[User] Please transcribe this text

[Assistant] How this is going to be used to create new content is a little bit limiting, right? It's a little bit like, hey, you know, you're doing a creative thing. Let me make a creative thing for you.


[User] Please transcribe this text

[Assistant] However, you know, so one of the things I want you to get out of this talk today is maybe a bit more of a critical eye when you see really exciting, uhm, funny machine learning systems that are out there doing creative work. I think they can be interesting for all sorts of reasons, but uhm, that doesn't mean that they're necessarily the right tools for human.


[User] Please transcribe this text

[Assistant] So I've been doing research in this area for about 10 years now. Uhm, I'm really interested in how machine learning can be used not to really, you know, replace people and give them this big red button, but to support people's existing creative practices.


[User] Please transcribe this text

[Assistant] And so we can break that down a bit using those definitions of machine learning that I started with at the beginning. And we can ask when might it be creatively useful to find patterns in data, to make predictions, to generate new similar examples.


[User] Please transcribe this text

[Assistant] Uhm, and I'm also really interested in making tools so that creative practitioners in different domains can use these techniques effectively in their own work because I think it's really


[User] Please transcribe this text

[Assistant] Uhm, short-sighted and limiting if people with engineering programming expertise are the only ones who can use them. We're gonna end up with just less interesting stuff, uhm, for one thing.


[User] Please transcribe this text

[Assistant] So one key to recognizing what all the opportunities with machine learning might be, is to start by thinking about what might the data be.


[User] Please transcribe this text

[Assistant] Right? What kind of data could we use to do these things? And there are potential sources of data everywhere, right? A lot of the cool generative modeling techniques have used large data- data sets, large image data sets and so on that either have, you know, arisen on the Internet or have been carefully curated. And yes, those are really exciting and they are really useful often.


[User] Please transcribe this text

[Assistant] But there are other sources of data that are often just as powerful, right? Data that you could collect from sensors, from cameras, from microphones about what people might be doing at this moment, how they're, um, how they're moving for instance.


[User] Please transcribe this text

[Assistant] Uhm, or data that creators make incidentally throughout their work, right? If I'm doing photo editing, the whole set of, uh, photos that I've edited in the past, that's data. The sequence of operations that I do within the software as I work, that's data. We could model that. We could find patterns in that. That could be really fun.


[User] Please transcribe this text

[Assistant] And then there's all sorts of data that might not be inherently creative, but which we care about a lot. Things like our social media data or the weather. Uhm, people have been making music and art out of these data sources for a long time. Uhm, and machine learning gives us some extra infrastructure for dealing with that data in perhaps more useful ways.


[User] Please transcribe this text

[Assistant] So let's just focus for the moment on this prediction paradigm. The same type of algorithms that we can use to predict


[User] Please transcribe this text

[Assistant] Which, you know, which album someone's going to buy, we can use that in other contexts. And for me, I got really interested in using machine learning with sensors and with real-time interactions.


[User] Please transcribe this text

[Assistant] And so instead of saying, well, here's demographic information, here's what they're going to want to purchase, we could say, well, maybe we're gonna listen.


[User] Please transcribe this text

[Assistant] To what sound is happening in the world or what sound is coming into a microphone and then use that to drive some kind of computer action and maybe this is a music visualization or maybe this is a voice controlled game. Lots of different things could fit into this paradigm.


[User] Please transcribe this text

[Assistant] Or uhm, the place that I really dug into when I was a PhD student was this, to say well, what if we take information from sensors that give us data about how people are moving and predict what sound we want the computer to make, right? And we can use this supervised learning infrastructure to make some cool stuff.


[User] Please transcribe this text

[Assistant] So uhm, I'm now gonna give you a demo of the software that I made starting in about 2008 to make it easy for composers to build new musical instruments with sensors. Uhm, and you can use this in all sorts of other domains. It's been used in a lot of different projects at this point.


[User] Please transcribe this text

[Assistant] It gives you this basic infrastructure where you start out and say I'm going to capture some data from the world or maybe read it from a file, but it's more fun if you're getting it in real time sometimes.


[User] Please transcribe this text

[Assistant] And I'm going to send that to a predictive model and that model is going to give me some kind of output that I can use to choose which sound to play or control the parameters of a synthesis algorithm or control how a robot moves or whatever I want, right? And this is modular, this can be any software, uhm, on the bottom here.


[User] Please transcribe this text

[Assistant] And of course, more interestingly, Wackinator doesn't just limit you to taking someone else's model that, you know, has been trained by an expert, uhm, to do a particular thing. It allows people, even if they don't have programming or machine learning expertise, to build that model themselves. So let's, uh, let's start out with a demo.


[User] Please transcribe this text

[Assistant] Uhm, so the first thing I'm gonna show you, uhm, I like, oops, not that one.


[User] Please transcribe this text

[Assistant] I like this example because I'm gonna run, uhm, for this first component, the world's worst computer vision system. And we're still gonna be able to build something useful with it. So this is just taking my webcam input, chopping it into 10 by 10, uhm, grid cells and sending that data, uhm, out as a single flat vector. So uhm, you know, if you're a programmer,


[User] Please transcribe this text

[Assistant] Even if you're a really good programmer, you're not gonna be happy if I say you have to write some code that looks at those hundred numbers and says something useful about what's happening in front of the camera. You might be able to do it, but it would be really annoying.


[User] Please transcribe this text

[Assistant] Uhm, so instead I'm gonna use Swackinator, I'm gonna use supervised learning to build a predictive model.


[User] Please transcribe this text

[Assistant] And I'm going to build a classifier and I'm going to use this classifier to control a drum machine.


[User] Please transcribe this text

[Assistant] And so I don't have a data set to start with, right? There's no ground truth data set of how I look in front of this camera in this room to control the sound, but I can make that. So I'm gonna start out by saying I want this sound to happen when I'm standing here. And I've just captured 15 examples, that's it.


[User] Please transcribe this text

[Assistant] And now, maybe I want another sound to happen when I'm not standing there. And I'm gonna train, I'm gonna build a machine learning model and I'm gonna run it and see how it does.


[User] Please transcribe this text

[Assistant] Okay, so it's working pretty well, and if I move around, I can see, well, it's making a mistake right here. Uhm, and that's okay, I can give it more examples, and say this, this is still me, now please run, and it's, yeaah.


[User] Please transcribe this text

[Assistant] Maybe I want to fine tune it and say this is also still me.


[User] Please transcribe this text

[Assistant] And I just keep iteratively improving the model by adding some more data.


[User] Please transcribe this text

[Assistant] And if I'm happy with it, I can maybe make it more complicated. I could say, well here's another thing. There's my hand.


[User] Please transcribe this text

[Assistant] Okay, so that's building a classifier to control sound. Pretty straightforward, pretty easy, like as you can see I didn't need to know much about machine learning to make that work. Uhm, and it worked even though I had really horrible data coming in.


[User] Please transcribe this text

[Assistant] Uhm, I wanna give you another example, uhm, of how,


[User] Please transcribe this text

[Assistant] This is actually how most composers used this when I started building this into a tool for them, uhm, in 2008, 2009. And, uh, you know, the music that I was playing for you just now, you could play that same music with a few buttons, right? It's creatively not giving you lots of different musical expressive possibilities.


[User] Please transcribe this text

[Assistant] However, there are lots of things that, you know, are hard to control with buttons, right? If you have lots of different parameters of sound changing at once, uhm,


[User] Please transcribe this text

[Assistant] That, uh, starts to become musically more of an interesting possibility space. So most composers weren't using classification like I just showed you. Most of them were very interested in regression and regression with multiple, uhm, predicting multiple continuous values at a time.


[User] Please transcribe this text

[Assistant] Uhm, and likewise, most of them weren't using this crappy webcam input, they were interested in using different kinds of sensors that might give different information about how people were moving or acting. So here I'm gonna use a leap motion, uhm, just use the shape and position of my hand to control some sound. And uhm, I'm gonna open up a fun little music program.


[User] Please transcribe this text

[Assistant] Okay, so there's my audio sample, and I might say I want to use my hands now to control how this sample gets played back. And maybe when I put my hand, I'll show you there, when I put my hand,


[User] Please transcribe this text

[Assistant] Kind of just a few inches above this, I want this normal sound to happen. And maybe I want the pitch.


[User] Please transcribe this text

[Assistant] To go up as I move my hand up. So I'll give some more examples with that new position and that new sound.


[User] Please transcribe this text

[Assistant] And now I have a continuous pitch controller, which is kind of fun.


[User] Please transcribe this text

[Assistant] And I can start making this more interesting and say maybe I also want different hand shapes to really do crazy stuff to the sound. So maybe I want


[User] Please transcribe this text

[Assistant] Maybe I like that and I want to sort of claw hand over here to give me that sound. And I can put that in, retrain with those additional examples.


[User] Please transcribe this text

[Assistant] And that's kinda cool. Maybe I like that, but maybe I wanna do some other totally weird stuff. Like that's a neat sound. And maybe I'll put that over, over here somewhere.


[User] Please transcribe this text

[Assistant] Alright, so that's, that's, you get the idea. Uhm, pretty easy to build, uhm, a new interaction that way. Uhm.


[User] Please transcribe this text

[Assistant] So that's the end of the first part of my talk. Does anybody have any questions about what I was doing right there? Okay, everybody more or less gets it. If you wanna, yeah.


[User] Please transcribe this text

[Assistant] Yeah, so uhm, you know, there are all sorts of really interesting questions here about, you know, certainly different learning algorithms are going to allow you to learn different things and not others or might require different amounts of data.


[User] Please transcribe this text

[Assistant] To learn different things, uhm, the, I can talk more about the details of this in the question period, but one of the things that I did with Wekinator is I, the first version of this I built.


[User] Please transcribe this text

[Assistant] Using a set of really intense participatory processes with composers to try to figure out like what is a reasonable default to put in there that's going to be able to more or less learn the stuff that I saw people doing and so the


[User] Please transcribe this text

[Assistant] Again, what is it that they're doing? Are the configurations the defaults? The way that this is set up? The number of models? Is that a good match to what people really wanna do? And how can we make it better? So actually that leads into the second part of my talk, which is uhm, really not just how do we make stuff like Wekinator that's usable,


[User] Please transcribe this text

[Assistant] Using machine learning in this context to build things, um, ends up looking a little bit different from using machine learning in more conventional applications.


[User] Please transcribe this text

[Assistant] Okay, so I've got sort of five really high level research findings that I want to throw at you and again we can go into more detail in any of these in the question period if you want. Uhm, so the first,


[User] Please transcribe this text

[Assistant] Really exciting thing for me when I started this work as a PhD student was that uhm, people immediately started using it to build really weird stuff that I hadn't expected. Uhm, and especially started building interactions with sensors. And so I wanna give you a couple examples.


[User] Please transcribe this text

[Assistant] Uhm, so you can see that it's not just like weird claw hands making weird sounds. Uhm, there's actually


[User] Please transcribe this text

[Assistant] You know, some really beautiful music that people have made, for instance. Uhm, this is a piece by a composer who, uhm,


[User] Please transcribe this text

[Assistant] We may never get out of this presentation, but that's okay. There we go. Here's Anne's piece.


[User] Please transcribe this text

[Assistant] So, you'll notice a couple things here. One of the reasons I really like this piece is, I mean, the sound is lovely, uhm, but the experience of watching that piece being performed is totally different from watching someone, you know, doing


[User] Please transcribe this text

[Assistant] Uhm, she was able to build that pretty easily with machine learning, uhm, to say here's how I want the performers to move, here's how the sound should change as they move.


[User] Please transcribe this text

[Assistant] Uh, whereas that kind of, uh, instrument would be a lot harder to build, uh, using programming.


[User] Please transcribe this text

[Assistant] Uhm, another one of my favorite really early interfaces that somebody built was, uhm, this one. This is Michelle Nagai, who's a composer who walked down the street one day and saw a piece of tree bark and said, I need to turn this into a musical instrument. So she brought it home.


[User] Please transcribe this text

[Assistant] So again, this is a really beautiful instrument, uhm, you can watch her play it on Vimeo, uhm, but it's an instrument where it just wouldn't have been feasible to build, uhm, to get 25 kind of weird, noisy inputs that are, uhm, also really, uh,


[User] Please transcribe this text

[Assistant] Impacted by the lighting conditions in the room and so on. She was able to make this quite easily with machine learning.


[User] Please transcribe this text

[Assistant] Since then, people have been using Wekinator for all sorts of other kinds of projects that I didn't envision, which I think is great.


[User] Please transcribe this text

[Assistant] Uhm, this is an art project featured in Motherboard by a student who, uhm, you put a camera on your head and if that camera detects something that looks like a screen, then it takes special glasses that you're wearing and makes them opaque. So you cannot look at a screen while wearing these glasses, which is pretty cool.


[User] Please transcribe this text

[Assistant] Uhm, I've also used Wekinator a lot of teaching, uhm, both teaching my students, uhm, at Goldsmiths and teaching interaction design to people who may or may not, uhm, be, you know, have done programming with sensors before, but also teaching workshops at various, uhm,


[User] Please transcribe this text

[Assistant] Other universities, conferences, workshop locations and so on. And a lot of the things that people have made, uhm, end up being just really fun and kind of whimsical. Uhm, this is high bot.


[User] Please transcribe this text

[Assistant] And I like that, you know, for this, the hard part of this project was thinking about what to do, gluing the servo motor behind the cardboard and not actually saying, well, I need to now figure out what is a wave computationally and find that, write the code to identify that.


[User] Please transcribe this text

[Assistant] Uhm, so there's lots of other really cool projects that people have done. Everything from sort of, uh, rock, uh, pop music to puppetry to starting companies with apps that, you know, react to you in really intelligent ways. Uhm, and I'll talk about some of the work I've done in the accessibility space.


[User] Please transcribe this text

[Assistant] I could give a whole talk on this. I'm not going to, you can go on the Wekinator website or YouTube Vimeo and see some really cool stuff. But again, lots, lots of the really fun projects are projects where people are using sensors and making things that might be really hard, uhm, or at least annoying and slow to make without machine learning.


[User] Please transcribe this text

[Assistant] The second point I want to make is that uhm, building these types of interactions with machine learning, uhm, especially building creative interactions, but even building things that might be, you know, not very creative, uhm, requires a few differences in the interfaces that we give people and the way that we think about the role of the algorithms. Uhm,


[User] Please transcribe this text

[Assistant] So in conventional machine learning context, the way that you would learn about machine learning in a computer science class, usually, uhm, the goal of using machine learning is to build an accurate model of your data, so that perhaps you can make predictions on new unseen data in the future, right? Super useful.


[User] Please transcribe this text

[Assistant] Uhm, if you build that model and it doesn't work very well, often you're going to change the algorithm and find a more suitable algorithm, or you might change the features, or you might say, oh, I've got more data that I can, you know, collect and use a bigger data set. That may often help as well.


[User] Please transcribe this text

[Assistant] Uhm, but you, you don't wanna mess around with the data too much because the data, you talk about it as a ground truth, right? It embodies some sort of truth about the world that you don't have access to other than through the data. Uhm,


[User] Please transcribe this text

[Assistant] When people are creating interactions with machine learning, a lot of these things change, right? People still might want to build a controller that they would call accurate, right? For instance, if I want to build a gaming controller that reacts to me, you know, punching, I want it to always react 100% of the time and not react otherwise. Uhm, but,


[User] Please transcribe this text

[Assistant] That accurate controller is in the context of a larger goal, which is I want to play a game that's fun, that's accessible to people, that doesn't, you know, doesn't take practice to learn how to use. Or maybe I want to build an expressive musical interface that gives me cool sounds that I can play on stage without looking stupid, right? There's all sorts of other subjective complex goals here.


[User] Please transcribe this text

[Assistant] And sometimes changing the algorithm or features are really good ideas, but often changing the training data is the most effective and reasonable way to change what the model is doing if it's not doing what you want. So you saw that in the, the camera example, right, when I found a place where it made a mistake, I was able to easily correct that by just giving it examples. I'm like, oh, I overlooked what it might, you know,


[User] Please transcribe this text

[Assistant] What I should tell it to do when I'm standing here so I can fix that and re-retrain and we're good.


[User] Please transcribe this text

[Assistant] Um, and so in HCI people- people often talk about this as human in the loop approaches to machine learning or interactive machine learning. Um, machine learning researchers may mean something slightly different, um, when they talk about interactive machine learning.


[User] Please transcribe this text

[Assistant] Uhm, this data often, yeaah, there may be some kind of hidden truth in it that we can't articulate.


[User] Please transcribe this text

[Assistant] Otherwise, but it's- the data is really sort of user interface or communication channel between the person and the machine, right? It's my first training set is my way of telling the computer what I want it to do. And my subsequent training sets might be refinements of that, uh, those instructions about what I want it to do.


[User] Please transcribe this text

[Assistant] Uhm, and so this actually requires some rethinking about what it is that we want the algorithm to do, what makes a good algorithm, and of course how we structure the user interface, right? Most other software environments, especially software environments for uhm,


[User] Please transcribe this text

[Assistant] Non-interactive, not more conventional machine learning, don't make it this easy to mangle your training data because that's a bad thing. Wekinator, we, you know, I learned right away that that was gonna be super useful for people.


[User] Please transcribe this text

[Assistant] So we need to have interfaces that allow the kinds of manipulations that make sense. But this leads to some interesting computational consequences in that, you know, people are gonna provide training data that is not,


[User] Please transcribe this text

[Assistant] IID, right? This is sort of an assumption that often we make, um, for convenience and maybe even with good reason when we're talking about machine learning theory. But people turns out are often smarter than that. And if we allow them to be smarter, we take advantage of the fact that they're smarter, we can learn more efficiently.


[User] Please transcribe this text

[Assistant] So let's imagine uhm, we're building a musical instrument and we're looking at maybe where my hand is in space and I want one sound to happen when my hand is in the purple and one sound when my hand is in the orange.


[User] Please transcribe this text

[Assistant] Um, if I were doing this in a more traditional machine learning context, I might go out and collect a bunch of data, try to get the best dataset possible, and get a nice even sampling maybe from these two regions. And that would be a pretty good if small dataset.


[User] Please transcribe this text

[Assistant] When people are using Wekinator to build examples, uhm, even when they don't know anything at all about machine learning, often they'll start by giving just a few examples, maybe in the center of the purple and the center of the orange, more or less. And they're not gonna spend too much time worrying about it. And then they'll build a model and see where that decision boundary is. And if it's not exactly where they want,


[User] Please transcribe this text

[Assistant] They're gonna add some corrective data. So they may say, well, this is really purple and this is really orange. And now I'm gonna retrain and get my boundary. Keep testing, keep doing that, but-


[User] Please transcribe this text

[Assistant] Here what I've done is I've learned essentially the right decision boundary, but I didn't have to provide all these other examples. So it's kind of like the same benefits that you would get from active learning, but here instead of the algorithm saying, hey, please label these unlabeled data points, it's the person just doing what they naturally do and saying, I'm gonna find where it makes a mistake and I'm just gonna give corrective examples there.


[User] Please transcribe this text

[Assistant] Another consequence of building the interface this way and so something that.


[User] Please transcribe this text

[Assistant] Uhm, really I hadn't thought about it all until I started working with people and observing how they were using the software, is that uhm, people were using this- these changes to the training data not only to fine tune the decision boundary and correct mistakes, but actually to try out different hypotheses about what they might build.


[User] Please transcribe this text

[Assistant] What if I put this sound here? What if I put this sound there? And as soon as I have that then I've got a prototype and I can play around with it and I can use my experimentation with that right away to say, well, was that a good idea or not? If it was a good idea, great.


[User] Please transcribe this text

[Assistant] Maybe it was a good idea but I found some other sound in there that I really liked and I could put that back in the data set or maybe I didn't like it and so I can roll back and so on. Right, so we can think about these changes to the data set as also, again, people communicating hypotheses about something that might be fun or,


[User] Please transcribe this text

[Assistant] Likewise, people exploring and saying, hey, I have an idea of the thing that might be cool. Is this learnable? Is this gonna be learnable given the fact that I'm not willing to spend more than five minutes giving training examples, right? And people often are involved in this negotiation with the algorithm to say, well, what's the most useful thing I can build in the amount of time that I have?


[User] Please transcribe this text

[Assistant] Uhm, so of course we can think about these as all really valuable types of activities in any kind of design context, right?


[User] Please transcribe this text

[Assistant] Supporting rapid prototyping, supporting exploration, recognizing the fact that people are in a dialogue with the materials that they're using and allowing them to have that dialogue and explore an experiment rather than assuming that, you know, this is ground truth training data and it's, it's never going to change.


[User] Please transcribe this text

[Assistant] So I did a project with a student a few years ago exploring this and saying well what if we were to make user interfaces for design with machine learning.


[User] Please transcribe this text

[Assistant] Assume from the get-go that you're going to change your mind and make it easier for to- for you to reason about what alternative designs might be good ones or might be problematic. And in the meantime, also make it possible to control sounds with- by beatboxing because that's fun.


[User] Please transcribe this text

[Assistant] In a more serious long-term project that just finished up, I've been working with a bunch of startups in music, tech, and gaming around Europe.


[User] Please transcribe this text

[Assistant] Um, and this project was a three-year effort, um, called RapidMix.


[User] Please transcribe this text

[Assistant] And we started out really recognizing that using interactive machine learning to build interactions is really powerful. Uhm, it's powerful as a design tool. Uhm, but Wekinator isn't for everybody, right? Wekinator is good for teaching. It's good for building musical instruments. Not so great for software developers who want to build their own app or want to build their own game.


[User] Please transcribe this text

[Assistant] Uhm, and so we started, my portion of the project started with asking, well what might it look like to build tools for developers that allow them to engage in the same kind of experimentation to build tools for developers that they can use without having to go take a machine learning course first.


[User] Please transcribe this text

[Assistant] Uhm, I don't think we've answered all those questions. These are, turned out to be really difficult questions. But the current answer that we have is embodied in this RapidMix API, which you can use in C++ or uhm,


[User] Please transcribe this text

[Assistant] Couple other environments, Open Frameworks, MaxMSP. Um, if you're a programmer and you want to do this kind of stuff in your own projects. Yeah, Michael.


[User] Please transcribe this text

[Assistant] Yeaah. Yeaah. So I have a PhD student who's still got a little bit of time left who is looking at this and saying well, you know.


[User] Please transcribe this text

[Assistant] Clearly, we- we don't have a guidebook that you can use to write a good API. There's lots of, you know, research that's been done on what makes APIs understandable or learnable and we-


[User] Please transcribe this text

[Assistant] built on that but machine learning is kind of its own beast in some ways. And so,


[User] Please transcribe this text

[Assistant] One of the things that we were doing was developing participatory processes to work with developers as


[User] Please transcribe this text

[Assistant] And a self-explanatory demo in the environment. So if you go to the website, you know, one of the things that we did on the website is have embedded code examples in JavaScript.


[User] Please transcribe this text

[Assistant] Paper is coming out about that soon though we've got a, a short paper about that, um, right now. All right. Um, next point I wanna make is that,


[User] Please transcribe this text

[Assistant] These tools ended up, when I asked folks that I was working with, you know, why are you using machine learning? Uhm.


[User] Please transcribe this text

[Assistant] Often it wasn't the answers that I just started with. It wasn't answers about, well, I have sensors and they're complex and I can't build something otherwise. Often it was about the way that they were able to think when they were engaged in designing interactions. Uhm, so the,


[User] Please transcribe this text

[Assistant] People using Wekinator are often, you know, people who are making music or who are moving or thinking about sound or animation and even if they're really good coders, they find that there's, you know,


[User] Please transcribe this text

[Assistant] There's a challenge in moving from thinking about how you want to move or how you want something to sound to turning that into a mathematical equation. And if we can give people example-based programming interfaces, then they don't have to make that cognitive leap back and forth, which slows them down and gives them stuff that they're less happy with.


[User] Please transcribe this text

[Assistant] So uhm, I wanna mention, uhm, not just, uh, this is something that's been important not just in the Wekinator project but in several other projects I've done with students. Uhm, this idea of how data can provide a foundation for building computer systems that understand something that we are doing, some, some, something that we're bodily engaged.


[User] Please transcribe this text

[Assistant] Uhm, this led my former PhD student Reid Oda to do a really cool project, uhm, recognizing that if we look at how a percussionist is moving a drumstick over time, we can actually predict pretty accurately when the next drumbeat is gonna happen, even if they're not playing at a steady tempo. And we can take this information and maybe send it over a network,


[User] Please transcribe this text

[Assistant] Before that hit happens and enable completely latency free collaborative performance. So I can be hitting my drum in New York and somebody in Tokyo can hear a sound of that drum synthesized exactly the same time. Even if the network is introducing some, you know, speed of light delay in the best case. So that was a cool project. Last thing I want to say, uhm, about,


[User] Please transcribe this text

[Assistant] So my research findings is that there's a whole space that I'm excited about right now. Uhm, looking at how machine learning does change people's


[User] Please transcribe this text

[Assistant] Possibilities for creative practice, right? So far I've been talking about how it supports things that people naturally do sort of with any medium. Um, but there are things that I think machine learning makes it easier to do that change people's relationships with their tools. And some of these are potentially very powerful.


[User] Please transcribe this text

[Assistant] A lot of the tools out there that are getting media attention or, you know, going viral on social media are really tools.


[User] Please transcribe this text

[Assistant] Uhm, where you can make a song by choosing what genre, what mood, duration, when you want the climax of the piece to happen, and then it'll make you a song with, with a name as well.


[User] Please transcribe this text

[Assistant] And so here I'll play you, this is my aggressive synth pop track that it made for me.


[User] Please transcribe this text

[Assistant] So you, you know, go try this out. This is free to try. I'm not gonna torture you with the rest of this track. It's not the worst music in the world. It's definitely not the best music in the world.


[User] Please transcribe this text

[Assistant] Uhm, so this, again, I, I don't think anybody's really, you know, thinks that this is gonna replace human composers. Uhm, so I wouldn't say that it's a tool for creativity in that way. However,


[User] Please transcribe this text

[Assistant] This is an awesome tool for YouTube video creators who might not be composers, might not have the money to hire a band or, you know, pay for the rights to a particular track.


[User] Please transcribe this text

[Assistant] Uhm, this allows them to put original music in their video without having expertise and probably come up with a slightly better end result, right? So they're delegating something that isn't really central, uhm, to their, uhm, to their goals and isn't really matched to their expertise. They're delegating that to a computer and I think that's, that's useful.


[User] Please transcribe this text

[Assistant] Uhm, there are a lot of other kind of tools out there. This is Edges to Cats. Uhm, this one allows you to, uhm, sketch sort of a line drawing and it'll fill it in with the closest thing to a cat.


[User] Please transcribe this text

[Assistant] I don't see this exact thing being very useful to professional artists.


[User] Please transcribe this text

[Assistant] Uhm, but I- probably we're gonna see other tools that allow similar, uhm, similar types of interaction which might actually add value. Uhm, and actually I wanna skip over to this. This is the smile vector. Uhm, so this is somebody using-


[User] Please transcribe this text

[Assistant] Deep learning based approaches to make random people on the internet and historical figures smile. Right? And this, again, this is kind of cute.


[User] Please transcribe this text

[Assistant] But this is useful, right? If you say I wanna manipulate content, maybe I'm a professional artist or graphic designer and I wanna manipulate content in a believable way, this gives me a set of tools to maybe let- make my life easier, make me, you know, able to do more things more flexibly.


[User] Please transcribe this text

[Assistant] And really a lot of my favorite work, my favorite creative work with machine learning on the Internet,


[User] Please transcribe this text

[Assistant] Taking advantage of the ways that these algorithms break down and exploiting that in creative ways, right? So the, the machine isn't being particularly creative here, but the people are saying, hey, I can appropriate this and do something with this that, um, you know, if you wanted to make a serious artist statement, you could say something,


[User] Please transcribe this text

[Assistant] There are other artists doing really, you know, serious work with machine learning. Uhm, and if you talk to them or study the ways that they're using these tools, it is often quite different


[User] Please transcribe this text

[Assistant] Uhm, from using programming or using uhm, analog materials. So this is one of my favorite artists, Mario Klingemann. Uhm, he uses neural nets to make visual art. He's also a very gifted programmer, uhm, does all the technical stuff himself as well. So arguably, a lot of his art making is acting as a programmer.


[User] Please transcribe this text

[Assistant] Um, but he's got neural network based systems that give him lots and lots of different content. And when he's, um,


[User] Please transcribe this text

[Assistant] Making work, he also, he made himself a Tinder-like interface to sort of swipe right or left based on whether the thing, he liked the thing that came out, right? So he's, at least in part of his art practice now, acting as a curator rather than as, you know, the person deciding which, you know, which color goes where.


[User] Please transcribe this text

[Assistant] One of the musicians I've worked with for a long time now, who I've learned a lot from, is somebody named Letitia Tsunami. And she's been using Wekinator for about six years. And uhm, she's very critical of this idea that when we make creative tools,


[User] Please transcribe this text

[Assistant] I think the machine learning allowed more of this fun of exploring instead of going, I have to have a result right away and then this thing is gonna do that and leaving it at that.


[User] Please transcribe this text

[Assistant] And again, I think lots of artists engage in, you know, different work with different media will say, if they're speaking honestly, that


[User] Please transcribe this text

[Assistant] The work that they make isn't always just that they had a brilliant idea and then they were able to use their expert skill to make that idea, right? People often get inspired by things that happen with the materials that they're working with that they didn't expect


[User] Please transcribe this text

[Assistant] But which they know to take advantage of and incorporate into.


[User] Please transcribe this text

[Assistant] The final piece. And again, I think some of the, the nature of the mistakes or oddities that you can find in machine learning may make these surprising pushbacks from the algorithms more fruitful than perhaps when you're coding. And the surprise is that you get a compiler error, right? Which is not likely to be creatively enriching.


[User] Please transcribe this text

[Assistant] Um, so I have another side project, um, with, uh, a student where we said, well, what, what might it look like to make these interactive interfaces where instead of trying to use machine learning, anything resembling the right way, what if we just try to make something that always is going to surprise you? Where you grab a sensor, you move it around, and then it makes you a mapping. So that was a fun project.


[User] Please transcribe this text

[Assistant] All right. So for the last few minutes, I wanna talk about, um, what I've been thinking about in the space of using machine learning to improve inclusion and access, and also improving inclusion, um, in terms of who gets to use machine learning, who gets to talk about it, and so on.


[User] Please transcribe this text

[Assistant] And in a lot of my work, one of the exciting, uhm, outcomes that I've, you know, tried to focus on and learn from and do more of is


[User] Please transcribe this text

[Assistant] Looking at how machine learning can enable more people to be creators. Uhm, so I do a lot of workshops. These are high school students, for instance, who didn't know about machine learning, didn't know how to code, didn't know anything about computer music, and I was able to get them building computer music instruments, uhm, in an hour or so.


[User] Please transcribe this text

[Assistant] And then later that afternoon, they taught the eight-year-olds in the classroom next to them how to build things. And this is exciting, right?


[User] Please transcribe this text

[Assistant] Arguably a lot of the tools that you see online, things like Edges to Cats or Neural Doodle, they also allow more people to become creators, but I want to, again, caution that I don't think that's where the creative value of these systems is because often the ways that these systems use data


[User] Please transcribe this text

[Assistant] Say, well no, that's not what I want. I want something that's totally different and my own.


[User] Please transcribe this text

[Assistant] Um, so I think the space of interactive machine learning, whether that's iteratively changing trade- training data or giving people other ways of influencing what an algorithm does and how, have the potential to be much more flexible, um, and, and also allow people to personalize systems in new ways.


[User] Please transcribe this text

[Assistant] So I have a project finishing up right now with a bunch of music therapists who wanted to use Wekinator style interactive machine learning to build musical instruments and we've got an open source project here. We're gonna release the final version next week.


[User] Please transcribe this text

[Assistant] And uhm, this has been super fun. They're building instruments for kids who cannot play most acoustic instruments.


[User] Please transcribe this text

[Assistant] And more interesting than that, they're not just building personalized instruments that are playable, they're actually developing a curriculum that takes advantage of the fact that they now can make a new interaction with sensors and sound in about 30 seconds. And so they often make many interactions for different reasons in the context of a session with a kid or a classroom teaching session.


[User] Please transcribe this text

[Assistant] Uhm, I've got a current project with Microsoft Research, which I can't say much about yet, but we're looking at a lot of these techniques in non-musical contexts saying how could we enable people who are blind and visually impaired to customize, personalize computer vision systems to get information that they really care about from the world around them. And so these projects have really, uhm,


[User] Please transcribe this text

[Assistant] It's gotten me thinking a lot about how, how this work might be in dialogue with what's in the news recently, right?


[User] Please transcribe this text

[Assistant] Most people in the world, when they experience data and machine learning, it's as people whose data is being sold, or they're, they're being disempowered, or they're being manipulated by it.


[User] Please transcribe this text

[Assistant] I've been starting to think about this question, you know, what can we do as computer scientists or HCI researchers or designers to make people's experiences with data machine learning less disempowering because


[User] Please transcribe this text

[Assistant] There are all these other ways to make, you know, to give people value from data that they create. Um, what, what if we were able to give people more value from their social media data or their other data for instance? And how can we get more people involved in the conversation about how they want machine learning to be used when it comes to their lives?


[User] Please transcribe this text

[Assistant] So in this vein, I've been involved increasingly in uhm, machine learning education efforts. I've had a MOOC online for about three years now, uhm, exploring how we teach machine learning to creative practitioners. Uhm, and that's been super fun. Uhm, I'll give you a hint, it's different from how I would teach it to a computer science class and it might actually


[User] Please transcribe this text

[Assistant] Look really similar to how I would teach machine learning to a non-creative group of people from the general public as well.


[User] Please transcribe this text

[Assistant] Uhm, and in fact, if you haven't seen Google's Teachable Machine demo, you should go look at that. Uhm, they explicitly modeled this cool web app on the interaction that Wekinator gives you, but you don't have to download anything. It's just a website where you can go and understand what supervised learning is doing and make cool interactions with it.


[User] Please transcribe this text

[Assistant] So I think there's a lot of space to explore, you know, both how creative applications of machine learning might be useful in teaching people about what these algorithms really do, how they break down, um, but also, um, thinking about, for instance, how


[User] Please transcribe this text

[Assistant] Applications of machine learning that foregrounds the impact of choice of training data in the output of the model, how that might be used to inform conversations about algorithmic bias, for instance.


[User] Please transcribe this text

[Assistant] The last thing I want to mention is that uhm, I recently learned about a really cool project in LA, which I had nothing to do with, but they used Wackinator in their work.


[User] Please transcribe this text

[Assistant] They're running a bunch of workshops with folks around LA, uhm, to


[User] Please transcribe this text

[Assistant] Accomplish something that's important to them. So one of the things they built was a virtual protest environment for people who couldn't, you know, leave their homes because of disability or carrying responsibilities and so on. This allowed them to actually interact with this virtual environment together. Uhm, so that's called, uh, post-human AI, uhm,


[User] Please transcribe this text

[Assistant] So check that out as well. So with that, I uhm, I'm happy to take any questions about any of the stuff that I talked about. And if you, again, if you wanna build some stuff with me tomorrow morning at 9.30, you should come along. You can also download Wekinator for free at that website. Thank you.


[User] Please transcribe this text

[Assistant] I think my, my beef with the jukeback, juke deck song is not that it's not new, it's that it actually feels a little bit off to me as a musician. Uhm, it's, it's new in a way that isn't how a human would do something new. Uhm, and again, I think that's gonna get better. Uhm,


[User] Please transcribe this text

[Assistant] That said, even in the current JukeDeck system, there's a lot of human infrastructure and human expertise that's gone into, you know, how to set that up. And it's not just, you know, a standalone machine learning algorithm that's spitting out stuff. There's all sorts of human heuristics and constraints.


[User] Please transcribe this text

[Assistant] Be part of the system as far as I can see into the future. Because why wouldn't you do that if you're trying to make money and make stuff that actually sounds good?


[User] Please transcribe this text

[Assistant] Yeah, I mean, uh, I do want to be clear that the developers I was working with, uhm, that I have worked with are primarily, they consider themselves to be like creative coders or they're working in creative industries making apps for sound stuff. So it's not like they're making finance apps or something. Uhm, so,


[User] Please transcribe this text

[Assistant] Not the question, not the question anybody's asked me before, so thank you. I think uhm, you know, in workshops, in both cases uhm, my, my learning goals for people have often been, you know, I need to get it clear, like what is, what is the,


[User] Please transcribe this text

[Assistant] Workflow of machine learning. What is, what is the basic model of what we're doing? And that's, you know, I, I showed that to you kind of


[User] Please transcribe this text

[Assistant] You know, this model, you would normally write code.


[User] Please transcribe this text

[Assistant] Uhm, and that's, you know, people usually don't come with that knowledge already, so I think that's...


[User] Please transcribe this text

[Assistant] One of the things that we've learned is not even about how to pitch that to developers versus other people, but how, how to.


[User] Please transcribe this text

[Assistant] Feasible, uhm, is really challenging in both those circumstances and I think probably more so with


[User] Please transcribe this text

[Assistant] Developers, right? When I'm running a workshop with creative, uhm, artists and so on, I give them a lot of examples to work with.


[User] Please transcribe this text

[Assistant] But, you know, developers, I give them example code, but they suddenly, you know, they know they can push that into a domain that they're more familiar with, but they don't have an intuition about like what features might be good.


[User] Please transcribe this text

[Assistant] Um, how do I test this algorithm? You know, do I- is this something I can do unit testing with? And so on. There's a lot, you know, their expertise, uh, means that they're asking really hard questions much sooner in some sense. So education- educational materials interspersed with our API documentation have just been really, really important. One final question.


[User] Please transcribe this text

[Assistant] Yeah, so not in the, not in the regular Wekinator interface and that's something that the, the Beatbox project, we started thinking about like how might you help people notice that some of their data overlaps in the feature space, right, which might be indicative that,


[User] Please transcribe this text

[Assistant] It's mislabeled or that the way that they've defined their classes is inherently problematic given the features that they have. So I-


[User] Please transcribe this text

[Assistant] In the regular Wekinator application, I haven't done that for various, various reasons. Uhm, but if you haven't, uhm, if you haven't looked at K.R. Patel's work for instance, I mean he's, he explored that in uhm, more conventional application context with software developers and I think James worked.


[User] Please transcribe this text

[Assistant] With him on that a bit. So that's, you know, I really found that work, um, inspiring and I think there's, there's lots more to do there and, and more that people at various companies now are thinking about how to build into their machine learning tools.


[User] Please transcribe this text

[Assistant] So whether you're young or you're old, we're able to find ways in which we can bring, uh,


[User] Please transcribe this text

[Assistant] Connections and build connections between people.


[User] Please transcribe this text

[Assistant] The video is increasingly becoming the way that we consume content online. And the power of a good launch video is massive. The highest grossing kickstarter of all time, the coolest cooler,


[User] Please transcribe this text

[Assistant] It failed its first shot, but ten months later, they came back with a retooled video.


[User] Please transcribe this text

[Assistant] Otherwise, Mr. Ingersoll, one of these fine days, you're going to drop dead. Good day, Mr. Ingersoll.


[User] Please transcribe this text

[Assistant] That's what people need, pure sweet air to fill your lungs with, and open up your pores to the sunshine.


[User] Please transcribe this text

[Assistant] Someday, some of those fantastic crusades of yours are going to kick back on you and crucify you. And then what'll be the good? Thank you.


[User] Please transcribe this text

[Assistant] I know it's not gonna be the easiest to do this on, but I really wanna play baseball. That's, that's what I wanna play. So that's what I'm going to play. But why I'm playing, where the, where's my pointer going? Hello? I don't think BeatBoss is very good at, uh,


[User] Please transcribe this text

[Assistant] Baseball, I think I'm gonna have to play on my OG me, but anyways why I'm playing. Yeah, look at that 1741 Wow, I'm actually really high


[User] Please transcribe this text

[Assistant] That's way more than I thought. I wanna get to skill level 2000. But realistically, I wanna get to skill level like 3000. Essentially, there's no cap.


[User] Please transcribe this text

[Assistant] From what I've read and what I've found out, there's no cap on skill level in Wii Sports. You can go to infinity, but once you get to around 3000 skill points,


[User] Please transcribe this text

[Assistant] It's like one of those exponential curves. It's like an exponential decay, you know what I mean? Or exponential growth. Wait, no.


[User] Please transcribe this text

[Assistant] It'd be decay. I don't know. It's like plateauing off to a point that you only gain like one skill point per game. Even at this point at 1741.


[User] Please transcribe this text

[Assistant] I wanna say 1738. I'm sure even if we beat Maria by like a run, we might not even get that much skill points from it. But nonetheless, I wanna get to that insane level of skill.


[User] Please transcribe this text

[Assistant] Level. That my graph is literally off the charts.


[User] Please transcribe this text

[Assistant] Well, that was a good start if that wasn't a terrible hit. Drop it! DROP IT!


[User] Please transcribe this text

[Assistant] Fucking laser-focused Maria. I'm gonna throw a slow splitter on him.


[User] Please transcribe this text

[Assistant] Wow, Pierre! Dumbass. Oh boy, what do I throw now? I'm gonna just try to get him twice with the splitters.


[User] Please transcribe this text

[Assistant] Usually at this skill level, they're, they're pretty good at picking up on that. Is this Abbey? It's Abbey. Sorry, I have like three Abbeys on my Wii now.


[User] Please transcribe this text

[Assistant] Oh, Abby! That was such a delay from when I swung my- Thank god that's a ball. That thing was coming at a hundred.


[User] Please transcribe this text

[Assistant] Alright, way to go, Abby. Oooooh, can they not catch it? That might be a home run.


[User] Please transcribe this text

[Assistant] Man, you can't get any closer to a home run than that.


[User] Please transcribe this text

[Assistant] Between me swinging and it actually swinging in the game, I swear.


[User] Please transcribe this text

[Assistant] Is that a home run? It might be. That time it actually is. Holy shit, we got three.


[User] Please transcribe this text

[Assistant] There's some weird delay going on right now, I swear to God.


[User] Please transcribe this text

[Assistant] Ninety-nine miles per hour, by the way, on that. Wooo! Holy crap! When did I get good at baseball?


[User] Please transcribe this text

[Assistant] Dude, my Wii Remote is being very strange.


[User] Please transcribe this text

[Assistant] This is a peach Wii remote with Wii Motion Plus inside that I'm using today. Not a beef remote. And I might have to switch to the beef remote. Because, uh, the peach is being a piece of poop, really. That was a hell of a-


[User] Please transcribe this text

[Assistant] That. We can mercy rule them if we don't give up a frickin' run.


[User] Please transcribe this text

[Assistant] Oh yeaah, of course. Oh yeaah, thank you! Holy shit, that's our best pitch though. I don't understand why that was an exclamation point though. Oh boy.


[User] Please transcribe this text

[Assistant] No effort. Zero fucking effort from goddamn black hair out there.


[User] Please transcribe this text

[Assistant] Wow! That one got the crowd going. That was an incredible catch.


[User] Please transcribe this text

[Assistant] What am I deserving to do exclamation points right now? Your mom's a ball.


[User] Please transcribe this text

[Assistant] Okay Maria, are you gonna swing at something or just stare at it all day?


[User] Please transcribe this text

[Assistant] That's a surprise I didn't choke. Thank god we gave up that one run though, now we're not done. We get to play a whole nother inning instead of Mercy ruling. I'm gonna have to replace this Wii Remote. Look at this Wii Remote.


[User] Please transcribe this text

[Assistant] Oh god, that's even worse. Something's interfering with it, I don't know what. Look at this, look at this.


[User] Please transcribe this text

[Assistant] Something, something is going wrong with my remotes right now and I don't understand it. Oh, okay, sensor bar, we just got an out by the way from that.


[User] Please transcribe this text

[Assistant] My sensor bar is fucking slippery as shit, so it just keeps on-


[User] Please transcribe this text

[Assistant] We should be getting insta-like hits, every time. We've got her to the point that all she does is exclamation point. I'm being nerf- NO, GET IT! Thank god.


[User] Please transcribe this text

[Assistant] Thank god she, she for whatever reason didn't feel like just grabbing that as she was right next to it.


[User] Please transcribe this text

[Assistant] I- I still have no idea what's going on with my- my Wii. Like, detecting my remote. It is not good at all today.


[User] Please transcribe this text

[Assistant] What an effort. Now we cannot choke a nine run lead. Surely, right?


[User] Please transcribe this text

[Assistant] Thank you. You have a reputation for gering it, but I appreciate you not.


[User] Please transcribe this text

[Assistant] Once Pierre cranked a homer though, I was thinking we might be screwed.


[User] Please transcribe this text

[Assistant] We might have the biggest upset of all time. See how we're already close to like going off the screen? Off the chart? With our line?


[User] Please transcribe this text

[Assistant] I wanna get up here. Like your bar will literally go up here.


[User] Please transcribe this text

[Assistant] That's where I wanna be. That was a good game though. I haven't played baseball in a long time. And I crushed it. But now we gotta play A.


[User] Please transcribe this text

[Assistant] What blade of grass did that ball touch? And let me personally thank it. I don't like A. I don't understand what A is.


[User] Please transcribe this text

[Assistant] I, hey, hey, even more now. We need to watch it here. They're, they're on something. Gonna have to call, oh yeaah, here we go. And I'm off something.


[User] Please transcribe this text

[Assistant] I just, I'm not swinging at these 100 mile an hour balls that grandma is apparently throwing here. I don't buy it.


[User] Please transcribe this text

[Assistant] Can we do anything other than hit it into the dirt? She's throwing nothing but splitters suddenly. Leave it to Matt. Uuuh, nevermind. Matt, you fucked us. I, I, you know, I'm holding- OH MY GOD! We're two, what, three games in.


[User] Please transcribe this text

[Assistant] I've seen my team exclamation point it, what, five times now? I've seen their teams do it zero.


[User] Please transcribe this text

[Assistant] I like David. I like David. I like playing David a lot.


[User] Please transcribe this text

[Assistant] You can't expect me to take this game seriously when my Wii remote's not being taken seriously right now.


[User] Please transcribe this text

[Assistant] Even after our errors there, we, we, we still made it through. I'm proud of my team.


[User] Please transcribe this text

[Assistant] Okay, that is really, uh, thank you for the attempt. Oh yeah. Oh yeah, thank you, infielder, for jumping six miles in the air to try to get it. And then, no thank you to Condom, who didn't even try to dive for it.


[User] Please transcribe this text

[Assistant] Check this man's glasses, they're clearly programmed with some sort of ball finding device that is making him not swing at anything. Oh yeaah, okay.


[User] Please transcribe this text

[Assistant] My team also doesn't know angles. See, this is how I'm used to baseball going. Which is why I was really surprised we won the first game almost 10 to 1. It was 10 to 3 at the end, but it easily could have been 10 to 1.


[User] Please transcribe this text

[Assistant] Oh my god, I had to get another weird- I had to get the beep remote back connected so I could pause the game. We had the easiest-


[User] Please transcribe this text

[Assistant] And we swing five minutes after it passes us. Not because I swung then, but because something's wrong.


[User] Please transcribe this text

[Assistant] Well, I'm just gonna turn off my Wii and turn it back on again. That game is now, that game is now lost. We are actually in a good position in that game. Maybe, just maybe my fit of anger and restarting the Wii.


[User] Please transcribe this text

[Assistant] I need to fix this now. Now that I've played Mr. Technician, I think I got this game working again. I think the Wii Remote's fixed too. It looks...


[User] Please transcribe this text

[Assistant] We'll see. I'm probably gonna have to stop playing before I lose my shit again.


[User] Please transcribe this text

[Assistant] Okay, that wasn't out of the park, but it was very close. This is exactly how we started against David last time. First pitch, home run.


[User] Please transcribe this text

[Assistant] I shouldn't have hit that. But if they're anything like my teammates, they won't get under it. No, no, no. They're not like my teammates that are just dilly-dallying around as the balls hang in there. They actually get under it. Wow team, maybe you should take notes.


[User] Please transcribe this text

[Assistant] I question myself sometimes. I'm a nice wing at that though.


[User] Please transcribe this text

[Assistant] I like Maria. Maria is my favorite. No, nevermind. She's a bitch. Like all the rest. Right? I already gave up a run. Two on the bases. Zero outs. We're doing nothing!


[User] Please transcribe this text

[Assistant] Thank you, team. They are so good right now. It's unbelievable.


[User] Please transcribe this text

[Assistant] I don't know, I try, I try to go from blistering fast to slow to switch up because they won't swing at ANYTHING! I'm trying every freaking combination of buttons. Thank you. Now that we're down by three runs, at this pace we only have to score


[User] Please transcribe this text

[Assistant] Wait, now we have to catch up three plus score another five to make up to, to let them have five runs.


[User] Please transcribe this text

[Assistant] Their players practically teleport under the ball. My players practically glitch out when they're trying to run under the ball.


[User] Please transcribe this text

[Assistant] It's- no. We're done. We're done! We're done! We're done! We're done! We're done! That's enough baseball today! We're done!


[User] Please transcribe this text

[Assistant] Excuse me. Father.


[User] Please transcribe this text

[Assistant] This thing, the little book here and the couple pens I always carry, I carry to


[User] Please transcribe this text

[Assistant] So I'm never bored and I can always do something. I can always, uh, find some downtime and work.


[User] Please transcribe this text

[Assistant] I think so far everything has been managed with great intelligence.


[User] Please transcribe this text

[Assistant] Oh well. Left for farts.


[User] Please transcribe this text

[Assistant] I got it! I got it!


[User] Please transcribe this text

[Assistant] Kefir grains basically consume, you're right, the sugar in, uh, dairy, lactose. However, these are slightly different kefir grains, it's water kefir, and therefore they


[User] Please transcribe this text

[Assistant] Eat, digest the other sugars, so natural sugars from fruit, and therefore you can make fruit-based, and I think this one started as apple juice, kefir. Same process, it's eating the sugars and turning it into acid. A bit like kombucha. A bit like kombucha, but instead of a scoby, grains.


[User] Please transcribe this text

[Assistant] So I think the logic behind it is it is a soft drink that is low in sugar, very low in calories, we're talking single digit calories per bottle, but has all the same goodness and the effervescence and the deliciousness that you want from something.


[User] Please transcribe this text

[Assistant] Subject to price of course, how much per bottle? That's a 330ml bottle.


[User] Please transcribe this text

[Assistant] Well Jay, we sourced all of these from a vegan butcher. Rudy's is based in London and it is an all vegan diner and butcher and they specialise in taking American classics and turning them vegan.


[User] Please transcribe this text

[Assistant] Very deliberately, we have chosen three sandwiches that are typically quite meat.


[User] Please transcribe this text

[Assistant] Heavy. And or cheese heavy. However, obviously no animal products in any of these.


[User] Please transcribe this text

[Assistant] The bacon is made with cannellini bean, wheat gluten or seitan, beetroot, coconut oil, nutritional yeast, the list goes on. The black pudding, the same but with kidney beans. The sausage patty in there is mostly soy based with soy protein.


[User] Please transcribe this text

[Assistant] Typically all the flavours you would expect, but with their pastrami, and the pastrami's made from red kidney beans, and seitan, and a whole bunch of other things. Probably worth saying when I say a whole bunch of other things, the list of ingredients is extensive. We're talking 20 plus ingredients.


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[Assistant] And therefore heavily or ultra-processed for sure, but most of it is vegetable protein in the form of legumes, beans, chickpeas, seitan, cashew nuts, soy, that kind of stuff.


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[Assistant] Which is 100 grams, so as a direct comparison, kilo for kilo, about twice as expensive as a higher-end supermarket smoked bacon.


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[Assistant] Clock it, log it, digest it, and plan.


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[Assistant] This is something we've seen pop up, chickpea ice cream. We couldn't actually get hold of any, so we made some.


[User] Please transcribe this text

[Assistant] The association of vegan food with health, I don't think is in itself healthy. And if you look at some of the examples we had recently, we did vegan fish and chips, with banana blossom, we did vegan KFC burger, uhm, with seitan and beans, you know, these are fast foods and fried foods, these are just vegan alternatives.


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[Assistant] Papa, all I ask is that you return home safely.


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[Assistant] Tell me, Beast. Who are you? Who are your parents?


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[Assistant] Believe me, a change of name will make my stay here more tolerable.


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[Assistant] Well, you needn't tell me who does your gardening. It's just that I would love to learn their secrets.


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[Assistant] Oh no. I just don't want to trample on any of the elves who do you got me.


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[Assistant] Are you laughing at me Lord Magic? Don't you believe in elves? Everyone in my family does.


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[Assistant] Yes, but you'd be hard pressed to find anything else.


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[Assistant] My schooling ended when my father lost his fortune and we moved to the country. I tend a tiny plot of herbs and have a few rose bushes.


[User] Please transcribe this text

[Assistant] I can tell the difference between a Caucasian rose and a Chinese rose merely by scent, but I could not tell you where to find China or Caucasia on a map.


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[Assistant] No, because I see your pain. Why are you so sad?


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[Assistant] As I see your pain, why are you so sad?


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[Assistant] Gentle eyes. We promise to speak only the truth here.


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[Assistant] Never mind, close your eyes. Now count to ten slowly. And don't think.


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[Assistant] Well if you wish. But this time I shall hide much better. Because you're not entirely to be trusted. Close your eyes. And don't he.


[User] Please transcribe this text

[Assistant] Oh good, uhm, two ropes, a plank for a seat, and a lovely tree to hang it from.


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[Assistant] You said you would not apologize. Please, do as you say.


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[Assistant] You may do as you please, but I'm going home.


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[Assistant] Papa, may I be excused? I would like to work in my garden for a bit and prune the roses.


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[Assistant] You're nearly a grown man. You're free to do as you choose.


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[Assistant] Hmm. Yes. I'm afraid I can no longer live without magic.


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[Assistant] I miss his courtesy, his wisdom, his kindness. That's the kind of magic I feel I can no longer live without.


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[Assistant] I think it's better that you don't know. I shall simply leave as I came. I don't want to hurt them, necklace. It's best that they think some enchantment took me away.


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[Assistant] Because if you hold it one moment longer, you will be transported to the Beast's Castle.


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[Assistant] What was the color of the rose that Beast gave me the day I left?


[User] Please transcribe this text

[Assistant] Indeed. Does that mean then that we shall quarrel? Wound each other? Be jealous? Will we lie? Break our vows? Never. No. Beast would never lie to me that way.


[User] Please transcribe this text

[Assistant] As you can see, we're going to be focusing on how you can get this view shop button. And when people click into it, they can have this feature and this feature. That's what we're going to be talking about today.


[User] Please transcribe this text

[Assistant] So this is the experience that we want to deliver. People come into here.


[User] Please transcribe this text

[Assistant] They click into view shop. This is what appears. They click into an item. This is what appears. That's...


[User] Please transcribe this text

[Assistant] How we want to set it up so then that way people can order right there and then.


[User] Please transcribe this text

[Assistant] In a simple four click format people can order from you.


[User] Please transcribe this text

[Assistant] That's the experience we're going to be set for setting up for you.


[User] Please transcribe this text

[Assistant] Do we want this feature? It is because it allows you to integrate your product catalog into your Instagram profile.


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[Assistant] After that you can actually promote your items on your Instagram post, your stories, your explore tab.


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[Assistant] All of this linking to your Instagram shop, making it super seamless.


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[Assistant] That's the reason why I think it is highly worthwhile for you to subscribe into a paid Instagram shop.


[User] Please transcribe this text

[Assistant] A Facebook page for your Instagram food business. Probably an hour to make.


[User] Please transcribe this text

[Assistant] And a Shopify page, which takes you roughly five to eight hours to make. Once you have these ingredients prepared, now you can actually link them up. You're going to link up your Instagram business page with your Facebook page as well.


[User] Please transcribe this text

[Assistant] Then you're going to link up your Shopify with your Facebook page and then you're going to link up your Shopify page with your Instagram page. And that's when you can have this view shop button. It might sound a little bit more complicated right now. However, once you set these three things up, linking them up is


[User] Please transcribe this text

[Assistant] As possible for you to understand. Ingredients to prepare. Let's dive into the Instagram business profile, Facebook page and Shopify page.


[User] Please transcribe this text

[Assistant] Instagram business profile. Just go to your Instagram settings and then.


[User] Please transcribe this text

[Assistant] Click onto the switch to professional accounts, select the business option, and voila, you're done. Five minutes and you're good to go.


[User] Please transcribe this text

[Assistant] Next up, your Facebook page. Go on Facebook. Create a Facebook page.


[User] Please transcribe this text

[Assistant] And add in all your business information, add in this, uh, this header, add in your logo, put in your descriptions, put on your website.


[User] Please transcribe this text

[Assistant] And that's how you set up your Facebook page. Okay. So quite simple, spend an hour or so on it. Doesn't need to be too thorough just to make sure you have that storefront. Pro tip is that once you set that up, it is time to invite your friends and family to like the page for you.


[User] Please transcribe this text

[Assistant] So then that way you can gain some initial traction so then it doesn't look like a scammy page. The next ingredient to prepare is a Shopify page.


[User] Please transcribe this text

[Assistant] Create your Shopify homepage and add in your product guys. Follow the steps in the tutorial setup to set up your Shopify website. These are the links to do so and I have included into the resources below. There's no point of me walking you through how specifically to do that when there are all these blogs.


[User] Please transcribe this text

[Assistant] All three of those ingredients, it is time for you to link them all up.


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[Assistant] Link your Instagram page with your Facebook page. Link Shopify with both your Facebook and Instagram page.


[User] Please transcribe this text

[Assistant] First step in doing so is to go to your Instagram settings, go to accounts, linked accounts, and to link your business profile to your Facebook page. It is very, very simple to do that.


[User] Please transcribe this text

[Assistant] Once again, follow this instructions and you're going to be able to link it up properly. Okay. You must be a Facebook page admin. So make sure that if you are getting someone else to help you to do so, make sure you give them the Facebook page admin. But if you're doing everything yourself, it should be quite simple.


[User] Please transcribe this text

[Assistant] Click here and log in and you're going to be able to connect your Instagram with your Facebook page.


[User] Please transcribe this text

[Assistant] Follow the instructions from this website in order for you to actually link.


[User] Please transcribe this text

[Assistant] Your Shopify to your Facebook page. Once again, this should take you no more than 10, 20 minutes to do so.


[User] Please transcribe this text

[Assistant] You're Shopify to your Instagram account. Once those are all linked, you're going to be able to have this experience to set up your shop and for people to click into the shop and for this to show up. And when they click onto this, they can have this visit the website and they can actually order.


[User] Please transcribe this text

[Assistant] Please, please, please feel free to rewatch this whole lesson because this should be quite simple for you to understand. It's just a lot of different components. It is now your turn.


[User] Please transcribe this text

[Assistant] To prepare your ingredients, your Instagram business profile, your Facebook page, and a Shopify page. Follow the three step recipe to link up your accounts. From your Instagram page to your Facebook page, Shopify to your Facebook page, and your Shopify to your Instagram business page.


[User] Please transcribe this text

[Assistant] Well, much obliged for the water. And good luck with your fishes.


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[Assistant] Without bothering to check the result Andrea pointed to the next target with the aiming lens.


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[Assistant] The magic power consumed in precise shooting varied with the difficulty of the shooting.


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[Assistant] The more difficult the target was, the more magic power she had to consume.


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[Assistant] That meant that her magic power, which could have been sufficient for arrow or stone shooting for two to four hours, would be exhausted in about eight minutes from shooting from such a long distance. She had to shoot as many bullets as she could. Her ability responded to her will. Andrea felt that she was dancing rather than shooting.


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[Assistant] The adjustment of the gun, the rising and falling of her arm, and every movement of her body seemed to integrate with the world in harmony.


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[Assistant] Shooting at every breath, Andrea soon used up 10 bullets and immediately changed to a new clip.


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[Assistant] The whole process was as smooth as water and as proficient as a well prepared performance.


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[Assistant] However, it was not so easy for the people on the Stonewall.


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[Assistant] Death was flying to Appen's army within seconds, while they were completely unaware of it.


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[Assistant] The knight who was commanding the mercenaries to adjust the crossbow machines became the first victim.


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[Assistant] The shooting range of these crossbow machines was about 120 meters, and the cast iron arrows would easily penetrate the large shields and armors of knights within 30 meters.


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[Assistant] This weapon could be described as the best weapon for defending the palace in terms of a normal attack.


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[Assistant] In order to withstand the monster soldiers of extraordinary strength, Appenet ordered them to move all inventory from the warehouse on the wall.


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[Assistant] Eight crossbow machines were arranged on the wall segment that directly faced Rising Sun Avenue.


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[Assistant] Considering the limited width of the avenue, any invader, no matter how strong he was, could not hide from the intensive shooting of the Iron Crossbows.


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[Assistant] The mercenaries heard a muffled sound and saw the knight who had been giving orders fall silently back onto the ground with his chest sunk in. Someone is attacking. A warning immediately came from the top of the wall.


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[Assistant] They drew their swords, but failed to find out where the attack came from.


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[Assistant] Followed by the second and third attack, Death raised his scythe again and again.


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[Assistant] The guards constantly fell while the enemy did not appear and inexplicable fear overwhelmed them.


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[Assistant] Death was not surprising for these people who had been used to fighting all their life. But it was a different story when they could do nothing but wait for death.


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[Assistant] Especially for a few mercenaries who were confident about their abilities.


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[Assistant] They found that their increasingly proficient skills were useless as their opponent did not even give them a chance to Convertac.


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[Assistant] Clumsy servants or experienced knights were no different in the face of such an unexpected attack.


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[Assistant] They saw their enemies in the Hermes battle at least, while they were just waiting for the call of death this time.


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[Assistant] Less than a minute later, over 20 people were killed on the wall segment.


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[Assistant] Upon hearing the painful moaning of the wounded, most people were about to collapse. Look for cover. There is a witch.


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[Assistant] Just then, the Order of the Chief Knight somewhat woke them up. As long as we hide behind the battlements and big logs, we can avoid the attack.


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[Assistant] Bring out the God's punishment arrows and shoot toward any possible direction to force that damn witch out. Andrea also noticed the change on the stone wall.


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[Assistant] A knight wearing golden lined armor seemed to be commanding their actions.


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[Assistant] Those guards were moving closer to him and hiding behind various obstacles while shooting arrows without targets. Some of them even threw arrows by hands. Their purpose was obvious. The actions of the enemy were not threatening to her at all.


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[Assistant] However, if she could not completely destroy the enemy, it would hinder the actions of the God's punishment witches in the siege.


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[Assistant] The position of the commander was a blind zone for Andrea from the Belfry.


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[Assistant] She could faintly see his arm, which was out of the battlement, and a small part of his helmet.


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[Assistant] Usually, it was impossible for her to hit such a target. However, she had a different idea since watching the first ARMY's canon show.


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[Assistant] Andrea constantly drew the magic power to fill her arms.


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[Assistant] Pushed by the invisible hands, the gun in her hand continued to rise up until it pointed to the sky.


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[Assistant] When the familiar feeling of harmony appeared again, she pulled the trigger without hesitation.


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[Assistant] At that moment Andrea felt that she saw the trajectory of the bullet.


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[Assistant] It was thrown high in the air, but did not lose all speed after passing the apex.


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[Assistant] On the contrary, it dived toward the target with forward momentum.


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[Assistant] Although the distance between the two was about 600 meters, it had flown a longer distance in the air, so the time for this bullet was much longer than the previous one she shot.


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[Assistant] Then she lowered the barrel aimed at the knight's helmet and fired.


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[Assistant] The second bullet arrived earlier and accurately hit the edge of the battlement.


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[Assistant] Bricks immediately splashed and a deformed bullet spun to hit the upper part of the helmet and knocked it away.


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[Assistant] The huge impact made the knight lose his balance and fall forward and exposed his soft neck.


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[Assistant] At the same time, the first bullet arrived and, as expected, pierced into his skin from an angle and fractured his cervical vertebra into several sections. The Chief Knight had no time to react.


[User] Please transcribe this text

[Assistant] He just heard a muffled sound behind his head, felt the chill on his neck, and then lost consciousness.


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[Assistant] This shot almost consumed all the rest magic power of Andrea.


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[Assistant] A strong sense of dizziness overwhelmed her and the consequence of excessive consumption made her hands tremble. She even had difficulty holding the butt of the gun.


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[Assistant] Nevertheless, the fall of the Chief Knight also became the last straw for the mercenaries.


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[Assistant] The defensive line of the stone wall immediately collapsed.


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[Assistant] Everyone turned around and ran toward the stairs, lest they would become the next target of death.


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[Assistant] No one even paid any attention to the big logs, hot oil, and crossbow machines, which were seen by the God's punishment witches.


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[Assistant] Woo woo woo woo woo woo. The horn for attacking was sounded.


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[Assistant] Alaina, who was in the siege team, rushed in first.


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[Assistant] In addition to carrying her commonly used tools, she brought a bundle of hemp rope in her hand. Just as she approached the foot of the wall, she threw the rope.


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[Assistant] At the end of the rope she had tightly tied a square shaped hook.


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[Assistant] A moment later, there were several hanging cables available for climbing the stone wall.


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[Assistant] The stone wall, which was about five meters high, was difficult for common people. But in the eyes of the gods' punishment witches, it was a fence which they could directly climb over.


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[Assistant] Elena casually climbed to the top of the wall via the hemp rope only to find out the castle district had been a mess.


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[Assistant] The supervising and preparatory teams arranged by Appen did not play their roles.


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[Assistant] Just when the guards on the stone wall were defeated, the guards of Ur-Oluwashi suddenly drew their swords toward the guard team of the Kingdom of Dawn.


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[Assistant] The guards on the stone wall wanted to run away as soon as possible, so the three parties created chaos on the spot.


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[Assistant] Alaina raised her lips, took out the huge sword on her back, and leaped over the stone wall. No one could withstand her frontal blows.


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[Assistant] As long as they were included in the range of the giant sword, the enemies were severely wounded or killed. By her power alone, she created a path in the crowd.


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[Assistant] As the god's punishment witches joined the battlefield, Earl Quinn had the situation well in hand.


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[Assistant] Appen, who was watching the battle from the top of the castle, furiously said, I'm going to kill him and his son. Ministers, where are my ministers?


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[Assistant] Your Majesty, Lord Kurlong said earlier that he was going to handle some things. After a while, the Chief Guard hesitated to reply, but I think he may not be back. What did you say? The King of Dawn turned suddenly and gasped at the latter. You agreed, said the Chief Guard hesitantly.


[User] Please transcribe this text

[Assistant] In addition to Lord Krillong, Lord Wyrent and Gold Hourglass Neel also left. Your Majesty, everyone is gone but me.


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[Assistant] It was only then that Appen noticed that only the Chief Guard and several servants were in the huge hall.


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[Assistant] He threw the scepter to the ground and said, gnashing his teeth, traitors, traitors. My reign is ruined by these traitors.


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[Assistant] He consented to their departure indeed, but what were their reasons?


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[Assistant] One said that he was going to check the defense of the stone wall. Another said that he was going to the inner court to supervise the maids preparing for war.


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[Assistant] Those were originally their duty, but they now turned out to be their excuses.


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[Assistant] Your Majesty, those cowards are bound to be punished in the end. But the immediate priority now is to withdraw from here as soon as possible.


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[Assistant] The chief guard approached and said, those mercenaries won't win us much time.


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[Assistant] Even the Imperial Guards could not withstand the enemy for over an hour. It'll be too late then. No, I want to see the traitors be punished.


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[Assistant] Said Appen, pushing the chief guard away, go to the underground cell and bring the head of Otto Lewashi to me. But, this is your king's command. He yelled at the top of his voice.


[User] Please transcribe this text

[Assistant] Yes, Your Majesty, the Chief Guard took a step back and bowed.


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[Assistant] After his only subordinate left, Appen felt that his fingers were slightly trembling, and his eyes seemed to swell, and even his vision was covered by a layer of light red.


[User] Please transcribe this text

[Assistant] When the mercenaries on the top of the city wall fled for no reason and gave up the stone wall, his failure had been assured. The rebellion of Uruguay was insignificant.


[User] Please transcribe this text

[Assistant] Why would Horford Quinn get the complete support of the other two families? He was unable to explain it.


[User] Please transcribe this text

[Assistant] Though the three families in the city of Glow were seen as a whole, their respective interests were not exactly the same.


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[Assistant] In this challenge which might cost their lives, he could not figure out who would be trusted by them to such a degree.


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[Assistant] Appen found that he did not know the city or the three families so deeply as he had thought. In the end, he did not get the answer of the Chief Guard.


[User] Please transcribe this text

[Assistant] A team of warriors, whom he had never seen before, opened the hall door.


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[Assistant] Their weapons were still dripping blood, and their armor was also splashed with bloodstains.


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[Assistant] However, there was no trace of exhaustion on their face.


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[Assistant] They were so relaxed, as if they had just gone through a street fight.


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[Assistant] The chief guard declared that they could resist for an hour, but in reality, they failed to even hold out for a quarter of an hour. The rebelon had overwhelming superiority.


[User] Please transcribe this text

[Assistant] Then he saw the usurper, Horford Quinn, who had vowed to always support the Moya family.


[User] Please transcribe this text

[Assistant] In addition to Earl Quinn, two other traders entered the hall together, along with their successors, Oro Tokat and Otto Lewashi.


[User] Please transcribe this text

[Assistant] When he saw the ladder, Appen knew that the revenge he wanted had become impossible.


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[Assistant] Why is this? Are you surprised as to why Otto is still alive?


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[Assistant] Oro interrupted him. It's not too hard to hide two warriors in the secret path of the palace. Not to mention that the ordinary iron gates and fences could not stop their actions.


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[Assistant] As for how they got into the castle district, you'd have to go to ask the guards.


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[Assistant] I don't think those guards, in their panic, may care much about the members of an acrobatics troop. Appen's pupils suddenly shrank.


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[Assistant] If that's not a bluff, does that mean that they could have entered my bedroom at any time? Yes, just as what you think.


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[Assistant] Oro spread out his hands and said, the king of Grey Castle needed to create a tribute, otherwise, you would have already been beheaded by them.


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[Assistant] To be honest, I'm so disappointed in you, your majesty. I thought you imprisoned Otto just out of anger.


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[Assistant] I had never expected that you would use him to threaten Earl Lewashi and even intend to kill him. He sighed and continued to say, I had thought.


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[Assistant] Even if we're not friends anymore, you would not forget the days when we were. You mean Roland Wimbledon? So that was all his doing?


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[Assistant] Appen did not care about the second half of Oro's words, as King of Greycastle mentioned by Oro had attracted all his attention. He said, Do you know what you're doing? You're helping a demon.


[User] Please transcribe this text

[Assistant] You not only betrayed your ancestors vows, but also sacrificed your kingdom and subjects to him. You're foolish. He pointed angrily to Horford Quinn and you. Do you think you can really sit on this throne? In fact, you're just a puppet. Haven't you thought about it? Why did he start the mutiny?


[User] Please transcribe this text

[Assistant] Why did he fight against me if he were not attempting to annex the Kingdom of Dawn?


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[Assistant] Don't forget, since these people can easily overthrow me today, they'll easily push you into the abyss one day.


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[Assistant] You're wrong. Suddenly a woman from outside said. He did it for two reasons. To save Otto and to protect the witches.


[User] Please transcribe this text

[Assistant] Absurd, Appen was ready to scold her presumption and ignorance, but his voice was suddenly stuck in his throat. You, you're.


[User] Please transcribe this text

[Assistant] She seemed to be very weak and could not stand up without the help of others. Even so her superior beauty could not be hidden.


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[Assistant] For a second, all the questions in his mind were answered.


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[Assistant] The reason why the Tokats supported Earl Quinn and why Earl Lwashi took risks. Indeed, there was one person who could gain the trust of both families at the same time. That was because both of their children had been in love with her.


[User] Please transcribe this text

[Assistant] The anger in his heart was quenched and replaced by desperation all of a sudden. He murmured for a moment and finally asked, why? Why did you finally choose them and not me?


[User] Please transcribe this text

[Assistant] If I'm doomed to be defeated by Roland Wimbledon, why did you also betray me? I can give you more than he does.


[User] Please transcribe this text

[Assistant] If it were not for that accident, you would have ruled the kingdom with me.


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[Assistant] Andrea seemed to read his mind and replied, because I'm a witch, Abban. A fallen who deserves to be killed in your mind.


[User] Please transcribe this text

[Assistant] In other words, that accident years ago was a life specifically set up by Earl Quinn to cover up the fact that his daughter became a witch. Andrea was a demonic fallen one all along. He never knew.


[User] Please transcribe this text

[Assistant] But the fact remained that the ones who murdered his father and brought destruction to his kingdom were indeed witches.


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[Assistant] If it weren't for their unimaginable abilities, the current situation would be totally different. His wish to avenge his father, was it wrong?


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[Assistant] The two thoughts were colliding in Appens mind, bringing him an unbearable headache. Your rule ends now. Earl Quinn came forward.


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[Assistant] No matter what, the Kingdom of Dawn must not continue to act according to your will.


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[Assistant] The witches will get their deserved recognition and obtain the same status and rights as the common people.


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[Assistant] As for you, what, do you intend to kill a member of the royal family? Appin glared at him with a ferocious scowl. Did you forget your oath to your ancestors? Did you forget my family name? Answer me, Horford Quinn.


[User] Please transcribe this text

[Assistant] His sharp questioning made everyone who was present retreat a step in fear, except for Andrea and the God's punishment witches.


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[Assistant] I am a direct descendant of the Royal House of Moya, and the one and only heir.


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[Assistant] Even if I am no longer a king, you will carry the title of Kingslayer from now on. He shouted, the Order of the Kingdom of Dawn will be destroyed by you and the great noble families will never trust you again.


[User] Please transcribe this text

[Assistant] I won't kill you, Horford's side. If my family wasn't at stake, I would have never chosen to act like this.


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[Assistant] But you need to swear to leave the Kingdom of Dawn forever and never come back.


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[Assistant] This way, you can take whoever you want with you and leave the King's City.


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[Assistant] The only alternative is that I imprison you in the castle's dungeon, just like what you did to the eldest son of the Lewashi family. Is this decision supported by all three big families? The Tokat family doesn't have any objections. Earl Tokat touched his chest. The Lewashi family agrees as well. Earl Lewashi continued.


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[Assistant] We aren't as cold-hearted as you," Oro Tokat said. Fortunately, your order was one step too late, or else. Enough. Otto interrupted him. Say no more. Such a group of considerate people.


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[Assistant] But when faced with the huge differences in power, how long will you be able to sustain such a ridiculous friendship?


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[Assistant] Appen gazed at the nobles in the hall coldly and thought for a long time before saying, I choose the first option.


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[Assistant] He could not let himself be imprisoned in a place where he might never see daylight again.


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[Assistant] As long as he was alive, he would always be a member of the royalty of the Kingdom of Dawn.