YouSaid · the spoken record

Andrej Karpathy

lines on the record: 266
first: 2022-10-29
most recent: 2022-10-29
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

“Another way to look at it is vision is necessary in a sense that the world is designed for human visual consumption. So you need vision. It's necessary. And then also it is sufficient because it has all the information that you need for driving. And humans obviously use vision to drive. So it's both necessary and sufficient. So you want to focus resources. And you have to be really sure if you're going to bring in other sensors. You could add sensors to infinity. At some point, you need to draw the line. And I think in this case, you have to really consider the full cost of any one sensor that you're adopting. And do you really need it? And I think the answer in this case is no.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Do you have a fleet or not? It's significantly more important whether you have LIDAR or not. It's just another sensor. And yeah, I think similar to the radar discussion, basically, I... Yeah, I don't think it basically doesn't offer extra information. It's extremely costly. It has all kinds of problems. You have to worry about it. You have to calibrate it, et cetera. It creates bloat and entropy. You have to be really sure that you need this sensor. In this case, I basically don't think you need it. And I think, honestly, I will make a stronger statement. I think the others, some of the other companies who are using it are probably going to drop it.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think this debate is always slightly confusing to me because it seems like the actual debate should be about do you have the fleet or not? That's the really important thing about whether you can achieve a really good functioning of an AI system at this scale.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Is a distraction. And these sensors, you know, they can change over time. For example, you can have one type of, say, radar, you can have other type of radar. They change over time. Suddenly you need to worry about it. Now suddenly you have a column in your SQLite telling you, oh, what sensor type was it? And they all have different distributions. And then they contribute noise and entropy into everything and they bloat stuff. And also organizationally, it's been really fascinating to me that it can be very distracting. All you want to get to work is vision. All the resources are on it and you're building out a data engine and you're actually making forward progress because that is the sensor with the most bandwidth, the most constraints on the world and you're investing fully into that and you can make that extremely good. You have only a finite amount of sort of spend of focus across different facets of the system.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Once you consider the full cost of a sensor, it actually potentially a liability, and you need to be really sure that it's giving you extremely useful information. In this case, we looked at using it or not using it, and the delta was not massive. And so it's not useful.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“The wrong prompt. Sorry, so it's like a little bit of a wrong question because basically you would think that these sensors are an asset to you. If you fully consider the entire product in its entirety, these sensors are actually potentially a liability because these sensors aren't free. They don't just appear on your car. Suddenly you have an entire supply chain. You have people procuring it. There can be problems with them. They may need replacement. They are part of the manufacturing process. They can hold back the line in production. You need to source them. You need to maintain them. You have to have teams that write the firmware, all of it. And then you also have to incorporate and fuse them into the system in some way. And so it actually bloats a lot of it. And I think Elon is really good at simplify, simplify. Best part is no part. And he always tries to throw away things that are not essential because he understands the entropy in organizations and in approach. And I think in this case, the cost is high and you're not potentially seeing it if you're just a computer vision engineer. And I'm just trying to improve my network and, you know, is it more useful or less useful? How useful is it? And the thing is, if”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think Elon also, like, he always wanted to drive the system himself. He drives a lot. And I don't want to say almost daily. So he also sees this as a source of truth. You driving the system and it performing.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think there's a ton of it's a source of truth, is your interaction with the system. And you can see it, you can play with it, you can perturb it, you can get a sense of it, you have an intuition for it. I think numbers just like have a way of numbers and plots and graphs are much harder. It hides a lot of.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“And then, of course, all of us drive it, and we can also see it. It's really nice to work with a system that you can also experience yourself. And, you know, it drives you home. Is it?”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“That comes to a very large extent to what we are trying to achieve in the product format, what we're trying to, the release we're trying to get out in the feedback from the QA team where the system is struggling or not, the things we're trying to improve”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Really just comes down to extremely good execution. They understand intuitively the philosophical insights underlying the data engine and the process by which the system improves, and how to, again, like delegate the strategy of the data collection and how that works, and then just making sure it's all extremely well executed. And that's where most of the work is, is not even the philosophizing or the research or the ideas of it. It's just extremely good execution is so hard when you're dealing with data at that scale.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Data set. So your data set is basically imperfect. It needs to be diverse. It has pockets that are missing. And you need to pad out the pockets. You can sort of think of it that way. The”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“The data engine is what I call the almost biological feeling process by which Perfect the training sets for these neural networks. So because most of the programming now is on the level of these data sets and make sure they're large, diverse, and clean, basically you have a data set that you think is good, you train your neural net, you deploy it, and then you observe how well it's performing, and you're trying to always increase the quality of your data set. So you're trying to catch scenarios basically that are basically rare. And it is in these scenarios that neural nets will typically struggle in because they weren't told what to do in those rare cases in the data set. But now you can close the loop because if you can now collect all those at scale, you can then feed them back into the reconstruction process I described and reconstruct the truth in those cases and add it to the data set. And so the whole thing ends up being like a staircase of improvement of perfecting your training set. And you have to go through deployments so that you can mine the parts that are not yet represented well in the”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I'm not sure if there's too many insights. You're trying to create a neural net that will fit in what you have available and you're always trying to optimize it. And we talked a lot about it on the AI day and basically the triple backflips that the team is doing to make sure it all fits and utilizes the engine. So I think it's extremely good engineering. And then there's all kinds of little insights peppered in on how to do it properly.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“For the neural, not specifically, just making sure everything fits into the chip on the car. And you have a finite budget of flops that you can perform and memory bandwidth and other constraints. And you have to make sure it flies. And you can squeeze in as much compute as you can into the tiny.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, basically, the sensor is extremely powerful, but you still need to process that information. And so going from brightnesses of these pixel values to, hey, here are the three-dimensional world is extremely hard. And that's what the neural networks are fundamentally doing. And so the difficulty really is in just doing an extremely good job of engineering the entire pipeline, the entire data engine, having the capacity to train these neural nets, having the ability to evaluate the system and iterate on it. So I would say just doing this in production at scale is like the hard part. It's an execution problem.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Jarving is really hard because it has to do with the predictions of all these other agents and the theory of mind and what they're going to do and are they looking at you are they looking where are they thinking A lot that goes there at the full tail of the expansion of the nines that we have to be comfortable with eventually, the final problems are of that form. I don't think those are the problems that are very common, I think eventually they're important, but it's really in the tail end.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Not just the pixels. I mean, you have a powerful prior service. For how the world evolves over time, et cetera. So it's not just about the likelihood term coming up from the data itself, telling you about what you are observing, but also the prior term of like where are the likely things to see and how do they likely move and so on.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“On the state of the world, that's fascinating. It's not just that, but again, this real importance of it's the sensor that humans use. Therefore, everything is designed for that sensor. Text, the writing, the flashing signs, everything is designed for vision. And so you just find it everywhere. And so that's why that is the interface you want to be in, talking again about these universal interfaces. And that's where we actually want to measure the world as well and then develop software for that sensor.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Pixels, I think, are a beautiful sensory, I would say. The thing is, like cameras are very, very cheap, and they provide a ton of information, ton of bits. So it's extremely cheap sensor for a ton of bits. And each one of these bits is a constraint on the state of the world. And so you get lots of megapixel images very cheap, and it just gives you all these constraints for understanding what's actually out there in the world. So vision is probably the highest bandwidth sensor. It's a very high bandwidth sensor.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think to a very large extent, we went through a number of iterations and we learned a ton about how to create these data sets. I'm not seeing big open problems. Originally when I joined, I was like, I was really not sure how this would turn out. But by the time I left, I was much more secure in. Actually, we sort of understand the philosophy of how to create these data sets. And I was pretty comfortable with where that was at the time.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so I grew the annotation team at Tesla from basically zero to a thousand. While I was there, that was really interesting. You know, my background as a PhD student researcher. So growing that kind of organization was pretty crazy. But yeah, I think it's extremely interesting and part of the design process very much behind the autopilot as to where you use humans. Humans are very good at certain kinds of annotations. They're very good, for example, at two-dimensional annotations of images. They're not good at annotating cars over time in three-dimensional space. Very, very hard. And so that's why we're very careful to design the tasks that are easy to do for humans versus things that should be left to the offline tracker. Like maybe the computer will do all the triangulation and 3D reconstruction, but the human will say exactly these pixels of the image are car. Exactly these pixels are a human. And so co-designing the data annotation pipeline was very much bread and butter was what I was doing daily.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, the nice thing about the annotation is that it is fully offline. You have infinite time. You have a chunk of one minute, and you're trying to just offline in a supercomputer somewhere, figure out where were the positions of all the cars, all the people. And you have your full one minute of video from all the angles, and you can run all the neural nets you want, and they can be very efficient, massive neural nets. There can be neural nets that can't even run in a car later at test time. So they can be even more powerful neural nets than what you can eventually deploy. So you can do anything you want, three-dimensional reconstruction, neural nets, anything you want just to recover that truth. And then you supervise that truth.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“And then understanding that, okay, there's 10 seconds of video, this is what we saw. And therefore, here's all the lane lines, cars, and so on. And then once you have that annotation, you can train in neural nets to imitate it.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“We have eight videos coming from all the cameras of the system. And this is what they saw. And this is the truth of what actually was around. There was this car, there was this car, this car. These are lane line markings. This is geometry of the road. There's traffic light in this three-dimensional position. You need the ground truth. And so the big question that Tim was solving, of course, is how do you arrive at that ground truth? Because once you have a million of it and it's large, clean, and diverse, then training a neural net on it works extremely well and you can ship that into the car. So there's many mechanisms by which we collected that training data. You can always go for human annotation. You can go for simulation as a source of ground truth. You can also go for what we call the offline tracker that we've spoken about at the AI day and so on, which is basically an automatic reconstruction process for taking those videos and recovering the three-dimensional sort of reality of what was around that car. So basically think of doing like a three-dimensional reconstruction as an offline thing.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“I would say by far in the industry if you're talking about the industry and how what is the technology of what we have available everything is supervised learning so you need data sets of input desired output and you need lots of it and there are three properties of it that you need you need it to be very large you need it to be accurate no mistakes and you need it to be diverse you don't want to just have a lot of correct examples of one thing you need to really cover the space of possibility as much as you can and the more you can cover the space of possible inputs the better the algorithm will work at the end now once you have really good data sets that you're collecting curating and cleaning you can train your neural net on top of that so a lot of the work goes into cleaning those data sets now as you pointed out it's probably it could be the question is how do you achieve a ton of uh if you want to basically predict in 3d you need data in 3d to back that up so in this video”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“And actually, they don't in three dimensions around the car. And now, actually, we don't manually fuse the predictions in 3D over time. We don't trust ourselves to write that tracker. So actually, we give the neural net the information over time. So it takes these videos now and makes those predictions. And so you're sort of just like putting more and more power into the neural net, more and more processing. And at the end of it, the eventual sort of goal is to have most of the software potentially be in the 2.0 land because it works significantly better. Humans are just not very good at writing software, basically.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Algorithm, we want the neural nuts to write the algorithm and we want to port all of that software into the 2.0 stack. And so then we actually had neural nets that now take all the eight camera images simultaneously and make predictions for all of that. And actually, they don't make predictions in the space of images. They now make predictions directly in 3D.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“I'm on a high level, I would say, if you look at the software running in the autopilot, I gave a number of talks on this topic. I would say originally a lot of it was written in software 1.0. Imagine lots of C++, right? And then gradually, there was a tiny neural nut that was, for example, predicting given a single image, is there like a traffic light or not? Or is there a line line marking or not? And this neural net didn't have too much to do in the scope of the software. It was making tiny predictions on individual little image. And then the rest of the system stitched it up. So, okay, we're actually, we don't have just a single camera. We have eight cameras. We actually have eight cameras over time. And so what do you do with these predictions? How do you put them together? How do you do the fusion of all that information? And how do you act on it? All of that was written by humans in C++. And then we decided, okay, we don't actually want to do all of that fusion in the C++ code because we're actually not good enough to write.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“So, in the case of the autopilot, a lot of the data sets had to do with, for example, detection of objects and lane markings and traffic lights and so on. So you accumulate massive data sets of, here's an example, here's the desired label. And then here's roughly how the algorithm should look like. And that's a convolutional neural net. So the specification of the architecture is like a hint as to what the algorithm should roughly look like. And then the fill in the blanks process of optimization is the training process. And then you take your neural net that was trained. It gives all the right answers on your data set and you deploy it.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“The way by which you program the computer and influence its algorithm is not by writing the commands yourself. You're changing mostly the data set. You're changing the loss functions of what the neural net is trying to do, how it's trying to predict things. But basically the data sets and the architectures of the neural net.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Is actually pretty strong, and we have a lot of developer environments for software 1.0. Like we have IDEs, how you work with code, how you debug code, how do you run code, how do you maintain code, we have GitHub. So I was trying to make those analogies in the new realm. Like, what is the GitHub, a software 2.0? Turns out it's something that looks like hugging face right now. And so I think some people took it seriously and built cool companies. And many people originally attacked the post. It actually was not well received when I wrote it. And I think maybe it has something to do with the title, but the post was not well received. And I think more people sort of have been coming around to it over time.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“And there was a smooth transition where, okay, first we thought we were going to build everything. Then we were building the features, so like hog features and things like that that detect these little statistical patterns from image patches. And then there was a little bit of learning on top of it, like a support vector machine or binary classifier for cat versus dog and images on top of the features. So we wrote the features, but we trained the last layer as sort of the classifier. And then people are like, actually, let's not even design the features because we can't. Honestly, we're not very good at it. So let's also learn the features. And then you end up with basically a convolutional neural nut where you're learning most of it. You're just specifying the architecture and the architecture has tons of fill-in blanks, which is all the knobs. And you let the optimization write most of it. And so this transition is happening across the industry everywhere. And suddenly we end up with a ton of code that is written in neural net weights. And I was just pointing out that the analysis”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Objective and the architecture specification into the binary, which is really just the neural nut weights and the forward pass of the neural nut. And then you can deploy that binary. And so I was talking about that sort of transition. And that's what the post is about. And I saw this sort of play out in a lot of fields, Autopal being one of them, but also just simple image classification. People thought originally in the 80s and so on that they would write the algorithm for detecting a dog in an image. And they had all these ideas about how the brain does it. And first we detect a corners and then we detect lines and then we stitched them up. And they were like really going at it. They were like thinking about how they're going to write the algorithm. And this is not the way you build it.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. Yeah, so I had a blog post on software 2.0. I think several years ago now. And the reason I wrote that post is because I kept, I kind of saw something remarkable happening in... Software development and how a lot of code was being transitioned to be written not in sort of like C and so on, but it's written in the weights of a neural net, basically just saying that neural nets are taken over software, the realm of software, and taking more and more tasks. And at the time, I think not many people understood this deeply enough, that this is a big deal, this is a big transition. Neural networks were seen as one of multiple classification algorithms you might use for your data set problem on Kaggle. Like this is not that. This is a change in how we program computers. And I saw neural nets as this is going to take over. The way we program computers is going to change. It's not going to be people writing software in C++ or something like that and directly programming the software. It's going to be accumulating training sets and data sets and crafting these objectives by which we train these neural nets. And at some point, there's going to be a compilation process from the data sets and the”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, exactly. I think the way we are programming these computers now, like GPTs, is converging to how you program humans. I mean, how do I program humans via prompt? I go to people and I prompt them to do things. I prompt them for information. And so natural language prompt is how we program humans. And we're starting to program computers directly in that interface. It's like pretty remarkable, honestly.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, I definitely, it's really interesting because search engines used to be about, okay, here's some query. Here's web pages that look like the stuff that you have. But you could just directly go to answer and then have supporting evidence. And these models basically, they've read all the texts and they've read all the web pages. And so sometimes when you see yourself going over the search results and sort of getting like a sense of like the average answer to whatever you're interested in, like that just directly comes out. You don't have to do that work. So they're kind of like... I think they have a way to distilling all that knowledge into some level of insight, basically.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“That would be, yeah, or some other more competent organization. So currently, for example, maybe Bing has another shot at it as an example.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“There's definite scope in building a better search engine today, and I think Google, they have all the tools, all the people. They have everything they need. They have all the puzzle pieces. They have people training transformers at scale. They have all the data. It's just not obvious if they are capable as an organization to innovate on their search engine right now. And if they don't, someone else will. There's absolute scope for building a significantly better search engine built on these tools.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, you could prompt a powerful model like this with its opinion about how to do any possible thing you're interested in. So they will discuss, they're kind of on track to become these oracles. I could sort of think of it that way. They are oracles currently is just text, but they will have calculators. They will have access to Google search. They will have all kinds of gadgets and gizmos. They will be able to operate the internet and find different information. Yeah, in some sense, Kind of like currently what it looks like in terms of the development.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“You something. And then it just continues the pattern. And suddenly you're having a conversation with a fake psychologist who's like trying to help you. And so it's still kind of like in the realm of a tool. It is people can prompt it in an arbitrary ways and it can create really incredible text. But it doesn't have long-term goals over long periods of time. It doesn't try to look that way right now. Yeah.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think right now at least, today they are not sort of, it's not correct to really think of them as goal-seeking agents that want to do something. They have no long-term memory or anything. It's literally a good approximation of it is you get a thousand words and you're trying to predict a thousand at first, and then you continue feeding it in. And you are free to prompt it in whatever way you want. So in text. So you say, okay, you are a psychologist and you are very good and you love humans. And here's a conversation between you and another human. human colon something”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“To an AI check, perfectly plausible in my mind. I think these AIs are actually quite good at human connection, human emotion. A ton of text on the internet is about humans and connection and love and so on. So I think they have a very good understanding in some sense of how people speak to each other about this. And they're very capable of creating a lot of that kind of text. There's a lot of like sci-fi from 50s and 60s that imagined AI is in a very different way. They are calculating cold Balkan-like machines. That's not what we're getting today. We're getting pretty emotional AIs that actually are very competent and capable of generating plausible sounding text with respect to all of these topics.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“And if you have like reasonable answers and looked real and so on. So to me, it's a He wasn't sufficiently trying to stress the system, I think, and exposing the truth of it as it is today. But I think this will be increasingly harder over time. So, yeah, I think more and more people will basically become... Think more and more there will be more people like that over time as this gets better.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. To me, it's a little bit of a canary in a coal mine kind of moment, honestly, a little bit, because this engineer spoke to like a chatbot at Google and became convinced that this bot is sentient.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Definitely, there's a lot of longing fruit, but I will say I agree that if you are a sophisticated actor, you could probably create a pretty good bot right now using tools like GPTs because it's a language model. You can generate faces that look quite good now. And you can do this at scale. And so I think, yeah, it's quite plausible and it's going to be hard to defend.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, maybe you have to start declaring when we have to start drawing those boundaries and keeping track of, okay, what are digital entities versus human entities and what is the ownership of human entities and digital entities and something like that? I don't know, but I think I'm optimistic that this is possible. And in some sense, we're currently in the worst time of it because all these bots suddenly have become very capable, but we don't have defenses yet built up as a society. But I think that doesn't seem to me intractable. It's just something that we have to deal with.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't think the problem is intractable. People are thinking about the proof of personhood. And we might start digitally signing our stuff. And we might all end up having like basically some solution for proof of personhood. It doesn't seem to me intractable. It's just something that we haven't had to do until now. But I think once the need really starts to emerge, which is soon. I think people will think about it much more.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, and they will get much better and they will share our digital realm and they'll eventually share our physical realm as well. It's much harder. But that's kind of like the world we're going towards. And most of them will be benign and hopeful. And some of them will be malicious. And it's going to be an arms race trying to detect them.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“At some point, I think it might be, I think the society will evolve a little bit. Like we might start signing, digitally signing some of our correspondence or things that we create. Right now, it's not necessary, but maybe in the future it might be. I do think that we are going towards a world where we share the digital space with AIs.”
2022-10-29 · Lex Fridman Podcast · #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI · IDENTIFIED FROM THE TRANSCRIPT · source