Rohit Prasad: Amazon Alexa and Conversational AI | Lex Fridman Podcast #57

The following is a conversation with Rohit Prasad. He's the Vice President and head Scientist of Amazon Alexa and one of its original creators. The Alexa team embodies some of the most challenging, incredible, impactful and inspiring work that is done in AI today. The team has to both solve problems at the cutting edge of natural language processing and provide a trustworthy, secure and enjoyable experience to millions of people. This is where state of the art methods in computer science meet the challenges of real world engineering. In many ways, Alexa and the other voice assistants are the voices of artificial intelligence to millions of people and an introduction to AI for people who have only encountered it in science fiction. This is an important and exciting opportunity. So the work that Rohit and the Alexa team are doing is an inspiration to me and to many researchers and engineers in the AI community. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, give it five stars on Apple Podcast, support it on Patreon, or simply connect with me on Twitter lexfriedman spelled F R I D M A n. If you leave a review on Apple Podcasts especially, but also castbox or comment on YouTube, consider mentioning topics, people, ideas, questions, quotes in science, tech or philosophy that you find interesting and I'll read them on this podcast. I won't call out names, but I love comments with kindness and thoughtfulness in them, so I thought I'd share them. Someone on YouTube highlighted a quote from the conversation with Ray Dalio where he said that you have to appreciate all the different ways that people can be a players. Let's connect with me too on Teams of Engineers it's easy to think that raw productivity is the measure of excellence, but there are others I worked with people who brought a smile to my face every time I got to work in the morning. Their contribution to the team is immeasurable. I recently started doing podcast ads at the end of the introduction. I'll do one or two minutes after introducing the episode and never any ads in the middle that break the flow of the conversation. I hope that works for you and it doesn't hurt the listening experience. This show is presented by Cash App, the number one finance app in the App Store. I personally use Cash App to send money to friends, but you can also use it to buy, sell and deposit Bitcoin in just seconds. Cash App also has a new investing feature. You can buy fractions of a stock save $1 worth no matter what the stock price is. Brokerage services are provided by Cash App Investing, a subsidiary of Square and member sipc. I'm excited to be working with Cash App to support one of my favorite organizations called first. Best known for their First Robotics and Lego competitions. They educate and inspire hundreds of thousands of students in over 110 countries and have a perfect rating on Charity Navigator, which means the donated money is used to maximum effectiveness. When you get Cash App from the App Store, Google Play and use Code Lex podcast, you'll get $10 and cash app will also donate $10 to first, which again is an organization that I've personally seen inspire girls and boys to dream of engineering a better world. This podcast is also supported by ZipRecruiter. Hiring great people is hard and to me is one of the most important elements of a successful mission driven team. I've been fortunate to be a part of and and lead several great engineering teams. The hiring I've done in the past was mostly through tools we built ourselves, but reinventing the wheel was painful. ZipRecruiter is a tool that's already available for you. It seeks to make hiring simple, fast and smart. For example, Codable co founder Gretchen Hebner used ZipRecruiter to find a new game artist to join our education tech company. By using ZipRecruiter screening questions to filter candidates, Gretchen found it easier to focus on the best candidates and and finally hiring the perfect person for the role in less than two weeks from start to finish, ZipRecruiter the smartest way to hire see why ZipRecruiter is effective for businesses of all sizes? By signing up as I did for free at ziprecruiter.com lexpod that's ziprecruiter.com Lex Pod and now here's my conversation with.

Rohit Prasad in the movie Her I'm not sure if you ever seen it. Human falls in love with the voice of an AI system Let's start at the highest philosophical level before we get to deep learning and some of the fun things. Do you think this what the movie her shows is within our reach?

I think not specifically about her, but I think what we are seeing is a massive increase an adoption of AI assistance or AI in all parts of our social fabric. And I think it's what I do believe is that the utility these AIs provide, some of the functionalities that are shown are absolutely within reach.

So some of the functionalities in terms of the interactive elements, but in terms of the deep connection that's purely voice based. Do you think such a close connection is possible with voice alone?

It's been a while since I saw Her. But I would say in terms of the, in terms of interactions which are both human like and in these AI assistants, you have to value what is also superhuman. We as humans can be in only one place. AI assistants can be in multiple places at the same time. One with you on your mobile device, one at your home, one at work. So you have to respect these superhuman capabilities too. Plus, as humans, we have certain attributes we are very good at, very good at reasoning. AI assistants not yet there. But in the realm of AI assistants, what they're great at is computation, memory, it's infinite and pure. These are the attributes you have to start respecting. So I think the comparison with human like versus the other aspect, which is also superhuman, has to be taken into consideration. So I think we need to elevate the discussion to not just human like.

So there's certainly elements where you just mentioned. Alexa is everywhere, computation is speaking. So this is a much bigger infrastructure than just the thing that sits there in the room with you. But it certainly feels to us mere humans that there's just another little creature there. When you're interacting with it, you're not interacting with the entirety of the infrastructure. You're interacting with the device. The feeling is, okay, sure, we anthropomorphize things, but that feeling is still there. So what do you think we as humans, the purity of the interaction with a smart assistant, what do you think we look for in that interaction?

I think in the certain interactions, I think will be very much where it does feel like a human because it has a Persona of its own. And in certain ones it wouldn't be. So I think a simple example to think of it is if you're walking through the house and you just want to turn on your lights on and off and you're issuing a command that's not very much like a human like interaction. And that's where the AI shouldn't come back and have a conversation with you. Just it should simply complete that command. So I think the blend of we have to think about this is not human human alone. It is a human machine interaction. And certain aspects of humans are needed and certain aspects and situations demand it to be like a machine.

So I told you, it's going to be philosophical in parts. What's the difference between human and machine in that interaction, when we interact two humans, especially those that are friends and loved ones, versus you and a machine that you also are close with, I.

Think you have to think about the roles the AI plays and it differs from different customer to customer, different Situation to situation especially I can speak from Alexev's perspective. It is a companion, a friend at times an assistant, and an advisor down the line. So I think most AIs will have this kind of attributes and it will be very situational in nature. So where is the boundary? I think the boundary depends on exact context in which you're interacting with the AI.

So the depth and the richness of natural language conversation is by Alan Turing been used to try to define what it means to be intelligent. You know, there's a lot of criticism of that kind of test. But what do you think is a good test of intelligence in your view, in the context of the Turing test and Alexa, with the Alexa prize, this whole realm, do you think about this human intelligence, what it means to define it, what it means to reach that level?

I do think the ability to converse is a sign of an ultimate intelligence. I think that there's no question about it. So if you think about all aspects of humans, there are sensors we have, and those are basically a data collection mechanism. And based on that, we make some decisions with our sensory brains. Right? And from that perspective, I think there are elements we have to talk about how we sense the world and then how we act based on what we sense. Those elements clearly machines have. But then there's the other aspects of computation that is way better. I also mentioned about memory, again, in terms of being near infinite, depending on the storage capacity you have, and the retrieval can be extremely fast and pure. In terms of like, there's no ambiguity of who did I see when. Right. I mean, machines can remember that quite well. So again, on a philosophical level, I do subscribe to the fact that to be able to converse and as part of that, to be able to reason based on the world knowledge you've acquired and the sensory knowledge that is there is definitely very much the essence of intelligence. But intelligence can go beyond human level intelligence based on what machines are getting capable of.

So what do you think, maybe stepping outside of Alexa, broadly as an AI field, what do you think is a good test of intelligence? Put it another way, outside of Alexa, because so much of Alexa is a product, is an experience for the customer. On the research side, what would impress the heck out of you if you saw, you know, what is the test where you said, wow, this thing is now starting to encroach into the realm of what we loosely think of as human intelligence?

So, well, we think of it as AGI and human intelligence all together. Right. So in some sense, and I think we are quite Far from that. I think an unbiased view I have is that the Alexis intelligence capability is a great test. I think of it as there are many other proof points like self driving cars, game playing like Go or chess. Let's take those two as an example. Clearly requires a lot of data driven learning and intelligence. But it's not as hard a problem. As conversing as an AI is with humans to accomplish certain tasks or open domain chat as you mentioned, like surprise in those settings the key difference is that the end goal is not defined. Unlike game playing, you also do not know exactly what state you are in in a particular goal completion scenario. In certain sense sometimes you can if it is a simple goal. But if you're even certain examples like planning a weekend or you can imagine how many things change along the way. You look for weather, you may change your mind and you change the destination. Or you want to catch a particular event and then you decide no, I want this other event I want to go to. So these dimensions of how many different steps are possible when you're conversing as a human with a machine makes it an extremely daunting problem. And I think it is the ultimate test for intelligence.

And don't you think that natural language is enough to prove that conversation, just.

Pure conversation from a scientific standpoint, natural language is a great test, but I would go beyond. I don't want to limited to as natural language as simply understanding an intent or parsing for entities and so forth. We are really talking about dialogue dialogue. So I would say human machine dialogue is definitely one of the best tests of intelligence.

So can you briefly speak to the Alexa prize for people who are not familiar with it and also just maybe where things stand and. And what have you learned, what's surprising, what have you seen that's surprising from this incredible competition?

Absolutely. It's a very exciting competition. Alexa Prize is essentially a grand challenge in conversational artificial intelligence where we threw the gauntlet to the universities who do active research in the field to say can you build what we call a social bot that can converse with you coherently and engagingly for 20 minutes? That is an extremely hard challenge. Talking to someone who you're meeting for the first time or even if you've met them quite often to speak at 20 minutes on any topic and evolving nature of topics is super hard. We have completed two successful years of the competition. First was one with the University of Washington, second University of California. We are in our third instance. We have an extremely strong team of 10 cohorts and the third instance of the Alexa prize is underway now and we are seeing a constant evolution. First year was definitely a learning. It was a lot of things to be put together. We had to build a lot of infrastructure to enable these universities to be able to build magical experiences and do high quality research.

Just a few quick questions. Sorry for the interruption. What does failure look like in the 20 minute session? So what does it mean to fail not to reach the 20 minute mark?

Awesome question. So there are one. First of all, I forgot to mention one more detail. It's not just 20 minutes, but the quality of the conversation too that matters. And the beauty of this competition before I answer that question on what failure means is first that you actually converse with millions and millions of customers as these social bots. So during the judging phases, there are multiple phases before we get to the finals, which is a very controlled judging. In a situation where we bring in judges and we have interactors who interact with these social bots, that is a much more controlled setting. But till the point we get to the finals, all the judging is essentially by the customers of Alexa. And there you basically rate on a simple question how good your experience was. So that's where we are not testing for a 20 minute boundary being crossed, because you do want it to be very much like a clear cut winner being chosen and it's an absolute bar. So did you really break that 20 minute barrier is why we have to test it in a more controlled setting with actors, essentially interactors, and see how the conversation goes. So this is why it's a subtle difference between how it's being tested in the field with real customers versus in the lab to award the prize. So on the latter one, what it means is that essentially there are three judges and two of them have to say, this conversation has stalled, essentially.

Got it. And the judges are human experts.

The judges are human experts.

Okay, great. So this is in the third year. So what's been the evolution? How far? So the DARPA challenge in the first year, the autonomous vehicles, nobody finished. In the second year, a few more finished in the desert. So how far along in this, I would say much harder challenge are we?

This challenge has come a long way to the extent that we're definitely not close to the 20 minute barrier being with coherence and engaging conversation. I think we are still five to 10 years away in that horizon to complete that. But the progress is immense. What you're finding is the accuracy and what kind of responses these social bots generate is getting better and better. What's even amazing to see that now there's humor coming in. The bots are quite awesome. You're talking about ultimate signs of intelligence. I think humor is a very high bar in terms of what it takes to create humor. And I don't mean just being goofy. I really mean good sense of humor is also a sign of intelligence in my mind, and something very hard to do. So these social bots are now exploring not only what we think of natural language abilities, but also personality attributes and aspects of when to inject an appropriate joke. When you don't know the domain, how you come back with something more intelligible so that you can continue the conversation. If you and I are talking about AI and we are domain experts, we can speak to it. But if you suddenly switch a topic to that I don't know of, how do I change the conversation? So you're starting to notice these elements as well. And that's coming from partly by the nature of the 20 minute challenge, that people are getting quite clever on how to really converse and essentially mask some of the understanding defects if they exist.

So some of this, this is not Alexa, the product. This is somewhat for fun, for research, for innovation and so on. I have a question, sort of in this modern era, there's a lot of, if you look at Twitter and Facebook and so on, there's discourse, public discourse going on and some things that are a little bit too edgy, people get blocked and so on. I'm just out of curiosity, are people in this context pushing the limits? Is anyone using the F word? Is anyone sort of pushing back, sort of, you know, arguing, I guess I should say, as part of the dialogue to really draw people in.

First of all, let me just back up a bit in terms of why we are doing this. Right. So you said it's fun. I think fun is more part of the engaging part for customers. It is one of the most used skills as well in our skill store. But that apart, the real goal was essentially what was happening is with lot of AI research moving to industry, we felt that academia has the risk of not being able to have the same resources at disposal that we have, which is lots of data, massive computing power and clear ways to test these AI advances with real customer benefits. So we brought all these three together in the Alexa prize. That's why it's one of my favorite projects in Amazon. And with that, the secondary effect is, yes, it has become engaging for our customers as well. We are not there in terms of where we want it to be. Right. But It's a huge progress. But coming back to your question on how do the conversations evolve? Yes, there is some natural attributes of what you said in terms of argument and some amount of swearing. The way we take care of that is that there is a sensitive filter.

We have built that certain keywords and so on.

It's more than keywords, a little more in terms of. Of course there's keyword base too, but there's more in terms of these words can be very contextual, as you can see. And also the topic can be something that you don't want a conversation to happen because this is a communal device as well. A lot of people use these devices. So we have put a lot of guardrails for the conversation to be more useful for advancing AI and not so much of these other issues. You attributed what's happening in the AI field as well.

Right. So this is actually a serious opportunity. I didn't use the right word. Fun. I think it's an open opportunity to do some, some of the best innovation in conversational agents in the world. Why just universities?

Why just universities? Because as I said, I really felt young minds. Young minds. It's also to. If you think about the other aspect of where the whole industry is moving with AI, the there's a dearth of talent given the demands. So you do want universities to have a clear place where they can invent and research and not fall behind with that. They can't motivate students. Imagine all grad students left to industry like us or faculty members, which has happened too. So this is a way that if you're so passionate about the field where you feel industry and academia need to work. Well, this is a great example and a great way for universities to participate.

So what do you think it takes to build a system that wins the Alexa prize?

I think you have to start focusing on aspects of reasoning that there are still more lookups of what intents customers asking for and responding to those rather than really reasoning about the elements of the. Of the conversation. For instance, if you have, if you're playing, if the conversation is about games and it's about a recent sports event, there's so much context involved and you have to understand the entities that are being mentioned so that the conversation is coherent rather than you suddenly just switch to knowing some fact about a sports entity and you're just relaying that rather than understanding the true context of the game. Like if you just said I learned this fun fact about Tom Brady rather than really say how he played the game. The previous night, then the conversation is not really that intelligent. So you have to go to more reasoning elements of understanding the context of the dialogue and giving more appropriate responses, which tells you that we are still quite far because a lot of times it's more facts being looked up and something that's close enough as an answer, but not really the answer. So that is where the research needs to go more an actual true understanding and reasoning. And that's why I feel it's a great way to do it, because you have an engaged set of users working to make help these AI advances happen in this case.

Right. So you mentioned customers there quite a bit. And there's a skill. What is the experience for the user that's helping. So just to clarify, this isn't as far as I understand, the Alexa. So this skill is a standalone for the Alexa prize. I mean, it's focused on the Alexa prize. It's not you ordering certain things on Amazon.com or checking the weather or playing Spotify. Right. This is a separate skill.

Exactly.

And so you're focused on helping that. I don't know, how do people, how do customers think of it? Are they having fun? Are they helping teach the system? What's the experience like?

I think it's both, actually. And let me tell you how the, how you invoke this skill. So all you have to say, alexa, let's chat. And then the first time you say alexa, let's chat, it comes back with a clear message that you're interacting with one of those three social bots. And there's a clear. So you know exactly how you interact. Right. And that is why it's very transparent you are being asked to help. Right. And we have lot of mechanisms where as the, we are in the first phase of feedback phase, then you send a lot of emails to our customers and then they know that the team needs a lot of interactions to improve the accuracy of the system. So we know we have a lot of customers who really want to help these university bots and they're conversing with that and some are just having fun with just saying Alexa, let's chat. And also some adversarial behavior to see whether, how much do you understand as a social bot? So I think we have a good, healthy mix of all three situations.

So what is the, if we talk about solving the Alexa challenge, the Alexa prize, what's the data set of really engaging, pleasant conversations look like? Because if we think of this as a supervised learning problem, I don't know if it has to be but if it does, maybe you can comment on that. Do you think there needs to be a data set of what it means to be an engaging, successful, fulfilling conversation?

I think that's part of the research question here. I think we at least got the first part right, which is have a way for universities to build and test in a real world setting. Now you're asking in terms of the next phase of questions which we're also asking, by the way, what does success look like from an optimization function? That's what you're asking in terms of we as researchers are used to having a great corpus of annotated data and then making then sort of tune our algorithms on those. Right. And fortunately and unfortunately in this world of Alexa prize, that is not the way we are going after it. So you have to focus more on learning based on live feedback. That is another element that's unique where just now I started with giving you how you ingress and experience this capability as a customer. What happens when you're done? So they ask you a simple question, on a scale of 1 to 5, how likely are you to interact with this social bot? Again, that is a good feedback. And customers can also leave more open ended feedback. And I think partly that to me is one part of the question you're asking, which I'm saying is a mental model shift that as researchers also you have to change your mindset that this is not a DARPA evaluation or an NSF funded study. And you have a nice corpus. This is where it's real world, you have real data.

The scale is amazing. That's a beautiful thing. And then the customer, the user can quit the conversation at any time.

Exactly, the user can. That is also a signal for how good you were at that point.

So and then On a scale 1 to 5, 1 to 3, do they say how likely are you or is it just a binary 1 to 5? 1 to 5? Wow, okay. That's such a beautifully constructed challenge. Okay. You said the only way to make a smart assistant really smart is to give it eyes and let it explore the world. I'm not sure, it might have been taken out of context, but can you comment on that? Can you elaborate on that idea? As I personally also find that idea super exciting from a social robotics, personal robotics perspective.

Yeah, a lot of things do get taken out of context. This particular one was just as philosophical discussion we were having on terms of what does intelligence look like and the context was in terms of learning. I think just we said we as humans are empowered with many different sensory Abilities. I do believe that eyes are an important aspect of it in terms of. If you think about how we as humans learn, it is quite complex. And it's also not unimodal that you are fed a ton of text or audio and you just learn that way. No, you learn by experience. You learn by seeing. You're taught by humans. And we are very efficient in how we learn. Machines, on the contrary, are very inefficient on how they learn, especially these AIs. I think the next wave of research is going to be with less data, not just less human, not just with less labeled data, but also with a lot of weak supervision and where you can increase the learning rate. I don't mean less data in terms of not having a lot of data to learn from, that we are generating so much data, but it is more about from an aspect of how fast can you learn.

So improving the quality of the data. That's the quality of data and the learning process.

I think more on the learning process. I think we have to. We as humans learn with a lot of noisy data. Right. And I think that's the part that I don't think should change. What should change is how we learn. So if you look at, you mentioned supervised learning, we have making transformative shifts from moving to more unsupervised, more weak supervision. Those are the key aspects of how to learn. And I think in that setting, I hope you agree with me that having other senses is very crucial in terms of how you learn.

Absolutely. From a machine learning perspective, which I hope we get a chance to talk to a few aspects that are fascinating there. But to stick on the point of sort of a body, you know, embodiment. So Alexa has a body, has a very minimalistic, beautiful interface, or there's a ring and so on. I mean, I'm not sure of all the flavors of the devices that Alexa lives on, but there's a minimalistic basic interface. What. And nevertheless, we humans. So I have a Roomba, I have all kinds of robots all over, everywhere. So what do you think the Alexa of the future looks like if it begins to shift what his body looks like? Maybe beyond the Alexa, what do you think are the different devices in the home as they start to embody their intelligence more and more? What do you think that looks like philosophically, a future. What do you think that looks like?

I think let's look at what's happening today. You mentioned, I think our devices as an Amazon devices, but I also wanted to point out Alexa is already integrated on a Lot of third party devices which also come in lots of forms and shapes. Some in robots. Right. Some in microwaves, some in appliances that you use in everyday life. So I think it's not just the shape Alexa takes in terms of form factors, but it's also where all it's available. It's getting in cars, it's getting in different appliances, in homes, even toothbrushes. Right. So I think you have to think about it as not a physical assistant. It will be in some embodiment. As you said, we already have these nice devices. But I think it's also important to think of it. It is a virtual assistant. It is superhuman in the sense that it is in multiple places at the same time. So I think the actual embodiment in some sense to me doesn't matter. I think you have to think of it as not as human like and more of what its capabilities are that derive a lot of benefit for customers and how there are different ways to delight it and delight customers and different experiences. And I think I'm a big fan of it not being just human like it should be human like in certain situations, Alexa price social bot in terms of conversation is a great way to look at it. But there are other scenarios where human like I think is underselling the abilities of this AI.

So if I could trivialize what we're talking about. So if you look at the way Steve Jobs thought about the interaction with the device that Apple produced, there was an extreme focus on controlling the experience by making sure there's only this Apple produced devices. You see the voice of Alexa being taking all kinds of forms depending on what the customers want. And that means it could be anywhere from the microwave to a vacuum cleaner to the home and so on. The voice is the essential element of the interaction.

I think voice is and essence. It's not all, but it's a key aspect, I think to your question in terms of you should be able to recognize Alexa and that's a huge problem. I think in terms of a huge scientific problem, I should say, like what are the traits, what makes it look like Alexa, especially in different settings and especially if it's primarily voice, what it is. But Alexa is not just voice either, right? I mean we have devices with a screen now you're seeing just other behaviors of Alexa. So I think we're in very early stages of what that means and this will be an important topic for the following years. But I do believe that being able to recognize and tell when it's Alexa versus it's not is going to be important from an Alexa perspective. I'm not speaking for the entire AI community, but I think attribution and as we go into more of understanding who did what, that identity of the AI is crucial in the coming world.

I think from the broad AI community perspective, that's also a fascinating problem. So basically, if I close my eyes and listen to the voice, what would it take for me to recognize that this is Alexa?

Exactly.

Or at least the Alexa that I've come to know from my personal experience.

In my home, through my interactions, that kind of thing.

Yeah. And the Alexa here in the US is very different. The Alexa in Ukraine and Alexa in India, even though they are all speaking English or the Australian version. So again, where. So now think about when you go into a different culture, different community, but you travel there, what do you recognize? Alexa? I think these are super hard questions, actually.

So there's a team that works on personality. So if we talk about those different flavors of what it means culturally speaking, India, uk, us, what does it mean to add Z? So the problem that we just stated, which is fascinating, how do we make it purely recognizable that it's Alexa, assuming that the qualities of the voice are not sufficient, it's also the content of what is being said, how do we do that? How does the personality come into play? What's that research even look like? It's such a fascinating experience.

We have some very fascinating folks who, from both the UX background and human factors are looking at these aspects and these exact questions. But I'll definitely say it's not just how it sounds. The choice of words, the tone, not just, I mean the voice identity of it, but the tone matters, the speed matters, how you speak, how you enunciate words, what choice of words are you using? How terse are you or how lengthy in your explanations you are? All of these are factors. And you also, you mentioned something crucial, that you may have personalized it, Alexa to some extent in your homes or in the devices you are interacting with. So you as your individual, how you prefer Alexa sounds can be different than how I prefer. And the amount of customizability you want to give is also a key debate we always have. But I do want to point out it's more than the voice actor that recorded and it sounds like that actor. It is more about the choices of words, the attributes of tonality, the volume in terms of how you raise your pitch and so forth. All of that matters.

This is such a fascinating problem. From a product perspective, I could see those debates just happening inside of the Alexa team of how much personalization do you do for. For the specific customer? Because you're taking a risk if you over personalize, because you don't. If you create a personality for a million people, you can test that better. You can create a rich, fulfilling experience that will do well. But if the more you personalize it, the less you can test it, the less you can know that it's a great experience. So how much personalization, what's the right balance?

I think the right balance depends on the customer. Give them the control. So I'll say I think the more control you give customers, the better it is for everyone. And I'll give you some key personalization features. I think we have a feature called Remember this, which is where you can tell Alexa to remember something. There you have an explicit sort of control in customer's hand because they have to say alexa, remember xyz.

What kind of things would that be used for? Something?

I have stored my tire specs for my car because it's so hard to go and find and see what it is right when you're having some issues. I store my mileage plan numbers for all the frequent flyer ones where I'm sometimes just looking at it and it's not handy. So those are my own personal choices I've made for Alexa to remember something on my behalf. Right. So again I think the choice was be explicit about how you provide that to a customer as a control. So I think these are the aspects of what you do. Like think about where we can use speaker recognition capabilities that it's. If you taught Alexa that you are Lex and this person, your household is person two, then you can personalize the experiences. Again, these are very in the cx, customer experience patterns are very clear about and transparent when a personalization action is happening. And then you have other ways like you go through explicit control right now through your app that you have multiple service providers. Let's say for music, which one is your preferred one. So when you say play Sting, depend on your whether you have preferred Spotify or Amazon Music or Apple Music that the decision is made where to play it from.

So what's Alexa's backstory from her perspective? Is there I remember just asking as probably a lot of us are just the basic questions about love and so on of Alexa just to see what the answer would be. Just it feels like there's a little bit of a back, like there's a feels like there's a little bit of personality but not too much does Alexa have a metaphysical presence in this human universe we live in? Or is it something more ambiguous? Is there a past, is there a birth, is there a family kind of idea, even for joking purposes and so on?

I think. Well, it does tell you if I think you, I should double check this. But if you said when were you born? I think we do respond. I need to double check that. But I'm pretty positive about it.

I think you do actually, because I.

Think I've tested that.

But that's like, that's like Halloween, like I was born in a brand of champagne and whatever the year kind of thing.

Yeah. So in terms of the metaphysical, I think it's early. Does it have the historic knowledge about herself to be able to do that? Maybe. Have we crossed that boundary? Not yet. Right. In terms of being thinking, have we thought about it? Quite a bit. But I wouldn't say that we have come to a clear decision in terms of what it should look like. But you can imagine though, and I bring this back to the Alexa Price Social Bot 1. There you will start seeing some of that, like these bots have their identity and in terms of that you may find, you know, this is such a great research topic that some academia team may think of these problems and start solving them too.

So let me ask a question. It's kind of difficult, I think, but it feels and fascinating to me because I'm fascinated with psychology. It feels that the more personality you have, the more dangerous it is in terms of a customer perspective, product. If you want to create a product that's useful, by dangerous I mean creating an experience that upsets me. And so how do you get that right? Because if you look at the relationships, maybe I'm just a screwed up Russian, but if you look at the human to human relationship, some of our deepest relationships have fights, have tension, have the push and pull, have a little flavor in them. Do you want to have such flavor in an interaction with Alexa? How do you think about that?

So there's one other common thing that you didn't say, but as we think of it as paramount for any deep relationship, that's trust.

Trust. Yeah.

So I think if you trust every attribute, you said a fight, some tension is all healthy. But what is sort of unnegotiable in this instance is trust. And I think the bar to earn customer trust for AI is very high in some sense more than a human. It's not just about personal information or your data. It's also about your actions on a daily basis. How trustworthy are you in Terms of consistency, in terms of how accurate are you in understanding me? Like if you're talking to a person on the phone, if you have a problem with your, let's say, your Internet or something, if the person is not understanding, you lose trust right away. You don't want to talk to that person. That whole example gets amplified by a factor of 10. Because when you're a human interacting with an AI, you have a certain expectation. Either you expect it to be very intelligent and then you get upset, why is it behaving this way? Or you expect it to be not so intelligent. And when it surprises you, you're like, really? You're trying to be too smart. So I think we grapple with these hard questions as well. But I think the key is actions need to be Trustworthy from these AIs, not just about data protection, your personal information protection, but also from how accurately it accomplishes all commands or all interactions.

Well, it's tough to hear because trust, you're absolutely right. But trust is such a high bar with AI systems because people, and I see this because I work with autonomous vehicles. I mean, the bar that's placed on AI system is unreasonably high.

Yeah, that is going to be. I agree with you and I think of it as it's a challenge and it also keeps my job. Right. So from that perspective, I totally, I think of it at both sides as a customer and as a researcher. I think as a researcher, yes, occasionally it will frustrate me that why is the bar so high for these AIs? And as a customer, then I say, absolutely, it has to be that high. Right. So I think that's the trade off we have to balance, but doesn't change the fundamentals that trust has to be earned. And the question then becomes, are we holding the AIs to a different bar in accuracy and mistakes than we hold humans? That's going to be a great societal questions for years to come, I think for us.

Well, one of the questions that we grapple as a society now that I think about a lot, I think a lot of people in the AI think about a lot and Alexis taking on head on is privacy is the reality is us giving over data to any AI system can be used to enrich our lives in profound ways. So basically any product that does anything awesome for you, the more data it has, the more awesome things it can do. And yet on the other side, people imagine the worst case possible scenario of what can you possibly do with that data. It boils down to trust as you said before there's a fundamental distrust in certain groups of governments and so on, depending on the government, depending on who's in power, depending on all these kinds of factors. And so here's Alexa in the middle of all of it in the home, trying to do good things for the customers. So how do you think about privacy in this context of smart assistance in the home? How do you maintain, how do you earn trust?

Absolutely. So as you said, trust is the key here. So you start with trust and then privacy is a key aspect of it. It has to be designed from very beginning about that. And we believe in two fundamental principles. One is transparency and second is control. So by transparency I mean when we built what is now called smart speaker or the first echo, we were quite judicious about making these right. Trade offs on customers behalf that it is pretty clear when the audio is being sent to cloud. The light ring comes on when it has heard you say the word wake word and then the streaming happens. Right. So and the light ring comes up. We also had, we put a physical mute button on it just so if you didn't want it to be listening even for the wake word, then you turn the power button, the mute button on and that disables the microphones. That's just the first decision on essentially transparency and control. Then even when we launched, we gave the control in the hands of the customers that you can go and look at any of your individual utterances that is recorded and delete them anytime. And we have cut true to that promise. Right. So and that is super again a great instance of showing how you have the control. Then we made it even easier. You can say, Alexa, delete what I said today. So that is now making it even just more control in your hands with what's most convenient about this technology is voice. You delete it with your voice now. So these are the types of decisions we continually make. We just recently launched this feature called what we think of it as if you wanted humans not to review your data because you mentioned supervised learning. Right. So in supervised learning, humans have to give some annotation. And that also is now a feature where you can essentially, if you've selected that flag, your data will not be reviewed by a human. So these are the types of controls that we have to constantly offer with customers.

So why do you think it bothers people so much that so, so everything you just said is really powerful. So the control, the ability to delete because we collect, we have studies here running at MIT that collects huge amounts of data and people consent and so on. The ability to delete that data is really empowering. And almost nobody ever asked to delete it. But the ability to have that control is really powerful. But still, you know, there's these popular anecdote, anecdotal evidence that people say they like to tell that them and a friend were talking about something, I don't know, sweaters for cats. And all of a sudden they'll have advertisements for cat sweaters on Amazon. There's that. That's a popular anecdote, as if something is always listening. What can you explain that anecdote, that experience that people have? What's the psychology of that? What's that experience? And can you. You've answered it, but let me just ask, is Alexa listening?

No, Alexa listens only for the wake word on the device. Right.

And the wake word is the words.

Like Alexa, Amazon, Echo. But you only choose one at a time. So you choose one and it listens only for that on our devices. So that's first from the listening perspective. We have to be very clear that it's just the wake word. So you said, why is there this anxiety, if you may? It's because there's a lot of confusion what it really listens to. Right. And I think it's partly on us to keep educating our customers and the general media more in terms of, like, what really happens. And we've done a lot of it and our pages on information are clear. But still, people have to have more. There's always a hunger for information and clarity, and we'll constantly look at how best to communicate. If you go back and read everything, yes, it states exactly that and then people could still question it. And I think that's absolutely okay to question. What we have to make sure is that we are because our fundamental philosophy is customer first, customer obsession is our leadership principle. If you put, as researchers, I put myself in the shoes of the customer and all decisions in Amazon are made with that. And trust has to be earned and we have to keep earning the trust of our customers in this setting. And to your other point on like, is there something showing up based on your conversations? No. I think the answer is like you, a lot of times when those experiences happen, you have to also be know that, okay, it may be a winter season, people are looking for sweaters. Right. And it shows up on your Amazon.com because it is popular. So there are many of these. You mentioned that personality or personalization. Turns out we are not that unique either. Yeah, right. So those things we as Humans start thinking oh, must be because something was heard and that's why this other thing showed up. The answer is no. Probably it is just the season for sweaters.

I'm not going to ask you this question because it's just because you're also because people have so much paranoia. But let me just say from my perspective, I hope there's a day when customer can ask Alexa to listen all the time to improve the experience to improve because I personally don't see the negative because if you have the control and if you have the trust, there's no reason why I shouldn't be listening all the time to the conversations to learn more about you. Because ultimately as long as you have control and trust, every data you provide to the device that the device wants is going to be useful. And so to me I as a machine learning person, I think it worries me how sensitive people are about their data relative to how empowering it could be for the devices around them enriching it could be for their own life to improve the product. So I just. It's something I think about sort of a lot how to make that devices Obviously Alexa thinks about it a lot as well. I don't know if you want to comment on that. So have you seen Let me ask you in the form of a question, okay. Have you seen an evolution in the way people think about their private data in the previous several years? So as we as a society get more and more comfortable to the benefits we get by sharing more data first.

Let me answer that part and then I'll want to go back to the other aspect you were mentioning. So as a society on a general we are getting more comfortable as a society doesn't mean that everyone is and I think we have to respect that. I don't think one size fits all is always going to be the answer for overall right by definition. So I think that's something to keep in mind in these going back to your on what more magical experiences can be launched in these kind of AI settings. I think again if you give the control it's possible certain parts of it. So we have a feature called follow up mode where if you turn it on and Alexa after you've spoken to it will open the mics again thinking you'll answer something again. Like if you're adding lists to your shopping item shopping list or to do list, you're not done you want to keep so in that setting it's awesome that it opens the mic for you to say eggs and milk and then bread right so these are the kind of things which you can empower. And then another feature we have, which is called Alexa Guard, I said it only listens for the wake word. All right, but if you have a, let's say you're going to say you leave your home and you want Alexa to listen for a couple of sound events like smoke alarm going off or someone breaking your glass. Right. So it's like just to keep your peace of mind. So you can say alexa on guard or I'm away. And then it can be listening for these sound events. And when you're home, you come out of that mode. Right. So this is another one where you again gave controls in the hands of the user or the customer and to enable some experience that is high utility and maybe even more delightful in the certain settings like follow up mode and so forth. Again, this general principle is the same control in the hands of the customer.

So I know we kind of started with a lot of philosophy and a lot of interesting topics and we're just jumping all over the place. But really some of the fascinating things that the Alexa team and Amazon is doing is in the algorithm side, the data side, the technology, the deep learning and machine learning and so on. So can you give a brief history of Alexa from the perspective of just innovation? The algorithms, the data of how it was born, how it came to be, how it's grown, where it is today?

Yeah, it starts with, in Amazon, everything starts with the customer. And we have a process called working backwards Alexa. And more specifically then the product Echo, there was a working backwards document essentially that reflected what it would be started with a very simple vision statement, for instance, that morphed into a full fledged document along the way, changed into what all it can do. Right. But the inspiration was the Star Trek computer. So when you think of it that way, everything is possible. But when you launch a product, you have to start with someplace. And when I joined, the product was already in conception and we started working on the far field, speech recognition, because that was the first thing to solve. By that we mean that you should be able to speak to the device from a distance. And in those days that wasn't a common practice. And even in the previous research world I was in was considered to an unsolvable problem then in terms of whether you can converse from a length. And here I'm still talking about the first part of the problem where you say get the attention of the device, as in by saying what we call the wake word, which means the word Alexa has to be detected with A very high accuracy because it is a very common word. It has sound units that map with words like I like you or Alec, Alex. Right. So it's a undoubtedly hard problem to detect the right mentions of Alexa's address to the device versus I like Alexa.

So you have to pick up that signal when there's a lot of noise.

Not only noise, but a lot of conversation in the house. Right. You remember on the device, you're simply listening for the wake word Alexa and there's a lot of words being spoken in the house. How do you know it's Alexa and directed at Alexa? Because I could say, I love my Alexa. I hate my Alexa. I want Alexa to do this. And in all these three sentences, I said Alexa. I didn't want her to wake up.

Can I just pause on that second? What would be your advice that I should probably, in the introduction of this conversation, give to people in terms of with them turning off their Alexa device, if they're listening to this podcast conversation out loud, like, what's the probability that an Alexa device will go off? Because we mentioned Alexa like a million times.

So it will. We have done a lot of different things where we can figure out that there is the device, the speech is coming from a human versus over the air. Also, I mean, in terms of like, also, it is think about ads. So we have also launched a technology for watermarking kind of approaches in terms of filtering it out. But yes, if this kind of a podcast is happening, it's possible your device will wake up a few times. It's an unsolved problem, but it is definitely something we care very much.