122 - Building Conversational AI's with Joe Bradley

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Hey everybody!
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So, one of my most fond memories is playing this game, this really esoteric game called Essex, which I think is named after a place in the UK. But it was this game where it was like Zork, if people are familiar with that, but it was one of these text-based story games where you would type, you know, go north, go west. And the thing that amazed me—I mean, I was maybe seven or eight years old—the thing that amazed me was that I could, I felt like I could just say anything to these computer players and that they would respond with really interesting things. And it ran on a single floppy disk. So, you know, honestly, to this day, it kind of blows my mind. But as a child, it just completely blew my mind. I mean, I was wondering if there was a real person somehow involved, like, you know, at real time. And I've always been really interested in conversational AI. And I feel like we're all just exceptionally lucky to have Joe Bradley here, who is the Chief Scientist at LivePerson, who is an expert in this field and really going to talk to us about how conversational AI kind of works, a bit of the history behind it, and, you know, how kind of LivePerson and other folks do it today. So thanks so much for coming on the show, Joe.
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Hey, thanks for having me. I'm really excited to be here and even more excited about the fact that you just brought up Zork. For me, that was the one. It wasn't the Essex or that I didn't know about. But yeah, I spent hours doing that as a kid. And it's funny, too. I have children myself now. And I see them, you know, just last week I went downstairs and they're not playing Zork, right? But they're playing the equivalent on, you know, the Amazon device on the Alexa. Somebody's coded up a little, like, exploration game and now they're talking to that. But it's essentially the same interface, just with the, you know, voice instead of text. And it's still fun for them, which I think is, like, really interesting. Even in this age of, like, hey, does everything have to be these, like, cool-looking graphics? And, you know, what's the UX got to be like? There's still something powerful about just talking to a machine, like you can, like you want to, and in a way that's easy, in a way that's natural for you. And then having the thing respond and tell you a story, right? That's cool, still.
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Yeah, I think that, you know, when it's abstract like that, you kind of, you know, your mind fills in those gaps and fills them in in a way that's, like, really interesting and pleasing to you, right? And so that's kind of the, I think, who's Scott McLeod, I want to say, if I'm getting that right. But he wrote a book about understanding comics. And he explains that the reason why comics are, you know, originally they're meant to be written really quickly. But another reason why comics work so well as a medium is that they're so abstract that when you watch, for example, Dilbert, you kind of put yourself into Dilbert versus if Dilbert was photorealistic, you wouldn't really be able to do that as easily. And so, yeah, games like Zork and Essex are, they do that. Your mind fills in all those gaps.
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Yeah, they hit this spot for us that's like, you know, if you think about how evolved we are to have conversation, right? And how important conversation is to us as like as human beings, right? It's the fundamental way in which, you know, we created efficiency, resources, wealth, right? All the things that we have today is that are founded on the abstractions that make conversational conversations possible, right? Language itself and then the ability to have those in a two-way or multi-way dialogue so that you can build more than just what's inside your own mind, right? So I think in some ways it's like surprising when I look at my kids playing Zork on that device. And in other ways it's, you know, it's like, oh no, wait, we're evolved to do this. Like this is what we want. We want to build stories. And now it's just interesting. It's kind of interesting that the last 30, 40 years we've begun to be able to do that with inanimate objects, right? And that like we sort of know and we sort of don't care and it's sort of still fun.
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Yeah, it's amazing. So I want to dive into, I'd love to know how these things, how Zork works and then also fast forwarding the future. But before we get into the tech side of it, you know, what is kind of your background and what led you to building conversational AI at LivePerson? What's that story like?
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Yeah, it's a little, I don't want to say topsy-turvy because it's not a bad story in my mind, but it's definitely a little all over the place. So I, you know, if you go back far enough, you start getting to, you know, like an interest in opera singing and, you know, in a major in English Literature and a whole bunch of stuff that like doesn't fit very well with, with where I am today, at least not in most people's minds. So for me, you know, it's like, I tend to find, like have moments of like five year kind of moments in my life where I get really, really passionate about something. And those, those end up like being a stepping stone to kind of somewhere else. And so when I was, you know, at undergraduate school and, and starting, you know, working as a classroom teacher in San Francisco, I got reminded about how, you know, I was teaching math and science to, to seventh graders, which is by the way, the hardest job I've ever done by a factor of 10. You know,
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It sounds difficult. I remember middle school as being one of the hardest points. I've only ever been in one fist fight in my entire life, other than martial arts, which doesn't count. And it was in middle school because, somebody—Oh, I was playing basketball and I blocked somebody because I'm a very tall, but I'm not, I'm not particularly athletic, but I do have the height and I blocked someone and he was upset. And actually I wouldn't really call it a fist fight. He just punched me in the face. And I was really upset about that. But yeah, I think middle school is extremely challenging age. And, you know, Patrick and I also have kids. And so that's, that's an age I'm not really looking forward to, but we'll have to get through it.
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Yeah. I mean, a couple of like, I will not forget the day one of my students—she was this probably about six foot tall, 12-year-old girl—looked at me and she said, 'I was like just getting started.' And I, so it's like a, you know, like I said, there was this undergrad English major who was like, 'I want to go be helpful. I want to go do something meaningful in a city.' And I just moved to San Francisco. So I got this job teaching. And I was a couple months in, I wasn't, I was not good at it yet. I hadn't figured it out. And she looks up at me and she says, 'Hey, Mr. Bradley, this class is just like WWF Smackdown.' And I like, I mean, I hung my head that day. Like it was, it was not like, cause she was right. Right. I was like, wow, this is, this is not good. I, and I mean, thankfully the institution of public school in San Francisco was very helpful. They brought me this mentor teacher who had all this knowledge about how to work with kids. It's really just like simple things that you'd never think of, right? Like don't talk about what one kid isn't doing well; stand next to them and talk to the kid who's doing it right and praise them. And then the other kid's going to suddenly look up and want to do the right thing. You know, all these sort of tricks of the trade that were just life-changing for me. But anyway, yeah. So to get back to your question, you know, I sort of rediscovered a love I had for math. I'd studied it a bit in college and I was doing teacher training on negative adding negative numbers or something like that. How do you teach a 12-year-old how to do that? And what are good visualizations and all these things? And it just brought back to me like, oh man, like I had so many questions and so much further. I wanted to take that—that led me back to school. So I went to school for a master's degree in mathematics at SF State University, San Francisco State University, part of the Cal State system, which, which I think deserves a plug because I feel extremely fortunate to have been able to go get an advanced degree in mathematics and pay something at the time, like 800 bucks a semester to do it.
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Oh my gosh. Amazing.
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It's more now. I know it's more now, but I think it's still a pretty good deal. And back then it's, you know, that's life-changing stuff, right? Like these are cultural institutions or, you know, that we have built for ourselves. That affect people's progress in life dramatically. And so that was a special, I think that's a special institution for me. That was a special time. You know, and that kind of led into physics, right? Like I was doing the math and I was like, well, I want to do something with this math as fun as the abstraction is. I've always kind of been attracted to abstraction. I want it, I want it to be good for something. So I ended up going up to University of Washington, moving up to Seattle and working on a PhD in physics. And that kind of led me to, you know, a lot, obviously more applied mathematics, obviously beginning to work in statistics much more deeply because I ended up doing a bunch of experimental work. And, you know, in addition to turning bolts and building instruments and firing X-rays and lasers at stuff, like you've got to go do the data analysis. And we started to think that there were some, you know, more advanced statistical analyses that would help us understand convolutions of data and all these things that, so that started, started to filter in. As I became a full-time scientist, I just found a lot more passion for that when I was working at the National Labs. And it was one like sort of component of a larger decision that led me to change tracks and end up kind of in, you know, in the industry I'm in today. And that was kind of by way of Amazon, right? So I sent all these resumes out because I was interested in machine learning. It's a live field. It's new. I think I have been reading the papers. I've been like, this is a fun time, you know, as opposed to physics, which is like hundreds of years old and everything's super sub-disciplined and, you know, very, very narrow and very, very tight. And it takes a long time to approach anything state of the art. I was like machine learning. Well, this is different, right? This is new. This is live. You can learn it, not quickly. It's not easy. I'm going to say that, but you can get to the front of the discipline much more effectively. And I think there's just a
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lot of ways in which knowledge is shared in the ML community and they're pioneering like this sort of academic research and shared code and all these things that wasn't really happening in physics that's starting to now.
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Yeah. I think I think you're seeing the same thing with economics. So I've always been really interested in economics and what you're seeing is ML start because ultimately economics is, is especially microeconomics is ultimately about people and the way they behave. And so, and so you need to, it's statistical by nature because we're not going to have a human brain processor. So we're going to be estimating that. And, and so, yeah, you're seeing ML make a ton of strides in that field. And I think at some point, ML will diffuse and just be a part of all of these fields. And ML, as we know it now will become more of like a core, you know, how do we make the learning really well? But I think we'll get to the point where maybe even conversational AI won't even be ML. It might be part of like a speech understanding or something like that.
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Yeah, I think that's right. I mean, I think if you really back up and think about what Machine Learning is, and even if you, even if you think about that in the context of Neural Nets, what are they really doing? They're taking these, like these qualitatively different kind of problems because they have such a high degree of dimensionality and such a weird set of correlations across the variables that describe them. And then they're trying to find these like really, really useful local potential minimum in them, right? Like these really, really useful like solutions, whether or not they're, you know, the perfect or the global, you know, I mean, I know there's a whole branch of ML that's devoted to finding, you know, convex problems and all that stuff, right? Like, but, but I think the way the field's evolving is we realize that like real problems are like way too dimensionally complex to solve in those exact ways or to, or to even approximate towards those global solutions. And we're all just okay with sort of these local solutions. But that's going to like, like any field that has anything approximating a real problem and most good fields do, it's going to benefit from, from that basic approach. I think, I mean, obviously creates new problems of like, well, how do you know if your answer is good enough and what is good enough mean? But for real problems, like those are, you know, questions you just have to cope with.
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Yeah. So, so you didn't have a CS, don't have a CS degree, correct?
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No, no. I'm a, like a research self-taught programmer at start anyway. And I got a little bit of a crash course about how bad a programmer that made me when I worked at Amazon for sure.
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So yeah, let's, let's go. Let's back up a tiny bit. How did you interview at Amazon? Cause this is something we get asked constantly. And, you know, someone says I have a background in physics or I have a background in economics, or I have a background in chemical engineering. And so, and I want to interview at Amazon. How did you get that right mindset to be able to do that?
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Yeah. So, so it was a case of like, there's a little bit of luck in there, but I also was intentional about how I did it. So I can't claim this will always work, but I think it was a relatively smart strategy. So I'll explain it a little bit. So I did, as I said, I sent out a bunch of resumes and heard very little back, right? It's like, who's this, you know, basically I think people would see that resume and be like, who's this national lab of business is like doing X-ray experiments. Like, yeah, get, get this thing off my desk is like, was kind of an easy response for, for most sane humans to have.
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Or sadly it's, it's even an AI. So a person didn't even have an opportunity to see the potential, right?
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Could be. I mean, we definitely are recruiters at live person, right? They look at a lot of resumes still, but, but I think there's search queries, you know, you gotta be smart about how you set up your resume. You want to have the right search terms on it, all this stuff. Like you gotta get past, you gotta get a human looking at it.
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That's true. Yep.
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And then what I did is I wrote, even though it wasn't asked for or common at the time, and it's probably less so now, I wrote what I thought was a thoughtful cover letter explaining what the heck I was doing there and why the heck I thought this made sense. And so I tried to break down my experience in particular. I, I spent, you know, like a while crafting this paragraph about how I had been doing these experiments in a national lab context where we'd go in and we'd have limited X-ray beam time and we'd sit down and, and we'd have, you know, 24 hours to conduct the experiment. And we had to be very thoughtful about how we planned it. We had to be very good at troubleshooting problems. We had to, you know, we had to really deliver in this, in this very tight, constrained environment, or we were not going to be successful. And my pitch was like, here's look at, look at what I've delivered. Here's the output, the output of these publications, this advance in this field. Right. So I tried to get, I didn't want to make that like a four page monologue. Right. That was like, had to whittle that down to, you know, to four or five good sentences. And there was some recruiter. I, I knew her at one point, I've forgotten her name now, because it's been quite a while. It'll probably come to me after the, after the call. But there was this woman who, who was a recruiter who saw it and read it and just thought like, she was just like, I think this could work. Right. I think we should talk to this guy. And I, I did talk to her, as I said, I talked to her, you know, later after I joined Amazon, I had a couple of conversations with her and she kind of relayed this to me that, that, that reading some of that text was important. It was an important part in her decision-making. It wasn't the only thing, of course, but I think that's smart to do. I think, you know, obviously you got to get past the, the sort of AI or the querying, all that stuff. But then you should remember there's a, if you remember, there's a human on the other end of this and they need me to make sense. If I'm not coming from what they see and what they expect, they need me to make sense of why my experience fits and, and how that story should go together. We're all narrative machines in the end. Right. So they need me to give them that narrative and, and take the time and the space in your resume or in a cover letter, however you do it to, to make sure that story is told and
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make sure that story is clear, but also make sure it's like super concise and tight. Like I can't make that point strongly enough. If you write well, you, you know, that also says something. And if you don't have a lot of attention, you should imagine you've got 30 seconds of attention. If you're lucky, how do I tell that story that quick?
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Yeah, absolutely. And so when you went to Amazon, were you working on conversational AI on day one, or is that something that you, a team that you transitioned to later?
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So I ended up doing, you know, a number of natural language applications at Amazon and at Nike subsequently. Right? A lot of it related to how people are, you know, like what interests, what kinds of interests they have in products, what's useful to them about products, things like that. What their website experience was like—a lot of analysis of text. And you know, I started building conversational AI with some of the experts we have at LivePerson. My real first foray into conversational AI was with LivePerson. Like that's where I began to build. And we brought on a lot of people. So I consider myself a little bit more of a generalist. You know, my background is more statistics, machine learning, et cetera. I obviously have learned a lot about Conversational AI in the last three, four years working at LP. But we of course have people that have spent their whole careers on it as well.
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Got it. Okay. So if you could kind of lay the land for us here, so there's Natural Language Processing, which I think—and please correct me here—is an umbrella that covers, let's say translation, maybe embeddings or semantic understanding of language. And conversationally, I would also be under that umbrella of NLP. Is that correct?
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Yeah. I think that most people would agree with that. It's like most designations like this, where there's always somebody who feels like, 'No, no, no, my semantic description of this field is more accurate.' But.
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Right.
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Yeah. But yeah, I mean, by and large NLP is like a big box, and inside that box goes the science of processing natural language, right? Like kind of quite literally, and conversationally, I—as a kind of processing of natural language, which is a very specific one, obviously a very important one we think. But yeah, I think that's fair. You also have an inside of Conversational AI, or inside of Natural Language Processing, you know, you have the subfield of Natural Language Understanding, right? Which is again very important to Conversational AI. And that's sort of this—you know, again, it's another one of these ones that like is what it says it is, right? So it is the science of trying to use algorithms to create what do we mean by understanding essentially a structured form of, or a structured interpretation of words of natural language that we use. You go from this unstructured text, you go from that format and sort of begin to categorize and adorn these natural language utterances with things that are meaningful to us in the abstract. Yeah.
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That makes sense. And then in the other direction, then you're talking about Natural Language Generation, where you say, 'I have this structure, this JSON, which has this information that I know somebody needs.' I, you know, the movie starts at this time, or this is when your pizza order is ready, and it has to get turned into text that people would feel natural to them.
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That's right. Yeah. You've got the sort of semantic box of metadata, however you get it—you know, wherever that arose from some system did some processing and said, 'Here's the semantics or the meaning of what I want you to say machine.' And then the NLG post portion, like basically input is that output is spoken language or written language or whatever it is. The other piece in the middle there that people can slice and dice in different ways, and it's still a very open sort of research question is really around all the dialogue management. Right? So you have the understanding, you have the text coming in—or the words coming in, and let's assume it's just written text. Cause then you can always kind of model the speech and the voice side at the other end of this pipeline. So you have texts coming in, you have this understanding capability whose job it is to decide what what it means that, what was said or what was said means. I guess it's the best way to say that.
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And then that comes in a structure now that interacts with the dialogue manager, or you know, and again, that can be a few pieces, but broadly speaking, dialogue manager's job is to take that information, understand it in the context of what's going on in the conversation up until now. And some managers do a lot of that and some managers do none of that, but functionally that's the responsibility of this piece of the system. And then to turn that into a next action. And then that next action gets passed out as like one element of that next—there may be lots of things that happen, right? It may go call an API and check your balance for your bank or whatever it is. But it's also gonna result very likely or most of the time in wanting to send you some text back. And so, in a system that has a decoupled NLG component, as you mentioned before, it will send back now this maybe this metadata blob and the NLG's job will be going to go and turn that into a language and thus kind of conversation. Right? So, I think are the like in big big animal letters, so to speak, those are kind of the big blocks here as far as a Conversational AI system goes. Yeah, that
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makes sense. That makes sense. So I think if I remember correctly, Zork didn't have conversation. It was more of like a pick-up sword hit, hit troll. I mean, it's been a long, long time since I played that, but there have been interactive fictions with conversation from a long time ago. Like this SX one, I'm sure there are other ones of its era and trying to unpack it today. I think what they were doing is they probably had some WordNet ontology on the disc. And so I think maybe what they were doing was they were taking your sentence of what you told the avatar and they were looking at each word, and they were maybe traversing up this WordNet. So just a bit of background for folks, WordNet is basically a tree of the English language, and it's been around for a very long time. So you can imagine I think the root word is entity if I remember correctly. So everything is a child of entity. And there's objects and abstractions. So if you were to take like happy, happy would be an emotion, which is an abstraction, which is an entity. And so you could kind of go up this chain. And so if I asked this avatar in this game from the 1980s, you know, 'Are you happy?' or 'Are you—I don't know, I guess elated or something like that.' What they would do is they would go up this WordNet chain and then they probably by hand kind of thought about what to do if they get these really high level words and they were able to kind of cover a pretty broad range there. And so my guess is there's something like that, and they were just pulling out individual words. And then if they see that word, then they know, 'Okay, this person asked me about the key,' or 'The person asked me about being sad.'
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So start the sad narrative where this person can go on this quest to make me not sad anymore. Things like that.
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Yeah. Yeah, no, I think you described then a lot of what Natural Language Understanding was and kind of had to be for a long time, right? And like that, there were sort of techniques like that. I know a lot of different very ornate ways of dealing with language because language is exceptionally complicated and dialogue even more so. Right? Like just the understanding piece of this has been an extremely important and huge research effort for a long, long time. Right? And I think a lot of times, a lot of times we sort of claim it's a lot more closed down today than it really is. Like that's sort of something interesting to talk about too, is how good are we at Natural Language Understanding? How good are we at dialogue handling? And my answer is we're not as good as we think we are, neither in the academic nor in the professional context. And like part of doing this work well is beginning with that recognition and then realizing that we have to build tools and capabilities to help us get to the level set where many of us already believe we are. And I think there's a lot of reasons for that. And I think there's a lot of reasons for that. There's hype in both the professional and the academic context, which will also be interesting to talk about. But you do see material advances that are very, very meaningful in the same way that we saw for computer vision starting back in 2006, the beginning of a neural net approach and the first kind of early autoencoders, right? That start to change that problem around and then the development of Convolutional Neural Networks that where the network sort of satisfies some of the symmetries of this of the system itself. And that allows it to work really well and kind of like reshape the functional capacity of these networks well enough so that they can make really good guesses at these approximate local minimum we were talking about before.
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You start to see some of that happen in the last five to 10 years in the Natural Language space. But it's hard. It's frankly harder in my opinion, because like I can talk about what the symmetries of physical space are, right? Like you know, move left, move right, move up, move down, rotate, reflect. Yeah.
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I mean, if you're training something to like play chess or something and you flip the board, you know, on its axis, your strategy doesn't have to change, or it's a mirror strategy. And so you could even just directly, you know, hack that into the system, and now it only has to learn half as much, but for language, it's totally unclear what that symmetry is.
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I mean, what is it like a symmetry in language? Is it like a synonym or an antonym? Yeah. And so how do you teach a computer to understand that? And I think what if you start to break the problems apart like that, like it starts to become clear, or at least you can make a good intuitive explanation for why attention mechanisms are really meaningful and why the neural nets for some of these modern applications have developed the way that they have, because they're trying to solve some of these symmetry problems in the same way that you and I do, like, you know, words through context. Right? And we're really good at recognizing which elements of context actually impact each other, even in a long form piece of text. So I think we've started to learn some of that. And I think obviously there's been a lot of major advances recently—separating out what's kind of hype or what it means to have advanced with something like GPT-3, for example; what, like, what that really is good at versus what it's really not good at. I think that's actually very hard for us in some ways because of the nature of what it's doing is producing such compelling text on its own that it's hard for us not to imagine, like a kind of Wizard of Oz entity back there that's all knowing and that really tease it out. Yeah.
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I thought the—there's one research, I'm sure it's a whole body of research, but I thought it was really fascinating. I can't remember if it's using GANs or Transformers. I don't totally remember, but it would caption an image. So you would give it an image and what would come out would be, you know, 'A girl is sitting on a red swing, talking to a boy near a tree.' And I thought that was so cool. When I saw that, that blew my mind. I felt like—I mean, well, I actually it would be great to know your opinion. It could be hype because I don't know the tech that well, but I feel like we reached a milestone when I saw that paper.
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I a hundred percent agree with you. So automatic image captioning is an example. It's almost an anti-example of GPT in some ways because what that's doing and there's a long way to go there. You're not going to approximate really complex tasks. It's not going to handle really complex tasks the way that you and I, with the eloquence that you and I would, or any human, but it did something fundamentally different, which is to do that research. And a lot of this came out of Google, in sort of the image search space. There's a lot of genius there, but to do that work well, what had to happen is you had to develop a shared mathematical representation of the visual image and of the text. And so the way you typically train models like this—I mean, it's been a while since I looked at this research, so this could have changed—but the last time I looked at it, which was several years ago, we typically train models like that as you have two vectors, one representing the sort of vectorized form of the speech or the language when representing the vectorized form of the image. And then you're trying to optimize their inner product. So they're getting closer and closer together.
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Yeah. Just to double-click on that. So we had an episode—our last episode was with the CEO of Pinecone, and Pinecone is a vector similarity database. And we talked about how you can take sort of any classification problem, or even in an unsupervised or self-supervised way, you can kind of take a chunk of data and you can create an embedding or a very relatively low dimensional representation of that. And what Joe's here talking about is that you can actually do this with two different media and try to join them together. When you've seen them together on the same website, or if you have a hand-curated set, and now you end up with two things that are projected into the same space and then you can go backwards with one of them.
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Yeah, that's exactly right. Sorry for not giving a little more background there, but to me, that's fascinating because what it is, it's like a step in teaching a system to have a multimodal representation of reality. So now the image and the texts are in a shared space. As you say, a shared mathematical space is shared semantic space, which is how you and I work. Like we don't differentiate the concept of 'red dress,' you know, the words and the picture of that, and a physical red dress—that's all those are all of a piece for us; those are all related. Obviously very strongly, it's just different representations of the same fundamental object. I think that, and that's very much not what GPT did. And don't get me wrong, I'm not trying to talk trash about GPT. I think it's an amazing advancement. In some ways, I think we should think of these big language models as like national resources that we're building. Cause they—I mean, I literally take as much power, like you measure the power required to train one of these things, like in units of Hoover Dam, like that kind of that, like, that's actually a reasonable scale. Like it's, I forget how many Hoover Dam days it is to train GPT, but it's not like 0.0005 or anything. It's like a real number.
0:31:21.260 --> 0:31:36.190
Yep. And you can transfer learn off of it. So, it really is—I think that's a beautiful way of looking at it. It really is like a national treasure that we've all spent a ton of time and energy, and now everyone can benefit. Yeah. And with a whole
0:31:36.629 --> 0:33:31.379
bunch of infrastructure required to do it. You can't go do that without the data centers. You can't go do that without industrial strength power lines going everywhere. Yup. But GPT is different, right? It's what it's trained on is not, and it's not trying to find this like multimodal representation, right? It's trained on basically most of the text of the internet. So, so it's a window into what it's different. It's a window into what for us is like a projection of reality onto text. That is GPT's reality. That's what it knows. Which is why and we start thinking about it like that—like the ways in which the model is amazing and the ways in which the model is confusing, at least to me, start to make a little bit more sense. You can ask: 'If I train this model on this and I ask it who the president of the United States is?' And in 1815, it's going to give me a good answer because it's got all these good relations. It's got a bunch of texts to work with that can tell it that answer. I mean, obviously it's an amazing advance that we're able to synthesize that into a system that can make the inference. But then it's also like if you go and ask it who was the president in 1705, right? Before the country was instantiated, it'll give you a reasonable answer of a person who's kind of sort of presidential. It could be like Ben Franklin or whoever—you know, that's probably too early for Ben Franklin—but yeah, you can go in and sort of trick it with these questions and these premises, and it doesn't do a great job without some further prompting and some further help in understanding that that question is, and I think that question doesn't make sense in that context because it's this really big and sophisticated association machine that doesn't have larger political context, like doesn't really understand what it means for some of these historical events to have taken place. It's really doing much more associative work.
0:33:31.840 --> 0:33:40.820
And a lot of that's that thin layer of like if language is kind of a membrane of our reality, right? You know, it's stuck on that membrane and it can't escape.
0:33:40.820 --> 0:34:08.679
Yep. Yep. Yeah. I think so. Correct me if I'm wrong. GPT-3 is trained such that they take like a string of N words and then they try to predict the N plus one word. And so what you end up with is now you can give GPT-3 a sentence or a paragraph, or what have you; it will generate the next word that it feels like belongs there.
0:34:09.240 --> 0:35:23.360
So what you just described is fundamentally what a language model is. And GPT-3 is a language model, right? So what mathematically the definition of a language model is given N words, predict the N plus one. And however you get there, however you want to do that, that could be a set of rules in the background. That could be whatever, like some algorithms got to do that job. And then once you have—the reason that's an important construct in the field is that once you have that, it unlocks all these other things, right? It's like now I can ask questions. I can turn that prediction and the underlying representations of the text and the knowledge that fuel it into really all the many of the applications that we care about today. So it's kind of a building block. And so GPT—you know, and you train these language models in different ways. Like you don't always train it specifically train it only for that with that task, right? Like that is the mathematical sort of fundamental definition of what task a language model has to perform, but there are lots of transfer learning, and like different ways you get the model to be good at doing that. And then of course, on the other side, there's a bunch of applications you'll go and turn on for it. So, but a lot of—yeah, a lot of the sort of self-supervised aspect of training language models is really about making adjudications of how good or how well a word fits in its context.
0:35:24.380 --> 0:35:50.440
And the nice thing about something like GPT-3 is that although you have to use Hoover Dam days worth of energy, you don't have to do any manual labeling because it's self-supervised. It's just scanning this data from the internet and it's trying to learn, to learn the rhythm of this data. And it can just see right away did I predict this word correctly or not without any human intervention?
0:35:51.000 --> 0:36:31.160
Yeah, no, that's why it's powerful. And that's also why it's limited, right? You there's only, you know, there's only so much you can learn that way. But there's a lot you can learn that way. And as a baseline for learning other things, it's kind of the best one we've got right now. That's why people are so excited about it. And it's pretty cool. I mean, we didn't have anything like this 15 years ago. And now, now we have models that can tell us stories that really are, you know, rapturous in their detail. Right. I think it was brilliant to bring out GPT-3 with the article that it wrote about itself. Right. It is an amazing way to illustrate that, hey guys, we're onto something new here. Like this is where, you know, we're in Star Trek territory kind of, sort of a little bit.
0:36:32.120 --> 0:37:45.840
Yeah. So, bringing us back down to earth, in my opinion, I feel like there are two big pieces that are missing and there's probably a lot more, but I'd love to get your take on it. I think one piece that's missing is getting uncertainty estimations about from really any of these models. And so to your point, you know, to where they can say what is the American president in 59 BC? The model should just say, well, like, I'm not confident about anything here. Right. And then the second point is having some kind of symbolic understanding to where, you know, it can understand, you know, in a modular way, it can understand and compartmentalize America as a concept. And when that concept started, and you can use like some first-order predicate logic to say, well, you know, the question is invalid because of these sort of symbols. I think that we've gone to this sort of embedding soup. And because of that, we've lost the ability to think about things and reason about things in like a logical methodical search kind of based way. And I feel like those are the two, at least in my opinion, two big missing pieces.
0:37:46.340 --> 0:39:40.300
Yeah. They're both really, really interesting and they're great points. Let me try to take on one of them. Maybe we'll get to both because I love to talk about both. But so as far as the logic point that you make, I look, I agree with both your points. First of all, I think those things are missing. I think the logical element or the higher-order logic, we don't know how to teach computers to do well. I personally don't think the answer is. At least not in as flexible a way as you and I do it when we sort of think through the universe as we do. I don't think the answer is to construct a kind of symbology of it. That, that like then these inferences are going to be mapped into, and then there'll be some kind of logical computation that happens on the symbology. And then that's going to go back to the embedding space and give it a different, I don't think, I actually don't think that'll work. I mean, I could be wrong. What do I know? I'm just one guy. But the reason I think that space, like that space of what is, what are the constructs and what are the abstractions that we use to make decisions at the higher-order logic is itself just as complicated as the reality it's trying to simplify. In terms of the relations between the constructs and the boundaries, a lot of things are complicated because the boundaries around what is this construct versus not like what's in the set and what's not in the set are just hard questions. And so I think in the end, we're going to have to find a way to teach a machine to construct representations like that for itself. But there is some going to have to be some notion or some parallel process that serves the same function of the logical hierarchies that you and I would use. And sometimes when you get into deep political disagreements, sometimes at the root of that is just a kind of a misalignment of some of these definitions of categories.
0:39:41.080 --> 0:41:39.775
I think just to riff on that, one thing that I think paves away is MuZero. I don't know if you've been following MuZero from DeepMind, but your DeepMind, their initial claim to fame was beating the world master at Go. So, Go is this board game. It's just a lot more complicated in terms of branching and other stuff than Chess. And they were able to beat Lisa Doll and now a computer is the world Go champion. But they didn't stop there. The next thing they did is they built something called AlphaZero. And AlphaZero removed all of the Go specific logic. And it treated it as just a field of, now they did have to do some feature engineering. So you can imagine, Go has just white and black stones. Chess doesn't. Chess has a whole bunch of variety. And so you have to do some proper feature engineering. But beyond that, the rules of Go and all these tricks were not in AlphaZero and it was able to perform just as well as AlphaGo. But what they did have written in hand by hand is the game tree. So when they're doing this game tree search, they're still, the mechanics of Go are built into that. So, in other words, AlphaZero will say, and this now it's AlphaZero. So it works on many games, AlphaZero will say, take this move, this Go move, this checkers move, this Chess move. And then that would go into some program that a person wrote that would make the move, you know, adjust the game appropriately, tell them if they want or not. And then and then it would go, and then it would and then that would conclude a simulation. So they would simulate using the hand-coded engine, and after so many simulations and they have a good action, right? So then they took it to the next level with MuZero, where in MuZero,
0:41:40.821 --> 0:42:28.080
they don't even use the rules of the game. And so actually in the neural network, it has to represent the rules of the game. And it actually has to do the simulation in the neural net. I think they're using like an LSTM or a Transformer or something. And so literally they've gotten to a point now where you give it a Go board and it thinks for a while and it does all these simulations and it just comes back and it says, you know, place the stone here. And I feel like that they're starting to unpack that planning and that reasoning and that symbology. It's totally uninterpretable to us, but they're starting to unpack that process, which I think is really exciting.
0:42:28.540 --> 0:44:23.600
Yeah. I mean, it's kind of like if you had a child, right. And you asked them to like, you never told them how to play the game, but you just let them watch, you know, a bunch of people play. Like, would they, like, how well would they do at understanding the rules and could they construct that abstraction? That's probably like, easier for some people, harder for others, but fundamentally possible, right? Like we don't, we wouldn't see that as beyond us. I think it's interesting. Yeah. I think you're right. I think that is like, I like the way you put that kind of what's fundamental about that advance. I think what it's important to remember too, is that that playing field, so to speak, or that area of board games like that is so ridiculously simpler than any sort of knowledge or human conversation or language-based kind of real application that we have. Like I used to give these talks where I would talk about like, okay, you know, everybody wants, everybody who's kind of first, who's learning a lot about Machine Learning. And I'm not trying to put you in this box. This is, you just referenced something in my mind, but everybody who's trying to first learn or who kind of sort of first learns about how to get Machine Learning done in one context rolls into the conversational context. And they have kind of the same idea, right? Which is they're like, cool. I just, I need a feedback loop here. Like I just need what I need is like somebody, you know, a person talking to the computer and good thing happens or bad thing happens at the end. And then I and as soon as I have that, I'm going to do this self-optimization thing, because that's what Machine, that's how Machine Learning works. And there's a reason no systems work like that in the world, right? There's a real, there are pieces of systems that work like that in real applications. But but there is no end-to-end trained dialogue machine. That means anything to any person that's used for real applications that is in that closed loop form.
0:44:23.600 --> 0:44:53.200
And it fundamentally comes down to how complex the space of language and dialogues really is. And even though the space of Go in a math, from a mathematical perspective is vastly complex, right? And two to the whatever, 113 or 111, or how many points there are on the board, you know, kind of combinations of stones and all this stuff. Like those are huge numbers. But when you start with language, you begin with what is literally mathematically an infinite-dimensional vector space.
0:44:54.020 --> 0:45:10.700
Yep. Yep. And the other part of it is there's no ambiguity in Go. I mean, you place the stone and the same thing happens every single time and there's no room for interpretation. But for language, it's the exact opposite. I mean, you couldn't build a language that didn't rely on assumptions.
0:45:11.100 --> 0:45:14.260
I mean, if you do, you kind of end up with something like a computer language, right?
0:45:14.480 --> 0:45:15.300
Yeah, that's right.
0:45:15.520 --> 0:45:23.300
Yeah. That's sort of where that goes in a lot of ways. And I mean, those are obviously meaningful and interesting, but like, you know, nobody talks in Python for a reason.
0:45:23.660 --> 0:45:26.420
Right. Except Patrick, but nobody else.
0:45:29.820 --> 0:45:36.920
Yeah. Well, that's actually interesting because it's another thing GPT is really good at. It's really good at writing computer code out of language, which I think is a fascinating application.
0:45:37.320 --> 0:45:39.300
Oh, right. The Codex, right? They're calling it Codex?
0:45:40.140 --> 0:45:58.660
Yeah. There's like a few different ways this has come to life. So, you know, there's a bunch of people that have done—you can go like poke through Twitter and find all sorts of good examples of how you can literally build a front end in some cases, you know, by telling the machine what you want the web page to look like.
0:45:59.620 --> 0:46:01.439
Oh, I've seen those demos. They're amazing.
0:46:01.440 --> 0:47:52.779
But to your other point, right? So sorry, we kind of meandered a bit. But to your other point around uncertainty, I also really, really agree with that, I think. And I think it's a big problem in conversational AI, right? Like you have these models that are built on, you know, especially natural language understanding that are built on these Transformers and these embedded representations. And they're, you know, really good in a lot of ways. They're very smart, but they can make really dumb mistakes still, right? That's not beyond them. And so most industrial-strength models, most real systems have some, you know, sort of are forced to have some combination of rules and backstops against these, you know, neural network approaches. And I think a lot of what's under that or a lot of what's missing is, you know, is there a system around that has a good idea about how much, how good this natural language understanding system is likely to be at this problem? And my personal opinion and some of the research work that we're doing is that you actually need that system to be fairly decoupled from the system that is whose job it is to make the prediction in the first place. I think you can't have zero coupling, and the art of it is kind of like in what way is, let's say, the natural language understanding understander decoupled and in what ways is it coupled to the system itself? Like those are—that's kind of like the hairy edge of this problem. But I think we haven't done a great job at that yet. And I think there's a lot of research, not we personally, but like we as a culture. And I think there's a lot of research still to be done. And I think it'll be important because I think it'll foundationalize some of the other problems that you talked about, right? So if we want to start building a better hierarchy of understanding for some of these models, like a step on the way there is to ask like, well, when is it wrong?
0:47:53.200 --> 0:48:19.160
Like have a separate opinion about when these models are wrong, which can help us develop separate, like an understanding of the categories of types of times when these models are wrong. So you can start to imagine an interplay, right? If I separate that system enough, it's going to begin to categorize, you know, a whole slew of cases where this thing is wrong that it doesn't know about. And then those slew of cases become the basis for an abstraction of, okay, what's a knowledge center, you know, that this thing, where this thing is weak.
0:48:20.299 --> 0:49:39.400
Yep. Yep. Yeah, totally. Yeah, I think as a field, handling uncertainty is very, very difficult right now with deep networks. I mean, there's—and this is, you know, this is for something simple, like a supervised model. Let's say the model that predicts cats and dogs or something. I mean, it's—there's still not any consensus. I mean, there's effectively two or three camps. There's one camp that says, well, train, you know, 10 different models either on one tenth of the data, or you shuffle the data for each model. And then now you'll get 10 different answers. And so, you know, depending on how much those answers vary, you can say how confident you are. There's another camp that is effectively doing the same thing, but within the model. So, you know, you kind of multiply the layers and now you have this distribution. And then there's this camp that says, let's put priors on everything and use kind of Bayesian approaches. And none of them work very well. I mean, the reason why we don't have—the reason why you can't just go on PyTorch.org or TensorFlow.com or whatever, and just get a model with uncertainty is because there's not really a method that satisfies it very well.
0:49:39.400 --> 0:51:26.820
There's a lot of reasons for that. But I think one of the most important ones is, you know, sort of broadly goes under the header of like, processes that generate language and how varied they are, right? And so I think, you know, this is one of the things we see in an industrial context all the time is that you can have models that are great and doing well, you know, based on real-life training data from real-live people that really live talk to the model and that you—but you have now learned to correctly predict. And then think, you know, something very subtle about the underlying environment can change. You know, people can start talking about the business promotion in a different way because it's, you know, there's an advertisement that describes in a different way. And all of a sudden, some of these things really start to fall apart. This is one of the areas that for me that illustrates like why NLU kind of isn't where we think it should be or where we think it is. And I think that, you know, mathematically what's going on into that is that, you know, there's the generating function for language and the infinite dimensionality of this vector space, right? Are, you know, allow for—they make it very hard to train models that are really rooted in the semantics as much as we'd like. And, and probably, you know, in the same way that, you know, GPT is existing on this membrane and, and, you know, really only seeing what it can see and missing these big pieces of reality and not having some of this like base symbolism to attach to, you know, the models that are doing the work of conversational AI today are still a little bit, you know, they can be infected with problems like that. And they can be confused by what we call lexical cues more so than we'd like, right? So a lexical cue for those who don't know, it's just like something more on the surface of language about the word choice and the top-level design of the words and the sentence versus semantic, which is like, okay, what's that deeper, you know, agreed upon meaning.
0:51:27.640 --> 0:52:28.879
Yeah. I mean, one thing—one really prime example of that is cats and dogs, right? I think that we always hear that analogy, like it's raining cats and dogs, or this person's a dog lover, this person's a cat lover. And so our mental model, you know, as a society is that cats and dogs are opposite. And all of us as children grew up watching movies where cats and dogs didn't like each other, right? I mean, that's a trope, right? And so in our mind, cats and dogs are really far apart, but if you were to use GPT-3, it's going to put them right next to each other. And the reason is websites that talk about cats—a lot of them talk about dogs too. I mean, imagine a website that sells pet food, right? I mean, they're going to be talking about cats and dogs at the same time. And so actually when you look at anything unsupervised, it's going to put cats and dogs very close together, especially in the grand scheme of anything that could be talked about. But we have this sort of a cultural separation, and those kinds of things are just very hard to put into the model in any meaningful way.
0:52:28.880 --> 0:54:23.759
Yeah. I mean, I think what you hope is that scenarios like that will be accomplished via like the high dimensionality of the embedding space, right? Like you can learn about some of that because on some dimensions and—high dimensionality is like really hard to visualize, as I'm sure, you know, and really hard to have intuition about because the distance metrics and all this start to behave really weird. But you would hope that there'd be some dimensionality where there's like this separation, right? Which in our minds would correspond to like a character of the animal or something like that, rather than a functional view of the system, right? Cause when we go—cause we're also not surprised when we go into a pet store and we see cat food next to dog food, right? Like that doesn't, you know, that doesn't make it say like, what's wrong with this world. Right? And, you know, that's so, so we can, you know, we have those like separate dimensions or something, you know? So I think I would hope that the representations can accommodate that. But—but I think the challenge to go back to the earlier point is, you know, what are those underlying dimensions? They're not stable, right? Like if I slant a concept like slightly differently, we might, we might redimensionalize that space a little bit differently. And so we sort of make them up on the fly in a lot of ways; obviously there's some touchstones, but there's sort of something that we create, you know, dynamically, like how do we interact with models at that level, you know, through the training process or through the—you know, even a discursive process in dealing with them, which is another thing I really like about GPT-3 is that you now sort of talk to the model by giving it examples and can kind of train it with real language. That's also an advance, I think is very important. And we're going to need to kind of cope with that and figure out how we use it because ultimately the better these things are at creating abstractions and redimensionalizing, you know, what they're talking about in the ways that we're kind of discussing, you know,
0:54:23.759 --> 0:54:40.720
the more we're going to need to converse with them to make sure we understand how their minds are working in the same way that when you and I talk to each other, like we got to get to a baseline about, like, you know, is he—you know, what does, is he actually a dog hater? Like I have to kind of figure that out first to talk to you appropriately about dogs or something.
0:54:40.720 --> 0:55:15.884
Yeah. I wonder if, if someone, you know, so if OpenAI is going to train a new GPT model, I wonder if they started with books for babies and—you know, the random network was biased in favor of baby books. And then they literally trained it on books meant for older and older people. And so it kind of followed the same path as a human in terms of what kind of material they consume. I wonder like what that model would presuppose. I feel like there's something maybe really interesting there. Yeah.
0:55:16.322 --> 0:56:03.032
Yeah. I agree. I think you quickly end up in the world of AGI, right? It's Artificial General Intelligence. And that definitely by no means an expert there, but I think that's kind of where a lot of this stuff goes in the end is you're like, well, I have to start teaching these things more like they're a child and I have to start relying. And again, this is why uncertainty is so important. I have to start relying on their own understanding of themselves and their ability to express their understanding of themselves in order to influence them the ways that I want to, and have them take right actions. And so you see, it sort of quickly starts to, if you really break it down, I think you start thinking like, wait a second, I've got to imagine this as the, as a more like a human thing that I'm teaching and less like a mathematical process that I'm training. Obviously the math doesn't go away.
0:56:03.994 --> 0:57:07.461
Right. Right. Right. So we'll dive into a little bit on transformers maybe. And then we can back out and look at the whole problem again. So if I remember correctly, my knowledge is very limited, but transformer somehow it takes in an arbitrary number of words, and then it creates some embedded space of that—that arbitrary, variable length paragraph, I guess. And now from that embedded—that's the encoder. And then from that embedded space, you have a decoder that can emit words and then also emit transformation to the space. So imagine like you have this embedding that contains all these things that you want to say. And as you say those things, you move to different points of the embedding where now there's less to say, and eventually you emit some special token that says I'm done. Is that—I mean, do I kind of get that right? Or like how do transformers work? Yeah.
0:57:08.288 --> 0:58:03.942
I mean, I think, you know, it's an area that's live, right? There's definitely a lot going on there. So I guess what I would reference, I think for folks, like one thing that I think is important is this notion of attention, right? So one of the hard problems in language for a long time has been how well can a model like reference across a great degree of space between concepts, right? And so when you look at a problem, like trying to think like a sequence problem, like a time series sequence, right? You'll—one of the longstanding problems in time series analysis is what if I have an effect last January and now it affects this March or something, right? And that's a macroeconomic underlying effect. And so there's all this work in sort of hand-curated feature building in those contexts, which are these are sequence models as well. So they're fundamentally the, you know,
0:58:03.942 --> 0:59:24.975
kind of like mathematically the same type of object that you have when you analyze language. You know, I think there's a lot of bespoke techniques to do that in time series analysis and, you know, all the ARIMA models and just they go tons and tons of work. And I think the same thing was done for many years, you know, in a hand-curated way when looking at language. And so I think one of the advances that's pretty important in this context is that, you know, we've begun to train these models and these transformer-based systems in a way that they can locate those long associations, right? And in a way that they can provide for us, you know, this is kind of another way to talk to the model, right. They can provide for us connections between them, right. You can ask, well, what other words in this sentence does this word relate to? And in an industrial context, that's super helpful because as a way of tuning and debugging and trying to improve systems, right. The those sort of like co-reference patterns—I don't mean co-reference in the literal sense of co-reference of language, but these patterns of correlation to these patterns of mutual interest or mutual effect become ways to find, you know, to go and kind of ferret out the misunderstandings that the models are having and try to improve.
0:59:26.072 --> 1:00:14.640
Them. Got it. Okay, cool. That makes sense. Yeah. I think I definitely have to get ramped up on it. You know, my background's in Reinforcement Learning. And I saw a paper recently about someone using transformers to do sequential decision-making. And so transformers are kind of coming into like a battering ram, just coming into my field and so many other fields. And so I have definitely to get ramped up on them. But yeah, I think that I've had seen the word attention a lot and maybe we'll—well, maybe we'll get you back on and we could do a whole show on transformers or we could somehow get ramped up on that. Yeah, let's pivot to LivePerson. So tell us about LivePerson and what you all do and what kind of services or products you provide.
1:00:15.920 --> 1:02:10.959
Yeah. So, all right. LivePerson, you know, fundamentally what we're trying to do—we have some new things we're trying to do as well. And in terms of relating directly to customers, but the core business of LivePerson for the last 20 years or so has been about essentially making the connection between us, the people in the world and the brands we have to deal with, or want to deal with a little bit better and making our lives a little easier because of it. And so that began back in the day with online chat, right. Which is sort of almost a dirty word now. And I think we actually would say at LivePerson that we thought it was a kind of a dirtier word first, right. Not that online chat doesn't have useful applications and we still do a lot of online chat, but we don't see it as the future of, like, Hey, I log into a website and I have a sort of not persistent or like a liminal connection with an agent on the other side. And I'm chatting with them for five minutes. And then, if I break the connection and it's gone and I have to start again, we don't see that as like a model for like a really good customer experience, or a really good brand experience for that matter. So, but that is kind of nonetheless, like how the company was built because that model does have some advantages over just the straight phone model. That was what was there before. But as time has gone on, right. And about—I guess it's about four years ago now, maybe five, LivePerson went early into a messaging context and what we call more asynchronous communication, right? So now you've got brands and consumers communicating through SMS messages through WhatsApp, through LINE in Japan, through whatever, right. There's a sort of umpteen messaging platforms out there. One of the things we do is we make sure that you can have a great conversation with your customers on these platforms, but where—that obviously starts to lead.
1:02:10.959 --> 1:02:19.463
And it's really interesting when you look at the conversations people want to have and what it means to shop that way compared to what it means to shop.
1:02:21.454 --> 1:04:02.805
In kind of the web signature way that we shop today, or the website context. I mean, the way that we shop today, it's fascinating. Right. And one of the things—the jokes I sometimes make is like one of my interview questions at Amazon was like, all right, well, help me build a model about whether or not the shopper is about to leave the website or help me build a model as whether or not this is a gift shop or how would you build that? Right. And these are things that people just tell you in a conversational context because they're trying to get help. Right. So one of my favorites is a woman talking to a sporting goods company and she's like, Hey, I—I'm late gift shopping for my 12 grandkids and my six great-grandkids. Like, can you please help me find some gifts for them? Right. These literally the kinds of things that a company like Amazon would be like, sifting through web search and web activity history to try and make an inference about are just things that people need to—that people they want to let you know that this is their problem. They want you to give them help in solving it. You know, that she was very interested in the fact that this service agent on the end of the line could next year reach out ahead of time with some proactive offers, right? Like all this stuff that can kind of feel seedy if a company is doing it behind the scenes on a website, but feels very natural. If you're the one telling the company, this is my problem and I'd like you to help me. Yeah. That makes sense. Yeah. So there's when I kind of drink the Kool-Aid on this problem and sometimes I drink the Kool-Aid, I feel like what we're building is a way to do a more open and transparent and a little bit better and warmer conversational experience for shopping, which takes you back to the world that we all used to live in kind of before the internet where most of shopping was like that. Yeah. I
1:04:03.447 --> 1:04:59.927
think that to your point. I mean, I've recently been buying all my clothes online since COVID. You know, once you've narrowed down the size and almost everything comes in almost every size. So narrowing down the size doesn't really do anything. Well, it's almost like how do you sort of search for your aesthetic? It's really difficult. Yeah. But I think that if it was sequential, like maybe I would search for blue shirt and I would look at what came back and I would realize, okay, really what I need is a blue striped shirt. And somehow knowing that this person pivoted to blue striped shirt that now it's like, okay, you have these two things and maybe you would show a striped shirt that's Navy because you know, that stripe part is really important. And so just, yeah, just handling that modality, I think is a conversation and whether you're doing it through a search engine or you're doing it with text generation, I think that's something that has to be addressed. Well, and
1:05:00.062 --> 1:05:53.040
if you think about it, like what you really want to understand if you're in that conversation with someone and trying to help them buy a shirt is something a little more fundamental about what they're doing, right? Like, are we on—are we in a situation here? I don't know how you shop, but like, I might be in a situation where I've already had three copies of this basic shirt and I'm trying to find it in a different color and I know it fits well. And I kind of just want to get moving and go get myself a fourth one because I love these shirts. Like, it's a pretty uncomplicated mission. And what I want in that situation is someone to show me all the different copies of that particular item. And then maybe, you know, I want a little back and forth on the style I'm looking for and I'm done, or I may be like, have been poking around the internet. I'm like, Oh, you know, I need a little bit of a fashion change. Like, I feel like I'm kind of boring right now and I'm looking for new ideas. And I, you know, I'm looking for a shirt that like, maybe I wouldn't have worn before and I might need a little—like I might need a really different experience.
1:05:53.040 --> 1:05:58.640
Maybe I'm teaching seventh grade and I need a periodic table. That's somehow also like WWF.
1:05:59.279 --> 1:06:05.919
Right. Right. I need this SmackDown. Like I need to turn it over and then Macho Man Randy Savage can jump out of it or something.
1:06:05.920 --> 1:06:09.300
Yeah. Like a hydrogen out of them and just crush it. Yeah, exactly. So
1:06:09.300 --> 1:08:07.279
Like, I think you want those—you know, you want those missions. I mean, this is a lot of where the through line for at least my career started, sort of matches between Amazon and Nike live persons. Like, what are those missions, and how do you define them? And I think the language context is obviously the best concept context to define them in. A lot of what we build is about allowing brands to sense that and to identify those missions from the language that their customers give them so that they can be more helpful. And so this, when we talk about conversational AI, or at least the tools of conversational AI and how we use them at LivePerson, you know, there's at least kind of two big categories, right? One category is around, of course, like helping brands build these systems so that they can have automated ways for customers to solve their problems so that it's easier and faster and better for everyone. And then the other side is like, okay, well, how do you are you set up to really listen to your customers? You know, for instance, that when, you know, your customer complains about having tried to call you, having tried to reach on your website, and having tried to text message with you, by the time they're at that point, they've got like an 80% chance that they're going to leave you because there's this canonical problem that just frankly makes everybody mad. And so being able to listen to stuff like that at scale, right, and understand that at a scale so that you can provide a better customer experience—like that's another sort of layer of the conversational AI offering that we think is really important for us and really important for brands. Because in the end, like, you know, consumers in America and other places now, we're pretty fickle beasts, right? Like, we've been kind of trained by like the Amazons of the world and other companies that have pioneered in customer service, to create like these really positive service experiences for us that are really different than 30 years ago, right? But now that's the norm. Like, and if you want to, you want to keep building your customer base, if you don't have a captive audience, if you're not
1:08:07.279 --> 1:08:30.559
like a cable company or something where nobody can leave you, then you have to be great at this now. And you have to kind of blur that line between what are you—you know, how are you solving their problems? And then how does that translate into future growth between you and them and building that strong relationship? So, we kind of build products on both sides of that, right? We build Natural Language Understanding with both of those use cases in mind, for instance.
1:08:30.559 --> 1:08:45.040
It sounds like another part on the product side that I think would be really tricky would be knowing when to hand off to a real person. That seems like something that a lot of companies wouldn't know how to do. They would really rely on you for that.
1:08:45.040 --> 1:10:44.240
Yeah, that's right. And I think like, that's actually like a nook in a much bigger problem, which is how do I understand how well the system is doing with my customers? How do I understand the quality of these conversations between the computer and the person? And there is not good tooling for that. This is one of these foundational areas where I think you just don't find anything good in the industry right now, because it's one of these annoyingly hard problems, right? And it's kind of related to all the abstract stuff we've been talking about. Like, when does the model know it's doing well? When does it not? And like, what—how deep and understanding and how introspective is that model in the first place? And the answer is typically not very. So, you can't rely on the model to do it. You need to kind of build some of these separate systems to be able to see this. And some of that requires like really rolling up your sleeve and sleeves and doing some dirty work and being like, what are like the top 10—you know, kind of, like, what's the right way to describe like the top 10 kinds of problems that people face when they're trying to talk to automated systems and how they break down and how that customer experience goes wrong. So we spent a lot of time on that. We have products that we're bringing to market right now. In fact, on measurement, you know, the this get this—like the product has this pithy name of Max, which is the Meaningful Automated Connection Score, which is really about like, 'Hey, how good or bad was your CX in these automated conversations? And like, where should you improve it?' So, and it's important for two reasons, right? One, you need to know how well you're doing in order to have any sensible business strategy. And two, tuning and optimizing these AI systems is a lot more of work and ongoing work than people at first often conceptualize that it's going to be. Right. You got to think of it like if you're building a website, you don't build it on Thursday, send it live and then don't touch it for a year. Like you would never do that. But a lot of people think about conversation—I'll build the bot. I won't touch it. It's done—but it's really an ongoing process iteration. And we need better
1:10:44.240 --> 1:11:35.600
tools to, in the same way we do website A/B testing and the same way that we have a bunch of tagging and kind of infrastructure and software built up around learning what's working about your web presence and improving it. We need the same thing. You need the same things if you're going to learn about how to improve your conversational AI. And that's part of what this metric and the system is about—is helping you quickly locate places where the conversations got wrong. Where did the bot get stuck in a circle, which drives us all crazy? Where did the NLU just totally barf and lose it and completely break the customer trust. Right. So our play in general has really been about quality. And one of the most critical pieces of quality is like, do you really have good measurement? Do you have good understanding about what quality is? And do you have good pointers as to where quality went
1:11:35.600 --> 1:12:57.120
wrong? Yeah. I mean, this is a huge problem. I think the people who if someone can even crack this in a generic way, it would move the entire industry a mile forward because I think we all have the same issues where it's just so hard to triage one of these situations. And what we end up doing—you're probably doing something similar—is just coming up with so many metrics on, for example, how many people are clicking on this, like, what's the click rate? What's let's use Amazon example. What's the click rate? What's the conversion rate? How many people abandoned the whole site? How many people bounce—all of that stuff. And then you have this other, you have this downstream problem where as soon as you define a metric, you create an incentive. And so people, machine learning engineers at your company are going to try to drive that metric up. And typically what happens is eventually something that's not being recorded will start to suffer. Right. And so now you have to make a metric to track that. And so you end up just adding more and more balls in the air and it's because you're in this arms race with yourself. Right. And so, yeah, yeah. It's just so difficult to go through that process.
1:12:57.389 --> 1:14:23.680
It's a little bit of a metrics alphabet soup in some ways. One of the things we did to deal with that. The first thing we did before we started working on this Max project was we sat down and said like, well, we need to organize. Like this system, like we need to organize the metrics somehow, because people are doing exactly what you're saying. And a conversation I probably had five times now is I'll go east before the pandemic, I would go physically to brands all the time. And now I talk to them on the phone, but they, you know, I'd sit down and be like, 'Okay, well, how's it going?' They're like, 'Oh yeah, we built this bot,'—you know, it's whatever platform they built it on. And they're like, 'It's great. It's got an 80% containment rate.' It's awesome. I'm like, 'That's great. Like, what are you doing with the 80% extra capacity you now have in the human contact center?' Like, what did you choose to do with all those people? You must have people at their desk with nothing to do. Right. And the answer is never like, 'Oh yeah, yeah. Here's what we did with them.' The answer is always like, 'Well, no, no, actually volumes in the human side have actually gone up or they basically stayed the same, but the bot's doing great.' Right. And it's because it's fundamentally because this concept of a containment is just a very broken way to think about whether or not the bot served the person in the way that it needed—it solved the problem or not. So one of the things we did was we sat down and we said, 'Well, let's organize the metrics into some conceptual framework that people can use so that they can see, cause we're not going to get out of this.' Like there's not going to be one magic number that does everything. This is actually a highly multivariate problem. There's lots of different ways to think about what good means.
1:14:23.680 --> 1:14:30.825
And people are adapting. As soon as you make a metric, someone will try to game it and then they'll cause another problem. Right. Right. So you can't,
1:14:31.197 --> 1:15:56.720
you can't kind of live on that one. It's just too unstable. Yep. So, so we built this framework, you know, we gave it the pithy name of the Four E's. Right. And there's an efficiency vector of metrics. There's a effectiveness in solving the customer problem vector of metrics. There's an emotion—right? What was the customer's emotional response? And now I'm going to totally blank on the fourth E, which is going to make me super embarrassed. It'll come to me in a minute. Anyway, so we've split into these categories and under them, we put both sort of existing contact center metrics that people are used to like repeat contact rate within an hour, within three hours, within a day. And then some of these newer metrics, like, 'Hey, what's the Max CX on this?' Or, you know, 'What's the sentiment of the customer,' as measured by these Machine Learning models, right? Now you can begin to give brands a sort of reasonable target to optimize for that makes conceptual sense to them. The fourth E, by the way, was effort, right? How much work did it take to solve this problem. And that's really—these are like much more connected to things that we care about as customers. Like when I tried to contact you, did it work or not? That's effectiveness. Like how much of a pain and frustrating was it? That's emotion. How much work did I actually have to go through to do it? That's my effort, right? So those correlate, and these concepts, I think, correlate a lot better to what a great CX is. So we spent a lot of time on that. And then that set up again like some of the Max work and some of the analytics work that we've done subsequently as well.
1:15:56.720 --> 1:16:25.559
Very cool. Very cool. So what is something that makes, and I know with COVID, we're all working from home. And so, and so everyone's job looks pretty much the same. There's kids running around and there's a dog and there's, you know, but like, but before COVID, or maybe projecting into the future, what about LivePerson is really unique, or you kind of walk into the office, or maybe you talk to the folks and you feel like this is something I haven't really seen anywhere else. Yeah. You
1:16:26.487 --> 1:16:30.689
mean like in terms of being inside the company or in terms of what the product offers? It could be,
1:16:30.891 --> 1:16:43.193
I know this is—this is inside the company. So it could be, it could be, you know, everyone plays ping pong on Thursdays, or it could be something about the nature of the kind of, the kind of ethos that you've built, something like that. Yeah.
1:16:43.429 --> 1:17:47.840
Yeah. So for me, I mean, I don't know, you probably have to bring people on, you know, on the, like on the team to make sure I'm not lying to you, but I can tell you about what some of the things we strive to do and some of the things that I see as successful at. I don't think it's easy. I guess let me talk about the science domain. Like, I don't think it's easy to have to combine two critical concepts that are important for real success in like scientific research and product development. And those two concepts are one, like truth-seeking—you know, are you really satisfied with what you've done? Or could it be better? You know, or what's really wrong here? Those kinds of questions. And then two, like a spirit of real collaboration and support. Right. So oftentimes, like when I was a physicist, right, the way physics solves this problem is by not solving it. They just let—like they just go after option one. And you know, it's—it's not at all uncommon for the chief discourse between two physicists to begin with, like, you know, for somebody to start out with, 'Well, that's great what you said, but let me tell you all the ways in which you are an idiot.' Right. Like literally, you know, this.
1:17:48.400 --> 1:17:57.443
We have a term for that. We call that the intelligent jerk. Yeah. It's—it's an archetype, an anti-hero of the tech world. Yeah. Yeah.
1:17:57.460 --> 1:19:04.000
Yeah. And I mean, it's very common and there's a sense to it logically. Like if you don't have some of that disagreeableness—and I mean that like literally in the psychological sense—you run into too much conformity, and you don't do great work. Right. Like you can't actually do well without some degree of that, but like to find a group of people that can marry that and can have that same impulse and understand how to express it in a way that is constructive, right? Rather than destructive, and like builds partnership rather than tearing it down. Those are special people. And we work very hard to, you know, to really staff ourselves with that combination of skills, like up and down the science org. I'm pretty pleased with how we've done. I think—I think it's without a doubt my favorite place to go to work and do science work and learn about research that I've ever had. And I had good situations at Amazon, a good situation at Nike. I'm not saying they were bad; they were great, but this, I think we've done something special by prioritizing, you know, that combination. We have not hired great people that we felt like just weren't going to hit dimension two very well.
1:19:04.000 --> 1:19:59.415
Yep. Yeah. It's a really good, really good point. Cause the other thing too is people who are really good, you know, and have the right, you know, emotional quotient and—and—and have that ethic and—and—and have all of those pieces, they're going to grow really quickly. And people who go quickly, you know, I don't know what data there is on this, but people who grow quickly will maybe want to change teams because there's an opportunity somewhere else or change companies or something like that. But people who are in over their head or who don't have the right character, those tend to be the people who stay forever. And then, so it's like—it's so difficult to, I mean, so much time needs to be spent to get that right. Because otherwise, you know, there could be whole years where there are problems that take a very long time to sort out. Yeah.
1:19:59.736 --> 1:21:16.799
I think there's some dynamics like you're talking about that are very real. You know, I felt like—I think one, you know, one partial antidote to at least the first part. I don't have a good antidote to the second one, but I think one thing we're also really fortunate about at LivePerson is we have pretty amazing data and capabilities to work with, right? There are hundreds of millions of goal-oriented dialogues coming through the platform every day. Obviously, we use those thoughtfully, carefully, and respecting all the contractual arrangements we make. We don't use all those conversations the same way, but different brands have different expectations about how we use that data. But nonetheless, there's lots and lots of amazing conversations to work with. And there are also tens of thousands of people sitting on the platform who can inform conversational AI with like their opinions and their expert opinions, right? All these agents sitting there are able to give feedback to the systems and tell them when they're working well and when they're not. And so some of those tools, I think like fuel much more so than anything that I or the other managers on the team do every day—fuel like people coming and really kind of hunkering down with LivePerson and doing good work for a long time. So it's been nice to experience that. But yeah, in general, I agree with your assessment of the dynamics. Like you get somebody who's struggling on one of these two dimensions of work, and it's hard for them to leave because there's like a lot more risk.
1:21:16.799 --> 1:21:46.160
For them. Yep. Yep. So, so for folks who are listening to this and completely enamored—they want to do conversational AI, they want to get into this field. Does LivePerson have any openings? And can you kind of break it down into, you know, does LivePerson have internships? Do you have full-time positions? And if there are, you know, post-COVID, you know, if there are geographic locations you want to focus on, what are those?
1:21:46.160 --> 1:23:45.675
Sure. So the first answer, or the first thing to say is we're hiring a lot. Like the company's grown; you can go read our public record reports. We are a public company so you can see how we're doing—we're growing rapidly. So we have a lot to do. You know, this year I'm really trying to focus on building an exceptional scientific research team that's going to look farther afield in time, right? So, so we've typically had at LivePerson a strategy of, well, like we want to work on research; we want to work on science, but we want it to be in the context of being productizable in the near to medium term. So really not much more than six months out, we want to see this come to life in a way that impacts the product. And I think that was right for where we were and all the stuff we needed to build. There was a lot of obvious stuff to do, but now we're in a different place. And I think we want to be looking on one, two, three, four-year timeframes for how some of this stuff turns into technology that we can use. And we want to go a little deeper with academic partnerships as well. So, so, you know, I'm staffing just a full team on research with kind of that mandate, looking for a lead for that who is—who wants to come in and say, 'Okay, I see this data you've got. I understand you guys have a basic dialogue problem you're trying to solve. And I want to go push science research in this direction and partnership with this academic institution over the next three, four years.' And here's the agenda, and is going to help drive that. And I mean, I have some ideas about where that should go, but I'm really looking for a very senior lead to come in and—and you know, and take the reins on that. So we're actively recruiting there, and they of course will actively recruit for the team who works with them. But we have other jobs and other opportunities as well. LivePerson is an engineering company. You know, on my team, you know, I hire analysts as well. We do it like—I run a lot of the analytics products for LivePerson. And we hire, of course, Machine Learning Engineers. We hire people to build backend systems that are supporting all the model building and the model training and the model management. And, you know, we're doing a bunch of work of migrating pieces of the platform into the cloud. So,
1:23:45.878 --> 1:25:01.022
so one vector that often works well is for if someone has a strong engineering background and they're interested in this space, right? There is—you know, there's definitely work to do to become an expert in the science and the field, but there are ways, I think, to the—there's a lot of engineering work that provides like a boots-on-the-ground introduction to the technology stack and kind of gets you, and then you kind of get into the flow where we're now like, 'Okay, you're coming to our Friday brown bags. You're talking to—you're working closely with scientists because you're building things that they're using.' And so you're in conversations with them in a different way. It's a really live place to learn and grow. So, I mean, I would—I would come if I were someone with a strong engineering background who was looking to get into this field, I'd be looking for a way to do it with a role like that rather than trying to say like, 'Well, okay, I'm going to go to bootcamp for six weeks. I'm going to read a bunch of papers. I'm going to do some prototype-like chatbots and then I'm going to be ready to do a research role tomorrow.' I think, you know, maybe there are some people out there that are just like astoundingly fast learners and that will work. But I think for most of us, regular humans, you want to kind of—you want to find a way, like, 'I'm going to build this into my life for a couple of
1:25:01.495 --> 1:25:15.940
years. Yep. And there's so much tribal knowledge, so much tribal knowledge that—I mean, it's that bootcamp doesn't exist where you're going to learn the tribal knowledge to be able to ACE in a science research scientist interview. So yeah, I think your advice is spot on. Yeah. And I
1:25:16.227 --> 1:25:25.280
mean, in addition to ACE in the interview, even if you can—like, and you show up on day one, like, are you really ready to do the job or are you setting yourself up to succeed? Right?
1:25:25.280 --> 1:25:35.616
Yeah. Yeah. Do you have internships? You have a lot of folks who are maybe in the middle of their college and they're looking for something over the summer, but then they're going to go back to college afterwards. Does that exist?
1:25:35.886 --> 1:27:03.679
Yeah. In fact, we just set up a meeting, I think for next week to sit down and talk about how we're approaching next year's crop of internships and what we'll do there. So yeah, we will be doing internships, and we do them all year. We have interns right now for different disciplines and different times makes sense. But there are a lot in the summer, and we've been focusing a little bit on some of the graduate students the last year or two. So it's been mostly PhD or Master's degree students who are coming in and doing internships, but I think we're going to change that this year and have a little bit more undergrad work to be done. The team's gotten a lot bigger, and one thing that means is that there's a wider variety of types of work that needs doing. And I think we have some spots for a really good undergrad research project or two that looked pretty interesting. Like I said, we're kind of still in the middle of figuring it out, but that's basically where we're headed. And for me, I like to make sure that if we're going to put an internship together, we want it to be something that's a little risky, right? It's a great time to take a risk, but also something where we see a legitimate chance for success. And it's going to, we're going to learn something either way and everyone's going to benefit from it. So I think we'll be in a position to do that with undergrads next year. We're definitely continuously in a position to do that with grad students; that's not going to change. So we'd love to hear from people who are interested in possibly joining us in that way.
1:27:03.680 --> 1:27:33.600
Cool. Excellent. And so folks want to check out LivePerson? You go to liveperson.com and you can see what they're all about. And if you want to reach Joe, you could reach Joe at jbradley@liveperson.com, and you can shoot him—I'm sure you can shoot him a resume. He'll forward it to the right folks, or there's probably a place on liveperson where there's like a careers page or something like that, where you can.
1:27:33.600 --> 1:27:57.282
You can definitely apply online. You can definitely reach out to me too. If you want to say, 'Hey,' and we'd love to hear from the audience here. As I mentioned before we got started, I think you guys do a pretty cool podcast here. I like how application-focused it is. And yeah, it feels like it has a lot of weight to it. So I'm sure your listenership are all pretty cool people.
1:27:58.125 --> 1:28:38.845
Yeah, definitely. And they're super motivated. We've actually connected a lot of interns to careers to at least to internships. And so, and we're constantly getting emails—the show has been out for a while. So we're constantly getting emails from folks who have landed full-time jobs or even been in them for several years, and that's really special. So I feel good about; I think there's going to be people out there who are a great match for LivePerson. And now if you're listening to this, you're one of those matches. Check out the show notes, check out the website, and get connected. It sounds really—oh, actually, so we didn't cover location. So where is LivePerson based? Yeah,
1:28:39.081 --> 1:30:21.679
We're a distributed company now. It's evolving. I think a lot of companies are, but when the pandemic started, we wanted to give certainty to everyone early on. So we said, 'Hey, if you want to move where you want to move, go move,' like as long as you can do your job, as long as it doesn't impact your work. We were pretty open and we're not demanding people be ready for a mass return or anything like that because we wanted people to live their lives. Now that we're coming out of the pandemic, I think we're hopefully resurging a little bit, but as we take steps out, we are beginning to open physical offices. Again, we have an office open in Seattle here that I've been to. We do COVID testing either the morning before you come in or you go do it at the office before you come in. We're obviously following all the policies and protocols that are local to the areas, and we have our own sort of standards on top of that. So I think we will be coming back more and more physically. We're probably going to maintain a high degree of flexibility about where people work, and how that comes to life. We have some policies and standards around how to manage the pay scales and things that like you sort of have to do from a corporate sense for the economics to make sense. But I think we've done that in a fair way. And I think we're open-minded, particularly when it comes to great science professionals and researchers, and folks with a lot of Machine Learning engineering expertise, and those people that are really doing great in these very hot fields. These are the kinds of people we want to be really flexible with. The most important thing is for you to come and work with us and for us to get some good work done together. And the exact details about the location and all that are pretty secondary.
1:30:21.679 --> 1:30:36.799
Yep. Totally makes sense. I think that's consistent with where everyone's at. I'm hoping they come up with some nice whiteboarding tool. As scientists, this is the thing we need—is some way to whiteboard together.
1:30:36.800 --> 1:31:01.920
Yeah. We're looking at that right now. We've got a couple of options that I want to get put in the office and try out to see how they work. I don't know if they're any good yet or not. And we haven't really tried out the tech, but I kind of agree. I'd love to be sitting here and have a whiteboard here that I can write on, and that like the writing of my partner on the other end of the communication appears, and it's almost as if they were here. Somebody solves that, they'll be billionaires overnight.
1:31:01.920 --> 1:31:26.400
Yeah. I wonder if in general the VR headset is never going to be so convenient that I would wear it all day, but I wonder if maybe that's—everyone keeps talking about the Metaverse. I have no idea what the Metaverse is. I'm assuming it's connected to VR, but that might be something where we could go into VR for an hour and then there could be some Holodeck-like thing, and then somehow at the end it would accumulate a whole bunch of notes.
1:31:26.400 --> 1:31:39.920
Yeah. I think it'll be interesting to see how we solve these problems. They're much bigger problems now or more important problems now than they were a couple of years ago. I wonder about VR too. I guess the Metaverse is the what Neil Stevenson reference from Snow Crash or something, which is.
1:31:39.920 --> 1:31:48.400
Oh, that's right. Oh, it's been so long. I think I had to read that in high school. It's been a long time since I should give it a reread.
1:31:48.400 --> 1:31:59.040
It's one of the only sci-fi books I know of that where the three ring binder appears in a starring role, at least for the first part of it. Yeah, that's right. It's been a while though.
1:31:59.679 --> 1:32:13.967
Yeah. That's where I think they were able to—someone, I think it's like a murder mystery or something, or so at some point I think somebody dies. Yeah. They're somehow killed in VR. It's very not lucid memories of that book.
1:32:14.271 --> 1:32:28.446
Language is a virus, right? Is this whole concept he's exploring. And I think there's this capability to like, you can show people information and it infects them and gets them sick. If you show it to them in the Metaverse or whatever, it's been a while for me too, but I think, I think it's something.
1:32:28.716 --> 1:32:32.125
Like that. Yeah. We should make that book of the show next time. We need to
1:32:32.125 --> 1:32:32.867
read it again.
1:32:32.867 --> 1:32:33.964
Yeah. Yeah. That'd be
1:32:33.964 --> 1:32:39.920
Fun. You guys ever want to do a Stevenson fest or something? I'd be happy to come back.
1:32:40.480 --> 1:32:55.328
Oh, cool. All right. We might take you up on that. Cool. Joe, thank you so much. I know we're over time by a lot, but I really appreciate you spending the extra time and chatting with us. It's been absolutely amazing, and it's been a real pleasure. Thank you so much.
1:32:55.497 --> 1:33:08.320
Yeah. The feelings mutual is a super fun conversation. Thank you so much for giving me the opportunity to come on and chat with you guys very much. I appreciate it, and I love like the depth and the quality of the discussion. So thank you so much for facilitating that.
1:33:09.200 --> 1:34:04.480
Cool. And thanks for everyone out there. I think you continue to support us on Patreon and on Audible, and we really appreciate that. I've been trying to post a bit more on Twitter. In the past, I've mainly used Twitter to send show notes, but I'm trying to put some more content on there. If I see something that I think is pretty cool, you know, and related to coding and tech, I've been trying to share more content there. So follow us on Twitter, follow us on all the other ones as well: LinkedIn and Facebook. Any of those will definitely—we'll definitely be posting the show notes there every time they come out, and subscribe, you know, we can. If we're not on a streaming platform at this point, let us know. I think enough people have let us know about enough platforms that were on all of them, but there's new ones coming out all the time. So definitely keep us up to date. Keep us honest if there's something we're not on, and we'll catch everyone in two weeks. Thank you so much.
1:34:18.480 --> 1:34:47.440
Music by Eric Barndoller. Music by Eric Barndoller. Programming Throwdown is distributed under a Creative Commons Attribution ShareAlike 2.0 license. You're free to share, copy, distribute, transmit the work, to remix, adapt the work, but you must provide an attribution to Patrick and I, and share alike in kind. Music by Eric Barndoller. Music by Eric Barndoller. Music by Eric Barndoller. Music by Eric Barndoller.