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Melanie Mitchell
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- 2019-12-28
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- 2019-12-28
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“So there's Douglas Hofstadter's book called Fluid Concepts and Creative Analogies talks in great detail about copycat. I have a book called Analogy Making As Perception, which is a version of my PhD thesis on it. There's also code that's available. You can get it to run. I have some links on my webpage to where people can get the code for it. And I think that would really be the best way to get into it.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So, I am really proud of my work on the copycat project. I think it's really different from what Almost everyone is done in AI. I think there's a lot of ideas there to be explored. And I guess one of the happiest days of my life. Aside from the births of my children. Was the birth of Copycat, what it actually started to be able to make really interesting analogies. And I remember that very clearly.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, and in AI, that was people used to work in these microworlds, right? Like the blocks world was very early important area in AI. And then that got criticized because they said, oh, you can't scale that to the real world. So people started working on much more real world like problems. But now there's been kind of a return even to the blocks world itself. We've seen a lot of people who are trying to work on more of these very idealized problems for things like natural language. And common sense. So that's an interesting evolution of those ideas.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So, one of the things he taught me was that when you're looking at Complex problem To idealize it as much as possible, to try and figure out what are really the essence of this problem. And this is how the copycat program came into being was by taking analogy making and saying, how can we make this as idealized as possible but still retain really the important things we want to study? And that's really been a core theme of my research, I think. And I continue to try and do that. And it's really very much kind of physics inspired Hofstetter was a PhD in physics. That was his background.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, they have sort of technical seminars and colloquia and they have a community lecture series, like public lectures, and they put everything on their YouTube channel so you can see it all.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So there's a few different things they do. They have a Complex system summer school for graduate students and postdocs, and sometimes faculty attend too. And that's a four-week, very intensive residential program where you go and you Listen to lectures and you do projects, and people really like that. I mean, it's a lot of fun. They also have some specialty. Summer schools, there's one on computational social science, there's one on Climate and sustainability, I think it's called There's a few, and then they have short courses, we're just a few days on different topics We also have an online Education platform that offers a lot of different courses and tutorials from SFI faculty. Including an introduction to complexity course that I taught.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“A very small group of resident faculties. Maybe about 10 who are there on five years that can sometimes get renewed. And then they have some postdocs. And then they have this much larger, on the order of 100 external faculty or people who come like me who come and visit for various periods of time.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Can arrow an economist, Nobel Prize winning economist, and they Started having these workshops. And this whole enterprise kind of grew into this research institute that's. Itself has been kind of on the edge of chaos its whole life because. Don't have any significant endowment. And it's just been kind of living on whatever funding it can raise through. Donations and grants and However, it can, you know, business associates and so on. But it's a great place. It's a really fun place to go think about ideas that you wouldn't normally encounter.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, exactly. So the Santa Fe Institute was started in 1984 and it was created by a group of scientists, a lot of them from Los Alamos National Lab, which is A 40 minute drive from Santa Fe Institute. We were mostly physicists and chemists. But they were frustrated in their field because they felt that their field wasn't approaching kind of big interdisciplinary questions like the kinds we've been talking about. And they wanted to have a place where people from different disciplines could work on these big questions without sort of being siloed into physics, chemistry, biology, whatever. So they started this institute. And this was people like George Cowan, who was a chemist in the Manhattan Project, and Nicholas Metropolis, a mathematician physicist Marie Gelman, a physicist. So some really big names here.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“It's definitely humbling how humbling in that also kind of awe inspiring That are awe inspiring, like part of mathematics that these incredibly simple rules can produce this very beautiful, complex, hard to understand behavior. And that's. It's mysterious, you know, and surprising still. Exciting because it does give you kind of the hope that you might be able to engineer complexity just from.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“It was very reductionist idea. You know, we figure out what all the parts are and then we would be able to figure out which parts cause which things. But it turns out that the parts don't cause the things that we're interested in. It's like the interactions. It's the networks of these parts. And so that kind of reductionist approach didn't yield the explanation that we wanted.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't think it's always possible to understand the things we want to understand the most, so I don't think it's possible to look at single neurons. Understand what we call intelligence, you know, to look at sort of summing up and the summing up is the issue here that we're, you know, one example is that the human genome, right? So there was a lot of work on excitement about sequencing the human genome because the idea would be that we'd be able to find genes that underlie diseases. But it turns out that”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Reductionism is when you try and take a system and divide it up into its elements Whether those be cells or atoms or Subatomic particles, whatever your field is, and then try and understand those elements. And then try and build up an understanding of the whole system by looking at sort of the sum of all the elements.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So, complexity is another one of those terms like intelligence. It's perhaps overused. But my book about complexity. Was about this. Wide area of complex systems studying different systems in nature in Technology, anciety, in which you have emergence, kind of like I was talking about with intelligence. You know, we have the brain, which has billions of neurons. And each neuron individually could be said to be not very complex compared to the system as a whole The interactions of those neurons and the dynamics creates these phenomena that we call intelligence or consciousness that we consider to be very complex. So the field of complexity is trying to find general principles that underlie all these systems that have these kinds of emergent properties.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I still think the original idea of the Turing test is a good test for intelligence. I mean, I can't think of anything better. You know, the Turing test the way that it's been carried out so far has been... Very impoverished, if you will. But I think a real Turing test that really goes into depth, like the one that I mentioned, I talk about in the book, I talk about Ray Kurzweil and Mitchell Kapoor have this bet, right? That in 2029, I think is the date there a machine will pass a Turing test and Turing says, and they have a very specific expert judges and all of that. And, you know, Kurzweil says, yes, Kapoor says no. We only have like nine more years to go to see.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I absolutely agree with Benjo there. And I think it's great that, you know, and it's great that New York Times will publish all this stuff.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I actually said, you know, something like superintelligence is not. Is not a Of coherent idea. That's not something the New York Times would put in.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Nuclear weapons climate problems, you know. Poverty, possible pandemics, you can go on and on. And I think though, you know, worrying about existential Threat from AI It's not the best priority for what we should be worried about. That's kind of my view because we're so far away. But I'm not. Not necessarily criticizing Russell or Bostrom or whoever for worrying about that. And I think it's some people should be worried about it. It's certainly fine. But I was more sort of getting at their view of intelligence, what intelligence is. Was more focusing on their view of the superintelligence than. Just the fact of them worrying. And the title of the article was written by the New York Times editors. I wouldn't have called it that.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Understanding of what Russell's argument was is more that the machine itself has the agency now. It's the thing that's making the decisions and it's the thing that has what we would call values. So whether that's just a matter of degree, it's hard to say, right? But I would say that's sort of qualitatively different than a face recognition neural network.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I guess the question here is sort of. Who has the agency? So you might say, for instance, we don't want our algorithms to be racist. And facial recognition, you know, some people have criticized some facial recognition systems as being racist because they're not as good on darker skin than lighter skin. Okay, but the agency there, the actual facial recognition algorithm isn't what has the agency. It's not the racist thing, right? It's the, I don't know, the combination of the training data, the cameras being used, whatever. But my understanding of, and I'll say, I agree with Benjio there that he, you know, I think there are these value issues with our use of algorithms. But”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, we're talking about people. Corporations are, their values are the values of the people who run those corporations.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Sure, but I guess the example that he gives there of these corporations, that's people, right? Those are people's values.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So I think that what my op-ed was trying to do was say that intelligence is more complex than these people are presenting it, that it's not like it's not so separable. The rationality The values, the emotions, all of that, that the view that you could separate all these dimensions and build a machine that has one of these dimensions, and it's super intelligent in one dimension, but it doesn't have any of the other dimensions. That's what I was trying to criticize that I don't believe that.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah, exactly. Bostrom had this example of the superintelligent AI that turns the world into paperclips because its job is to make paperclips or something. And that just as a thought experiment didn't make any sense to me.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Can't just be intelligent along this one dimension of okay, I'm going to figure out all the steps, the best optimal path to solving climate change and not be intelligent enough to figure out that humans don't want to be killed, that you could get to one without having the other. And Bostrom in his book talks about the orthogonality hypothesis where he says he thinks that systems Remember exactly what it is, but like a system's goals and it's. Values don't have to be aligned. There's some orthogonality there. Make any sense to me”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So it was spurred by an earlier New York Times op-ed by Stuart Russell, which was summarizing his book called Human Compatible. And the article was saying, you know, if we have superintelligent AI, we need to have its values aligned with our values and it has to learn about what we really want. And he gave this example. What if we have a super intelligent AI and we give it the problem of solving climate change and it decides that the best way to lower the carbon in the atmosphere is to kill all the humans? Okay, so to me, that just made no sense at all because a super intelligent AI. First of all, trying to figure out what a super intelligence means. And it seems that. Something that's super intelligent”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“At least the way the humans work, it's a big part of how it affects how we perceive the world. It affects how we make decisions about the world. It affects how we interact with other people. It affects our understanding of other people, you know. Me to understand What you're likely to do, I need to have kind of a theory of mind, and that's Very much a theory of emotion and motivations and goals. And to understand that, We have this whole system of mirror neurons. I sort of understand your motivations through sort of simulating it myself. So, you know, it's not something that I can prove. Necessary, but it seems very likely.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“By the way, did you see that there was this recent thing going around the internet? Some, I think he's a Russian or some Slavic, had written this thing sort of anti-the idea of superintelligence. Forgot, maybe he's Polish. Anyway, so he had all these arguments, and one was the argument from Slavic pessimism. My favorite.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“That's a good question. I haven't really thought about that, but I think both, I would guess, because I think Think intelligence is so hard to separate it from our desire for self-preservation, our emotions, our all that non-rational stuff that kind of gets in the way of Logical thinking. But because we the way If we're talking about human intelligence or human level intelligence, whatever that means, a huge part of it is social. We were evolved to be social and to deal with other people. And that's just so ingrained in us that it's hard to separate intelligence from that. I think AI for the last 70 years or however long it's been around, it has largely been separated. There's this idea that there's like, it's kind of very Cartesian, there's this thinking thing. We're trying to create, but we don't care about all this other stuff”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Especially where you know driving's kind of boring, and we have these phones to play with and everything. I think What's going to happen is that for many reasons, not just AI reasons, but also like legal and other reasons, that The definition of self driving is going to change or autonomous is going to change. It's not going to be just. I'm going to go to sleep in the back, and you just drive me anywhere. It's going to be more. Certain areas are going to be instrumented to have the sensors and the mapping and all of the stuff you need for that the autonomous cars won't have to have full common sense. And they'll do just fine in those areas as long as pedestrians don't mess with them too much. That's another question. But, um, I don't think we will”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“That it's a very fair thing to say that autonomous vehicles will be ultimately safer than humans because humans are very unsafe. It's kind of a low bar.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“And not because they weren't intelligent enough. Right. Whereas the accidents with autonomous vehicles is because they weren't intelligent enough.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So, I don't think that. Ultimately, driving so it's a trade off in a way. You know, being able to drive and deal with any situation that comes up does require kind of full human intelligence. And even humans aren't intelligent enough to do it because humans, I mean, most human accidents are because the human wasn't paying attention or the human's drunk or whatever.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“It's the paddle moved problem. Right. And so, my understanding, and you probably are more of an expert than I am on this, is that. Current self driving car vision systems have problems with obstacles, meaning that Don't know which obstacles, which quote unquote obstacles they should stop for and which ones they shouldn't stop for. And so a lot of times I read that they tend to slam on the brakes quite a bit. And the most common accident with self-driving cars are people rear-ending them because they were surprised. They weren't expecting the machine, the car to stop.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“It's difficult because of the world is so open-ended as to what kinds of things can happen. Have sort of what normally happens, which is just you drive along and nothing surprising happens and autonomous vehicles can do the ones we have now evidently can do really well on most normal situations as long as long as the weather is reasonably good and everything but if some Have this notion of edge case or things in the tail of the distribution call it the long tail problem, which says that there's so many possible things that can happen that was not in the training data of the machine that Won't be able to handle it because it doesn't have common sense.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think, fair enough. Self play is amazingly powerful. And, you know, that goes way back to... Arthur Samuel, right, with his checker plane program, and that which was brilliant and surprising that it did so well.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“And in that example, it didn't. Sure, if you train it on moving, you know, the paddle being in different places, maybe it could deal with maybe it would learn that concept. I'm not totally sure. But the question is, you know, scaling that up to more complicated worlds, to what extent could a machine that only gets this very raw data learn to divide up the world into relevant concepts? And I don't know the answer, but I would bet that without some innate notion that it can't do it.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Because the reason I brought up this example was because you were asking do I think that learning from data could go all the way. That this was why I brought up the example because I think, and this was, it's not at all to. Take away from the impressive work that they did. But it's to say that when we look at what these systems learn, Do they learn the human, the things that we humans consider to be the relevant concepts?”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“There was a group who did an experiment where they took the paddle, you know, that you move with the joystick and moved it up two pixels or something like that. And then they looked at a deep Q learning system that had been trained on breakout and said, could it now transfer its learning to this new version of the game? Of course a human could. And it could. Maybe that's not surprising, but I guess the point is it hadn't learned the concept of a paddle. It hadn't learned the concept of a ball or the concept of tunneling. It was learning something. We looking at it kind of Anthropomorphized it and said, Oh, here's what it's doing and the way we describe it, but it actually didn't learn those concepts. And so because it didn't learn those concepts, it couldn't make this transfer”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't think so I think it's an open question, but I tend to be on the innateness side that there's some things that We've been evolved Be able to learn that learning just can't happen without them. So, one example here's an example I had in the book that I think is useful to me at least in thinking about this. This has to do with the deep minds Atari gameplay program. Okay, and it learned to play these Atari video games just by getting input from the pixels of the screen. And it learned to play the game breakout. Thousand percent better than humans. Okay, that was Of their results, and it was great, and it learned this thing where it tunneled through the side of the bricks in the breakout game, and the ball could bounce off the ceiling and then just wipe out bricks.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think deep learning as it's currently. As it currently exists, we'll place that kind of thing, we'll play some role. But I think that there's a lot more going on in perception. But who knows? You know, the definition of deep learning, I mean,”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think the idea of the dynamic Perception is key here. The idea that Moving your eyes around and getting feedback And that's something that, you know, there's been some models like that. There's certainly recurrent neural networks that operate over several times. But the problem is that the recurrence Basically, the feedback is at the next time step is the entire hidden state. Of the network, which is, and it turns out that that's That doesn't work very well”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, it's not, it's not also, okay, so it's not dynamic. I mean, in the sense that as a perception of a new example, being. Process Those attentions weights don't change.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Just going back and forth. So, right, so that is extremely important. And one thing about deep neural networks is that in a given situation, like, you know, they're trained, right? They get these weights and everything. But then now I give them a new. A new image, let's say. Treat every part of the image in the same way. They apply the same filters at each layer to all parts of the image. There's no feedback to say, like, oh, this part of the image is irrelevant. Shouldn't care about this part of the image or this part of the image is the most important part. And that's kind of what we humans are able to do because we have these conceptual expectations.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“The goal of the original goal of deep learning in at least visual perception was that you would get the system to learn to extract features that at these different levels of complexity, maybe edge detection, and that would lead into learning simple combinations of edges and then more complex shapes and then whole objects or faces. This was based on the ideas of the neuroscientists, Hubel and Weasel, who had seen Laid out this kind of structure in brain. And I think that's right to some extent. Of course, people have Found that the whole story is a little more complex than that in the brain, of course. Always is, and there's a lot of feedback. So I see that. As absolutely a Good brain inspired approach to some aspects of perception. But one thing that it's lacking Example, is all of that feedback? Which is extremely important”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Also, you know, I have a new situation, so another example with the walking the dog thing is sometimes people, I see people riding their bikes with a leech holding a leech and the dog's running alongside. Okay, so I know that I recognize that as kind of a dog walking situation. Though the person's not walking right and the dog's not walking, because I have these models that say, okay, riding a bike is sort of similar to walking or it's connected, it's a means of transportation, but I because they have their dog there, I assume they're not going to work, but they're going out for exercise. And, you know, these analogies help me to figure out kind of what's going on, what's likely.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, the idea is that you have this pretty complicated conceptual space. You can talk about a semantic network or something like that with these different kinds of concept models in your brain that are connected. So let's take the example of walking a dog. So we were talking about that. Okay. Let's see. I see someone out on the street walking a cat. Some people walk their cats, I guess. Seems like a bad idea, but. So, my model, my, you know, there's connections between my Model of a dog and model of a cat. And I can immediately see the analogy of Those are analogous situations, but I can also see the differences, and that tells me what to expect.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“And you also, that generative model is telling you where to look and what to look at and what to pay attention to. And I think it affects your perception. It's not that just you compare it with your perception. It becomes your perception in a way. It's kind of a mixture of Bottom up Information coming from the world and your top down model being imposed on the world is what becomes your perception.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“That's what I'm working on now, is trying to take some of those ideas and extending it. So I think There are some really promising approaches that are going on now that have to do with more active, generative models. So this is the idea of this simulation in your head When you're perceiving a new situation, you have some simulations in your head. Those are generative models. They're generating your expectations. They're generating predictions.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Guess it is a question. People have brought up this question. And when you asked about is our current hardware Will our current hardware work? Well, Turing computation says that our current hardware Is in principle a Turing machine, right? So, all we have to do is make it faster and bigger. But there have been people like Roger Penrose, if you might remember that he said Turing machines cannot produce intelligence because intelligence requires continuous valued numbers. I mean, that was sort of my reading of his argument and quantum mechanics. What else? Whatever, you know. But I don't see any evidence for that, that we need new computation paradigms But I don't know if I don't think we're going to be able to. Scale up our current approaches to programming these computers.”
2019-12-28 · Lex Fridman Podcast · Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source