40:24,389 --> 00:40:26,655
to these the sound of this, you know,

1143
00:40:26,655 --> 00:40:28,675
structure, and you can then ask the question,

1144
00:40:28,815 --> 00:40:29,795
how would that

1145
00:40:30,494 --> 00:40:32,015
sound like if you were to,

1146
00:40:32,974 --> 00:40:34,335
listen to that? And, again, you can't hear

1147
00:40:34,335 --> 00:40:35,775
that with human ears. You know, you have

1148
00:40:35,775 --> 00:40:37,235
to transpose that to human,

1149
00:40:37,535 --> 00:40:39,715
audible frequencies, but you can do that with,

1150
00:40:40,179 --> 00:40:42,019
the algorithms, and now you have a way

1151
00:40:42,019 --> 00:40:44,179
of listening to that. And there's different levels

1152
00:40:44,179 --> 00:40:45,780
of interpretation we have done with this. One

1153
00:40:45,780 --> 00:40:47,640
is to listen to the whole structure,

1154
00:40:48,099 --> 00:40:49,860
but we can also go to finer detail

1155
00:40:49,860 --> 00:40:51,719
and look at and listen to the individual

1156
00:40:51,780 --> 00:40:53,480
amino acids and the individual

1157
00:40:53,940 --> 00:40:55,894
folds in the protein. And and this particular

1158
00:40:56,054 --> 00:40:59,195
the spike protein is a incredibly complex protein.

1159
00:40:59,255 --> 00:41:01,815
You know, it's it's, quite quite interesting actually

1160
00:41:01,815 --> 00:41:02,315
and

1161
00:41:03,014 --> 00:41:03,655
very big,

1162
00:41:04,295 --> 00:41:06,614
and has thousands of amino acids. And so

1163
00:41:06,614 --> 00:41:08,315
it's very lots of different

1164
00:41:08,760 --> 00:41:11,500
small structures and larger structures and repetitions

1165
00:41:11,880 --> 00:41:13,719
and variations, and this is sort of what

1166
00:41:13,719 --> 00:41:14,699
makes this protein,

1167
00:41:15,239 --> 00:41:16,679
look like the way it does. But

1168
00:41:17,480 --> 00:41:19,480
and so what we've done also in and

1169
00:41:19,480 --> 00:41:21,480
this work has has received a lot of

1170
00:41:21,480 --> 00:41:22,519
attention at the time,

1171
00:41:23,000 --> 00:41:25,574
is just created a whole symphony essentially from

1172
00:41:25,574 --> 00:41:27,974
that structure because now we have lots of

1173
00:41:27,974 --> 00:41:30,054
information about the structure, you know, at a

1174
00:41:30,054 --> 00:41:32,215
DNA level all the way to the entire

1175
00:41:32,215 --> 00:41:35,094
structure of this whole, molecule. And and we

1176
00:41:35,094 --> 00:41:36,795
can translate this actually into,

1177
00:41:37,640 --> 00:41:38,140
into,

1178
00:41:38,840 --> 00:41:39,500
a multi,

1179
00:41:40,119 --> 00:41:40,619
instrumental,

1180
00:41:41,800 --> 00:41:44,519
realization, right. Where, you know, each instrument would

1181
00:41:44,519 --> 00:41:44,920
play,

1182
00:41:45,320 --> 00:41:45,820
model

1183
00:41:46,680 --> 00:41:48,519
a particular part of that protein, right. So

1184
00:41:48,519 --> 00:41:50,619
from the smallest to the highest level and,

1185
00:41:51,375 --> 00:41:53,295
that that whole piece is an hour and

1186
00:41:53,295 --> 00:41:54,114
fifty long

1187
00:41:54,414 --> 00:41:56,335
because the structure is so complex. It takes

1188
00:41:56,335 --> 00:41:58,894
that much time to to build an audible

1189
00:41:58,894 --> 00:42:00,655
model of this entire spike protein, which is

1190
00:42:00,655 --> 00:42:02,094
only a small part of the entire virus.

1191
00:42:02,094 --> 00:42:04,559
So it just shows how how much complexity

1192
00:42:04,619 --> 00:42:06,219
and how much music is out there. If

1193
00:42:06,219 --> 00:42:07,819
you wish, if you were to listen to

1194
00:42:07,819 --> 00:42:10,619
all musical forms in nature and you have

1195
00:42:10,619 --> 00:42:13,579
an endless source of playlist basically. But yeah,

1196
00:42:13,579 --> 00:42:14,559
so that's that piece.

1197
00:42:16,175 --> 00:42:18,034
And it was a way I think for,

1198
00:42:18,494 --> 00:42:20,414
it's interesting to see, you know, at the

1199
00:42:20,414 --> 00:42:22,735
time we, we, of course everyone was talking

1200
00:42:22,735 --> 00:42:24,414
about COVID as a beginning of the pandemic.

1201
00:42:24,414 --> 00:42:26,255
And obviously, I guess we still are talking

1202
00:42:26,255 --> 00:42:28,094
about it today, but at that time it

1203
00:42:28,094 --> 00:42:29,934
was an unknown thing. We didn't know where

1204
00:42:29,934 --> 00:42:31,074
it's gonna go. And,

1205
00:42:32,150 --> 00:42:33,050
and by

1206
00:42:33,349 --> 00:42:35,030
listening to it, you had a lot of

1207
00:42:35,030 --> 00:42:37,989
people had just a different connection relationship to

1208
00:42:37,989 --> 00:42:38,969
that virus.

1209
00:42:39,349 --> 00:42:41,269
You can't see it, but now you could

1210
00:42:41,269 --> 00:42:43,590
hear it. And you couldn't just I mean,

1211
00:42:43,590 --> 00:42:44,710
you wanna look at a picture on the

1212
00:42:44,710 --> 00:42:47,030
screen, that's one thing, but the vibrations is

1213
00:42:47,030 --> 00:42:47,530
really

1214
00:42:48,684 --> 00:42:50,704
a reflection of the structure of this protein.

1215
00:42:50,764 --> 00:42:51,264
And

1216
00:42:52,284 --> 00:42:54,704
it was nice for me as an educator

1217
00:42:54,764 --> 00:42:56,764
is that it gave me the opportunity to

1218
00:42:56,764 --> 00:42:58,304
talk to a lot of people about proteins.

1219
00:42:58,925 --> 00:42:59,984
I had so many

1220
00:43:00,360 --> 00:43:02,380
interviews and articles and so on where

1221
00:43:02,760 --> 00:43:04,280
I could say, yeah, this is how it

1222
00:43:04,280 --> 00:43:05,720
sounds like and this is what a protein

1223
00:43:05,720 --> 00:43:06,700
actually is. Here's

1224
00:43:07,160 --> 00:43:09,720
an atom, a molecule, that's what DNA is,

1225
00:43:09,720 --> 00:43:11,800
this is what amino acid is. It was

1226
00:43:11,800 --> 00:43:13,614
a great way to reach, I think the

1227
00:43:13,614 --> 00:43:16,175
general public and get them maybe excited about

1228
00:43:16,175 --> 00:43:17,795
STEM and science and engineering

1229
00:43:18,414 --> 00:43:20,594
and physics and all these different areas. But

1230
00:43:20,894 --> 00:43:23,055
yeah, and it opened, a lot of people

1231
00:43:23,055 --> 00:43:25,215
don't know about what protein actually is. And

1232
00:43:25,215 --> 00:43:26,655
I think protein is something to eat, which

1233
00:43:26,655 --> 00:43:27,599
it is too. But

1234
00:43:28,159 --> 00:43:30,160
so it was a good way to, to

1235
00:43:30,160 --> 00:43:31,220
reach out and

1236
00:43:31,760 --> 00:43:32,579
have a discussion

1237
00:43:33,200 --> 00:43:35,140
on all sorts of questions, you know, from

1238
00:43:35,680 --> 00:43:36,660
the, you know,

1239
00:43:37,039 --> 00:43:39,300
the people that are more interested and more

1240
00:43:39,519 --> 00:43:42,155
familiar with music and art, had a way

1241
00:43:42,155 --> 00:43:42,815
of understanding

1242
00:43:43,755 --> 00:43:46,175
this this virus now from a much more

1243
00:43:46,474 --> 00:43:49,355
quantitative perspective. Now is, probably the most interesting

1244
00:43:49,355 --> 00:43:50,795
part of that is that outreach we could

1245
00:43:50,795 --> 00:43:52,414
do with this with this music.

1246
00:43:56,089 --> 00:43:58,329
It feels like the sort of thing where

1247
00:43:58,329 --> 00:44:00,650
people might latch onto it, like when people

1248
00:44:00,650 --> 00:44:03,150
latch onto quantum physics. You're talking about vibrations.

1249
00:44:03,210 --> 00:44:05,049
You're talking about music. Do you worry that

1250
00:44:05,049 --> 00:44:07,289
people might stretch this too far and start

1251
00:44:07,289 --> 00:44:08,670
using it for pseudoscience?

1252
00:44:09,144 --> 00:44:11,065
Oh, absolutely. Yeah. No. They are. In fact,

1253
00:44:11,065 --> 00:44:12,204
I there are

1254
00:44:12,905 --> 00:44:14,285
even before we were

1255
00:44:14,825 --> 00:44:17,224
in, there are people that are, you know,

1256
00:44:17,224 --> 00:44:19,065
claim that and and and it's actually comes

1257
00:44:19,065 --> 00:44:20,825
from so I wanna take a step back.

1258
00:44:20,825 --> 00:44:23,085
There are, different cultures and civilizations

1259
00:44:23,545 --> 00:44:25,289
or or, you know, kind of schools of

1260
00:44:25,289 --> 00:44:27,289
thought in in other cultures, not the western

1261
00:44:27,289 --> 00:44:28,110
culture, others,

1262
00:44:28,410 --> 00:44:30,730
where vibrations do actually play a very important

1263
00:44:30,730 --> 00:44:32,650
role in healing. And that's, for example, in

1264
00:44:32,650 --> 00:44:35,210
in in Asia, India in particular, many other

1265
00:44:35,210 --> 00:44:35,710
cultures.

1266
00:44:37,125 --> 00:44:39,545
Certain tones and and instruments

1267
00:44:39,844 --> 00:44:42,585
are part of ceremonies and healing processes. And

1268
00:44:42,724 --> 00:44:44,644
and so, yeah, there's a there's sort of

1269
00:44:44,644 --> 00:44:47,385
a a already, some heritage out there. And

1270
00:44:47,525 --> 00:44:49,045
and, yeah, you're right. And there are people

1271
00:44:49,045 --> 00:44:51,844
latching onto this who are basically saying, oh,

1272
00:44:51,844 --> 00:44:53,429
here's the, you know,

1273
00:44:53,890 --> 00:44:55,110
the Southeast Asian,

1274
00:44:55,489 --> 00:44:57,409
you know, way of healing, and here's the

1275
00:44:57,409 --> 00:44:59,170
coronavirus. Well, can we get rid of the

1276
00:44:59,170 --> 00:45:01,170
coronavirus? But and and then we'll be very

1277
00:45:01,170 --> 00:45:03,510
careful, of course. You know, obviously, that's there's

1278
00:45:03,570 --> 00:45:04,070
definitely

1279
00:45:05,025 --> 00:45:06,704
lots you know, could be nonsense out there

1280
00:45:06,704 --> 00:45:08,304
or or let's not put it that way.

1281
00:45:08,545 --> 00:45:09,824
I wanna say nonsense, but I wanna say

1282
00:45:09,824 --> 00:45:11,344
things that aren't explored yet. And I think

1283
00:45:11,344 --> 00:45:13,025
if we don't understand that, we can't really

1284
00:45:13,025 --> 00:45:14,545
make any claims. And but there are people

1285
00:45:14,545 --> 00:45:16,324
that probably stretch it a little too thin.

1286
00:45:16,545 --> 00:45:17,525
Absolutely. Yes.

1287
00:45:18,809 --> 00:45:19,630
You know, and

1288
00:45:20,329 --> 00:45:22,730
that's definitely something that, you know, can happen,

1289
00:45:22,730 --> 00:45:24,989
but I, you know, for us, we

1290
00:45:25,610 --> 00:45:27,130
we we try to keep that line very

1291
00:45:27,130 --> 00:45:29,130
clear. I mean, we're not claiming, you know,

1292
00:45:29,130 --> 00:45:31,390
that we can, well, treat diseases,

1293
00:45:31,849 --> 00:45:33,994
with this, but but we really talk about

1294
00:45:33,994 --> 00:45:35,994
the physics and the mathematics, the material science,

1295
00:45:35,994 --> 00:45:37,755
and chemistry behind that, and I think that's

1296
00:45:37,755 --> 00:45:39,594
a very solid ground we can stand on

1297
00:45:39,594 --> 00:45:40,094
there.

1298
00:45:40,635 --> 00:45:42,235
And and also the like, we talked a

1299
00:45:42,235 --> 00:45:44,555
lot about the translation of information. That's that's

1300
00:45:44,555 --> 00:45:46,635
a very rigorous discipline. But, yeah, you're right.

1301
00:45:46,635 --> 00:45:48,655
I mean, that's definitely something that I always,

1302
00:45:49,510 --> 00:45:51,349
you know, I I did get lots of

1303
00:45:51,349 --> 00:45:53,110
these emails, and I was right back and

1304
00:45:53,110 --> 00:45:54,869
I say, hey. I don't I don't I

1305
00:45:54,869 --> 00:45:56,809
can't say this is gonna work and,

1306
00:45:57,349 --> 00:45:58,950
you know, you could try to try to

1307
00:45:58,950 --> 00:46:01,349
say that. But, yeah, definitely, it's there's definitely

1308
00:46:01,349 --> 00:46:02,410
people out there that,

1309
00:46:02,985 --> 00:46:04,425
that think that. And and like I said,

1310
00:46:04,425 --> 00:46:06,744
so there might be something to that, you

1311
00:46:06,744 --> 00:46:09,865
know, because other cultures have explored vibrations as

1312
00:46:09,865 --> 00:46:11,305
a way of healing, and and and we

1313
00:46:11,305 --> 00:46:13,244
might just not understand that yet. But,

1314
00:46:13,865 --> 00:46:15,780
I would not I mean, I just we

1315
00:46:15,780 --> 00:46:17,719
can't under underwrite that anyway.

1316
00:46:21,940 --> 00:46:23,780
At the beginning, you talked about this the

1317
00:46:23,780 --> 00:46:26,820
spider's web and sort of translating back into

1318
00:46:26,820 --> 00:46:27,880
that and communicating.

1319
00:46:28,535 --> 00:46:30,855
Do you literally mean communicating with the spider?

1320
00:46:30,855 --> 00:46:32,215
And if so, what would you say to

1321
00:46:32,215 --> 00:46:32,715
it?

1322
00:46:33,335 --> 00:46:35,015
Well, I think that is a great question.

1323
00:46:35,015 --> 00:46:36,614
So we we are actually trying to,

1324
00:46:37,175 --> 00:46:39,414
you know, communicate with different species and try

1325
00:46:39,414 --> 00:46:41,655
to figure out how can we influence I

1326
00:46:41,655 --> 00:46:42,795
mean, of course, the

1327
00:46:43,730 --> 00:46:45,489
the the human interest is very different than

1328
00:46:45,489 --> 00:46:47,650
the spider's interest. Right? So we're not the

1329
00:46:47,650 --> 00:46:49,250
spider is not gonna be interested in telling

1330
00:46:49,250 --> 00:46:51,409
us probably about the weather or, you know,

1331
00:46:51,409 --> 00:46:53,909
I don't know, politics or whatever.

1332
00:46:54,530 --> 00:46:56,289
The spider is gonna be interested in finding

1333
00:46:56,289 --> 00:46:57,684
the next next fly. And so well, so

1334
00:46:57,684 --> 00:46:59,684
our communication is gonna be something like,

1335
00:47:00,484 --> 00:47:03,224
you know, can we can we pretend that

1336
00:47:03,364 --> 00:47:05,385
there's a fly here, and can we

1337
00:47:05,684 --> 00:47:07,284
attract the spider to come to that point?

1338
00:47:07,284 --> 00:47:09,284
You know, can we understand exactly how that

1339
00:47:09,284 --> 00:47:10,184
works, and

1340
00:47:10,550 --> 00:47:12,949
can we trick, quotation marks a spider to

1341
00:47:12,949 --> 00:47:14,550
think there's a fly? And and what is

1342
00:47:14,550 --> 00:47:15,829
the signal we need to give to the

1343
00:47:15,829 --> 00:47:17,130
web to make that happen?

1344
00:47:17,670 --> 00:47:19,510
You know, can we pretend we're another type

1345
00:47:19,510 --> 00:47:21,690
of spider? You know, spiders use this to,

1346
00:47:22,309 --> 00:47:23,989
attract mates. You know, that's how they find

1347
00:47:23,989 --> 00:47:25,994
each other when they, when they look,

1348
00:47:26,635 --> 00:47:28,394
you know, to mate, and they they use

1349
00:47:28,394 --> 00:47:29,835
for patient signals for that. They have a

1350
00:47:29,835 --> 00:47:32,155
way of of alerting, you know, you know,

1351
00:47:32,155 --> 00:47:34,315
in the different colonies, other spiders of threats,

1352
00:47:34,315 --> 00:47:36,235
and so there's a whole spectrum. So the

1353
00:47:36,235 --> 00:47:38,075
the the communication is not is gonna be

1354
00:47:38,075 --> 00:47:40,159
around those context, you know, because that's the

1355
00:47:40,159 --> 00:47:42,000
that's the things they're interested in. Yeah. They're

1356
00:47:42,000 --> 00:47:44,159
not interested in so we have to come

1357
00:47:44,159 --> 00:47:46,799
to the same level of of communication in

1358
00:47:46,799 --> 00:47:48,400
terms of what we're gonna talk about, I

1359
00:47:48,400 --> 00:47:49,219
guess. So,

1360
00:47:49,920 --> 00:47:51,039
but I think there,

1361
00:47:51,440 --> 00:47:52,000
there are,

1362
00:47:52,905 --> 00:47:54,425
you know and and there are people that

1363
00:47:54,425 --> 00:47:56,364
actually are are interested in

1364
00:47:56,905 --> 00:47:58,204
in, you know, exploring

1365
00:47:58,664 --> 00:47:59,164
the

1366
00:47:59,545 --> 00:48:01,704
the questions of of the, you know, the

1367
00:48:01,704 --> 00:48:04,105
the the intelligence of spiders or the animals

1368
00:48:04,105 --> 00:48:05,864
in much more rigorous ways. And maybe that's

1369
00:48:05,864 --> 00:48:06,925
a community that

1370
00:48:07,299 --> 00:48:09,539
could be brought into this if once we

1371
00:48:09,539 --> 00:48:11,639
understand how to communicate with different

1372
00:48:11,940 --> 00:48:12,440
species,

1373
00:48:12,980 --> 00:48:14,339
there's a there's a there's a lot of

1374
00:48:14,339 --> 00:48:17,159
biologists that are essentially looking at understanding communication

1375
00:48:17,219 --> 00:48:17,719
between

1376
00:48:18,179 --> 00:48:20,359
species themselves. And so I think there's some

1377
00:48:20,545 --> 00:48:22,144
there's some interesting overlap there. Yeah. But it

1378
00:48:22,144 --> 00:48:23,264
but it is gonna be,

1379
00:48:24,385 --> 00:48:25,905
you know, it's gonna be a simple it

1380
00:48:25,905 --> 00:48:27,264
is a simple thing in the beginning,

1381
00:48:27,664 --> 00:48:29,105
and then we can see where it would

1382
00:48:29,105 --> 00:48:30,085
take us. But,

1383
00:48:31,184 --> 00:48:32,945
yeah, I mean, I was so excited that

1384
00:48:32,945 --> 00:48:35,860
humans are just one species out of many

1385
00:48:35,860 --> 00:48:37,699
that are you know, they're all we all

1386
00:48:37,699 --> 00:48:39,460
live in our own world, basically. And and

1387
00:48:39,460 --> 00:48:41,460
I think we all we all have different

1388
00:48:41,460 --> 00:48:43,460
interests what we wanna do. And but but

1389
00:48:43,460 --> 00:48:45,059
there there could be an overlap for sure.

1390
00:48:45,059 --> 00:48:46,739
And and I think for a fast at

1391
00:48:46,739 --> 00:48:48,420
at a minimum level, we we look a

1392
00:48:48,420 --> 00:48:50,655
lot to spiders and spider webs, as we

1393
00:48:50,655 --> 00:48:52,414
have understanding how they build materials. And it's

1394
00:48:52,414 --> 00:48:54,335
a fascinating problem by which they they make

1395
00:48:54,335 --> 00:48:55,555
this web. I mean, that's

1396
00:48:55,934 --> 00:48:58,515
a level of intelligence or or or capability

1397
00:48:58,574 --> 00:48:59,855
that we just don't have. You know, we

1398
00:48:59,855 --> 00:49:00,594
don't have,

1399
00:49:01,055 --> 00:49:03,454
a manufacturing capability that that does that. You

1400
00:49:03,454 --> 00:49:05,474
know, we have three d printers, but they're

1401
00:49:05,829 --> 00:49:07,589
very simplistic compared to what the spider can

1402
00:49:07,589 --> 00:49:09,510
do. You know, spiders can make a strength

1403
00:49:09,510 --> 00:49:10,650
of steel cables,

1404
00:49:11,589 --> 00:49:13,130
on demand as a living

1405
00:49:13,429 --> 00:49:14,949
three d printer. They eat the fly, they

1406
00:49:14,949 --> 00:49:16,630
make the swab, and they they can change

1407
00:49:16,630 --> 00:49:18,089
the web, they live in it. And

1408
00:49:18,414 --> 00:49:20,335
that's something we can't we can't program anything

1409
00:49:20,335 --> 00:49:21,534
like this. So there's a lot of really

1410
00:49:21,534 --> 00:49:23,375
exciting things. So so your question I mean,

1411
00:49:23,375 --> 00:49:24,974
but but I'm more interested in perhaps as

1412
00:49:24,974 --> 00:49:26,974
I would say it is, can we understand

1413
00:49:26,974 --> 00:49:28,974
how the spider lives in this world, and

1414
00:49:28,974 --> 00:49:29,634
can we

1415
00:49:29,934 --> 00:49:32,070
mimic that in a way, you know, not

1416
00:49:32,070 --> 00:49:34,070
using an actual spider, but can we create

1417
00:49:34,070 --> 00:49:36,010
a technology that would behave like a spider

1418
00:49:36,550 --> 00:49:38,309
and but that would, you know, produce a

1419
00:49:38,309 --> 00:49:38,809
material,

1420
00:49:39,510 --> 00:49:41,750
maybe not silk, maybe a different material, maybe

1421
00:49:41,750 --> 00:49:42,329
a different,

1422
00:49:43,030 --> 00:49:44,950
maybe a polymer, maybe a metal, or maybe

1423
00:49:44,950 --> 00:49:46,489
a combination of metals, polymers,

1424
00:49:47,355 --> 00:49:49,775
that would reflect something that's much more

1425
00:49:50,074 --> 00:49:51,994
relevant for us, you know, which would be

1426
00:49:51,994 --> 00:49:54,875
an engineered system, maybe a fabric, maybe clothing,

1427
00:49:54,875 --> 00:49:56,235
you know, and and so on. So, you

1428
00:49:56,235 --> 00:49:58,315
know, beginning to think about materials as a

1429
00:49:58,315 --> 00:49:59,054
way of

1430
00:49:59,440 --> 00:50:01,840
building an intelligence in a material system that

1431
00:50:01,840 --> 00:50:03,760
would allow it to respond to the environment

1432
00:50:03,760 --> 00:50:06,340
and have a some little conscious behavior where,

1433
00:50:06,400 --> 00:50:08,260
you know, your your fabric would

1434
00:50:08,640 --> 00:50:11,360
become would become thicker when you when it's

1435
00:50:11,360 --> 00:50:13,565
cold, and and it would shrink when it's

1436
00:50:13,565 --> 00:50:15,824
warm. And and and there have been incipient

1437
00:50:15,964 --> 00:50:17,964
attempts to do that, of course, but not

1438
00:50:17,964 --> 00:50:19,885
at the fidelity that nature does. So that's

1439
00:50:19,885 --> 00:50:22,224
a big space out there for mimicking biological

1440
00:50:22,284 --> 00:50:23,964
systems in that way. Thank you so much

1441
00:50:23,964 --> 00:50:26,000
to professor Marcus Buhler for talking to me

1442
00:50:26,000 --> 00:50:28,079
and for allowing us to share with you

1443
00:50:28,079 --> 00:50:29,059
some of these sonifications

1444
00:50:29,599 --> 00:50:31,359
that he's made as part of all this

1445
00:50:31,359 --> 00:50:33,599
fascinating work. If you'd like to know more

1446
00:50:33,599 --> 00:50:35,119
about the work that Marcus and his team

1447
00:50:35,119 --> 00:50:37,219
are doing, I can highly recommend the feature

1448
00:50:37,359 --> 00:50:38,684
on the Physics World magazine

1449
00:50:43,005 --> 00:50:45,505
signifying science from an amino acid

1450
00:50:45,965 --> 00:50:48,144
scale to a spider silk symphony.

1451
00:50:48,445 --> 00:50:50,045
And, of course, we'll be back soon with

1452
00:50:50,045 --> 00:50:52,385
an episode exploring the major breakthrough

1453
00:50:52,684 --> 00:50:55,420
at The UK's jet laboratory in the quest

1454
00:50:55,559 --> 00:50:57,500
for practical nuclear fusion.

1455
00:50:57,800 --> 00:50:59,740
And thank you very much for listening.

1456
00:51:04,200 --> 00:51:05,500
Physics world.