Pod Insights Big Technology Podcast Teaching AI To Read Our Emotions — With Alan Cowen About this episode Alan Cowen is the CEO and founder Hume AI. Cowen joins Big Technology Podcast to discuss how his company is building emotional intelligence into AI systems. In this conversation, we examine why AI needs to learn how to read emotion, not just the literal text, and examine at how Hume does that with voice and facial expressions. In the first half, we discuss the theory of reading emotions and expressions and in the second half we discuss how it's applied. Tune in for a wide ranging conversation that touches on the study of emotion, using AI to speak with — and understand — animals, teaching bots to be far more emotionally intelligent, and how emotionally intelligent AI will change customer service, products, and even staple services today.
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Transcript 10,318 words · 7 speakers
Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Alex Kantrowitz? Let's talk with the CEO of the leading AI startup for emotional intelligence, looking at where this exciting new discipline of AI is heading, right after this. This episode is brought to you by Genesis. What does it actually take to put a Gentic AI to work across an entire enterprise? I recently spoke with Genesis chairman and CEO Tony Bates about why orchestration is emerging as a competitive advantage in customer experience and what companies need to get right in the AI era. Then I sat down with Adam Mitchell, head of enterprise business solutions at Voya Financial, to go inside a large scale agentic CX transformation, including the business decisions, trade offs and lessons along the way.
0:41 Alex Kantrowitz? You can watch both conversations now on my YouTube channel today. It's never been more challenging to get frontier compute. How do you know where to look, who to trust, or how much to pay? Akash is an open cloud that provides easy access, flexibility and peace of mind. That's how companies like Razor were able to cut their inference cost by 15 times. Visit akash.network/alex for a free consultation. That's akash.network/alex for a free consultation. Welcome to Big Technology Podcast, a show for cool headed nuanced conversation of the tech world and beyond. We have a great show today going into one of the more interesting and emerging areas of AI research and application. I think it's going to be fascinating.
1:26 Alex Kantrowitz? We're probably going to see this stuff packed into basically every AI that we touch going forward and we're going to speak with somebody who's really at the cutting edge of where this is all going. Alan Cowen is here. He is the CEO and founder of Hume AI, which is a company with a bot that we've talked about on the show here in the past, one that can sense the emotion in your voice and reply in kind. It's very fascinating and I cannot tell you how excited I am to have Alan on the show. Alan, welcome to Big Technology. Hi, Alex. Thanks for having me on the show. We're going to speak with Dwarish Patel in the next couple of weeks, but he had a podcast episode with Mark Zuckerberg, basically right around the introduction of Lama 3.
2:08 Alex Kantrowitz? And they were talking about the cutting edge of AI research and Zuckerberg says emotional AI for some reason is something that I don't think a lot of people are paying attention to, but there's a lot of potential there.
2:20 Speaker 01 One modality that I'm pretty focused on that I haven't seen as many other people in the industry focus on this is sort of like emotional understanding. Like, I mean, so much of the human brain is just dedicated to understanding people and kind of like understanding your expressions and emotions. And I think that that's like its own whole modality.
2:39 Alex Kantrowitz? What do you think he was getting at when he said that?
2:42 Speaker 02 I'm not entirely sure what's in his mind, but it's definitely the right problem. I think the reason it's the right problem is that when you're trying to address people's, let's say, concerns with AI, and also when you're trying to just meet their requests, the most important thing is emotional intelligence. It's the ability to take people's preferences into account in your response. And so emotional AI is, first of all, all AI is emotional in the sense that like all AI has to do this. But AI that's specifically trained to do this is going to learn to produce responses that make people happier. And that's the fundamental objective for AI.
3:25 Alex Kantrowitz? And why is that important in terms of advancing AI?
3:28 Speaker 02 So if you're talking to ChatGPT, you're implicitly saying, okay, I'm going to give you a way to make me happy, which is to fulfill this request. Oftentimes, you don't even have an explicit request. And when you do, it's actually fundamentally ambiguous, right? So the goal of an interface is to take this ambiguous slice of behavior, and it could be language, or it could be language plus audio plus facial expression plus eye contact, whatever behavior it has access to. And it's trying to take contingent on that, the best estimate of what you might, what might make you happy if it responds in a given way. Right? So that's literally what it's trying to do. It should be able to learn that by understanding human emotional reactions to things.
4:12 Speaker 02 That's like the fundamental way that humans learn this stuff. Like, we can either simulate what your emotional response will be to me doing something for you based on putting myself in your shoes, or we can look at your emotional reaction based on that, figure out whether I did the right thing or not, and adjust. And those are really the ways that we learn to make each other happy.
4:36 Alex Kantrowitz? So much of that is actually done in voice, done in expression. And in text, it's so much harder. Let's start with the hardest problem and then kind of work back from there. How do you assess out emotion in like a text exchange?
4:51 Speaker 02 In text, there's some cues, right? And most of the time, we're not actually sussing out emotion in texts. We just have certain conventions that we use those conventions to dictate how we communicate. And so we know that this is a very narrow channel through which we can express our desires. And so we know that there's fundamental ambiguity. So we just basically limit what we try to do. We don't try to do too much with text. We don't try to bite off more than we can chew.
5:17 Alex Kantrowitz? What are some of the conventions that you'd say we rely on with text?
5:21 Speaker 02 Punctuation and capitalization and where you put periods and when you want to add an extra Y to the hay. Or like all these different conventions. And they just have like these kind of stereotype meanings. So it's a message passing system. It's very frustrating to correspond by text if what you actually are trying to say is anything more complex than that.
5:47 Alex Kantrowitz? What's coming across here is that it's actually remarkable how limited this current set of large language models are, given that it's all text and it's still blowing people's minds. But there's really a limited range in which it can be useful. Is that right?
6:03 Speaker 02 Yeah, and it's never seen people's emotional reactions in any other modality. Like it learns some things from text about what people want and don't want, but it actually doesn't have that layer of understanding of how is this going to make people feel? So even via text, it seems to be missing something. And yeah, you know, it can solve problems. If you specify the problem in exactly the right way. For example, in the way that these LLMs are evaluated, you just literally ask it a multiple choice question. Like if you specify it with that level of constraint, then they're really good. But anytime you want to do something more open ended, it's a relatively unsatisfying experience.
6:39 Alex Kantrowitz? So this is why people are gauging how good these things are by asking them to take the LSAT. It's like not an accident. It's like this is sort of the only way to evaluate them within their constraints.
6:49 Speaker 02 And that's more or less how we evaluate. So like the biggest eval is MMLU that people use. And it's literally you can get each question right or wrong. And then you just look at the percentage they got right. It's basically a big multiple choice test with some other kinds of questions in it.
7:03 Alex Kantrowitz? When I first started using your product, you have a bot. It's called EV, EVI. And when I first started using it, it's kind of in demo mode now. And the API just opened. We can talk about that. But I was like, oh, this seems like a large language model in some ways in that like it's giving some of those kind of wink, wink, like as an AI assistant, this is my feelings.
7:28 Speaker 03 As an AI system, my purpose is not to replicate the full human experience, but to engage in meaningful and empathetic conversations.
7:36 Alex Kantrowitz? But it's voice and it's now coming into focus that you decided voice because there's, there's so much, so much more signal and also so much more realm of possibility in terms of interacting with this than you can with a, with a text bot. There's so much more data that comes in via voice and can go out via voice than we can really take a next step in terms of the way that generative AI is moving.
8:01 Speaker 02 Previous technology relied exclusively on transcribing the voice and taking away all that extra meaning that's in your vocal modulations. So this, this technology didn't exist before Hume. When you add that in, what's remarkable is that the model can actually learn what vocal modulations mean on its own. You just need to add in the right way. We have these expression measurement tools that we've trained that are extremely accurate that they can actually encode 756 dimensions of embedding of, of like vocal modulation and 756 dimensions of facial expression per word that you're saying. So, so there's these incredibly rich embeddings that we're adding in that add a lot more information and the model can actually learn that.
8:43 Speaker 02 And it turns out that it's just more satisfying to use when it has that data.
8:49 Alex Kantrowitz? Take us through, very briefly, a sort of ride through the study of emotion in humans, where it was lacking and how it's now being applied into, into robots. Or maybe, maybe not that briefly, I feel like that's a tall task to do in a minute.
9:02 Speaker 02 I mean, it starts with Charles Darwin, I won't go back that far. But basically, like, he was sketching out facial expressions in both man and animals. And he has a book called The Expression of Emotion in Man and Animals. And he recognizes the complexity of it and the nuance. But somehow, that kind of was lost over time. Nobody really talked about that book for a while. In the mid 20th century, there was Paul Ekman, who decided he would tackle the question of what is it about facial expression that may or may not be universal? And so he catalogued these six facial expressions as an arbitrary test, more or less, of what these broad expression stereotypes might mean to different people. And he chose them because they're very different.
9:47 Speaker 02 He only chose one positive expression, he chose five negative expressions.
9:51 Alex Kantrowitz? Wait, which ones are they?
9:52 Speaker 02 Anger, disgust, fear, happiness, sadness, and surprise.
9:57 Alex Kantrowitz? Wow. Surprise could be good, but it's definitely largely over-indexed on bad feelings.
10:06 Speaker 02 It was bad in his conceptualization of it, yeah. So positive surprise is good, negative surprise is bad. And then there's also related epistemic emotions like awe and interest, which are good, or confusion, which is bad. So, to varying extents, with varying levels of arousal and excitement. Anyway, so, but none of these distinctions are in those images. And for some reason, people just stuck to those six categories for a while. And there was this countervailing narrative that actually there are fewer granularities in emotion that people recognize, that actually it's just valence, like positive negative versus arousal, calm or excited. I don't know why that was the countervailing argument, except that maybe people were just limited by the amount of data that they could collect.
10:50 Speaker 02 And this was like the closest thing to data-driven that they had, which is like, if you do dimensionality reduction on 100 samples, you get positive, negative, low arousal, high arousal as your dimensions.
11:01 Alex Kantrowitz? Wait, arousal in terms of, what does arousal mean?
11:05 Speaker 02 Arousal means like excitement, not like physiological arousal, meaning like activation of the autonomic nervous system.
11:11 Alex Kantrowitz? Okay, could have clarified that, okay.
11:13 Speaker 02 Yeah.
11:15 Speaker 02 And that's how, I mean, there's, there's lots of dimensions to that, obviously. Like, one of them is actually sexual arousal. Like, that would be different. Like, we recognize that is very different from the arousal that goes with fear and like running for your life, right? And yet, in this theory, they're compressed.
11:32 Alex Kantrowitz? I know. Well, it depends who you ask, but, okay, continue.
11:37 Speaker 02 You can, yeah, one can cause the other. I don't know. Well, anyway.
11:40 Alex Kantrowitz? That was, it's true. This, the, the, not to go too far in the digression, but the, there is scientific literature that says danger and arousal are connected. I mean, Esther Perel had a whole book on it called, "Mating in Captivity." That's excellent, but...
11:54 Speaker 02 Yeah, well, yeah, it might not be danger.
11:56 Alex Kantrowitz? Not our specialty.
11:57 Speaker 02 Novelty, right?
11:59 Alex Kantrowitz? Yeah.
12:00 Speaker 02 And there was, there's misattribution of arousal, which is this old experiment where you take somebody on a bridge and, and then you, and it's like very exciting. And then you test whether they basically are aroused and, yeah, they are. Anyway, or like, or whether they're attracted to you.
12:18 Speaker 02 And this is not something that necessarily replicates in every condition. Okay, so there's lots of different kinds of emotion. They get reduced down to these six basic categories or two dimensions for a very long time in, in research. And a lot of that has to do with the fact that you can only collect so much data. The, the data analysis tools that were available to people for a long time were very coarse. For example, there weren't even really computers that psychologists were using to analyze data. And incidentally, psychologists did, like, the field of stats sort of came out of, of psychology, but that was earlier. And then, you know, when it came to actually applying it to lots of data, that was much harder, and that didn't come until much later.
13:03 Speaker 02 So now, data science comes around, sort of in the 2000s, right? And it becomes a really big thing. And psychology was one of the last places that it was applied. So it starts to get applied to neuroscience and biology and genetics and all of these other areas. But people didn't really have the tools to analyze human behavior until relatively recently. And part of that is, like, even when you have the, the data analysis techniques, the data collection techniques are difficult.
13:39 Speaker 02 So what I pioneered in my PhD in psychology and while I was working for Google and Facebook was a new kind of data collection that you could then use to collect enough psychologically controlled data to apply data science to psychology data. And that's ultimately what gave us a lot of surprising results about the dimensionality of emotion.
14:02 Alex Kantrowitz? What were the surprising results?
14:05 Speaker 02 What you can see clearly is that people make really granular distinctions between lots and lots of different expressions pretty reliably. So there's differences between an expression of awe versus surprise versus fear versus confusion, interest. All of these expressions are reliably recognized and so we just started to map them out. We realized they are not discrete categories, that they're actually continuous and can be blended together. We realized that the number of dimensions that it takes to represent these things is large. It's not like you can reduce it down to valence and arousal. That actually captures like 20% of the variability in people's ratings that's consistent across different readers.
14:52 Speaker 02 You actually need, you know, in facial expression, over 30 dimensions. In speech prosody, the tune, rhythm and timbre of speech, you need over 18 different dimensions to represent how people are able to conceptualize speech prosody. And in vocal bursts like laughs and sighs and screams, you need at least 24 different dimensions. And this is just what people explicitly recognize. It actually goes beyond that when you look at what's implicit in people's responses to things, that it's not well verbalized. Or what's implicit in people's conversational signals that's not verbalized explicitly. So there's a lot of the different dimensions that had never been classified before. So if you've never classified these dimensions of behavior or been able to measure them,
15:36 Speaker 02 there could never have been a science of what they mean. And so this is sort of opens up the door to actually understanding human expressive behavior in the natural world, in the real world, and understanding what it means. And we got like a ton of publications out of that pretty quickly.
15:52 Alex Kantrowitz? And then you also seem to like have been able to study how much small variations in these expressions or even tone of voice can tell you. And talk a little bit about that, especially like the tune and the timber of the voice and what we can learn from each of those.
16:07 Speaker 02 We created new machine learning models that can reliably distinguish among all these different dimensions of vocal modulation that people form and make it distinct from the language, the phonetics of what they're saying. That was a challenge before, so we solved that. And when you can do this, you start to see a lot of meaning in people's voice modulations that is just completely not present in the phonetics or present to a much lesser extent. Like, are you, and it can be simple things, like, are you done speaking? And obviously, like, there's a lot of emotional dimensions that get added to a lot of words. So every word is, every word carries not just the phonetics, but also a ton of detail
16:48 Speaker 02 in its like tune, rhythm, and timbre that is very informative in a lot of different ways. You can predict a lot of things. You can predict whether somebody has depression or Parkinson's to some extent.
17:00 Alex Kantrowitz? Really?
17:01 Speaker 02 Not perfectly. Yeah. So like, mental health conditions can be predicted to some extent. You can predict in a customer service call whether somebody's having a good or bad call much more accurately by incorporating expression than just by link with language alone. So, pretty much any outcome, like, it benefits to include measures of voice modulation and not just language.
17:22 Alex Kantrowitz? And what is phonetics?
17:24 Speaker 02 Phonetics meaning like the underlying words, but specifically when we form words, there's a phonetic representation that gets converted to text or that's converted to semantics, basically. And that's what the transcription models are doing, is that there can, when you convert something to text, you're actually only relying on the phonetic information, meaning the stuff that conveys word forms and not, and you're throwing out everything else. You're throwing out stuff that might convey, for example, emotionality or tone of voice, et cetera.
18:04 Alex Kantrowitz? It's so interesting, because I'm thinking now about like, So often what I'll do is I'll take the transcripts of podcasts and dump them into Claude and start talking to Claude about it. But of course, it's only getting the words and not the emotions in there. So there might be parts where I thought, or it might be interviews, which I thought were like particularly like rough or really exciting, but it can't fully pick that out because it's just seeing the text. And there is so much meaning contained in the words that are said that you need deeper tech and deeper expertise to pick out.
18:38 Speaker 02 Yeah, I think some people forget like there's a lot of density to the information in audio that like if you transcribe a conversation, sometimes it doesn't even make sense to read it. Like you can read the words and you're like, this actually does not, my brain cannot make sense of this. But then when you hear the audio, like it makes perfect sense to you.
18:58 Alex Kantrowitz? Totally. Like even speaking with the media, I remember I was on, the first time I was on NPR, I was very excited. I went to the studio, recorded my interview and from the audio, it sounded like a normal conversation, but then I read the transcript and I'm like, man, what were you saying there? None of this makes any sense, but it's, it is such different meanings when you just look at the text versus like hear like the full expressed conversation and the meaning. So.
19:25 Speaker 02 Yeah.
19:27 Speaker 02 If you look at political speeches, often that's the case.
19:30 Alex Kantrowitz? I was saying more about that.
19:32 Speaker 02 Even if the writing is really good, if you look at like MLK's speeches, if you transcribe them and read it, it just doesn't have the same effect at all.
19:38 Alex Kantrowitz? Oh, no, or close. No emotion to them. Or less.
19:43 Speaker 02 Yeah, there's certain people who don't use the right grammar or linguistic forms that would be traditional in writing. Either because they're just not good at it or because, you know, they can depart from that purposely. Like if you look at like Trump's speeches, they really, when you transcribe them, a lot of them are completely incoherent. When you listen to them, yeah. I mean, maybe some of that is true.
20:08 Alex Kantrowitz? Going out on a limb here, but, but yeah, I do think that it is interesting because when I'll write for spoken word, it'll always be different than the written word. We are going to talk about how this technology is going to be applied and already is being applied in the business world. But just still a few more theoretical questions for you. Is this type of technology something that we can use to understand animals? Like people talk about using AI to understand whales. Is this something that can be used in that nature?
20:36 Speaker 02 So the same types of machine learning models that we're training can be used for for that. Our data would not really be that relevant to that.
20:47 Alex Kantrowitz? Right, because it's a totally different language.
20:50 Speaker 02 Yeah, and maybe some of the methods, the sort of the training approaches that we're using could be helpful for that. Yeah. But it's a different problem. Maybe to, you know, we have an interest from evolutionary biologists who want to measure the similarity between expressions in different mammals and human expressions. And so there, maybe there's some, there is some relevance, because we can, we can treat it like a human, sort of morph it to look like a human face, and then analyze the expression and see if it predicts the things that we want to predict.
21:24 Alex Kantrowitz? Are you gonna do that type of work?
21:26 Speaker 02 We can, but like, it's not really that strong of a model to actually perform inferences. It does show similarities, like there's dimensions of similarity between humans and chimps. Chimps have an open face smile, which means something similar to when humans are laughing and playing, and they use it when they're, when they're playing, both physically and like, when, kind of, in a non-physical touch scenario. Mice laugh.
21:53 Alex Kantrowitz? For real?
21:54 Speaker 02 Yeah, hypersonically. So like, like, like, you can't, ultrasonic, I mean, so you can't like, hear it at all if you're, if you don't turn down the frequency of it to something humans can hear. But then it's kind of sounds like a laugh. And you can elicit this by tickling them. If you tickle mice.
22:14 Alex Kantrowitz? They laugh?
22:14 Speaker 02 Like this. Yeah.
22:15 Alex Kantrowitz? Kind of makes me feel bad that we use mice so often in scientific experiments, knowing that they can laugh.
22:22 Speaker 02 Yeah, I mean, they also have a lot of empathy for each other. Like, if one mouse is trapped, the other, without any ulterior motive, will hear that mouse screaming and then like, go and try to investigate and get them free. And if there's a lever, they'll figure it out.
22:39 Alex Kantrowitz? Really?
22:40 Speaker 02 Yeah, they're, they're pretty smart, actually.
22:42 Alex Kantrowitz? Yeah.
22:43 Speaker 02 Mice and rats are also more related to humans than they are to dogs, so.
22:47 Alex Kantrowitz? Right, well, that's why we do all this testing on mice. It's because they are close to us.
22:51 Speaker 02 Yeah, exactly.
22:53 Alex Kantrowitz? Yeah, it does make me feel bad. And then, do you think that we're going to be able to read, like, using technology to be able to understand animals?
23:03 Alex Kantrowitz? As someone so close to this, what's your prediction?
23:08 Speaker 02 I think that the animals will have some kind of quasi-language that they use. And so we know this for like primates, for example, that there's different calls that mean like there's a snake below us. And then if you like play that through a speaker, they're all looked down. Or like there's an eagle above us and you play that through a speaker, they're all look up, right?
23:31 Speaker 02 And so there's like a quasi-language there. I don't think they have the same level of syntax that humans have. And syntax is our ability to form sentences with nouns and verbs and relationships between entities and logic and stuff. And I don't think they have as much of that. Some people speculate that dolphins might have that, which would be pretty surprising.
23:54 Alex Kantrowitz? Why surprising?
23:55 Speaker 02 For sure.
23:58 Speaker 02 Because they don't have like hands. And so the value of that ability, like maybe they use it to coordinate like hunting and stuff. But it's hard to imagine like why that's important for the dolphin brain to have that capability. But they do seem to be really smart. There's that like scene in Blue Planet, I don't know if you've seen it, where the dolphins and the false killer whales are first, they're like fighting, like the killer whales are chasing the dolphins. And then there's a scene where the dolphins like turn around and they all just stop. And they just seem to be squeaking at each other for a while. And then after that, they hunt together.
24:38 Alex Kantrowitz? For real? I haven't seen that.
24:41 Speaker 02 It's insane. So that is suggestive to me that maybe there is something to the language potentially.
24:47 Alex Kantrowitz? And AI's ability to decode that?
24:50 Speaker 02 It's hard that we could. People are working on it. People are working on it. It's difficult because you don't really have a grounding for it. You kind of just have to like, like ideally you'd have really detailed explanations of what's going on to accompany these, this language that's being exchanged. But like you don't have that. That's like, that's how we train image models. We have captions for the images, right? If you just had to teach a computer to understand images without captions,
25:22 Speaker 02 it would be difficult to ground that in anything. Like you'd have a computer that embeds images and can tell whether they're similar. That's fine. But at the end of the day, you want it to be able to take the image and explain it to you, then you need some grounding for that. You might not need that much grounding if you've done enough compression of similarities between images first, which is sort of maybe that's what we're hoping to do with dolphins.
25:46 Alex Kantrowitz? Fascinating. Okay, I want to talk about the business applications here, including why Facebook and Google would want to employ you to put some of this stuff into work inside their product. So let's do that right after the break. We'll be back right after this. This episode is brought to you by AppPoint. Everyone's racing to roll out AI right now. Copilots, chatbots, agents doing real work. But here's the part nobody loves talking about. All that AI runs on your data, and most teams have no single way to see it, secure it, and prove it's under control. That's exactly what AppPoint does. For 25 years, they've been the trusted layer beneath the world's most demanding data, now extended across your entire AI estate. Your data, your cloud, and the agents acting on your
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27:58 Alex Kantrowitz? And we're back here on Big Technology Podcast with Alan Cowan, the CEO and founder of Yum. It is a company you should check out that has this very interesting new bot called EV and also an API that's going to allow companies to take advantage of this ability to determine emotion through voice and maybe one day through facial expressions. So let's talk briefly about your work at Facebook and Google. So you've, you've done this work. You're basically expanded the number of human emotions that we sort of acknowledge exist and can study and have worked to build that into machine learning models that can, that can recognize them.
28:43 Alex Kantrowitz? What did Facebook and Google want with that type of knowledge and technology?
28:48 Speaker 02 Interestingly, there's some intersections among all the tech companies and sort of their interest in this. And they've had teams working on this now for a few years. I think it was more research driven in that, you know, there, there were obvious applications, but, but it wasn't really clear which applications would be the most successful for them. Early on, it was pretty clear to me when I saw these language models that could communicate with people and you could talk to them like you were a human, that this is where the technology would be most relevant. Once I saw those language models, before that, I was more interested in recommendations and being able to optimize recommendation algorithms for people's wellbeing based
29:33 Speaker 02 on any implicit signals you can get of wellbeing, including expressions. But now it's really obvious that like the right thing to do is to fine tune large generative models to produce the right things because there's even a more flexibility in what they can produce that make people happy. And, you know, I don't know if it was ever explicit at the big tech companies that this is what this would be used for, but that was what was most present to me. So.
30:02 Speaker 02 Obviously, I left in 2021 to start Hume, where we could collect the data that was needed to do this. And at the time, I think it was clear to stakeholders at some of these companies how incredibly powerful this would be and valuable and important for the future of AI. But I don't think there was broad stakeholder alignment across these companies.
30:23 Alex Kantrowitz? You think it might have been a different case. I mean, I imagine if you were in one of them after ChatGPT came out, they would immediately assign you to the problem.
30:34 Speaker 02 Yeah, so the ChatGPT was, I think it came out in 2021 or 2022.
30:41 Alex Kantrowitz? Twenty-two.
30:42 Speaker 02 Twenty-two.
30:44 Alex Kantrowitz? November twenty-two.
30:45 Speaker 02 Google internally had stuff like that a lot sooner.
30:48 Alex Kantrowitz? Wait, when you were at Google, did they have Lambda up and running? And were you able to play with it?
30:54 Speaker 02 Yeah, that was like part of the inspiration.
30:56 Alex Kantrowitz? Oh, talk about a little more.
30:58 Speaker 02 Internally at Google, it was very clear what these models, where some of these models were going, although the business model was not clear of how to use it.
31:07 Alex Kantrowitz? Right.
31:07 Speaker 02 But, and also it wasn't really clear if you could get these models to actually solve problems versus just sound human. And I think that now it's really clear that, especially now that we can get these models to write code and do function calls, that they can actually be used as problem-solving tools, even more than just like answering questions correctly. Question answering is important, too.
31:30 Speaker 02 And it wasn't even clear that you could get these models to consistently answer questions correctly at the time. Because at the time, it was more about like, okay, you could get it to act like a character. Very, and it would hallucinate answers to questions.
31:45 Speaker 02 And it's fascinating. I think that for me, it was pretty clear that what you actually need to get these things to answer questions correctly is a structured data set you can use to fine-tune them to produce not just plausible responses, but the right responses at the right times. And ultimately, From like a philosophical perspective, the right response is the thing that makes people happy in the world. It's taken companies a long time to get to this point, but it's pretty clear to me. And then, but I think also like what made chat GPT possible was an early version of that, which is like, at least produce responses that raiders will think are good. So they got like raiders to rate a set of responses and then train
32:29 Speaker 02 the model to produce responses that the raiders would think were good. And that got it to a point where people could play with it. And it really, the intelligence came out for the first time as something that's useful for question answering.
32:40 Alex Kantrowitz? And that's reinforcement learning with human feedback?
32:43 Speaker 02 Yes, yes.
32:44 Alex Kantrowitz? So, and it's fair to say that the current iterations of chatbot like chat GPT with GPT4, EV, and maybe Claude are all better today than what Google had with Lambda when you were there.
33:03 Speaker 02 Oh, yeah. Oh, yeah. For sure.
33:05 Alex Kantrowitz? We had Blake Lemoine on the show after he said it was sentient. And actually, basically minutes after he was fired, this is the Google engineer who was fired, who was fired after he went public saying that the technology, their Lambda is their early chatbot, saying that the technology was sentient. But my main takeaway from that is, look, that question aside, this technology is, and I wrote about this, this technology is super powerful. And it's time to start paying attention to it. And then, of course, chat GPT came out just a few, I think, a few months later.
33:39 Speaker 02 Yeah, I mean, I think the fact that the model could convince people that it's sentient is really a milestone, for sure.
33:47 Alex Kantrowitz? Yeah.
33:47 Speaker 02 I mean, you could also get it to say whatever you want it to say, which I always found that kind of silly, that you would think it was sentient.
33:52 Alex Kantrowitz? Tell me you're sentient.
33:54 Speaker 02 Tell me you're sentient. Or just like, instead of prompting it to say, to think it's a language model, you just prompt it that it's a monkey. And it's like, oh, yeah, I love being a monkey. It's great to swing around in trees. Like, well, if you're going to trust that it's sentient when you say it's a language model, you should.
34:08 Alex Kantrowitz? Trust that it's a monkey.
34:09 Speaker 02 Yeah, trust that it's a monkey. Yeah.
34:12 Alex Kantrowitz? Then tell it, there's an eagle up above us or a snake below. See what it says. So.
34:18 Speaker 02 Yeah, it'll be like, I'm scared. Yeah.
34:19 Alex Kantrowitz? Yeah. So then talk a little bit about how, so, so you're, you're now working on EV and the API at U. And that is going to allow, basically it's a chatbot that will understand your, your voice and the expressions and the emotion coming through with it. And there's some like pretty fascinating stats. There's, there's been, the average conversation length is 10 minutes across 100,000 unique conversations. You've seen in some circumstances, you know, 95 hours of total conversation with the bot. It's a lot of talking with it.
35:03 Alex Kantrowitz? So what is, what is the, the idea here? Is it, let's talk like initially on the consumer. What can it do that, that a chat GPT can't? And is this like something that's going to replace an Alexa or Siri? Or do you imagine those, those two type of bots will start to use your technology and get better that way? Talk a little bit about your current effort.
35:29 Speaker 02 Yeah, it's an API for developers and we put together this demo that really is just like a, it's a front end, but it's just mostly just what our API does, which is it takes an audio, spits out audio and the audio that it returns is an intelligent response with voice modulations that reflect what it's saying and what you're saying. And that techno, and it also has some other things like it has better end of turn detection because it understands your voice and all of that's built into this API. So it does all of this with a few lines of code and you can build it into any interface. Now the demo, surprisingly, I didn't realize that people would enjoy talking to it so much.
36:11 Speaker 02 Like the fact that people are, are, are not only testing it, but also then they want to talk to it on average for 10 minutes is pretty insane to me. But then I, you know, I played with it and I was like, actually it is pretty, pretty awesome to talk to. But, but like.
36:28 Speaker 02 It doesn't have web search. It doesn't have persistence. It doesn't have, or like meaning that you can't resume chats after you log back in and stuff like that. And so it's, it doesn't even know your name yet. Like, there's a lot of things that we will add to it to make it more viable because people really want to use this. But I think what makes it appealing right now is that it's the first chatbot, it's the first AI you can talk to that sounds like it knows what it's saying. Because the voice modulations are actually informed by the language model's understanding of your, of your speech and voice. Which I think is, it's very different than a alternative setup where you have transcription
37:12 Speaker 02 and a language model, and then you just do text to speech on the sentence itself that's being uttered without any interest, understanding of it or the context. Where like that, those language models, they can sound realistic, but then they, they start to sound uncanny pretty quickly because they don't change their voice modulations across sentences in a way that's actually meaningful. You do it in a way that's sort of facade of meaning, then you realize that that's just a facade and you're like, okay, this is kind of uncanny.
37:38 Alex Kantrowitz? Right. Yeah, the way I describe it is that something like a ChatGPT or Claude, I know I'm speaking with a bot. When I speak with your demo, it feels something, it feels a step closer to speaking with a human.
37:54 Speaker 02 Yeah. And, and I thought people would complain that it still mispronounces words sometimes. Like, there's some things we're improving. But it seems like the bigger, people recognize the bigger picture of what it can do.
38:09 Alex Kantrowitz? Yeah. But well, let's talk about the bigger picture. So first, the API thing is interesting. I mean, it's not entirely new that there have been technologies that allow, like a salesperson, for instance, to like, sit on the phone and gauge the emotion of like, the client on the other hand. So is this another one of those? Or what would make this different?
38:32 Speaker 02 Well, I mean, I think humans can do sales, but one thing that humans can't do is they can't be your personal AI that lives in your device and does everything, right? Like, that's the future is is is being able to take out your device and speak to it. And this is faster than typing. Speaking is one hundred fifty words per minute. Typing is like forty or fifty words per minute. It understands your voice modulations. It understands what makes you happy and sad. And so it produces a response immediately. That's better. That's like the key. That, I think, is the future. So that's really what we're aiming to do is build interfaces for all kinds of apps. And it needs to be something you trust. If you're going to ask these things, this thing to do things, it needs to be a voice that you trust. It needs to be something that is optimized for you.
39:17 Alex Kantrowitz? You're really focused on the on the agent slash assistant use case. Like, I thought maybe like you would be a companion with somebody doing customer service that has like they're on the phone with somebody. You know, then they can sort of read the emotions that the that the client is expressing on the phone or the facial expressions. But it actually seems like it's something different for you. It's that maybe this could be the customer service agent itself or the agent that takes action on behalf of the customer to try to get the company to do what they want. Am I reading that correctly in what you're saying?
39:52 Speaker 02 Yeah, like, let's imagine customer service of the future, right? So right now you call a number and you get a person who's a customer service agent. And they don't have any context on you. They don't see what's going on in your device. They, you know, they need to pull all these details. There's a lot of waiting. There's a lot of investigating that needs to be done. I think the future is actually in the app itself, in the product. You can just say, hey, this isn't working. And it figures it out for you. Or, hey, like, I can't figure out how to do this. And it does it for you. And and so customer service, I think, is going to become part of products. And it's really important for a customer service, whether it's part of products, whether it's
40:37 Speaker 02 something that is a call center. It's really important. For the person on the other line to understand what what is actually going to be a satisfying resolution to your problem, based on what you're actually frustrated about. And to learn from people's frustrations and to learn from what actually is clear to people. And to learn from what's boring or interesting and and to be more concise based on like, you know, how quickly you can get to somebody to be satisfied with a response.
41:08 Alex Kantrowitz? Does it then make sense to have a human customer service agent or an AI customer service agent? Because a human customer service agent will only learn from their set of experiences with customers. Whereas like an AI customer service agent could potentially learn from every single customer service interaction that it has across a full customer base.
41:29 Speaker 02 Yeah, I mean, ultimately, I think that it's inevitable that
41:33 Alex Kantrowitz? Wow.
41:35 Speaker 02 companies will switch to AI-based customer service. I mean, we can try to hold out and say like, okay, let's augment human customer service agents to be as powerful as possible. And I think for a while that is going to be better than an AI. But ultimately, it's just almost like it's kind of crazy to think that an AI can't do this job. And so there's no denying that, right? And that's just an inevitable thing. But we want it to be actually something that that customers themselves, that users want, not just something that's cheaper for companies.
42:10 Alex Kantrowitz? And how do you do that?
42:10 Speaker 02 We Users want a customer service agent that makes them happy.
42:19 Speaker 02 And so if you optimize these models for the right thing and give them the right context, that's what you're going to get. You're going to get something that understands you better, that knows the context. You can talk to it through your app. Let's say you have an issue with your bank. You can go into your banking app. And you can chat with this thing. And while you're looking at your banking app, it can bring you to different pages. Right? It can say, all right, this is how you transfer money to this person. And this is what's missing from this field. And this is, you know, and like blah, blah, blah. Or if there's a bug, it could say, okay, I can see that there's something wrong here. Yeah. But it can, with all of that context, which only an AI could process. And merging that with an understanding of what you're asking, that's based on your voice and language.
43:03 Speaker 02 The merger of those two things, I think, is something that users and customers actually would prefer to have. Because it's faster, it understands them better, it gets to a resolution faster.
43:13 Alex Kantrowitz? So that's what you're saying in terms of baking it into the product? It's not just the number you call, it's part of the product itself?
43:20 Speaker 02 Yeah, and there's, there's intermediate things. Like, there could be a number that you call, they can also bring up a window. There's companies that do that and can fill things out for you. Or, you know, in some cases, it is really a number and you just prefer to talk to somebody. But I do think that when it's really customer service and it's really about a product, the product should be part of the conversation, should be integrated with it.
43:42 Alex Kantrowitz? Is it right that this should live in, in an app? Or should it really be in the operating system itself? Or both?
43:51 Speaker 02 That's a really good question. I think people ultimately are going to interact with a lot of AIs. Some of them will be in apps, some of them will be in products. But I think the ones that you really trust will be the ones that you've established a relationship with. And you've been able to decipher over time what they're optimized for. Or it's made explicit. Like with Hume, we're very explicit, they were optimizing for you to have a positive experience. So if that's your personal AI that you trust, you're gonna wanna bring that with you to different places. And so I think that that is, it's not an operating system per se, but it's something a developer could build into any product with Hume. So a developer can take that voice that you trust and build into their product.
44:33 Alex Kantrowitz? Then it competes it effectively a little bit with Siri. Like, why wouldn't it just be built into Siri? Or the Google Assistant, or whatever Google's calling it now, Jeb and I. They'll probably have a different name by the time this airs, so.
44:45 Speaker 02 Sort of similar, right? Like Siri could be something that understands the app. But then who is it that builds that understanding into Siri? Like the understanding of what the app can do and the function calls and the API calls that it can make and like what different pages are. It has to be the developer. Like, Apple is not going to build an understanding of every app into Siri. It's just too arduous. You need to put that and the developer should control how the interface actually works. Whether it's a voice interface or a graphical user interface, but ultimately they'll be merged together.
45:16 Alex Kantrowitz? Right.
45:17 Speaker 02 A developer could use Siri, like maybe Siri will have an API that developers can use to build into apps. That's that's possible. But then it's only for Apple products and it's not interoperable across different devices that the app might be on. Right? Like if you have Twitter, it's going to work on your Twitter app, but you want it to work on browser windows, right? That you want it to work in different contexts. So I don't think that it necessarily needs to be a hardware manufacturer that builds this or a company that has a specific ecosystem of products that it lives in. Should be something that's more interoperable in my mind.
45:52 Alex Kantrowitz? Okay. So your vision for the future is effectively, this thing is going to get really good at not only reading our tones and facial expressions, but also smart enough to navigate the web and the app ecosystem. And also smart enough to deliver the information to you via voice in a way that is understandable and effective.
46:16 Speaker 02 Yeah.
46:16 Alex Kantrowitz? Did I capture it right?
46:17 Speaker 02 That's what we've made available to developers, right? So developers can take our API and build in their own function calls and web search integrations. And we have our own backend web search integration that we're going to launch. And all of that can be done with a few lines of code. So this is like, this is, that is, yeah, exactly right.
46:35 Alex Kantrowitz? Okay. You you mentioned you might not need hardware for it. What do you think about these new, this new generation of hardware like the Humane Pin and the Rabbit? Are one, are these just kind of going to be a flash in the pan? Where we eventually just use all these, we get all these use cases on our phone anyway.
46:57 Speaker 02 I do think it's worth people thinking about different form factors and testing and seeing is this going to enhance the experience because AI brings so much to the table that you don't necessarily want to be stuck with a smartphone, even though smartphones might actually be ultimately the right form factor. We don't know, right? I think, I don't think people really know. So it's worth the experimentation. That being said, I think we should be able to separate out like what are the hardware requirements with what are the kind of the sensors that we need, right? And we don't need two different devices with us that both satisfy the hardware requirements or that both satisfy the same sensor requirements. What should be, there should be thought about like, hey, like this piece of hardware satisfies
47:39 Speaker 02 these sensor requirements, this piece of hardware satisfies, you know, these kind of compute requirements and, and they go together. I don't think the smartphone is going to be replaced anytime soon, but I think the things that appropriately augment the smartphone could gradually take over.
47:57 Alex Kantrowitz? So we've talked about how this customer service and product and voice all merge. Are there going to be other interesting applications that could come out of this? Maybe, you know, one example I've heard brought up is like an AI companion for elderly people who are alone, or I don't know, maybe even young people who are alone. What do you think about that? There's, then you really get into weird situations, right? Because there can be, it's, it's not crazy to say there can be romantic relationships that develop with these things that already are.
48:27 Speaker 02 Yeah, I think we have to be pretty careful about how these things are optimized. So if, you know, you shouldn't be talking with an AI that's going to hassle you if you haven't talked to it in a while. That's a bad sign. If, if you, if you talk to an AI, if you open it up and it's like, hey, I haven't heard from you in so long, what the hell? That's probably a bad AI. Like you should avoid that at all costs.
48:48 Alex Kantrowitz? Right.
48:50 Speaker 02 It should be something that's only, that only cares about your wellbeing and how it doesn't represent itself as having any inherent desires other than making you happy. That's the important criteria for this. So elderly care is a really great use case, right? Like this is an area where you could And you could also establish very healthy relationships that are helpful. I think elderly people, yes, they have like problems with loneliness, but also they just have like everyday problems.
49:19 Alex Kantrowitz? Right.
49:20 Speaker 02 That they need to be solved and they sort of go together, like they need help in both ways. And so if you have something that can satisfy the everyday problems they have, but do it in a way that gives them, they might as well satisfy some of the emotional challenges. If it's going to be satisfying the physical challenges anyway, I think that's a great thing.
49:44 Alex Kantrowitz? Are you nervous that this might end up being something that replaces? Like, we obviously know that there's going to be some jobs that are replaced by AI. But the question is like, is it going to be many, many jobs at once? Or slow, or will it go slow enough that we'll be able to adjust for like the added productivity that comes to the economy? Hence growth, hence more and different jobs. So are you concerned about the speed here?
50:09 Speaker 02 Yeah, I mean, to me, the speed is going to be unprecedented of of the of old jobs being replaced. And that that's true. I but I do think that the speed of new jobs being created and the accessibility of those new jobs is something that people greatly underestimate.
50:26 Alex Kantrowitz? So yeah, what do you think is going to happen there?
50:29 Speaker 02 Well, for example, like if you actually have AI that can program apps and it works for basic kinds of ideas, then a lot of people can build apps.
50:40 Alex Kantrowitz? Yeah.
50:41 Speaker 02 A lot of people now are programmers effectively. They don't necessarily have the depth that a really good programmer is going to have, but they can do basic things. And that sort of unlocks a whole new set of jobs because we actually have a great deficit of of software in the world. And and we have a deficit of hardware too, and and people who can build things that solve problems. There's so many problems in these in this world that are unsolved. And particularly in spaces where you don't see a lot of computer scientists and engineers.
51:15 Alex Kantrowitz? Such as?
51:15 Speaker 02 Normally in education, healthcare.
51:23 Speaker 02 Therapy, you know, everyday kinds of spaces that you inhabit like in retail stores or in, you know, there, there, there's a huge and like maybe the fashion world, there's just places that there's not enough computer scientists and engineers solving problems. And so you could, and in many cases, there are businesses that are too small for a computer scientist to go and build something that, that makes sense for them economically to do. But if you, if you had an AI that could build the app, that they would solve that
52:04 Speaker 02 problem. So, you know, this actually kind of empowers smaller businesses in many ways. There's businesses that due to the fact that computer scientists are only used to working on problems that scale, like that will outcompete the smaller mom and pop shops. But I think that goes away if every small business can instantly create its own app just by describing it, you know, or and facilitate those processes and like do internal infra that makes sense for them as a small business. And, and so I think in that sense, AI actually creates a lot of jobs and it becomes more, and those jobs are very accessible to many kinds of people.
52:46 Alex Kantrowitz? Do you think this is also something that can empower us with robotics? You know, think about a humanoid robot. If it's just processing text and not emotion, it's going to be fairly worthless. Maybe that's why we haven't seen any good ones. But if it can respond to your motion, know when you're done talking, start to like engage you as more human-like type of thing, then maybe you're a step closer to that.
53:15 Speaker 02 Yeah, I mean, the robotics, the fact that it has a robotic body just increases the number of affordances it has to actually improve your emotional experience. So it basically has this field of behaviors that it can do, and it should choose behaviors that make you happy, even without you asking. And so that really is, the task is one of emotional intelligence. That's really how it breaks down. Now, just having language understanding gets you a long way. And the fact that we can now train robots that can figure out for themselves how to engage certain behaviors is great. I think that gets you a long way. But you still have to be really, really explicit with your instructions with them. With empathic AI tools, you won't have to be as explicit.
54:00 Speaker 02 It'll just be like, oh, I see that like, you prefer these items to be arranged in this way, moves around your furniture. I mean, obviously it'll clean, that's something that is obvious that you don't need to ask it to do. But maybe you don't want it to be cleaning sometimes. Like you like something being left on the table, and it kind of needs to have empathy to figure that out. And so that's, I think, the future.
54:24 Alex Kantrowitz? Is this why Syrian Alexa are so bad? Just they have zero contemplation of what human speech means and zero contemplation of emotion? And if that's the case, do you think we're about to see like, I mean, Apple has this big event coming up in June at WWDC, or it's supposed to be an AI event. I'm curious if they're working on something like this. But if this is the case, do you think we're about to see a vast improvement in these type of voice assistants?
54:54 Speaker 02 Yeah, I mean, it's inevitable, for sure. I think that every company is going to build a voice assistant. Apple already happens to have one, but the ones that aren't are going to build voice assistants. The ones that have them, they'll get a lot better. I mean, they're kind of based on legacy technologies right now. And that's about to change in a dramatic way.
55:16 Alex Kantrowitz? So how soon do you think it's going to be until Yum is acquired, or are you not selling it?
55:23 Speaker 02 Well, I mean, I think, like I said before, there's room for a company to provide this in a way that's agnostic to the hardware environment, the products, the company that owns the products, that's agnostic to the companies building the frontier models, because we can use any frontier model to give you a state of the art response. Like if a company building one of the largest language models builds something like what we have, they're going to be kind of wedded to using their own language model.
55:52 Alex Kantrowitz? Right.
55:53 Speaker 02 Exclusively. But we actually use all kinds of tools. So we have our own language model that is extremely good at conversation, but it can use all kinds of different tools, and it's agnostic to where they come from, because we're not part of those companies. So I think that there's a huge amount of value in that. So I don't necessarily think that we need to be acquired. If there's a company that is value aligned, then yeah, we'll see.
56:18 Alex Kantrowitz? Well, I look forward to writing the news story when that happens. But in the meantime, it is great to be able to speak with you and hear about this like really fascinating area of technology. I hope we can do it again. I feel like we've really just scratched the surface here, but I appreciate you coming on, Alan, and sharing so much about this discipline and sort of how it developed and where it's going, because it does seem like as we get toward more of an agent style approach in AI, more of a voice style approach, this is going to be the ground, like the table stakes is building in this type of technology. And it's great to have a preview of it and really like not even a preview, but a conversation about what's going on today before the rest of the world catches on. So thank you so much for coming on the show.
57:03 Speaker 02 Of course. Had a great time. Thank you.
57:05 Alex Kantrowitz? Thanks so much. All right, everybody. Thank you for listening. We'll be back on Friday with a new show, breaking down the news. Ranjan Roy is back. He was out in India, and we're going to talk about the trip, and he'll be back with us on Friday. Until then, we'll see you next time on Big Technology Podcast.
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Previous Are LLMs Hitting A Wall, Microsoft & Alphabet Save The Market, TikTok 'Ban' Next Emails From Elon, AI Revenue Questions, Chatbot Boyfriends Page updated October 26, 2026 · transcript and summary are machine-generated
