YouSaid · the spoken record

Rohit Prasad

lines on the record: 120
first: 2019-12-14
most recent: 2019-12-14
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“To the bar, right, that we felt would be. Where people will use it, which was critical. Because you really have one chance at this. If we had launched in November 2014 is when we launched, if it was below the bar, I don't think this category exists if you don't need the bar.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“In terms of accuracy and customer experience features, some stakes on the ground saying here's where I think it should get to. So you established a bar. And then how do you measure progress towards a given, you have no customer right now? So from that perspective, so first was the data without customers. Second was doubling down on deep learning as a way to learn. I can just tell you that the combination of the two cut our error rates by a factor of five from where we were when I started to within six months of having that data, we at that point I got the conviction that this will work right so because that was magical in terms of when it started working and that”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“No customer base, right? So that was first innovation. And once we had that, the next thing was, okay, if you have the data, first of all, we didn't talk about what would magical mean in this kind of a setting? What is good enough for customers, right? That's always, since you've never done this before, what would be magical? So it wasn't just a research problem. You had to put some...”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think as you said first, there was a lot of skepticism on whether Farfield Speech recognition will ever work, to be good enough, right? And what we first did was got a lot of training data in a far field setting. And that was extremely hard to get because none of it existed. So how do you collect data in far field setup, right?”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I'll tell you this neuroscientist, and a lot of my scientists have adopted that. They have now, they love it. As a process because it was very, as scientists, you're trained to write great papers, but they are all after you've done the research or you've proven and your PhD dissertation proposal is something that comes closest or a DARPA proposal or a NSF proposal is the closest that comes to a press release. But that process is now ingrained in our scientists, which is delightful for me to see”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Very close, I would say, in terms of the, as I said, the vision was Starter Computer, right? Or the inspiration. And from there, I can't divulge all the exact specifications. But one of the first things that Was magical on Alexa was music. It brought me back to music because my taste was still in when I was in undergrad. So I still listened to those songs and I Was too hard for me to be a music fan with a phone. And I hate things in my ear. So from that perspective, it was quite hard. And music was part of at least the documents I've seen. So from that perspective, I think, yes, in terms of how far are we from the original vision, I can't reveal that, but that's why I have a ton of fun at work because every day we go in and thinking like these are the new set of challenges to solve.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Was the one was me saying, and actually, I should say, and one was semi-optimistic. Yeah. And eight were trying to convince let's go to the management and say, let's not work on this problem. Let's work on some other problem like either telephony speech for customer service calls and so forth. But this was the kind of belief you must have. And I had experience with Farfield speech recognition and my eyes lit up when I saw a problem like that saying, okay. We have been in speech recognition always looking for that killer app. And this was a killer use case to bring something delightful in the hands of customers.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I'll give you a very interesting anecdote on that. When I joined the team, the speech recognition team was six people. My first meeting, and we had hired a few more people, it was 10 people. Nine out of ten people thought it can't be done.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“The combination of large scale data, deep learning progress, near infinite GPUs we had available on AWS even then, all came together for us to be able to solve the far field speech recognition to the extent it could be useful to the customers. It's still not solved. It's not that we are perfect at recognizing speech, but we are great at it in terms of the settings that are in homes, right? So that was important even in the early stages.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“But we just now build on that on the very first thing we did when I joined and with the team and remember it was a very smut of a startup environment, which is great about Amazon. And we doubled on deep learning right away. And we knew we'll have to improve accuracy fast. And because of that, we worked on the scale of data once you have a device like this, if it is successful, will improve big time. Like you'll suddenly have large volumes of data to learn from to make the customer experience better. So how do you scale deep learning? So we did one of the first works in training with distributed GPUs and where the training time was linear in terms of in the amount of data. So that was quite important work where it was algorithmic improvements as well as a lot of engineering improvements. To be able to train on thousands and thousands”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, this is around. So I joined in 2013 in April, right? So the early research in neural networks coming back and showing some promising results in speech recognition space had started happening, but it was very early.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Need to be recognized with very high accuracy Now we are still just in the recognition problem. We haven't yet come to the understanding one, right?”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“That's the first The world's best wakeward detector in a far field setting, not like something where the phone is sitting on the table. This is like people who have devices 40 feet away in my house or 20 feet away, and you still get an answer. So that was the first part. The next is, okay, you're speaking to the device. Of course, you're going to issue many different requests. Some may be simple. Some may be extremely hard, but it's a large vocabulary speech recognition problem, essentially, where the audio is now not coming onto your phone or a handheld mic like this or a closed talking mic, but it's from 20 feet away where if you're in a busy household, your son may be listening to music, your daughter may be running around with something and asking your mom something and so forth, right? So this is like a common household setting where the words you're speaking to Alexa”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Million times. So 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 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, right? It's a non-solved problem, but it is definitely something we care very much about.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 it to wake up.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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, right? So it's undoubtedly hard problem to detect the right mentions of Alexis addressed to the device versus I like Alexa.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 some place. And when I joined, the product was already in conception and we started working on the for 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 be 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.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“It started with Amazon, everything starts with the customer. And we have a process called working backwards. Alexa and more specifically than the product echo, there was a working backwards document essentially that reflected what it would be, started with a very simple”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 fall of mode and so forth. Again, this general principle is the same, control in the hands of the customer.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“First, let me answer that part and then I'll want to go back to the other aspect you were mentioning. 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 all by definition. So I think that is something to keep in mind in these. Going back to 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 will add.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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, is there something showing up based on your conversations? No, I think the answer is a lot of times when those experiences happen, you have to also 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. Are many of these, you mentioned that personality or personalization turns out we are not that unique either. 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.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 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 in Lance.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“The words like Lexir, Amazon, Echo, but you only choose one at a time. So you choose one and it listens only for that on our devices That's first. From a listening perspective, we have to be very clear that it's just the wakewood. So you said, why is there this anxiety, if you make?”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 you 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.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 lightering comes on when it has heard you say the word wake word and then the streaming happens, right? So when the lightering 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 wakewood, then you turn the power button and the mute button on. 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 through to that promise, right? So that is super, again, a great instance of showing how you have the control.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 build what is now called smart speaker or the first echo.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, that is going to be a. 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 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. 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 is, 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 question for years to come, I think, for us.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“You are 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.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So there's one other common thing that you didn't say, but we think of it as paramount for any deep relationship. That's trust So, I think if you trust every attribute you said, a fight, some tension, Is unhealthy, 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? 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's 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's surprised.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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. You can imagine, though, and I bring this back to the Alexa Prize social bot one, there you will start seeing some of that. Like these bots have their identity. And in terms of that, you may find this is such a great research topic that some academia team may think of these problems and start solving them too.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 whether you have preferred Spotify or Amazon music or Apple music that the decision is made where to play it from.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Or song title or 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. So I store my mileage plan numbers for all the frequent flyer. 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. Think about Where we can use speaker recognition capabilities that it's if you Alexa that you are lex and this person in your household is personally. 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.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think the right balance depends on the customer. Give them the control. So I'll say 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. Of control in customers' hand because they have to say, Alexa, remember X, Y, Z.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 customer is ability 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.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, and the Alexa here in the US is very different. The Alexa in UK and the Alexa in India, even though they are all speaking English, or the Australian version. So again, so now think about when you go into a different culture, a different community, but you travel there, what do you recognize, Alexa? I think these are super hard questions, actually.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think voice is an 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. That's a huge problem. I think in terms of huge scientific problem, I should say, what are the traits? What makes it look like Alexa, especially in different settings, and especially if it's primarily voiced 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 they'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 from, but I think”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 to 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 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, like surprise 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.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 is not just the shape Alexa takes in terms of form factors, but it's also, where are 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.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 how they learn, especially these AIs. I think the next wave of research is going to be with less data, not just less 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?”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Annotated data and then making 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 life 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 one to five, how likely are you to interact with this social part 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”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think that's part of the research question here. 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 a optimization function? That's what you're asking in terms of we as researchers are used to having a great corpus of”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it's bought, actually. And let me tell you how you invoke the 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 university 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 a 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 this 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”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 in 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.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think you have to start focusing on aspects of reasoning that there are still more look ups of what intents the customer is asking for and responding to those rather than really reasoning about the elements of the conversation. For instance, if you have 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. 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”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Young minds, it's also to, if you think about the other aspect of where the whole industry is moving with AI, 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”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source