What would it take for a machine to jam? This question was first posed
in Computer Music Journal in 1988 as a form of musical turing test, a way of identifying human-like intelligence in machines. And this test is very simple. Blues and E flat, one two, a 1234. What would it take in this case for a machine to convince me and you that it was human? Well, very quickly, a number of things need to happen in real time. First, the machine would need to identify where one was and entrain to my pulse using not only auditory but visual cues. We're very good at figuring out if other people are locked in with our pulse. And so I would need to sense that the machine was feeling the groove. Two, the machine would need to identify what it was that I was doing on my bass guitar, and then fit that within the paradigm of a twelve bar blues. Am I doing the quick IV, for example, or am I playing a II-V on the turnaround instead of a V VI? So am I playing a jazz blues versus a delta blues versus a Chicago style blues? It would then need to take that information and respond in kind in an improvised solo, using meaningful blues vocabulary. Now, music is not really a language, but it certainly feels like a language to those who play music. And so if I ask a musical question, does it feel like I'm getting a meaningful answer in response? Does it feel like I'm connecting with somebody, that there is a real ghost in the machine on the other side of the algorithm? I'm fairly convinced that this will never happen. No AI will be able to pass a true musical Turing test. Call Ray Kurzweil. Tell him hes a hack. Singularity, my ass. Now, I'm not going to bullBASS you in this video. I'm not going to appeal to some vague sense of the uniqueness of human musical creativity - "AI has no soul" - because it's gonna get there, right? I mean, Red Lobster is already using AI generated music in its ad campaigns. And who are we to question the aesthetic sensibilities of Red Lobster? Red Lobster, you got the magic
touch. "Cheddar Bay Biscuits I love you so much" There have
been some incredible advances in generative AI from companies like Udio and Suno AI. They let you generate full pieces of music from text prompts. Just type in what you want and it will do a pretty good job of giving it to you. "In Tommy's Shack Grill late at night. Big Joe Flippin those paddies, fine.
American Cheddar melting just right." That's horrifying. It's like spitting on Muddy Waters' grave. Upon hearing these kinds of results, many techno-optimists have breathlessly extolled that the musical Turing Test has been passed. Machine musical intelligence is upon us. Daddy Elon is so excited. So why am I so doubtful here, right? Why am I saying that music AI
will never pass the Turing Test? Well, I think there's a
pretty profound category error that's going on here that we need to always be aware of going forward. What generative AI does is not music. Let me explain. This video was brought to you by Nebula. Hope of an extraordinary aesthetic success based on extraordinary technology is a cruel deceit. Iannis Xennakis, 1985. In 1950, Alan Turing first wrote about what he called the Imitation Game, what we now call the Turing Test. In this test, an interlocutor asked questions of two entities, one machine and one human. And if at the end of a conversation - traditionally done through a text prompt - the interlocutor is unable to tell which one is the machine and which one is the human, then we say that the machine passed the Turing Test. It displays human level intelligence. Now, how might we take that idea and expand it out into the world of music? Well, one scenario that we showed at the beginning of this video involves a "Turing Jam Session". During the improvisation among an interlocutor and two musicians, the interlocutors task would be to identify who is the machine and who is the human. I'm really drawn to this test because jam sessions are a great way to get to know people, know what they're about, know their musical taste. They're a lot of fun. They're a social activity. But there are other possible musical Turing tests. Christopher Ariza loosely categorizes them as either musical directive tests, which involve ongoing musical interaction between the interlocutor and two agents, and musical output tests, which involve no interaction. The listener simply judges the human-like quality of a given musical output. I have no doubt that generative AI will be able to pass musical output tests. Red Lobster's marketing team apparently thinks so, too. But musical directive tests, where there's a continuous interaction between agents, put the emphasis on process, not product. Alan Turing's original imitation game envisioned a conversation between agents, not just somebody passively looking at text and determining whether or not it was computer generated, whether chatGPT did your homework, in other words. And so to pass a musical Turing test, a machine has to do what musicians do when they make music. The process has to feel human. One way that visual artists have already started to push back at the flood of generative AI images is to document their process and tell the story of making the art. This approach will become more and more common with musicians over the next couple of years as a means of pushing back against the inevitable tide of generative AI slop. So back in 2008, Jack Conte, current CEO of Patreon, was releasing music with Nataly Dawn under the name Pomplamoose. They were releasing these YouTube videos in a format that they called video song. Video songs had two one, what you see is what you hear, and two, if you hear it, at some point, you saw it. Now, this seems kind of like obvious now because this format is so ubiquitous. But back then, back in 2008, it was a revolutionary approach to releasing art. You were seeing the music as it was actually made - an appeal that I imagine will carry some resonance in the future in the face of generative AI. The music theorist William O'Hara expands on this idea of showing your work for musicians. Borrowing a term for the ancient greek word for craft, he calls it the techne of YouTube. A techne of YouTube performance, then, is a form of music theoretical knowledge that exists at the intersection of analytical detail, virtuosic performance ability, practical instrumental considerations, and an awareness of one's audience and the communicative tendencies of social media. Pomplamoose has since expanded on this idea of techne in social media musical performance by releasing these short form video things where you see the musicians actually working out the music in the studio and joking around, you get to see how the sausage is made, so to speak. Can we try snapping a tight one? Let's try a tight one. Sorry, you want to snap a tight one? You also get to see what exactly would be necessary for a machine intelligence to do if it was to pass the musical directive test. It would need to understand
and respond to musical jokes. It was just like, we're feeling this in two. I just think about everything in one. One. One is a 400 bar form. It would need to understand
and take musical direction. It would need to vibe with Jack Conte, so to speak. I talk about him all the time on this channel, but the musicologist Christophe Small talks about how music is not really a noun, but in fact a verb. In his book Musicking, he writes that music is not a thing at all, but an activity, something that people do. The apparent thing "Music" is a figment, an abstraction of the action, whose reality vanishes as soon as we examine it at all, closely. Singing protestant hymns in a church, dialing in sick lead tones on a quad cortex freestyling live on the radio, facing the wall of death at a metal festival, watching singers perform o
sole mio in a concert hall, sitting in at a jazz jam session, streaming yourself on Twitch
producing music in FL Studio, its very to difficult to see what these things have in common besides somehow reacting to organized sound. And any one of these activities might be a good candidate for a musical directive test. They are all different examples of ongoing musical interactions. Generative AI, on the other hand, is very good at creating products, musical recordings. But that product is only ever useful for passing the output test. Passing the directive test, though, would require AI researchers to treat music as a verb, like Christopher Small suggests a process, a thing that two or more people do together, like in Alan Turing's original imitation game. And when you do that, you have to take a look at the dynamic relationships between audiences and performers and the technology they use and the spaces that they make music in. And by the way, all of this is just for western music so far. The whole thing is a lot. I am not gonna pretend like I know how AI works. I am but a simple bass player. I have tried to read those papers and I am just not smart enough. I do recommend Valerio Velardo's videos on AI music if you want to get into some of the technical weeds about these things. But basically, the way I understand it is that a large language model will train on a bunch of data and then use that data to try and accurately predict what the next thing will be in a sequence. This is basically what the computational cognition model is for humans, which says that we take information, information in from the world through our senses as input, and then we process it in our brain, and then our brain outputs behavior. This is a fairly outdated model, and one that I don't feel like applies to how we think about music. And if the point is to pass the Turing test, the machine has to think like a human. Anybody who has ever performed knows that getting stuck inside your head is the worst possible thing. Thinking too much means you cannot react with meaningful musical ideas. But your mind isn't blank, you're still thinking about things. Its just very fragmented. One hip new theory in philosophy of the mind that accounts for this is called 4E cognition, after the four E's Embodied, Extended, Embedded, and Enacted cognition. They represent a dynamic relationship between the brain, the body, and your environment. The first of these is Embodied cognition. Your body shapes how you think. When it comes to music, this means that if it sounds good, it's because it feels good. And this is backed by two decades of music neuroscience research, especially when it comes to auditory motor coupling. The areas of your brain which process movement through space are the same areas of your brain that process rhythm and music in general. The vestibular system that governs balance influences your sense of downbeat, where one is. If you're not physically balancing your body, you might lose the downbeat. A very common occurrence, like what I just did here in this jam with Rotem Sivan and James Muschler. You see me swaying maybe a little bit too much there in the background, and then the rhythm gets all ...floaty. Cool, but maybe not intentional. Failure is actually something that Alan Turing identified as a means of getting a machine to fool people into thinking that it was human. If a machine is too good at answering questions, it wont seem human. To err is human, and so to pass a Turing test, an AI might need to lose where one is. The second form of cognition is Extended cognition, where the world is your brain. Thinking requires a lot of energy - evolutionarily speaking - your brain gets tired sometimes. And so humans have figured out ways of extending our thought patterns into the physical world as a means of reducing cognitive load. The classic example of this is writing. We write things down so we dont have to remember them anymore, freeing up cognitive capacity in our brain. Plato famously complained about this, how people were getting lazy because they relied on writing too much. Smartphones are the latest example of this, extending our brains into the world with technology. We could say that music notation is a form of extended cognition, by letting us remember more music than we would normally be able to with our mere brains. Orchestral composers don't have to remember every note for every instrument that they have ever written, and so they are freed from the constraints of their own memory to imagine larger and grander musical designs - music shaped by extending our brains past their limitations. An AI might need to show forgetfulness if it's going to pass a musical Turing test. The third form of cognition is Embedded cognition. Patterns of thought are
embedded in external systems. One way to think of this is
how I have embedded my musical vocabulary into a system of tuning for bass. 4th tuning. Like, I don't have to think that much to be able to express myself with my bass tuned like this. The notes are just there where my fingers expect them to be. But if I was to detune my bass a little bit to an unfamiliar tuning system, I don't know where anything is anymore. So my cognitive load has increased as I hunt and peck for each individual note on my instrument. Anybody who's ever tried to type with a Dvorak keyboard knows what I'm talking about. The patterns of thought embedded in the technology that we use, like the bass guitar, guide our musical intuitions. You can tell if somebody wrote a piece in a digital audio workstation versus musescore, for example, would an AI need to mimic this pattern of embedded cognition to pass a Turing test? I don't know, but this is how we do it, you know? The fourth form of cognition is Enacted cognition. Doing, is thinking. You process the world through action. It basically says that there are certain activities which are meaningless if you are passive, like sports, for example. We don't say that you're a good soccer player if you spend a lot of time thinking about soccer, because you have seen a lot of other people do it, you know, you kind of gotta get out there and actually run and do the thing yourself. The very first question from this video, what would it take for a
machine intelligence to jam? Is like asking, what would it take for a machine
intelligence to play soccer? And the answer is, a body. We need replicants. We need androids out there on the field. Otherwise, it's just a supercomputer thinking about soccer. Without a body, AI sports intelligence is meaningless. You gotta have robots on the field, processing in real time what's going on with their robot bodies. Without a body, AI music intelligence is meaningless. You gotta have robots on the bandstand, processing in real time what's going on with their robot bodies. As the great jazz educator Hal Galper said, we musicians are athletes of the fine muscles, and like athletes of the larger muscles, our meaning is created by our bodies doing things. Like our siblings in sports, we share in vivo, the struggle, the joy, and the experience of our lived selves moving through the world. Embodied AI is a long way off, but I don't see any technical reason why we can't have musical terminators. Again, I'm not an AI researcher, so I don't know any of the actual nitty gritty with any of this stuff, but, you know, it could happen. And I also see how it would be possible to treat music more as a conversation between human and machine, passing a musical directive test by valuing and creating meaning in the process of musicking. So why am I so doubtful? Why is the thesis of this video that the musical Turing test will never be passed? We will never have musical machine intelligence. [CAPITALISM] The presumed autonomous thingness of works of music is, of course, only part of the prevailing modern philosophy of art in general. What is valued is not the action of art, not the act of creating, and even less that of perceiving and responding, but the created art object itself. You can sell a thing. It's harder to sell the process. If the process of making quality recorded music can be made more efficient, the market is incentivized to make the process as efficient as possible. Generative AI creates recorded music extraordinarily cost effectively compared to the other ways you might do it. It fully automates a process that had previously required human input, much the same way that industrial capitalism automated making cars, making food, and making things, as long as those things are good enough. In other words, as long as they pass the musical output test, the processes are relevant, only the product. There are billions of dollars now being thrown at developing generative models for language, images, and now music. Because there are potentially billions of dollars to be made in the market. Spotify is now in on the generative AI music trend. Theres just, you know, no money to be made in passing a music directive test. You're just making the process to get to the product less efficient. And I mean that very literally, too. The current prize for passing an improvisation based Turing test is only $1,000. And with such weak market pressure to do something like this - I mean, I guess you could sell tickets to see this as part of a live show - theres just no reason to spend that kind of computational energy into doing this. The cloud computing power required to run large language models uses absurd amounts of energy consumption is a great bottleneck for AI. Based on the extremely intensive computational demands of training. The carbon cost of image generation is staggeringly high, and raw audio generation is slated to be much higher. I mean, just to, I guess, put this in perspective, I think a gigawatt, it's like around the size of a meaningful nuclear power plant, only going towards training a model. Who is going to build the equivalent of nuclear power plants so that robots can jam with me? It's just so much more efficient to have humans do the jamming. I'm old fashioned and very idealistic about that. My feeling is I'll outplay anybody using the machine or I'll die. I don't care. The day that the machine outplays me, they can plant me in the yard with the corn. And I mean it. I'm very serious. I will not permit myself to be outplayed by someone using the machine. I'm just not going to permit that. You know, there are people that I respect that use this technology to make beautiful music that somehow captures what it means to be human in the year 2024. And I think that's exciting. But then there are people that I do not respect, like the people who run companies like Suno, Udio, and other AI companies, who have a very accelerationist mindset when it comes to this, it feels like music is just one more box to tick on the way to the singularity. Music is a problem that technology can solve. There seems to be a profound disinterest in the artistic process, why music sounds the way that it does. And so you get things like the beautiful, rich history of the blues, a Black American tradition, reduced to typing into a text prompt. Delta blues about cheeseburgers. That's why I refuse to call this stuff music, because the technology behind it is so aggressively anti human, anti history, anti music. And that's why I also feel like the musical directive test will never be passed because the people running the show just don't care. You know, one of the things that I've learned over the years talking about musicking on this channel is that you can change your relationship to music that you hate by choosing to musick it differently. And I kind of hate this Red Lobster tune because it kinda slaps red lobster. You got me up every single time. There's like a phrase extension too disgusting. Now, I owe a massive amount of context for everything that we've talked about here today to a video that I saw from the science creator Tibees - Toby Hendy, where she goes over Alan Turing's original 1950 paper, where he first details his imitation game. She highlights just how visionary Turing's paper truly was, like how he predicted that machines would need a degree of randomness in them to evolve to have human like intelligence. This has actually turned out to be an essential part of modern machine learning. She also covers how Turing thought of potential objections to the idea that machines could become intelligent, including some weird ones like Turing felt like he seriously needed to address the prospect of extrasensory perception. Anyway, this video was a great one, giving me some extra context about the history of machine learning. And you can find it and many more like it over on my streaming service, Nebula. Nebula is a creator-owned streaming service that was originally started as a means for creators to make interesting videos and essays free of the constraints of the recommendation algorithm. But it's since organically grown into, like, one of the genuinely best places on the Internet for curious folks to find exclusive, interesting, and enriching content. Like for example, on there you'll find science creators like Tibees and Jordan Harrod, who does some fantastic stuff with AI. You'll find amazing video essayists like the OG video essayist herself, Lindsay Ellis is on Nebula. And also Jacob Geller. If you have never seen a Jacob Geller video essay, I highly recommend you check out some Jacob Geller. Like some of this stuff is so beautiful. I think he's one of the the best people in the game making video essays. Go check out some Jacob Geller. You'll also find some of my fellow music creators that I deeply love and respect, like the queen of jazz education herself, Aimee Nolte is on Nebula. You also have the wonderful music theorist 12Tone making videos over there. I genuinely love the community of creators over on Nebula. They are such a wealth of inspiration for me, and I know they will be a wealth of inspiration for you too. If you're already on Nebula, we're making it easier to find content for both new creators and existing favorites. There are categories now, news, culture, science, history, podcasts and classes. Each category is kind of like its own mini service. Like, I have some classes over there. I have a class on vlogging, which you might enjoy. I also have a class that I did with Aimee Nolte on jazz and blues that I know you will enjoy. If you like my nerdy music theory channel, if you sign up using my link at Nebula.tv/adamneely, or use the link below, you can support me and all the creators over on Nebula directly and get Nebula for 40% off annual plans, which is as little as $2.50 a month. What's exciting, though, and genuinely unique to nebula, I think, is that now Nebula is offering lifetime time subscriptions, which means, yes, now until the singularity, the end of time. Thank you, Ray Kurzweil. You can be enjoying Nebula and all it has to offer. There are some big concept, high octane Nebula originals to be excited for coming this summer. Like Identiteaze, the debut short film from Jessie Gender coming this June. And of course, Jet Lag season ten is now in production. You actually might remember Toby from season five of Jet Lag The Game, but she'll also be appearing in the latest season alongside Ben, Adam, and Sam from Wendover. I'm a fan of Jet lag the game, by the way. It's kind of like reminds me of tour. It feels almost weirdly comfy watching them run around because it's like me running around Europe. Anyway, $300 gets you lifetime access to all of this great stuff and everything that Nebula will ever produce from now until the end of time. I love reading that, by the way. Now until the end of time. That's. That's fun. I'm very excited for the future of Nebula, and I think you will enjoy this community that aims to engage the world in a more meaningful human way. Thank you so much for watching. You can sign up today for either 40% off annual plans or $300 off lifetime access. And until next time, guys,
