YouSaid · the spoken record

David Ferrucci

lines on the record: 179
first: 2019-10-11
most recent: 2019-10-11
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

“I mean, we were supposed to take things and say this is an active research area. It's our obligation to kind of, if we have the opportunity, to push it to the limits. And if it doesn't work to understand more deeply why we can't do it. And so I was very committed to that notion saying, folks, this is what we do. It's crazy not to do this.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“That we mentioned earlier hard to interpret the question, hard to do it quickly enough, hard to compute an accurate confidence. None of this stuff had been done well enough before. But a lot of the technologies were building where the kinds of technologies that should work. But more to the point, what was driving me was I was in IBM research. I was a senior leader in IBM research. And this is the kind of stuff we were supposed to do. In other words, we were basically supposed to.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So, I thought it was possible, and a lot of people thought it was impossible. I thought it was possible. The reason why I thought it was possible is because I did some brief experimentation. I knew a lot about how we were approaching open domain factoid question asking. We've been doing it for some years. I looked at the Jeffrey stuff. I said, this is going to be hard for a lot of the...”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“It was 2004, I think, was on his winning streak, and someone thought, hey, that would be really cool if the computer complained Jeopardy. And so this was like in 2004. They were shopping this thing around. And everyone was telling the research execs, no way. Like, this is crazy. And we have some pretty senior people in the field and they say, now this is crazy. And it came across my desk and I was like, but that's kind of what I'm really interested in doing. But there was such this prevailing sense of this is nuts. We're not going to risk IBM's reputation on this. We're just not doing it. And this happened in 2004. It happened in 2005. At the end of 2006, it was coming around again. And I was coming off of a, I was doing the open domain question answering stuff, but I was coming off a couple other projects. I had a lot more time to put into this. And I argued that it could be.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I had been working in AI at IBM for some time. I had a team doing what's called open domain factoid question answering, which is, you know, we're not going to tell you what the questions are. We're not even going to tell you what they're about. Can you go off and get accurate answers to these questions? And it was an area of AI research that I was involved in. And so it was a very specific passion of mine. Language understanding had always been a passion of mine. One sort of narrow slice on whether or not you could do anything with language was this notion of open domain, meaning I could ask anything about anything, factoid, meaning it essentially had an answer. And being able to do that accurately and quickly. So that was a research area that my team had already been in. And so completely independently, several IBM executives like, what are we going to do? What's the next cool thing to do? And Ken Jennings was on his winning streak. This was like...”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so the story was that it was coming up, I think, the 10-year anniversary of Big Blue, not Big Blue. Deep Blue. Deep Blue. IBM wanted to do sort of another kind of really fun challenge, public challenge that can bring attention to IBM research and the kind of the cool stuff that we were doing.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, they were two different things. In fact, we had multiple stages, whereas we would say let's estimate our confidence, which was sort of a shallow answering process. And then ultimately decide to buzz in. And then we may take another second or something. Kind of go in there and do that. But by and large, we're saying, like, we can't play the game. We can't even compete if we can't, on average, answer these questions in around three seconds or less.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So you're already down at that level just to get started. And so it was important to get a very quick sense of do you think you know the right answer to this question? So we have to compute that confidence as quickly as we possibly could. So in effect, we have to answer it. At least, you know, spend some time essentially answering it and then judging the confidence that our answer was right and then deciding whether or not we were confident enough to buzz in. And that would depend on what else was going on in the game because there was a risk. So like if you're really in a situation where I have to take a guess, I have very little to lose, then you'll buzz in with less confidence.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so for example, I mean, we had this thing called recall, which is like how many of all the Jeopardy questions, you know, how many could we even find like find the right answer for like anywhere? Like, could we come up with if we look, you know, we had a big body of knowledge some order of several terabytes. I mean, from a web scale, it was actually very small, but from a book scale talking about millions of books, right? So they call it millions of books, encyclopedias, dictionaries, books. It's a ton of information. Only 85% was the answer anywhere to be found.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think a lot of humans will assume they'll look at their process of very superficially. In other words, what's the topic? What are some keywords? And just say, do I know this area or not before they actually know the answer? Then they'll buzz in and then they'll buzz in and think about it. So it's interesting what humans do. Now some people who know all things like Ken Jennings or something or the more recent big Jeopardy player, I mean, they'll just buzz it. They'll just assume they know all of Jeopardy and they'll just, you know, Watson, interestingly, didn't even come close to knowing all of Jeopardy, right? Watson really.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“The questions were much more direct. They weren't quite like that. They got sort of more and more interesting. The way they asked them that sort of got more and more interesting and subtle and nuanced and humorous and witty over time, which really required the human to kind of make the right connections and figuring out what the question was even asking. So yeah, you have to figure out the questions even asking. Then you have to determine whether or not you think you know the answer. And because you have to buzz in really quickly, you sort of have to make that determination as quickly as you possibly can. Otherwise, you lose the opportunity to buzz in.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, they're asked exactly in numerous witty, tricky ways. Exactly what they're asking is not obvious. It takes inexperienced humans a while to go, what is it even asking? And it's sort of an interesting realization that you have when somebody says, Oh, what's Jeopardy is a question answering show? And then he's like, oh, like, I know a lot. And then you read it and you're still trying to process the question and the champions have answered and moved on. There are three questions ahead by the time you figured out what the question even meant. So there's definitely an ability there to just parse out what the question even is. So that was certainly challenging. It's interesting historically though if you look back at the Jeopardy Games much earlier,”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Have to understand the question What is it asking? And that's a good point because the questions are not asked directly, right?”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“It's really to get a question and answer, but it's what we call a factoid Really relates to some fact that few people would argue whether the facts are true or not. In fact, most people wouldn't. Jeopardy kind of counts on the idea that these statements have factual answers. And the idea is to, first of all, determine whether or not you know the answer, which is sort of an interesting twist.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Right, so it's to take a Question and answer it. It's just the opposite. It's the opposite. Well, no, but it's not, right? It's really not. It's really.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Break that argument down and say, wait a second, what do you really think about this? So essentially holding us accountable to doing more critical thinking.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Broader the content. Richard is. And so you and I can have very different experiences with the same text, obviously We're committed to understanding each other Start, and that's the other important point if we're committed to understanding each other, we start decomposing and breaking down our interpretation towards more and more primitive components until we get to that point where we say, oh, I see why we disagree and we try to understand how fundamental that disagreement really is. But that requires a commitment to breaking down that interpretation in terms of that framework in a logical way. Otherwise, and this is why, like I think of AI as really complementing and helping human intelligence to overcome some of its biases and its predisposition to be persuaded by more shallow reasoning in the sense that we get over this idea, well, I'm right because I'm Republican or I'm right because I'm Democratic and someone labeled this as a democratic point of view or it has the following keywords in it. And if the machine can help us break.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“And in fact, you know, to help people reason and say, Oh, I see where our differences lie. I have this fundamental belief about that. I have this fundamental belief about that”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“We would want out of the AI is to be able to tell you that this perspective, one perspective, one set of assumptions is going to lead you here, another set of assumptions is going to lead you there.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, I mean, I think. Right, you're talking about political and social ways of interpreting the world around them. And I think these frameworks are still largely, largely similar. I think they differ in maybe what some fundamental assumptions and values are. Now, from a reasoning perspective, like the ability to process the framework might not be that different, the implications of different fundamental values or fundamental assumptions in those framework may reach very different conclusions. So from a social perspective, the conclusions may be very different. From an intelligence perspective, I just followed where my assumptions took me”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Depends on how you bound them, right? So, in other words, how big or small their individual scope. But there's lots and there are new ones. I think the way I think about it is kind of in a layered. I think the architecture is being layered in that there's a small set of primitives that allow you the foundation to build frameworks and then there may be many frameworks, but you have the ability to acquire them. And then you have the ability to reuse them. I mean, one of the most compelling ways of thinking about this is the reasoning by analogy where I can say, oh, wow, I've learned something very similar. You know, I never heard of this. I never heard of this game soccer. But if it's like basketball in the sense that the goals like the hoop and I have to get the ball in the hoop and I have guards and I have this and I have that, like where does the similarities and where are the differences? And I have a foundation now for interpreting this new information.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“And somewhere in the text, it says, and electricity is produced by water flowing over turbines or something like that. And then there's a question that says, well, how is electricity created? And so my daughter comes to me and says, I mean, I could create and produce or kind of synonyms in this case so I can go back to the text and I can copy by water flowing over turbines. But I have no idea what that means. Like I don't know how to interpret water flowing over turbines and what electricity even is. I mean, I can get the answer right by matching the text, but I don't have any framework for understanding what this means at all.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Another example is where the machine's literally talking to you and saying, look, I'm reading this thing. I know that the next word might be this or that, but I don't really understand why I have my guess. Can you help me understand the framework that supports this and then can kind of acquire that, take that and reason about it and then reuse it the next time it's reading to try to understand something, not unlike... Human student might do. I mean, I remember when my daughter was in first grade and she had a reading assignment about electricity.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, no, that's exactly right. That's a moment of interaction where the machine has learned some stuff. It has a failure. Somehow the failure is communicated. The human is now filling in the mistake, if you will, or maybe correcting or doing something that is more successful in that case. The computer takes that learning. So I believe that the collaboration between human and machine, I mean, that's sort of a primitive example and sort of a more”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Acquire these patterns to induce the generalizations from those patterns, but then ultimately to work with humans to connect them to frameworks, interpretations, if you will, that ultimately make sense to humans. Of course, the machine is going to have the strength that it has, the richer and longer memory, but it has the more rigorous reasoning abilities, the deeper reasoning abilities. So it would be an interesting complementary relationship between the human and the machine.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I am. My sort of approach to AI is because I've set the goal for myself. I want machines to be able to ultimately communicate. Understanding with humans. I want to be able to acquire and communicate, acquire knowledge from humans and communicate knowledge to humans. They should be using what inductive machine learning techniques are good at, which is to observe patterns of data, whether it be in language or whether it be in images or videos or whatever.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, and let's be clear, let's be clear only for the express purpose that you're designing an intelligence that can ultimately communicate with humans. In the terms of frameworks that help them understand things So, to be really clear, you can create, you can independently create a machine learning system and intelligence that I might call an alien intelligence that does a better job than you with some things, but can't explain the framework to you. That doesn't mean it might be better than you at the thing. It might be that you cannot comprehend the framework that it may have created for itself that is inexplicable to you. That's a reality.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Representation like you saying, like these graphs of logic, if you will, there are also neural networks that acquire certain class of information. Then they align them with these frameworks. But there's also a mechanism to acquire the frameworks themselves.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so I mean, I think asking the question they look like neural networks is a bit of a red herring. I mean, I think that they will certainly do inductive or pattern-matched reasoning. And I've already experimented with architectures that combine both that use machine learning and neural networks to learn certain classes of knowledge, in other words, define repeated patterns in order for it to make good inductive guesses, but then ultimately to try to take those learnings and marry them, in other words, connect them to frameworks so that it can then reason over that in terms of other humans understand. So, for example, at elemental cognition, we do both. We have architectures that do both. But both those things, but also have a learning method for acquiring the frameworks themselves and saying, look, ultimately I need to take this data. I need to interpret it in the form of these frameworks so they can reason over it. So there is a fundamental knowledge.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“To acquire, to have access to and acquire learn the frameworks as well and connect the frameworks to the data. I think this can be done. I think we can start, I think machine learning, for example, with enough examples, can start to learn these basic dynamics. Will they relate them necessarily to gravity not unless they can also acquire? Those theories as well And put the experiential knowledge and connect it back to the theoretical knowledge. I think if we think in terms of these class of architectures that are designed to both learn the specifics, find the patterns, but also acquire the frameworks and connect the data to the frameworks. If we think in terms of robust architectures like this, I think there is a path. Get”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“No, I think it is solvable. I mean, I think that, first of all, it's about getting machines to learn, learning is fundamental. And I think we're already in a place that we understand, for example, how machines can learn in various ways. Right now, our learning stuff is sort of primitive in that we haven't sort of taught machines to learn the frameworks. We don't communicate our frameworks because of how shared them. In some cases, we do, but we don't annotate, if you will, all the data in the world with the frameworks that are inherent or underlying our understanding. Instead, we just operate with the data. So if we want to be able to reason over the data in similar terms in the common frameworks, we need to be able to teach the computer, or at least we need to program the computer.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So I think if you're talking about sort of the physics, the basic physics around us, for example, acquiring information about acquiring how that works, yeah, I think there's a combination of things going. I think there's a combination of things going on. I think there is fundamental pattern matching, like what we were talking about before, where you see enough examples, enough data about something you start assuming that, and with similar input, I'm going to predict similar outputs. You don't necessarily explain it at all. You may learn very quickly that when you let something go. It falls to the ground.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, because what the frameworks do is they give you now the ability to interpret in reason and to interpret in reason to interpret and reason over the specifics in ways that other humans would understand.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“A tremendous amount of detailed knowledge in the world. You can imagine effectively infinite number of unique situations and unique configurations of these things. But the knowledge that you need, what I refer to as like the frameworks, for you need for interpreting them, I don't think. I think those are finite.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“That can be given in those terms, in the terms of that underlying framework that most humans share. Now you could find humans that come and interpret events very differently than other humans because they're using different framework. The movie Matrix comes to mind where they decided humans were really just batteries. And that's how they interpreted the value of humans as a source of electrical energy. But I think that for the most part, we have a way of interpreting the events or the social events around us because we have this shared framework. It comes from, again, the fact that we're similar beings that have similar goals, similar emotions, and we can make sense out of these frameworks make sense to us.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it is possible to imbue a computer with that stuff that humans take for granted when they go and sit down and try to interpret things. And then with that foundation, they acquire, they start acquiring the details, the specifics in any given situation, are then able to interpret it with regard to that framework. And then given that interpretation, they can do what? They can predict. But not only can they predict, they can predict now with an explanation.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Their fundamental economics around scarcity of resources. And when humans come and start interpreting a situation like that, because you brought up like historical events, they start interpreting situations like that. They apply a lot of this fundamental framework for interpreting that. Well, who are the people? What were their goals? What resources did they have? How much power influence did they have over the other? Like there's fundamental substrate, if you will, for interpreting and reasoning about that.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So in other words, We view the world in a particular way. So in other words, we Have you will, as humans, we have a framework for interpreting the world around us. So we have multiple frameworks for interpreting the world around us. But if you're interpreting, for example, sociopolitical interactions, you're thinking about whether there's people, there's collections and groups of people, they have goals, goals largely built around survival and quality of life.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it is possible to learn for a machine, to program a machine to acquire that knowledge with a similar foundation. In other words, a similar interpretive foundation for processing that knowledge.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Humans come with a lot of pre programmed stuff. Ton of program stuff, and they were able to communicate because they have a lot of, because they share that stuff.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Cracked. We have the shared experience and we have similar brains. So we tend to, in other words, part of our shared experience is our shared local experience, like we may live in the same cultural, we may live in the same society, and therefore we have similar educations. We have similar what we like to call prior models about the world prior experiences. And we use that as a, think of it as a wide collection of interrelated variables, and they're all bound to similar things. And so we take that as our background and we start interpreting things similarly. But as humans, we have a lot of shared experience. We do have similar brains, similar goals, similar emotions under similar circumstances because we're both humans. So now one of the early questions you asked, well, how is biological and computer information systems fundamentally different? Well, one is”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“But when I want to align our understanding of that, I have to specify a lot more stuff that's actually not directly in the artifact. Now I have to say, well, how are you interpreting this image and that image? And what about the colors and what do they mean to you? What perspective are you bringing to the table? What are your prior experiences with those artifacts? What are your fundamental assumptions and values? What is your ability to kind of reason to chain together logical implications? So, your reasoning processes and how they work, your prior models and what they are, your values and your assumptions, all those things now come together into the interpretation. Getting in sync with that is hard.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“We try to get more rigorous with our communication, we try to really nail down that meaning so we go from abstract art to precise mathematics, precise engineering drawings and things like that, we're really trying to say, I want to narrow that space of possible interpretations because the precision of the communication ends up becoming more and more important. So that means that I have to specify, and I think that's why this becomes really hard. Because if I'm just showing you an artifact and you're looking at it superficially, whether it's a bunch of words on a page or whether it's brushstrokes on a canvas or pixels on a photograph, you can sit there and you can interpret lots of different ways at many, many different levels.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Meaning is often relative, but meaning implies that the connections go beneath the surface of the artifacts. If I show you a painting, it's a bunch of colors on a canvas. What does it mean to you? And it may mean different things to different people because of their different experiences. It may mean something even different to the artist who painted it.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Because you have to start getting at the meaning of the stuff, of the content. You have to get at how humans interpret the content. Relative to their value system and deeper thought processes.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Or how long the material is, or other very superficial features, what colors are being used in the material. Like I don't know why you're clicking on the stuff you're clicking or if it's products, what the price is or what the categories and stuff like that. And I just feed you more of the same stuff. That's very different than kind of getting in there and saying, what does this mean? The stuff you're reading, like why are you reading it? What assumptions are you bringing to the table? Are those assumptions sensible? Does the material make any sense? Does it lead you to thoughtful, good conclusions? Again, there's interpretation and judgment involved in that process that isn't really happening in the AI today That's harder.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“And it's interesting to think about why it's harder. And because you're interpreting, you're interpreting the data in the context of prior models. In other words, understandings of what's important in the world, what's not important, what are all the other abstract features that drive our decision making, what's sensible, what's not sensible, what's good, what's bad, what's moral, what's valuable, what isn't, where is that stuff? No one's applying the interpretation. So when I see you clicking on a bunch of stuff and I look at these simple features, the raw features, the features that are there in the data like what words are being used.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So that I agree with you. That's the interesting dichotomy, right? Because on one hand, we're sitting there and we're sort of doing the easy part, which is finding the patterns. We're not building, the system's not building a theory. That is consumable and understandable by other humans that we can explain and justified. And so on one hand to say, oh, you know, AI is doing this. Why isn't doing this other thing? Well, this other thing is a lot harder.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Think we're seeing this not just in buying stuff, but even in social media. You're reading this kind of stuff. I'm not judging on whether it's good or bad. I'm not reasoning at all. I'm just saying I'm going to show you other stuff with similar features. And that's it. And I wash my hands from it and I say, that's all that's going on.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Explanation for why it's important to you, and the algorithms are saying, hey, that's like whatever. Like, that's your problem. All I know is you're buying stuff like that. You're interested in stuff like that. Could be a bad reason, could be a good reason. That's up to you. I'm going to show you more of that stuff. And so I think that that's not good or bad. It's not reasoned or not reasoned. The algorithm is doing what it does, which is saying you seem to be interested in this. I'm going to show you more of that stuff.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source