Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey there, and welcome to Forward Thinking, the
Speaker 1: podcast that looks at the future and says I write
Speaker 1: the songs that make the whole world sing. I'm Jonathan Strickland,
Speaker 1: I'm Lauren bo and I'm Joe McCormick. And today we
Speaker 1: wanted to talk about musical composition. We talked about musical
Speaker 1: performance in a previous podcast, but now we're going to
Speaker 1: talk about actually writing music and composing it. And uh,
Speaker 1: you know, of course some of our most famous music
Speaker 1: creators are now decomposers. That's a pun. Lauren's just silently
Speaker 1: shaking her head. So the discussion I thought would be fun,
Speaker 1: you know you Joe came up with a great little
Speaker 1: point of of of research about the earliest known written
Speaker 1: musical notation, right, because of course we've been creating music, um,
Speaker 1: you know, even even singing or um you know, percussion
Speaker 1: on drums or rib cages or etcetera, for for for many, many, many,
Speaker 1: many many. But so yeah, we talked about in our
Speaker 1: last podcast, so we don't even know how old music is, right,
Speaker 1: We imagine we've been making it for you know, tens
Speaker 1: of thousands of years. We know it's at least about
Speaker 1: forty years old because that's how old the oldest bone
Speaker 1: flutes are. But when it comes to written music, I mean,
Speaker 1: obviously anything that's a written account, it's going to take
Speaker 1: a lot for it to be able to survive centuries
Speaker 1: and centuries. Yeah, we when you think about it, this
Speaker 1: is this is kind of strange and creepy. We don't
Speaker 1: know what that music sounded like. Yeah, we have no
Speaker 1: way of knowing. You could just try to guess, but
Speaker 1: you but there's no way to know. Those those vinyl
Speaker 1: albums are no longer on store shelves. Yeah, no, there's
Speaker 1: no there's no there's no way of recording it from
Speaker 1: you know, they didn't have any way of recording it
Speaker 1: back then, apart from writing things down. In the earliest
Speaker 1: version we have of that came from a city called Yogurt.
Speaker 1: It's in Syria. It was actually written on clay tablets
Speaker 1: and the Babylonians No No is the Hurrians um in
Speaker 1: fact that the Hurrians were an ancient people who spoke
Speaker 1: hero Eurytian. It was a language, and they were from
Speaker 1: what is now well what would eventually be northern messa potami,
Speaker 1: let's be fifty twos for you. But no, they were
Speaker 1: from that that area Mesopotamia, but now it's known as Syria,
Speaker 1: or at least that's where the clay tablets were found,
Speaker 1: these specific tablets, and the tablets had two sections to them,
Speaker 1: and the top section were lyrics, they were words that
Speaker 1: was that this was a song, not just a a
Speaker 1: piece of music, but a song, and the lower section
Speaker 1: was musical annotation. It was actually as if it were
Speaker 1: sheet music for us today. Of course, the problem is
Speaker 1: that we don't have any ancient Hurrians sitting around to
Speaker 1: tell us exactly what this musical note annotation meant. So
Speaker 1: it took some interpretation upon the part of scholars, particularly
Speaker 1: Dr Richard drumbrell Um. He'd spent a great deal of
Speaker 1: time trying to figure out exactly what this annotation meant.
Speaker 1: And the surviving tune is called Hurrying Him number six
Speaker 1: and it comes from about four b c E. Wow. Yeah,
Speaker 1: and uh, it's essentially a hymn to the moon God's
Speaker 1: wife Nicole. We should put up a link to this
Speaker 1: because I've heard some of these reproductions and obviously these
Speaker 1: are just interpretation, because again we don't know for sure
Speaker 1: if the Yeah, it's like it's like it has a
Speaker 1: like a series of sort of number symbols. I think
Speaker 1: that that we're just guessing, like, well, we think that
Speaker 1: means this series of notes within a mode or something. Yeah, exactly.
Speaker 1: We it's it's our best guests essentially. I mean, it's
Speaker 1: it's an educated guess, but it's still a guess. Yeah.
Speaker 1: And but but anyway, these interpretation sens of the notation
Speaker 1: are pretty haunting that they sound pretty cool to me. Yeah,
Speaker 1: I've got Well, we'll do a blog post that goes
Speaker 1: along with this one, just as we did with the
Speaker 1: last one, where we'll have some links to to some
Speaker 1: sound samples. Will also include the videos. There's a great
Speaker 1: video of a musician named Michael Levi who was playing
Speaker 1: on a solo liar uh and that his version is
Speaker 1: very haunting and beautiful. And then there was another one
Speaker 1: that we listened to by the musician Malek Gendali who
Speaker 1: is a pianist, but he performed also with the Ludwig
Speaker 1: Symphony Orchestra. And so you have a full orchestral version
Speaker 1: and a solo liar version. And it's interesting to listen
Speaker 1: to these two very different interpretations of the same set
Speaker 1: of music. Yeah, and then there are even much more
Speaker 1: different interpretations than that, sure that I saw. And it
Speaker 1: seems a lot of people have been taking a stab
Speaker 1: at what this sounded like. Terrific. Uh, yeah, it's really cool.
Speaker 1: I want to get in on this game, but I
Speaker 1: just don't know enough about music. Yeah, and same here.
Speaker 1: I I'm pretty sure that if I were to attempt
Speaker 1: the hurry and hymn number six on my ukulele, it
Speaker 1: would not sound anything like the the Hurrians would have imagined. Okay,
Speaker 1: but so that kind of thing we can't uh, we
Speaker 1: don't know exactly what it's supposed to sound like. But
Speaker 1: but eventually we did get some modes of musical notation
Speaker 1: that are comprehensible to us today that we're more standardized
Speaker 1: within their culture, and and that now we pretty much
Speaker 1: know how we're supposed to interpret right. Right, even if
Speaker 1: you are looking at an antiquated piece of music, we
Speaker 1: have enough information about what those those different scales of
Speaker 1: music and different keys, all that kind of information. We
Speaker 1: already know that. So even if it was written in
Speaker 1: a way that's antiquated, we can translate that into a
Speaker 1: more modern version. So that your typical musician would be
Speaker 1: able to yeah and again, you know, like just like
Speaker 1: we said in the musical performance, uh episode, A lot
Speaker 1: of this ends up being down to to musical expression
Speaker 1: as well. So even if something is written one way
Speaker 1: on the page, just as you would expect today, you
Speaker 1: get two different musicians to play the same piece of music,
Speaker 1: you're going to get two different experiences from that. Yeah,
Speaker 1: and so in the sort of the Western classical tradition,
Speaker 1: we have a very like notation based way of thinking
Speaker 1: about music. So you have you have a musical staff,
Speaker 1: you know, and you've written out the notes on it
Speaker 1: in the standard notation, and you know, yeah, and you
Speaker 1: you can read from that, and and that's how you
Speaker 1: deal with music. But I think there are tons of
Speaker 1: musicians these days who really never even deal with standard
Speaker 1: Western classical music notation. Now. They learn their instrument, They
Speaker 1: they learn what their instrument is capable, the sounds that
Speaker 1: the instrument makes. They know, you know, they may either
Speaker 1: have a a true understanding or maybe an intuitive understanding
Speaker 1: of the way that chord progress sians need to go,
Speaker 1: that sort of thing, the things that sound pleasing to
Speaker 1: that particular cultures ear well. Also, we don't need to, um,
Speaker 1: we don't need to write those things down in order
Speaker 1: to reproduce them later, because we can make audio records
Speaker 1: of that music. Exactly. Yeah. I feel like to some extent,
Speaker 1: the audio recording has eroded the place that was once
Speaker 1: held by by written music notation, because when you can
Speaker 1: record a song, it's not so imperative that you write
Speaker 1: it down. People can well, we can always go listen
Speaker 1: to it and then just reverse engineer how it was played.
Speaker 1: Sure yeah. And in fact, there are computer programs that
Speaker 1: can do that that are pretty interesting. Exactly So. I
Speaker 1: think a lot of people who compose music these days
Speaker 1: use something use electronically aided composition. Sure yeah. And I've
Speaker 1: actually got quite a few examples of that. One of
Speaker 1: the earliest ones that ultimately was not successful in the market,
Speaker 1: but I thought was interesting to talk about, came from
Speaker 1: a guy named Sid Meyer. Now, Joe, have you ever
Speaker 1: heard the name said Meyer? Everyone in Civilization has that
Speaker 1: is that is a little bit of a pun because
Speaker 1: Sid Meyer was the original creator and designer for for
Speaker 1: the Civilization series of video games that you guys might
Speaker 1: be familiar with. He also created an awesome game called Pirates. Anyway, yeah.
Speaker 1: So Sid Meier known as a video game designer. He
Speaker 1: had formed a company called micro Pros along with Bill
Speaker 1: Steely or Wild Bill Steele as he's known, and the
Speaker 1: two of them started making and selling video games, and
Speaker 1: h after he experienced some early success, Sidmyer decided he
Speaker 1: wanted to try something new. He didn't want to keep
Speaker 1: making military simulations, which was chiefly what Micropros was known
Speaker 1: for in those early days. He wanted to branch out
Speaker 1: do something different and uh, and he was a big
Speaker 1: fan of music. He um, I believe things in his
Speaker 1: church choir. Yep, yep. So he thought, why don't I
Speaker 1: create some software that allows people to create their own
Speaker 1: compositions in different styles. And that's when he went out
Speaker 1: and made a program called cpu box and actually watched
Speaker 1: a demonstration of this software and it was played on
Speaker 1: the three d O. Do you guys know where the
Speaker 1: three d O is? Yes? It sounds funny, Okay, So
Speaker 1: the three d O was a game console system is
Speaker 1: meant to be like a full entertainment system. It's a
Speaker 1: predecessor to the systems that we see today. Things like
Speaker 1: the Xbox and PlayStation models are really kind of the
Speaker 1: next step of what the three D O was supposed
Speaker 1: to be. But the problem was the three D O
Speaker 1: came out a little too early and way too expensive.
Speaker 1: This this was what was it the late eighties? Did
Speaker 1: the three D O have some of those interactive movies
Speaker 1: on it? Oh? Yeah, No. This was the same time
Speaker 1: that we started seeing full motion video get incorporated into
Speaker 1: video games. And the problem was that everyone was putting
Speaker 1: full motion video into video games without thinking about how
Speaker 1: to actually make it part of the game. So it
Speaker 1: became a real gimmick and you ended up getting these
Speaker 1: terribly acted shots a full motion video that just were
Speaker 1: not you know, it looked like lesslike the kind of
Speaker 1: stuff you would get on your television if you had
Speaker 1: really bad reception. It just was not a very satisfying
Speaker 1: was their full motion video in CPU Box? No? It
Speaker 1: was actually it was actually all graphics from what I
Speaker 1: could tell, or at least the stuff I saw was
Speaker 1: all graphics. So it wasn't. I was kidding, but but
Speaker 1: it could have been. It could have been because it
Speaker 1: was for the three D O. And so the way
Speaker 1: it worked was that you would be able to create
Speaker 1: music in various uh classical mode, so you could do
Speaker 1: a concerto, or you could even do a symphony if
Speaker 1: you wanted to, and you or chamber orchestra piece, and
Speaker 1: you would sit down and you would start to compose
Speaker 1: this stuff through this interface, and then at the end
Speaker 1: of it it would have a full piece of music.
Speaker 1: But it did not sell very well. Very few people
Speaker 1: really picked it up. I don't think the market was
Speaker 1: quite ready for it at the time that it came out,
Speaker 1: so ultimately this was not a success. But there are
Speaker 1: plenty of programs out there that have various UH features
Speaker 1: that are great for musicians, like UM here's an example
Speaker 1: of one. There's one called the Finale Songwriter. Finale Songwriter
Speaker 1: allows you to create musical notations, you know, so you
Speaker 1: can get that cheap music you want if you want
Speaker 1: to have it written down. UH. You could play a
Speaker 1: song on a MIDI device, so any device that is
Speaker 1: MIDI compatible. MIDI is a set of standards for music
Speaker 1: for for computer music. If you were to have, say
Speaker 1: a keyboard that's a MIDI keyboard, and you have it
Speaker 1: plugged into your computer and you play the song exactly
Speaker 1: the way you want it, this program can then translate
Speaker 1: that into musical notations, so you can actually print that
Speaker 1: out and hand that to someone else and say here's
Speaker 1: the song I wrote. Now you can play it too. Um.
Speaker 1: You could also if you wanted to program the song
Speaker 1: note by note as instead of playing something dynamically and
Speaker 1: have it be recorded and transformed, you could very systematically
Speaker 1: go through and say I want this this particular note
Speaker 1: at this pitch, at this tempo and for this length,
Speaker 1: and go. You could build it like you could build
Speaker 1: a computer algorithm. If you wanted to do that, you could.
Speaker 1: You could essentially be data from star Trek. That's interesting
Speaker 1: to me, I mean because with that kind of capability,
Speaker 1: you sort of expand what the uh what the average
Speaker 1: person can do, especially like say, if you're not the
Speaker 1: greatest performer, Like what what if you want to write
Speaker 1: music but you don't have really good rhythm yourself, or
Speaker 1: if you're incapable of hitting a note on a particular
Speaker 1: instrument right or yeah, or you just have some form
Speaker 1: of impairment that would you know, maybe you aren't, maybe
Speaker 1: you don't have the full use of both of your hands,
Speaker 1: but you have a very musically tuned mind. You can
Speaker 1: think of the music you want to play, and if
Speaker 1: you had that capability, you could play it, but perhaps
Speaker 1: you've lost it, or perhaps you never had in the
Speaker 1: first place. This gives you that freedom of creating that
Speaker 1: piece of music. It's kind of like thinking about how
Speaker 1: Beethoven was able to create incredible symphonies even as his
Speaker 1: hearing was going away. It's that you know, you never know,
Speaker 1: there may be the next Beethoven who is incapable of
Speaker 1: playing a musical instrument. But the one other reasons I
Speaker 1: bring up Beethoven is to talk about the third piece
Speaker 1: of software. And keep in mind, like I said, there
Speaker 1: are lots of different examples. I just picked three kind
Speaker 1: of uh. This piece of software is called Ludwig, and
Speaker 1: Ludvig is music software. It's specifically for the Windows platform,
Speaker 1: and again there are different ones for for Max as well,
Speaker 1: but lets you create music. So what you do is
Speaker 1: you you insert a melody into this software, so you
Speaker 1: have to create the melody. But once you insert the melody,
Speaker 1: it starts to fill out the rest of the song
Speaker 1: with chords and other elements to make it a full
Speaker 1: song and not just a melody. And it does this
Speaker 1: by following specific rules for specific genres. So you pick
Speaker 1: the genre you want and then it will fill that
Speaker 1: out and put it to the right tempo for that
Speaker 1: genre with the right sound in the background. Um, it's interesting.
Speaker 1: I would say that it it comes out sounding very
Speaker 1: much like MIDI music. So in other words, it's not
Speaker 1: sounding like a big, rich, you know, diverse set of instruments,
Speaker 1: but something a little bit fancier than what you would
Speaker 1: get on say, electric organ the way that like my
Speaker 1: grandparents had in their house that you could just put
Speaker 1: like Ragtime Beach number two in the background of Yeah,
Speaker 1: so the the you know, it's it's interesting because what
Speaker 1: they'd ended up doing was they pulled in some sheet music. Again. Um,
Speaker 1: in this case, they pulled out any a piece of
Speaker 1: music that was in public domain, find the video I watched,
Speaker 1: and then applied different h musical genres to it. So
Speaker 1: they could play like two different styles of music with
Speaker 1: the same set of notes, the same melody. And that
Speaker 1: was kind of interesting to hear what it how it
Speaker 1: came out. So we're starting to see computer assisted composition here,
Speaker 1: not just assisted in that it's giving you some tools
Speaker 1: to work with, but assisted that it's actually sort of
Speaker 1: helping generated for content. Yeah. So I mean, and this
Speaker 1: kind of thing goes back to I I know that
Speaker 1: one of Ray kurtz Wild's first projects was I mean
Speaker 1: when he was like a teenager. This is probably about
Speaker 1: in N five I believe it was, was a piece
Speaker 1: of software that would analyze classical compositions and um and
Speaker 1: compose its own based on those rules. There's those algorithms
Speaker 1: that are first well, of course well known for his
Speaker 1: work and artificial intelligence voice recognition. Also a well known futurist,
Speaker 1: aspiring cyborg. He's waiting for that singularity to hit us
Speaker 1: any day now y forty years. But yeah, so that's
Speaker 1: one of the things he was known for, was this
Speaker 1: early computer program that could compose its own music. Well,
Speaker 1: that leads me to a really interesting idea, um, and
Speaker 1: it's something I touched on in the script I wrote
Speaker 1: about this episode. Um, but the idea of algorithmic composition. Okay,
Speaker 1: so we already know this is possible, that we can
Speaker 1: get computers to write viable music themselves. And actually we
Speaker 1: already talked about in another podcast the idea of say
Speaker 1: Ai storytelling. But that's actually a lot more difficult, right
Speaker 1: because the rules of human language are are such that
Speaker 1: they're really really complicated and kind of squeege you need
Speaker 1: millions of rules in order to create a coherent story
Speaker 1: out of nothing, unless you were just using like some
Speaker 1: pre built skeleton or whatever um mad libs. But to
Speaker 1: create a piece of music, that's actually a lot easier
Speaker 1: because music already is it's very susceptible to data. It's
Speaker 1: it's pretty mathematical when you break it down to its
Speaker 1: bare bones. Yeah, and it's easier to tell a computer
Speaker 1: what the rules of music are than say, what the
Speaker 1: rules of storytelling and language are. And so I can
Speaker 1: very much see, even more so than the AI storyteller.
Speaker 1: We discussed the possibility of an AI musical composer. Okay,
Speaker 1: So imagine you've got a music service like Pandora. Okay, okay,
Speaker 1: and Pandora what does it do to give you the
Speaker 1: songs that you want to hear? It follows the music
Speaker 1: genome projects, So in other words, you tell Pandora what
Speaker 1: kind of song or what kind of band you are
Speaker 1: interested in. It starts to look for other bands or
Speaker 1: other songs that share those same sort of qualities, and
Speaker 1: then it serves those up to you. Yeah, what does
Speaker 1: it do? So you tell it, say, you know, I
Speaker 1: really like a song by Meat Loaf? Alright, So Paradise
Speaker 1: by the Dashboard Life? Yeah, I really that's the song
Speaker 1: I give it? So does how does it know what
Speaker 1: I like? Well? Does it just give me other songs
Speaker 1: by meat Loaf? No? No, let's say, does it just
Speaker 1: give me other songs that are filed under the same
Speaker 1: genre category as meat Loaf? No? No? Well what does
Speaker 1: it just give me songs that are on the same
Speaker 1: label that that album was on? No? Joe, would you
Speaker 1: like me to tell you what it gives you? Yeah?
Speaker 1: How does it figure out what songs go with Paradise
Speaker 1: by the Dashboard? Like? It's really cool because what they
Speaker 1: do is they they have people listening to songs and
Speaker 1: they start to classify all the different elements that go
Speaker 1: into making that song. So it's kind of the meta
Speaker 1: data for an individual song. So in the musical feature,
Speaker 1: the musical features everything from is it a single vocalist?
Speaker 1: Is it a duet? In case of Paradise by the
Speaker 1: Dashboard Light? Yes it is? Uh? Is it um you know?
Speaker 1: Is it a ballad? Is it rock and roll? It
Speaker 1: doesn't have a lot of electric guitar? And it does.
Speaker 1: There's there there are all these different elements that go
Speaker 1: into it, and so a single song may have forty
Speaker 1: or fifty or more different specific features that have been
Speaker 1: singled out, and so then it starts to look for
Speaker 1: other songs that contain a certain number of those tags,
Speaker 1: and then it serves that song up to you. Then
Speaker 1: you have the option of either hitting the thumbs up,
Speaker 1: I love this song and make sure you play this
Speaker 1: kind of stuff more frequently, or thumbs down, this is
Speaker 1: not what I'm looking for. And as you give input
Speaker 1: back into the program, it starts to refine that algorithm.
Speaker 1: Maybe it starts to think that, Okay, this guy really
Speaker 1: likes cheesy songs, but every single solo artist I've ever
Speaker 1: given him he has thumbs down. So we are not
Speaker 1: going to do any more solo from now on. It's
Speaker 1: all going to be duets or bands or whatever. And
Speaker 1: then it'll start to refine your channel over time. Okay,
Speaker 1: so imagine a program like this. It starts to get
Speaker 1: a really specific musical taste profile on you. It tells
Speaker 1: you know, it figures out, and let's imagine that it's
Speaker 1: even more advanced than pandoras today. Like it, it gets
Speaker 1: a really specific profile you, like these fifty features in
Speaker 1: in songs UM. Right, Now, what it has to do
Speaker 1: is try to go through its whole library and match
Speaker 1: songs that have as many of the features you like
Speaker 1: and as few of the features you dislike as possible. Sure,
Speaker 1: but using um algorithmic composition, we could imagine that it
Speaker 1: and technically create songs that have exactly what you want.
Speaker 1: So let's just kind of creepy. So if you if
Speaker 1: you really love Paradise by the Dashboard Light and you
Speaker 1: love all the things that go into making that song
Speaker 1: what it is, but you can't just listen to the
Speaker 1: one song you three times and that takes up half
Speaker 1: a day. The song is long, it's like a twelve
Speaker 1: minute long song. But yeah, no, yeah, it could. In theory,
Speaker 1: let's say it's following those rules, it could start to
Speaker 1: produce other songs that match the same sort of criteria dynamically.
Speaker 1: So this is you're literally hearing this song for the
Speaker 1: first time. In fact, you're the first person to hear
Speaker 1: this song for the first time, and maybe no one
Speaker 1: ever hears that song again because it's making it specifically
Speaker 1: to match what you want to hear at that moment. Now,
Speaker 1: we already have algorithmic composition, and we already have UM
Speaker 1: things like Pandora that can create a really intense taste
Speaker 1: profile for the listener. So why don't we already have this? Well,
Speaker 1: I'd say one thing that standing in the way is
Speaker 1: that UM algorithmic composition, while it's really good, probably isn't
Speaker 1: quite there yet in terms of its precision. And also,
Speaker 1: I'd say the biggest impediment right now is just the
Speaker 1: fact that the computer generated sounds aren't as pleasing to
Speaker 1: people as like actually recorded instruments and voices are in
Speaker 1: most cases. Would would you agree? Yeah, No, I completely agree.
Speaker 1: Though I wonder if as time goes on and we
Speaker 1: get better and better at creating synthesized UM, you know,
Speaker 1: instrument sounds that are almost indistinguishable from real recorded instrument sounds,
Speaker 1: if this will become more and more of a possibility. Sure.
Speaker 1: This discussion always reminds me of that one episode of
Speaker 1: Next Generation where where Data was playing a violent concert
Speaker 1: and UM, and that one doctor who wasn't Crusher, who
Speaker 1: was really mean to him all the time, really got
Speaker 1: on his case about it. She was like, yeah, you
Speaker 1: were technically perfect, but there was no emotion in the music.
Speaker 1: And whether or not whether or not a motion, whether
Speaker 1: or not flaws in the playing of music are are
Speaker 1: somehow critical to the human experience of that kind of sound.
Speaker 1: To be fair, that the woman only knew one emotion
Speaker 1: that was irritated. Um, I sympathize with that point of view.
Speaker 1: While on the one hand, I'm excited about the possibility
Speaker 1: for this kind of technology, on the other hand, I think, like, well,
Speaker 1: so many of my favorite songs, what's my favorite thing
Speaker 1: about them is I don't know something imperceptible about the
Speaker 1: sort of emotional presentation that the performer brings to the song,
Speaker 1: and not just like the sequence of notes in in
Speaker 1: its relative harmonies. Although at the same time, you could
Speaker 1: argue that if a musician is creating an algorithm that's
Speaker 1: creating a song, it's just a form of an instrument
Speaker 1: could be. But then again, I think about so I'll
Speaker 1: just make it more concrete. One of my favorite musicians
Speaker 1: is Neil Young. Um. I love Neil Young, but he's
Speaker 1: he's got this sloppiness about how he plays, about how
Speaker 1: he plays his guitar. There's nothing precise about it. It's
Speaker 1: it's very half drunk kind of sound. I was about
Speaker 1: to say, similarly, like if you're a big Tom Waits fan.
Speaker 1: I don't think you're ever going to get a song
Speaker 1: like the algorithm has been drinking. I kind of hope
Speaker 1: not um And it's it's hard for me to imagine that,
Speaker 1: uh A an algorithm could reproduce that, because it seems
Speaker 1: so essentially human. But then again, you know, I I
Speaker 1: don't wanna. I don't want to overestimate my powers of detection.
Speaker 1: Who knows. I mean, somebody maybe could create something that
Speaker 1: I just wouldn't be able to tell the different maybe
Speaker 1: in twenty to forty years. And of course, depending upon
Speaker 1: your opinion of certain pop song artists, you might think
Speaker 1: that if you had a computer create that's exactly what
Speaker 1: would sound like. Oh well yeah, in some cases with
Speaker 1: some types of music, uh and not even to deride it,
Speaker 1: I'd just say, like, nothing at all against electronic music.
Speaker 1: But let's say electronic music, I think is a much
Speaker 1: is much more susceptible to something like this, the kind
Speaker 1: of not being played by by three dimensional instruments to
Speaker 1: begin with, so or it might not be in, or
Speaker 1: it might not be right, or it might be I mean,
Speaker 1: there's a lot of great electronic music that's not even
Speaker 1: performed in real time. It's just programmed music. It's programmed
Speaker 1: by a human composer. Yeah. Yeah, And that again, it
Speaker 1: doesn't mean that that's you know, any less valuable or
Speaker 1: or or you know, quality wise, that it's any different
Speaker 1: from any other music. Obviously, the music, uh evokes some
Speaker 1: response from you. There's something about it that is meaningful
Speaker 1: to you. So we don't mean to say that electronic
Speaker 1: is somehow absolutely not now if you're very very no.
Speaker 1: And in the end, I think the hardest turtle. So
Speaker 1: we've mentioned all these things that the instruments, the hardest
Speaker 1: one is going to be vocals. Vocal music is most
Speaker 1: of the most popular music I think involved vocalization human voice. Uh.
Speaker 1: I don't even know how we're going to begin to
Speaker 1: have the idea of re producing that. So mostly what
Speaker 1: I'm thinking about with this AI composition is instrumental music.
Speaker 1: So I could actually easily imagine, you know, in a
Speaker 1: maybe a decade's worth of time, that will have uh
Speaker 1: a like a movie score that was completely composed by computer.
Speaker 1: I can actually imagine that, particularly if you are able
Speaker 1: to feed into the computer's algorithm the time stamps of
Speaker 1: your movie, so you know that this moment, I need
Speaker 1: to have a sense of urgency and this moment I
Speaker 1: need to build tension. This needs to be a release moment.
Speaker 1: That kind of thing. I can actually see that happening
Speaker 1: at one point, kind of the opposite of having a
Speaker 1: having a visualization of music and autogenerated visualization. Actually, one
Speaker 1: way I can really see this being a big deal
Speaker 1: is in uh scoring video games. In video games, you
Speaker 1: want your music to respond dynamically to what's happening in
Speaker 1: the game, But there you don't have direct control like
Speaker 1: you do when you're scoring a movie over the exact timing.
Speaker 1: So you could have have this AI composition responding to
Speaker 1: the player in real time. That would be wonderful. That's
Speaker 1: one of the things that can really take me out
Speaker 1: of a video game. Actually, when the when the music
Speaker 1: is either too repetitive or or isn't or is lags
Speaker 1: behind following me in what I'm doing. I can tell
Speaker 1: you that if the music didn't change when I was
Speaker 1: being chased by a ghost to the point where I'm
Speaker 1: chasing the ghost after I've eaten the power pellet, I
Speaker 1: would never play Batman. Well, you know you're you're talking
Speaker 1: about about the incorporation of data there, Yeah, that kind
Speaker 1: of thing. It would be data, the data about what's
Speaker 1: going on in the game would be informing the algorithm
Speaker 1: that tells what the music should sound like at any
Speaker 1: given moment. But yeah, there are all kinds of other
Speaker 1: ways you can use data to create music, right. The
Speaker 1: soonification or oddification of data is a big topic these
Speaker 1: days in music composition. UM there's a few people who
Speaker 1: are who are working more than a few people who
Speaker 1: are working in this. One of the more interesting stories
Speaker 1: I think is a gentleman by the name of Robert Alexander.
Speaker 1: He's a currently a NASA fellow, started off as a
Speaker 1: University of Michigan student who is working with um ROS
Speaker 1: satellite data from the Solar and Heliospheric Observatory, which is
Speaker 1: one of those NASA E s A satellites that that
Speaker 1: has been recording data from the Sun for the past
Speaker 1: thirteen years. Um back in two thousand nine, he created
Speaker 1: a story of a musical journey from the Earth to
Speaker 1: the Sun based on a year of data of from
Speaker 1: two thousand three and did things like added drums to
Speaker 1: represent the passage of time. Because he was compressing all
Speaker 1: of these data points down into something like forty four thousand,
Speaker 1: one hundred data points per second, which happens to be
Speaker 1: about the hurts of ceed equality audio sound UM, which
Speaker 1: which happens to equal out to UM every eight measures
Speaker 1: being a single solar day and UM really interesting. I
Speaker 1: also just a trivia note. So Robert Alexander, you said,
Speaker 1: was went to the University of Michigan. Uh, that's also
Speaker 1: where sid Meyer went. Really apparently the computer musician types
Speaker 1: all like to gather over at the University of Michigan. UM.
Speaker 1: But you know, he started. The really interesting part about
Speaker 1: this is that he was looking at lots of data,
Speaker 1: like many different streams of data from these spector scopes
Speaker 1: and satellites and U. One one element that he started
Speaker 1: noticing a pattern from was carbon. And the people at
Speaker 1: NASCA that he had been talking to about this data
Speaker 1: had never really mentioned carbon before. And through his creation
Speaker 1: of this music and through his analyzing it as a
Speaker 1: sound pattern, realized that UM that you could use soonified
Speaker 1: data to analyze solar wind and flares and and so new.
Speaker 1: I mean this this changed the way that we study
Speaker 1: the sun. His music has changed that. And that's that's
Speaker 1: really you know, And there's and there's there's a whole
Speaker 1: bunch of people who are working and stuff like that.
Speaker 1: The astrophysicist Luciana Walkowitz has taken data from the Kepler
Speaker 1: which which searches for planets and other solar systems. The
Speaker 1: exoplanet search right right, and you know uses the light
Speaker 1: frequency of stars, which changes as stars and objects move around.
Speaker 1: Um mixes in things like Buddhist chants and uh creates
Speaker 1: some some really haunting things. The folks at the Large
Speaker 1: Hattern Collider have started up a soonification department and so
Speaker 1: you can listen to musical compositions based on the sweep
Speaker 1: of the Atlas detector. I heard something like this. Maybe
Speaker 1: it might not have been from that, but where did
Speaker 1: I hear it? The somebody created Essentially it was the
Speaker 1: sound of the Big Bang. Yeah, yeah, because because because
Speaker 1: you can I you know, because it starts with our
Speaker 1: whole universe was in a hot, dense state. You know,
Speaker 1: you can you can track the the motion of sub
Speaker 1: atomic particles and and turn that I mean basically anything
Speaker 1: in the electromagnetic spectrum. Most of it's out of our
Speaker 1: audible spectrum, but you can kind of scale and trans
Speaker 1: for things down into something that you can hear and
Speaker 1: and all of these sounds of the universe, um, right
Speaker 1: down to one of my favorite examples, which is, um,
Speaker 1: you know, the magnetosphere around the Earth creates non audible
Speaker 1: sound continuously. It's kind of a very chirping sort of
Speaker 1: noise and um and it. I wouldn't necessarily call it music,
Speaker 1: but I wouldn't necessarily call Japanese noise music either, So
Speaker 1: I you know, it's it's really interesting to listen to
Speaker 1: kind of cool. Yeah, and you mentioned in the script
Speaker 1: Joe for the for the video show the the guy
Speaker 1: who used points of GPS data to turn into that. Yeah,
Speaker 1: that's uh so that's a little closer to home, I
Speaker 1: guess than these amazing sort of atmosphere where you are
Speaker 1: when you get the GPS right. Uh so, yeah, you
Speaker 1: can turn like these big worldwide kind of data points
Speaker 1: from the natural world into music. But we can also
Speaker 1: and so that gives us a new perspective on you know,
Speaker 1: what's out there. Yeah, yeah, we can also get a
Speaker 1: new perspective on ourselves because yea, what this guy did,
Speaker 1: he was the artist Brian House. Um so he made
Speaker 1: house music. He uh, he made this really cool song.
Speaker 1: There's a great article about it. We will link up
Speaker 1: when we put the blog post, uh, and we'll put
Speaker 1: a link to the song up to because you should
Speaker 1: hear it. He turned a year's worth of location tracking
Speaker 1: data on himself into music, and so it worked. The
Speaker 1: way most data or all data generated music works is
Speaker 1: that you have a data set and each different value
Speaker 1: represents ay a different rhythm or a different pitch um.
Speaker 1: And so he the way this music worked was everywhere
Speaker 1: he went, it would track his location and that would
Speaker 1: represent a different pitch along the musical scale, and and
Speaker 1: and played at at high speed, you can hear yourself
Speaker 1: traveling throughout the day and these very predictable patterns. But
Speaker 1: then say, like when you go on vacation, there's this
Speaker 1: strange sort of like bridge that goes into a different
Speaker 1: sonic territory and the song it creates is really hypnotic.
Speaker 1: It's cool to listen to. You should check it out.
Speaker 1: And it it's cool not just because of how it sounds,
Speaker 1: but because of how it gives you a new way
Speaker 1: of thinking about what you do with your life. Sure,
Speaker 1: and you know, all all of us, lets us look
Speaker 1: at at things from a slightly different angle and notice patterns.
Speaker 1: I mean, the human ear and the human brain interpret
Speaker 1: music much differently than than other data, and so um,
Speaker 1: you know, this kind of thing has been used to
Speaker 1: study epilepsy e e g. S electro and cephalograms to
Speaker 1: figure out what exactly is going on from people's brains,
Speaker 1: and m seismographic data collected from volcanoes. It's it can
Speaker 1: potentially be used to help blind people see paintings or
Speaker 1: or other images. So I can imagine even this being
Speaker 1: connected to something like an activity tracker, and then you
Speaker 1: can turn that into some sort of sound and you
Speaker 1: can actually hear what your activity would be like during
Speaker 1: a day, and maybe it gets to a very soothing,
Speaker 1: lulling kind of tone where you realize, man, I might
Speaker 1: need to get up and move around a little more.
Speaker 1: I want to I want to have more of a
Speaker 1: Ramans lifestyle and less of the Yanni lifestyle. No, I'm
Speaker 1: being serious though, I mean that can really be a
Speaker 1: way of learning more about yourself. I mean we're talking
Speaker 1: like on different levels, whether it's uh, you know, the
Speaker 1: activity of your brain, or really just learning in a
Speaker 1: new way, like how you are behaving throughout the day. Yeah, totally, Yeah,
Speaker 1: anything you know, we've talked before about big data, and
Speaker 1: I think that that really feeds into this. I mean,
Speaker 1: whenever we are collecting that many points of information. Um. Yeah, Well,
Speaker 1: let me ask you guys a question. What if we
Speaker 1: were to ask a bunch of people what things they what?
Speaker 1: What were the things they liked about music? And then
Speaker 1: take all the information we had gathered. Oh, I know
Speaker 1: where you're going with this. Where they've told us all
Speaker 1: the things that make music great, the best of the best,
Speaker 1: this is the stuff that resonates, and then we made
Speaker 1: the best song ever written based upon that data. Okay,
Speaker 1: so I found a couple old articles. I actually remember
Speaker 1: when these came out. But here's the here's the thing.
Speaker 1: I never listened to these songs until I made you,
Speaker 1: until you made me, and I am so thankful you did.
Speaker 1: There are a couple of songs that I think mitigate, uh,
Speaker 1: our enthusiasm for the well for the self tailored music.
Speaker 1: Remember we talked about, yes, the AI composition, the idea
Speaker 1: that you could get a song tailored to exactly all
Speaker 1: the things you want. Turns out it might be that
Speaker 1: getting exactly the song you want is not a good idea. Yeah,
Speaker 1: so this was something that a trio of gentleman attempted
Speaker 1: to do a vitally Coomar Alex Melamed and David Soldier,
Speaker 1: and they did a survey where they asked people about
Speaker 1: the things they liked about music, piled all that information
Speaker 1: and then ranked it, and then through that compilation they said,
Speaker 1: all right, these are the different elements that people really
Speaker 1: enjoy and among those. By the way, it was this
Speaker 1: was five person survey, so you might argue it's not
Speaker 1: very scientifically rigorous people that that represents the entire width
Speaker 1: and breadth of musicality. So but the most yeah, this
Speaker 1: is what they These are the things and songs that
Speaker 1: they liked the most. That includes songs about love, uh,
Speaker 1: the soprano saxophone, also the tenor saxophone, the concept of
Speaker 1: humble ambition. Also uh, cheesy electronic drums, um drum fills, right,
Speaker 1: drum fills, also working the night shift. Apparently someone was
Speaker 1: thinking of a very specific song on that one. The
Speaker 1: world loves guess so strings, swells, also power chords and
Speaker 1: uh finally, and these are these These come from an
Speaker 1: article that our blog posts from Wired and which is
Speaker 1: a fantastic one. And the Wired posts actually contain the songs.
Speaker 1: You can actually download the MP three's if you like,
Speaker 1: and we will include yes. But the final touch that
Speaker 1: they mentioned is a saccharine male and female harmonies, also
Speaker 1: Paradise by the dashboard light. Like, no, this is turning
Speaker 1: it into easy listening. I wouldn't call Paradise by the
Speaker 1: dashboard light easy listening. Wait does that have male and female?
Speaker 1: Or was that Paradise by the dashboard lights? Okay, I'm
Speaker 1: not thinking if I would do anything. Okay, that's all
Speaker 1: I can say. Go on, let me do anymore. No, anyway,
Speaker 1: they that. So they took all these things and they
Speaker 1: wrote a song, and it's possibly one of the worst
Speaker 1: songs I've ever is off Well, first of all, I
Speaker 1: think consciously, so we're not missing them, No, I think
Speaker 1: I think even they were doing this totally tongue in cheek. Um.
Speaker 1: So it was It's an experience. You really have to
Speaker 1: go and listen to it. But when I first started listening,
Speaker 1: the first thing I thought was, I feel like I'm
Speaker 1: in the dentist's waiting room. This is this is the
Speaker 1: uncomfortable experience I have before I have to go and
Speaker 1: have an even more uncomfortable experience of how my teeth
Speaker 1: drilled so well, this introduces the idea that, um, maybe
Speaker 1: we shouldn't get everything we want. Maybe it's worth trusting
Speaker 1: some experts to create music for us. Before we get
Speaker 1: too far into that, let's also point out this most
Speaker 1: Wanted song, which by the way, is preceded by a
Speaker 1: voicing Most Wanted Song, and then it goes into It
Speaker 1: is about five minutes long. They also did one for
Speaker 1: the Most Unwanted Song, where they tried to pull together
Speaker 1: all the elements that people said they did not like,
Speaker 1: typically like holiday music, bagpipes of pipe organ, children's choruses,
Speaker 1: m accordion, ban joe, Walmart, rapping, and opera. You have
Speaker 1: an operaretic singer rapping about being a cowboy, and then
Speaker 1: you have children burst in to sing about holidays and children, Yeah,
Speaker 1: had children in general. Um, the children's course jumps in randomly,
Speaker 1: it starts to sing about various holidays. We should just
Speaker 1: stop describing it and let them listen. Well, it's also
Speaker 1: I have to say that I don't think I'll ever
Speaker 1: be able to hear any kind of holiday song ever
Speaker 1: again without immediately thinking of the phrase shop at Walmart.
Speaker 1: This had this had all three of us just just
Speaker 1: doubled over at our desks earlier. And while the most
Speaker 1: wanted song is five minutes long, the most unwanted song
Speaker 1: is about twenty two minutes long and well worth the
Speaker 1: twenty two minute time commitment it will take to listen.
Speaker 1: From me, I found it quite enjoyable um and reminded
Speaker 1: me less of the couple's skate at at roller skating
Speaker 1: rinks when I was like twelve, so over you that
Speaker 1: less than the want, right, So I will say that
Speaker 1: the unwanted song also to me sounded like, uh, if
Speaker 1: you had mixed they might be giants song Fingertips, which
Speaker 1: was a collection of little songs all all smashed together.
Speaker 1: So it wasn't there's no real theme, and there's no
Speaker 1: like each little vignette is very different from the one
Speaker 1: that precedes it and the one that follows it. Uh.
Speaker 1: So it was kind of like that because there are
Speaker 1: a lot of mishmashes in this twenty two minute long song.
Speaker 1: It also reminded me of the musical comedy of p
Speaker 1: d q Bach, which is Peter Shile is behind that
Speaker 1: and it's a he has invented a whole cloth, a
Speaker 1: fictional son of Johann Sebastian Bach, who is a who
Speaker 1: wanted to be a great composer but failed in almost
Speaker 1: every respect. And if you ever listened to p D
Speaker 1: Box Music and you listen to this Most Unwanted Song,
Speaker 1: you're going to recognize some themes. And part of that
Speaker 1: is because Peter Shiley, when he makes these PDQ back pieces,
Speaker 1: is specifically incorporating things that are a quote unquote wrong.
Speaker 1: They are breaking the rules, not following the rules of
Speaker 1: musical composition. Uh. In that case, he's doing it for comedy.
Speaker 1: You could argue that the Most Unwanted Song does the
Speaker 1: same you know, does it for the same reason. I
Speaker 1: found it hilarious, and every time a new holiday came up,
Speaker 1: I was like, hey, um, yeah, As I was saying
Speaker 1: a minute ago, I think an interesting thing that the
Speaker 1: Most Wanted Song points out is is it sort of
Speaker 1: subtly underlines the idea of artistic integrity, like that by
Speaker 1: by saying that you want all these things is like
Speaker 1: trying to to say what you would want in a
Speaker 1: piece of music. You're ignoring the fact that a piece
Speaker 1: of music as it is now is typically created by
Speaker 1: a person, right, and it's not like a burger. It's
Speaker 1: not like you can just go in and say like, hey,
Speaker 1: so I want these things and none of those other things.
Speaker 1: I mean, apparently you can do that, but it turns
Speaker 1: out the burger might be better if you just trust
Speaker 1: the chef. Now, what's interesting is I would argue that
Speaker 1: a lot of pop music that comes out, not all
Speaker 1: of it, but a lot of it is essentially made
Speaker 1: to a formula. So there is some very formulaic music
Speaker 1: out there that is very popular. It's just that this
Speaker 1: was taking it to a ridiculous extreme. And it's also
Speaker 1: a lot of pop music is also written by a
Speaker 1: few of by a very few people, and um farmed
Speaker 1: out to the artists. Hey, let's not just say pop music.
Speaker 1: I mean I think in in some ways pretty much
Speaker 1: all music, or almost all music, is formulaic, even the
Speaker 1: most you know what you was say quote authentic music
Speaker 1: is formulaic in a way. I mean, there there are
Speaker 1: like sure, right, that is true. I'm just thinking of
Speaker 1: a much narrower set of conventions for the particular kind
Speaker 1: of like you know, like when you're thinking about very
Speaker 1: specific subgenre like bubble gum pop, it's got this very
Speaker 1: specific sound to it. And you know, that's one of
Speaker 1: those things where I'm not saying you don't have to
Speaker 1: have talent to produce it. It's just that it's one
Speaker 1: of those things where if I listen to more than
Speaker 1: five or six songs, I can't tell where it stopped
Speaker 1: and where it began. But then I'm also old and
Speaker 1: I yell at people to get off my lawns. So yeah, yeah,
Speaker 1: so I mean there's that I'm curious to see where
Speaker 1: musical composition goes in the future. I am too, I
Speaker 1: am too. And also, you know, again, just to remind everybody,
Speaker 1: we are going to have blog post up where we're
Speaker 1: gonna have links to all this stuff, and you can
Speaker 1: get those MP three's of the Most Wanted Song and
Speaker 1: Most Unwanted Song. They are still available, so if you
Speaker 1: want to get your your grubby little musical hands on,
Speaker 1: those will show you where to go. And uh, and
Speaker 1: I don't know why you wouldn't. These things are brilliant
Speaker 1: in their own kind of awful way. Uh. Meanwhile, if
Speaker 1: you want to join in on the conversation, what you
Speaker 1: should do is go to f w thinking dot com.
Speaker 1: That's the website where we've got all the podcasts, all
Speaker 1: the blog posts, the videos. We've got articles there you
Speaker 1: can join in on the conversation. Let's know what you think.
Speaker 1: Is the best song ever written or maybe the worst
Speaker 1: song ever written. I'm curious to hear your thoughts on
Speaker 1: both of those, or if you can imagine listening to
Speaker 1: a song created by a computer. Yeah, let's know. I
Speaker 1: mean maybe you already have. Yeah, So get in touch
Speaker 1: with us, let's know what you think, and we will
Speaker 1: talk to you again. Really similar. For more on this
Speaker 1: topic and the future of technology, visit forward thinking dot Com,
Speaker 1: brought to you by Toyota. Let's go Places,