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

“Just to be part of whether it's a medical diagnosis or whether it's the various treatment options or whether it's a legal case or whether it's a social problem that people are discussing, like be part of the dialogue, one that holds itself and us accountable to reasons and objective dialogue. I get goosebumps talking about it, right? It's like this is what I want.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So you threw me a little bit with finding the truth at the end. The truth is a whole nother topic. I think the beauty of it. If I really have that system, I don't have to pick. So, in other words, I can go to and say, this is what I care about today. And that's what we mean by it, like this general capability. Go out, read the stuff in the next three milliseconds. And I want to talk to you about it. I want to draw analogies. I want to understand how this affects this decision or that decision. What if this were true? What if that were true? What knowledge should I be aware of that could impact my decision? Here's what I'm thinking is the main implication. Can you find that out? Can you give me the evidence that supports that? Can you give me evidence that supports this other thing? Boy, would that be incredible? Would that be just incredible?”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Goes back to the cognition here's how your brain works here's how easy it is to trick your brain right there are fundamental you should appreciate the different the different types of thinking and how they work and what you're prone to and you know and what and what do you prefer and under what conditions does this make sense versus that make sense and then say here's what ai can do here's how it can make this worse and here's how it can make this better”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Humans, machines empower that. So that's what I mean by leverage. It's not new. But wow, it's powerful because machines can do it more effectively more quickly. And we see that already going on in social media and other places. That's scary. And that's why I'm. That's why I go back to saying one of the most important public dialogues we could be having is about the nature of intelligence and the nature of inference and logic and reason and rationality. Us understanding our own biases, us understanding our own cognitive biases and how they work, and then how machines work, and how do we use them to complement and basically so that in the end we have a stronger overall system. That's just incredibly important. I don't think most people understand that. So, like telling your kids or telling your students.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“The ease at which humans could pre persuaded one way or the other, and now we have algorithms that can easily take control over that and amplify noise and move people one direction or another. I mean, humans do that to other humans all the time. And we have marketing campaigns. We have political campaigns that take advantage of our emotions or our fears. And this is done all the time. With machines, machines are like giant megaphones, right? We can amplify this in orders of magnitude and fine-tune its control so we can tailor the message. We can now very rapidly and efficiently tailor the message to the audience taking advantage of their biases and amplifying them and using them to persuade them in one direction or another in ways that are not fair, not logical, not objective, not meaningful.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Think there are things to worry about. I think the giving machines too much leverage is a problem. And what I mean by leverage is Too much control over things that can hurt us, whether it's socially, psychological, intellectually, or physically. And if you give the machines too much control, I think that's a concern. You forget about the AI. Just when you give them too much control, human bad actors can hack them. Produce havoc So, you know, that's a problem. And you imagine. Hackers taking over the driverless car network and creating all kinds of havoc. But you could also imagine given”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Worry about biases, you know, obviously, so in other words, if you develop an emotion relationship with a machine, all of a sudden you start are more likely to believe what it's saying, even if it doesn't make any sense. So I worry about that. At the same time, I think the opportunity to use machines to provide human companionship is actually not crazy and intellectual and social companionship is not a crazy idea.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“There are other companies that are creating physical thought partners, it's the physical partners for humans, but that's kind of not where I'm at. But the important point is that a big part of what we process. Is that physical experience of the world around us?”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Riding along is different. I meant if you want to create an intelligence That is human compatible, meaning that it can learn and develop a shared understanding of the world around it. You have to give it a lot of the same substrate. Part of that substrate is the idea that it generates these kinds of internal features, like sort of emotional stuff. It has similar senses. It has to do a lot of the same things with those same senses. So I think if you want that. Again, I don't know that you want that. Like, that's not my specific goal. I think that's a fascinating scientific goal. I think it has all kinds of other implications. That's sort of not the goal. I want to create, I think of it as I create intellectual thought barters for humans, that kind of intelligence.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“And they don't have the feedback that says, okay, I've gotten this, I've gotten this emotion, or I've gotten this idea. I now want to process it, and then I can, it then affects me as a physical being, and then I can play that out. In other words, I could realize the implications. Internal features are generated. I learn from them. They have an effect on my mind, body complex. So it's interesting when we think, do we want a human intelligence? Well, if we want a human compatible intelligence, probably the best thing to do is to embed it, embed it in a human body”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So I think Going back to that shared understanding bit, humans are very connected to their bodies. I mean, one of the reasons, one of the challenges in getting an AI to kind of be a compatible human intelligence is that our physical bodies are generating a lot of features that make up the input. So in other words, our bodies are the tool we use to affect output, but they also generate a lot of input for our brain. So we generate emotion, we generate all these feelings, we generate all these signals that machines don't have. So machines that have this is the input data”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“This is what is the potential of this? What are the business potential? What's the societal potential to that? And to build up that incentive system around that.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Have to get the machine, so it's a little bit of a bootstrapping thing. Can we get the machine engaged in the intellectual? Calling you a game, but in the intellectual dialogue with the humans, are the humans sufficiently in intellectual dialogue with each other to generate enough, to generate enough data in this context? And how do you bootstrap that? Because every one of those conversations, those intelligent interactions require so much prior knowledge that it's a challenge to bootstrap it. So the question is, and how committed, so I think that's possible, but when I go back to are we incentivized to do that, I know we're incentivized to do the former. Are we incentivized to do the latter significantly enough to people understand what the latter really is well enough? Part of the elemental cognition mission is to try to articulate that better and better through demonstrations and through trying to craft these grand challenges and get people to say, look, this is a class of intelligence, this is a class of AI.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“In a continuous basis. So, how do you get in there? How do you get the machine in the game? How do you get the machine in the intellectual game?”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“And machines will outpace humans in a variety of those things. The underlying mechanisms for doing that may be the same, meaning that maybe these are deep nets. There's infrastructure to train them, reusable components to get them to do different classes of tasks. And we get better and better at building these kinds of machines. You could argue that the general learning infrastructure in there is a form of a general type of intelligence. Think what starts getting harder is this notion of. Can we effectively communicate and understand and build that shared understanding because of the layers of interpretation that are required to do that and the need for the machine to be engaged with humans at that level?”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Think that when you get into a general intelligence for learning physical tasks, and again, I want to go back to your body question because I think your body question was interesting, but. Want to go back to learning the abilities to do physical tasks, you might have, we might get, I imagine in that timeframe, we will get better and better at learning these kinds of tasks, whether it's mowing your lawn or driving a car or whatever it is. I think we'll get better and better at that, where it's learning how to make predictions over large bodies of data. I think we're going to continue to get better and better at that”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“How incentivized we are. I think from a task perspective. If we see business opportunities to take this technique or that technique to solve that problem, I think that's the main driver for many of these things. From a general intelligence thing is kind of an interesting question. Are we really motivated to do that? And we just struggled ourselves right now to even define what it is. So it's hard to incentivize when we don't even know what it is we're incentivized to create. And if you said mimic a human intelligence, Just think there are so many challenges with the significance and meaning of that that there's not a clear directive, there's no clear directive to do precisely that thing.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“It is hard to make these predictions. I mean, I would be guessing. And there's so many different variables, including just how much we want to invest in it and how important we think it is. What kind of investment are we willing to make in it Kind of talent we end up bringing to the table, all you know, the incentive structure, all these things. So I think it is possible. To do this sort of thing. I think it's Think trying to sort of. Ignore many of the variables and things like that. Is it a 10 year thing? Is it a 23? It's probably closer to a 20 year thing, I guess. But not. No, I don't think it's several hundred years. I don't think it's several hundred years. But again, so much depends on how committed we are to investing and incentivizing this type of work. And it's sort of interesting. I don't think it's obvious.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“And now you're going to look at that and you can say, well, that's intelligence because it's protecting its power source. Maybe, but that's again this human bias that says the thing I identify my intelligence and my conscious so fundamentally with the desire or at least the behaviors associated with the desire to survive. If I see another thing doing that. Going to assume it's intelligence.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“The interesting thing to reflect on is how trivial that would be. And I don't think if you knew how trivial that was, you would associate. With being intelligence. I mean, I literally put in a statement of code that says, you know, you have the following actions you can take. You give it a bunch of actions. Like you mount a laser gun. Or you give the ability to scream or screech or whatever. And you say, if you see your power source threatened and you could program that in. You're going to take these actions to protect it.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“To really code, I mean, you can get a robot now, and you could say, you know, plug it in and say, protect your power source, you know, and give it some capabilities and it'll sit there and operate to try to protect its power source and survive. I mean, so I don't know that that's philosophically a hard thing to demonstrate. It sounds like a fairly easy thing to demonstrate that you can give it that goal. Will it come up with that goal by itself? I think you have to program that goal in.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“You could have an intelligence capability and a capability to learn, a capability to predict. But I think without Again, your fear, but essentially without the goal to survive.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“People used to ask me if Watson was conscious, and I was just like, and he said conscious of what exactly. I mean, I think, you know, maybe it depends what it is that you're conscious of. I mean, So, you know, did it, if you, you know, it's certainly easy for it to answer questions about it would be trivial to program it so that to answer questions about whether or not it was playing jeopardy. I mean, it could certainly answer questions that would imply that it was aware of things, exactly what does it mean to be aware and what does it mean to conscious them. It's sort of interesting. I mean, I think that we differ from one another based on what we're conscious of.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Understand it help you understand something that you didn't really understand before So you're not again, it's almost like can it teach you? Can it help you learn? And in an arbitrary space, so it can open those domain space. So can you tell the machine? And again, this borrows from some science fictions, but can you go off and learn about this topic that I'd like to understand better? Then work with me to help me understand it.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Don't think so, and I think that's where we have to set the higher standard for ourselves. This goes back to rationality and it goes back to objective thinking and can you produce, can you acquire information and produce reasoned arguments and to those reasoned arguments pass a certain amount of muster? And can you acquire new knowledge? For example, can you reasonably, I have acquired new knowledge, can you identify where it's consistent or contradictory with other things you've learned? And can you explain that to me and get me to understand that? So I think another way to think about it, perhaps. Can a machine teach you?”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Again, that's why I think this is such a challenge because we go back to the emotional persuasion. We go back to, now we're checking off an aspect of human cognition that is in many ways weak or flawed, right? We're so easily manipulated. Our minds are drawn for often the wrong reasons, right? Not the reasons that ultimately matter to us, but the reasons that can easily persuade us. I think we can be persuaded to believe one thing or another for reasons that ultimately don't serve us well in the long term.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“What intelligence is, we're not sure how to determine whether or not people actually understand each other and in what depth they understand it to what depth they understand each other. So the challenge becomes something along the lines of can you satisfy me? That we have a shared understanding. So if I were to probe and probe and you probe me, can machines really act like thought partners where they can satisfy me that we have our understanding is shared enough that we can collaborate and produce answers together and that they can help me explain and justify those answers.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Know, look, I mean, I think there are lots of really great ideas for Grand Challenges. I'm particularly focused on one right now, which is, can you demonstrate that they understand that they could read and understand, that they can acquire these frameworks and communicate, reason and communicate with humans? So it is kind of like deterring test, but it's a little bit more demanding than the Turing test. It's not enough. It's not enough to convince me that you might be human because you could parrot a conversation. I think, you know, the standard is a little bit higher. For example, can you, the standard is higher? And I think one of the challenges of devising this grand challenge is that we're not sure.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Even if the Sussex said males are more likely to be violent criminals, we still take each person as an individual and we treat them based on the logic and the knowledge of that situation. We purposefully and intentionally. Reject Statistical inference We do that at a respect for the individual.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“We are challenged, we are deeply, deeply challenged. We have big segments of the population getting hit with enormous amounts of information. Do they know how to do critical thinking? Do they know how to objectively reason? Do they understand what they are doing, never mind what their AI is doing? This is such an important dialogue to be having. And we are fundamentally, our thinking can be and easily becomes fundamentally biased. And there are statistics, and we shouldn't blind statistical inference, but we should understand the nature of statistical inference. As a society, we decide to reject statistical inference, to favor understanding and deciding on the individual. We consciously make that choice. So even if the statistics said,”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So, this is one of my very positive and optimistic views of why the study of artificial intelligence, the process of thinking and reasoning, logically and statistically, and how to combine them is so important for the discourse today because it's causing a, regardless of what What state AI devices are or not Causing this dialogue to happen. This is one of the most important dialogues that, in my view, the human species can have right now, which is how to think well. To reason well, how to understand our own cognitive biases and what to do about them. That has got to be one of the most important things as a species can be doing, honestly. We've created an incredibly complex society. We've created amazing abilities to amplify noise faster than we can amplify signal.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“And have you done enough studies to compare it to say, well, what if we dug in in a more direct, you know, let's get the evidence, let's do the deductive thing and not use statistics here, how often would that have done better? So you have to do the studies to know how good the AI actually is. And it's complicated because it depends how fast you have to make decision. So if you have to make decisions super fast, you have no choice. If you have more time, but if you're ready to pull the plug, and this is a lot of the argument that I had with a doctor, I said, what's he going to do if you do it? What's going to happen to him in that room if you do it my way? Well, he's going to die anyway. So let's do it my way then.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“The medical system overall was doing more good than bad. Now, there's another argument that suggests that that wasn't the case, but for the sake of argument, let's say like that's a net positive. And I think you have to sit there in there and take that into consideration. Now you look at a particular use case, like for example, making this decision. Have you done enough studies to know How good that prediction really is.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, it's hard because it's hard to know that. You'd have to go back and you'd have to have enough data to essentially say, and this goes back to how do we case of how do we decide whether AI is good enough to do a particular task? And regardless of whether or not it produces an explanation. And what standards do we hold for that? If you look at more broadly, for example, as my father as a medical case, Medical system ultimately helped him a lot throughout his life. Without it, it probably would have died much sooner. So, overall, it sort of worked for him in sort of a net kind of way. Actually, I don't know that's fair.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“That the data that the data that's being used to make statistical inferences doesn't adequately reflect the phenomenon. So in other words, you're getting shit wrong. I'm sorry, you're getting stuff wrong because your model is not robust enough and you might be better off not using statistical inference and statistical averages in certain cases when you know the model is insufficient and that you should be reasoning at about the specific case more logically and more deductibly. Hold yourself responsible or hold yourself accountable to doing that.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Big long story, but the bottom was a fascinating story, by the way, but how I reasoned and how the doctors reasoned through this whole process. But I don't know, somewhere around 24 hours later or something, he was sitting up in bed with zero brain damage.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, they decided that my father was brain dead. He went into cardiac arrest and it took a long time for the ambulance to get there and he wasn't not resuscitated right away and so forth. And they came and they told me he was brain dead. And why was he brain dead? Because essentially they gave me a purely statistical argument under these conditions with these four features, 98% chance he's brain dead. I said, but can you just tell me not inductively, but deductively go there and tell me his brain's not functioning is the way for you to do that? And the protocol in response was, no, this is how we make this decision. I said, this is inadequate for me. I understand the statistics and I don't know there's a 2% chance he's still lie. I just don't know the specifics. I need the specifics of this case and I want the deductive logical argument about why you actually know he's brained it. So I wouldn't sign the do not resuscitate. And I don't know. It was like they went through lots of procedures.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“The reality is that it's not, I'm not sure humans are making reasonable choices when they do these things. They are using statistical hunches, biases, or even systematically using statistical averages to make calls. I mean, this is what happened my dad, and if you saw the talk I gave about that, but.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“There you go. I think that's going to be interesting. I mean, I think this is where technology and social discourse are going to get deeply intertwined in how we start thinking about problems, decisions, and problems like that. I think in other cases it becomes more obvious where It's like Why did you decide to give that person a longer sentence? Or does deny them parole? Again, policy decisions, or why did you pick that treatment? Like that treatment ended up killing that guy? Like, why was that a reasonable choice to make? And people are going to demand explanations. Now, there's a reality, though, here.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“You know, you were negligent in the training data that you gave that machine. Like, how do we drive down the reliability? So I think those are, I think those are interesting questions.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think the reality is it's going to be a mix. I'm not sure I have a problem with that. I mean, I think there are tasks that are perfectly fine with. Machines show a certain level of performance and that level of performance is already better than humans. So for example, I don't know that I take driverless cars. If driverless cars learn how to be more effective drivers than humans but can't explain what they're doing, but bottom line, statistically speaking, they're 10 times safer than humans. I don't know that I care. I think when we have these edge cases, when something bad happens and we want to decide who's liable for that thing and who made that mistake and what do we do about that? And I think those edge cages are interesting cases. And now do we go to designers of the AI? And the AI says, I don't know, that's what it learned to do. And he says, well, you didn't train it properly.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Toward a goal, imagine you're sentencing your judging, you're sentencing people. You're setting policy or you're making medical decisions. And you can't explain. You can't get anybody to understand what you're doing or why. It's an interesting dilemma. The applications of AI? Do we hold AI to this accountability that says, you know, Humans have to be able to take responsibility For the decision. In other words, can you explain why you would do the thing? Will you get up and speak to other humans and convince them that this was a smart decision? Is the AI enabling you to do that? Can you get behind the logic that was made there?”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I found the go function. Can I even comprehend the go function? Can I talk about the go function? Can I conceptualize the go function like whatever it might be?”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Think it was a giant landmark for AI. I think it was phenomenal. I mean, it was one of those other things nobody thought like solving Go was going to be easy, particularly because, again, it's hardly hard for humans, hard for humans to learn, hard for humans to excel at. And so it was another measure, a measure of intelligence. It's very cool. I mean, it's very interesting, you know, what they did. And I loved how they solved the data problem, which again, they bootstrapped it and got the machine to play itself to generate enough data to learn from. I think that was brilliant. I think that was great. And of course, the result speaks for itself. I think it makes us think about, again, okay, what's intelligence? What aspects of intelligence are important? Can the GO machine help me make me a better Go player? Is it an alien intelligence? Am I even capable of like, again, if we put in very simple terms, it found the function.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Should be, but I think depending on the content, one of the problems we have there is that if that large community of people are not judging it with regard to a rigorous standard of objective logic and reason, you still have a problem, like masses of people can be”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Your answers justify your predictions and your beliefs why you think they make sense. Can you convince me what the implications are? Can you reason intelligently and make me believe that the implications of your prediction and so forth? So what happens is it becomes reflective. My standard for judging your intelligence depends a lot on mine”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So it's interesting because humans are really good at in their own mind justifying or explaining what they hear because they project their understanding onto yours. So you could say you could put together a string of words and someone will sit there and interpret it in a way that's extremely biased to the way they want to interpret it. They want to assume you're an idiot and they'll interpret it one way. They will all assume you're a genius and they'll interpret it another way that suits their needs. So this is tricky business. So I think to answer your question, as AI gets better and better mimicked, we create the superparrits. We're challenged just as we are with humans. Do you really know what you're talking about? Do you have a meaningful interpretation, a powerful framework that you could reason over and justify?”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So I was going to say it's very possible humans do behave that way too. And so upon deeper probing and deeper interrogation, you may find out that there isn't a shared understanding because I think humans do both. Like humans are statistical language model machines and they are capable reasoners. They're both. And you don't know which is going on, right? So, and I think it's an interesting problem. We talked earlier about like where we are in our social and political landscape. Can you distinguish someone who can string words together and sound like they know what they're talking about from someone who actually does? Can you do that without dialogue, without integrative appropriing dialogue?”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I would just pause on that. Like, that's a superparrot, right? So given similar circumstances, its faces in similar ways, changes its tone of voice in similar ways, produces strings of language that would similar that a human might say, not necessarily being able to produce a logical interpretation or understanding that would ultimately satisfy a critical interrogation or a critical understanding.”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Think machines will, in many measures, will be better than us, will become more effective, in other words, better predictors about a lot of things than ultimately we can do. I think where they're going to struggle is what we've talked about before, which is. Relating to communicating with and understanding humans. In deeper ways. And so I think that's a key point. Like, we can create the superparrot. What I mean by the superparrot is given enough data, a machine can mimic your emotional response, can even generate language that will sound smart and what someone else might say under similar circumstances”
2019-10-11 · Lex Fridman Podcast · David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI · IDENTIFIED FROM THE TRANSCRIPT · source