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

“These kind of agents out there being used, they get better for your customers. And I think that's where the amount of research topics we are throwing out at our budding researchers is just going to be exponentially hard. And the great thing is you can now get immense satisfaction by having customers use it, not just a paper and neurops or another conference.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I feel, I tell every researcher that joins or every member of my team that this is a unique privilege. I think, and we have, and I would say not just launching Alexa in 2014, which was first of its kind, along the way, when we launched Alexa Skillskit, it became democratizing AI. Before that, there was no good evidence of an SDK for speech and language. Now we are coming to this where you and I are having this conversation where I'm not saying Ox planning a night out with an AI agent impossible. I'm saying it's in the realm of possibility and not only possibility will be launching this, right? So some elements of that will keep getting better. We know that is a universal truth once you have”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it's a privilege. I'm so fortunate where I ended up, right? And it's been a long journey. I've been in this space for a long time in Cambridge, right? And it's so heartwarming to see the kind of adoption conversational agents are having now. Five years back, it was almost like should I move out of this because we are unable to. Find the skiller application that customers would love that would not simply be a good to have thing in research labs. And it's so fulfilling to see it make a difference to millions and billions of people worldwide. The good thing is that it's still very early. So I have another 20 years of job security doing what I love. So I think from that perspective.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“And that, I think, is the first five year transformation. The next five year transformation would be, okay, I can plan my weekend with Alexa or I can plan my next meal with Alexa or my next night out with seamless effort.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Even if you limit to the human intelligence, we know we are quite far from that. In fact, every aspect of our sensing to neural processing, to how brain stores information and how it processes it, we don't yet know how to represent knowledge. So we are still in those early stages. I wanted to start. That's why at the five year. Because the five year success would look like that in solving these complex goals, and the 40 year would be where it's just natural to talk to these in terms of more of these complex goals. Right now, we've already come to the point where these transactions you mentioned of asking for weather or reordering something or listening to your favorite tune, it's natural for you to ask. It's now unnatural to pick up your phone.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Is a nice bar for it? I think you will, it's a nice ambition. And do we have press releases for that? Absolutely. Can I tell you what specifically the roadmap will be? No. And will we solve all of it in the five-year space? No, this is, we'll work on this forever, actually. This is the hardest of the AI problems. And I don't see that being solved even in a 40-year horizon.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So, I think I think of it as how do you complete goals with minimum steps for our customers, right? And when you think of it that way, the distinction between goal-oriented and conversations for open domain's sake goes away. I may want to know what happened in the presidential debate. And is it, I'm seeking just information and I'm looking at who's winning the debates, right? So these are all quite hard problems. So even the five-year horizon problem, I'm like, I sure hope will solve these.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So now you think even for something that you felt like is a finite goal, I think the space is huge because even products, the attributes are many. And you want to look at reviews, some on Amazon, some outside, some you want to look at what CNET is saying or another consumer forum is saying about even a product, for instance. So that's just shopping where you could argue the ultimate goal is sort of known. And we haven't talked about Alexa, what's the weather in Cape Cod this weekend? Right. So, why am I asking a question? Right.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Then you make a decision. So, is that a goal-oriented dialogue when somebody says Alexa, find me a camera? Is it simply inquisitiveness? So, even in something that you think of it as shopping, which you said you yourself use a lot, if you go beyond where it's reorders or items where you sort of are not brand conscious and so forth. So that was just in shop.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think the five years where, I mean, I think of in these spaces it's hard, especially if you're in thick of things, to think beyond the five-year space because a lot of things change, right? I mean, if you ask me five years back, will Alexa will be here? I think it has surpassed my. Imagination of that time, right? So I think from the next five years' perspective, from an AI perspective, what we're going to see is that notion which you said, goal-oriented dialogues and open domain, like surprise, I think that's Closed. They won't be different. And I'll give you why that's the case. You mentioned shopping. How do you shop? Do you shop in one shot? Sure, your AA batteries, paper towels, yes. How long does it take for you to buy a camera? You do a ton of research.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think you have to solve that problem. Otherwise, and everyone's very different. Like, I mean, we see this already in terms of the skills, right? I mean, if you're an average surfer, which I am not, right? But somebody is asking Alexa about surfing conditions, right? And there's a skill that is there for them to get to, right? That tells you that the tail is massive, like in terms of what kind of skills people have created, it's humongous in terms of it, and which means there are these diverse needs. And when you start looking at the combinations of these, right, even if you had pairs of skills and $90,000 too, it's still a big set of combinations. So I'm saying there's a huge to-do here now. And I think customers are wonderfully frustrated with things. And they have to keep getting to do better things for them. And they're not.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think yeah you write in terms of the head of the distribution of all the possible things customers may want to accomplish but the tail is long and it's diverse right so from that many many”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“The hypothesis space is really, really large. And when you go back in time, like you were saying, I want Alexa to remember more things. Once you go beyond a session of interaction, which is by session I mean a time span, which is today, two versus remembering which restaurant I like. And then when I'm planning a night out to say, do you want to go to the same restaurant? Now you're up the stakes big time. And this is where the reasoning dimension also goes way, way bigger.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't think so. I wouldn't say deep learning is enough. I think for the purposes of Alexa accomplish the task for customers, I'm saying there are still a lot of things we can do with prediction-based approaches that do not reason. I'm not saying that, and we haven't exhausted those. But for the kind of high utility experiences that I'm personally passionate about of what Alexa needs to do, Reasoning has to be solved to the same extent as you can think of Natural language understanding and speech recognition to the extent of understanding intents has been how accurate it has become. But reasoning we are very, very early days.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“About is incredible. So transfer learning is also super critical, especially when you're thinking about Applying knowledge from one task to another or one language to another, right? It's really ripe. So these are great pieces. Deep learning has been useful too. And now we are sort of marrying deep learning with transfer learning and active learning, of course, that's more straightforward in terms of applying deep learning and an active learning setup. But I do think in terms of now looking into more reasoning-based approaches is going to be key for our next wave of the technology. But there is a good news. The good news is that I think for keeping on to delight customers, that a lot of it can be done by prediction tasks. And so we haven't exhausted that. We don't need to give up on the deep learning approaches for that. So that's just I wanted to sort of creating a rich.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Absolutely. I think there has to be a lot more investment, and I think in many different ways. And there are these, I would say, nuggets of research forming in a good way, like learning with less data or zero shot learning, one-shot learning.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“There's a whole field of affective computing, something that MIT has also done a lot of research, is super hard. And you're now talking about a far field device as in you're talking to a distance noisy environment. And in that environment, it needs to have a good sense for your emotions. This is a very, very hard problem.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“You can get from voice, but it's very hard. Frustration as a signal historically, if you think about emotions of different kinds”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, let me again bring back to what it already does. We talked about how based on you bargain over Alexa, clearly it's a very high probability it must have done something wrong. That's why you barged in. The next extension of whether frustration is a signal or not.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Here, but there are certain similarities, but if you think about how many decisions Alexa is making or evaluating at any given time, it's a huge hypothesis space. And we are only talked about so far about what I think of reactive decision in terms of you asked for something and Alexa is reacting to it. If you bring the proactive part, which is Alexa having hunches, so any given instance, then it's really a decision at any given point based on the information. Alexa has to determine what's the best thing it needs to do. So these are the ultimate AI problem about decisions based on the information you have.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think first and foremost, as I mentioned, Get the basics right are still true. Basically, One shot request, which we think of as transactional requests needs to work magically. No question about that. If it doesn't turn your light on and off, you'll be super frustrated. Even if I can complete the night out for you and not do that, that is unacceptable as a customer, right? So that you have to get the foundational understanding going very well. The second aspect, when I said more conversational is, as you imagine, is more about reasoning. It is really about figuring out what the latent goal is of the customer based on what I have the information now and the history. What's the next best thing to do? So that's a complete reasoning and decision making problem. Just like yourself driving car, but the goal is still more finite. Here it evolves. Your environment is super hard and self-driving and the cost of a mistake is huge.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“That latter part is definitely our responsibility in terms of when the request is not fully specific, how do you figure out what's the best skill or a service that can fulfill the customer's request? And it can keep evolving. Imagine going to the situation I said, which was the night out planning that the goal could be more than that individual request that came, a pizza ordering could mean a nighting where you're having an event with your kids in the house and so this is welcome to the world of conversational AI.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“It's both. I think the large problem in terms of making sure your skill quality is high, that has to be done by our tools because these skills, just to put the context, they are built through Alexa Skillskipt, which is a self-serve way of building an experience on Alexa. This is like any developer in the world could go to Alexa Skillskit and build an experience on Alexa. Like if you're a Domino's, you can build a Domino skills. For instance, that does pizza artering. When you have authored that, you do want to now, if people say Alexa open dominoes or Alexa ask dominoes to get a particular type of pizza, that will work, but the discovery is how you can't just say Alexa, get me a pizza and then Alexa figures out what to do.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“We were talking about lists, for instance, and you want to say Alexa, add milk, Alexa, add eggs. Alexa add cookies No, Alexa add cookies, milk, and eggs, and that in one shot, right? So that works, that helps with the naturalness. We talked about memory. Like if you said you can select, so remember I have to go to mom's house, or you may have entered a calendar event through your calendar that's linked to Alexa. You don't want to remember whether it's in my calendar or did I tell you to remember something or some other reminder? So you have to now, independent of how customers create these events,”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Versus a left, or based on your preferences, and then you can rank the responses from the skill and then choose the best response for the customer. So that's on the more natural. Other examples of more natural is like...”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Now if you have to remove the skill name, that means the discovery and the interaction is unnatural. And we are trying to solve that by what we think of as, again, this was, you don't have to have the app metaphor here. These are not individual apps, right? Even though you're not sort of opening one at a time and interacting. So it should be seamless because it's voice. And when it's voice, you have to be able to understand these requests independent of the specificity, like a skill name. And to do that, what we have done is again built a deep learning-based capability where we shortlist a bunch of skills when you say, let's say, get me a car. And then we figure it out, okay, it's meant for an Uber skill.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I would think in terms of there's a lot of controls in each of the places for how, I mean, the speed of the war is the prosthetic patterns, the actual smoothness of how it sounds, all of those are factored, and we do a ton of listening tests to make sure. But naturalness, how it sounds should be very natural, how it understands requests is also very important. Like, and in terms of like we have 95,000 skills, and if we have imagine that in many of these skills, you have to remember the skill name.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Customers helping Alexa is a tenet for us in terms of improving it and more self-learning is by, again, this is like fully unsupervised, right? There is no human in the loop and no labeling happening. And based on your actions as a customer, Alexa becomes smarter. Again, it's early days. But I think this whole area of teachable AI is going to get bigger and bigger in the whole space, especially in the AI assistant space. So that's the second part where And we have done a lot of advances in our text to speech by using again neural network technology for it to sound very human-like.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I would say it's a spectrum. Some customers do think that way, and some would be annoyed by Alexa acknowledging that. So there's again no one, you know, while there are certain patterns, not everyone's the same in this way. But we believe that, again,”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“And it's very common with station names because play NPR, you can have n be confused as an M and then for a certain accent like mine, people confuse my N and M all the time because I have an Indian accent. They're confusible to humans. It is for Alexa too. And in that part, but it starts autocorrecting. And we collect a lot of these automatically without a human looking at the failures.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, and not only utilizing, it's already doing some of it, we call it where Alexa is becoming more self-learning. So Alexa is now auto-correcting millions and millions of utterances in the US without any human supervision involved. The way it does it is, let's take an example of a particular song didn't work for you. What do you do next? No, that's not the song I want. Or you say, Alexa, play that you try it again. And that is a signal to Alexa that she may have done something wrong. And from that perspective, we can learn if there's that failure pattern or that action of song A was played when song B was requested.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“That would happen. You should send me your next time it fails. Feel free to send it to me. We'll take care of it. Because Led Zeppelin is one of my favorite granted works for me. So I'm shocked it doesn't work for you”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Some memories for the customers. It is using a lot of memory within that. So right now, not so much in terms of, okay, which restaurant you prefer? That is a more long term memory, but within the short term memory, within the session, it is remembering how many people did you. So if you said buy four tickets, now it has made an implicit assumption that You're going to have, you need at least four seats at a restaurant, right? So these are the kind of contexts it's preserving between these skills, but within that session. But you're asking the right question in terms of for it to be more and more useful, it has to have more long-term memory. And that's also an open question. And again, this is still early days.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Based on what you say, whether either you have completed the interaction or you said, no, get me an Uber now so it will shift context into another experience or skill on another service. So that's a dynamic decision making that's making Alexa, you can say more conversational for the benefit of the customer rather than simply complete transactions which are well thought through. You as a customer has fully specified what you want to be accomplished. It's accomplishing”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“From the customer to the AI, where it's thinking of what is your it anticipates your goal and takes the next best action to complete it. Now that's the machine learning problem. But essentially the way we solved this first instance and we have a long way to go to make it scale to everything possible in the world, but at least for this situation, it is from at every instance Alexa is making the determination whether it should stick with the experience with atom tickets or offer you.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“And let's say I now complete the movie ticket purchase. Maybe I would like to get dinner nearby. So, what is really the goal here? Is it night out or is it movies? As in just go watch a movie. The answer is we don't know. So, can Alexa now figure we have the intelligence that I think this meta goal is really night order at least say to the customer when you have completed the purchase of movie tickets from atom tickets or Fandango or Picure anyone, then the next thing is do you want to get to get an Uber to the theater, right? Or do you want to book a restaurant next to it? And then not ask the same information over and over again. What time how many people in your party, right? So this is where you shift the cognitive burden”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Am I trying to just buy movie tickets? Am I actually even do you think I'm looking for just movies for curiosity? Avengers are still in theater, or when is it? Maybe it's gone, and maybe it will come on my missed it. So I may watch it on Prime, which happened to me. So from that perspective now, you're looking into what is my goal?”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“And with Alexa conversations, the way it is that you just provide a sample interaction data with your service or your API, let's say your Atom tickets that provides a service for buying movie tickets, you provide a few examples of how your customers will interact with your APIs. And then the dialog flow is automatically constructed using a recurrent neural network trained on that data. So that simplifies the developer experience. We just launched our preview for the developers to try this capability out. And then the second part of it, which shows even increased utility for customers, is you and I, when we interact with Alexa or any customer, As I'm coming back to our initial part of the conversation, the goal is often unclear or unknown to the AI. If I say Alexa, what movies are playing nearby?”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So that's again, this is language understanding. Now things have evolved, right? So, where we want Alexa definitely to be more accurate, competent, trustworthy based on how well it does these core things. But we have evolved in many different dimensions. First is what I think of it being more conversational for high utility, not just for chat, right? And there at Remars this year, which is our AI conference, we launch what is called Alexa Conversations. That is providing the ability for developers to author multi-turn experiences on Alexa with no code, essentially, in terms of the dialogue code. Initially, it was like all these IVR systems, you have to fully author if the customer says this, do that, right? So the whole dialog flow is hand-authored.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So I don't remember everything we had, but the timers were the big ones. That was, the timers were very popular right away. Music also, like you could play, song, artist, album, everything. And so that was a clear win in terms of the customer experience.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Garage door, you can check your status of the garage door and things like that, and we have gone make Alexa more and more proactive where it even have hunches now that Or hunches like you left your light on. Let's say you've gone to your bed and you left the garage light on. It will help you out in these settings.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So we think of it as music, information, right? So whether it's a part of information, right? So when we launched, we didn't have smart home, but by smart home I mean you connect your smart devices, you control them with voice. If you haven't done it, it's worth, it will change your life.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Sort of 13 big domains, I would say, in terms of our thing, we think of it as 13 big skills we had. Like music is a massive one when we launched it. And now we have 90,000 plus skills on Alexa.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“There is both, right? You want 90% or high 90s to be done without any further questioning or UX, right? But it's absolutely okay, just like as humans, we ask the question, I didn't understand you, Lex It's fine for Alexa to occasionally say, I did not understand you, right? And that's an important way to learn, and I'll talk about where we have come with more self-learning, with these kind of feedback signals. But in those days just solving the ability of understanding the intent and resolving to an action where action could be play a particular artist or a particular song was super hard. Again, the bar was high as we are talking about, right? So while we launched it”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“In terms of what we think of it as an entity resolution problem, right? Because which one is it, right? I mean, even if you figured out the stones as an entity. You have to resolve it to whether it's the stones or the temple pilots or some other stones.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Stone Temple pilots or Rolling Stones, right? So you don't know which one it is. So these kind of other signals to, now there we had great assets from Amazon in terms of...”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, it is contingent on that, and that's why it came back to how do you get the data before customers, the fact that this is why data becomes crucial to get to the point that you have the understanding system built in. And notice that we were talking about human machine dialogue, and even those early days, even it was very much transactional, do one thing, one-shot utterances in great way. There was a lot of debate on how much should Alexa talk back in terms of You misunderstood you, or you said play songs by the stones and let's say it doesn't know early days knowledge can be sparse, who are the stones, right? It's the rolling stones, right? And you don't want the match to be...”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Again, no customer base. So now you're looking at meaning understanding and intent understanding and taking actions on behalf of customers based on their request. And that is the next hard problem, even if you have gotten the words recognized. How do you make sense of them? In those days, there was still a lot of emphasis on rule-based systems for writing grammar patterns to understand the intent, but we had a statistical first approach even then, where for our language understanding we had even those starting days. An entity recognizer and an intent classifier, which was all trained statistically. In fact, we had to build the deterministic matching to fix bugs that statistical models have, right? So it was just a different mindset where we focused on data-driven statistical understanding.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So, the next one was what I think of as multi domain natural language understanding. I wouldn't say easy, but it is during those days solving it, understanding in one domain a narrow domain was doable, but for these multiple domains like music, like information, other kinds of household productivity, alarms, timers, even though it wasn't as big as it is in terms of the number of skills Alexa has and the Confusion space has grown by three orders of magnitude, it was still daunting even those days.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Was very high, and we talked about how also errors are perceived from AIs versus errors by humans. But we are not done with the problems that ended up, we had to solve to get it to launch. So, do you want the next one? Yeah.”
2019-12-14 · Lex Fridman Podcast · Rohit Prasad: Amazon Alexa and Conversational AI · IDENTIFIED FROM THE TRANSCRIPT · source