We started OpenAI seven years ago we felt like something really interesting was happening in AI we wanted to help steer it in a positive direction. It’s just really amazing to see how far this whole has come since then. And it’s really gratifying to hear from people like Raymond who using the technology we are building, and others, for so many things. We hear from people who are excited, we hear from people who are concerned, we hear people who feel both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an historic period now where we as a world are going to define a technology that will be so for our society going forward. And I believe that we manage this for good.
So today, I want to you the current state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m going to you is what it’s like to build a tool for an AI rather than building it for human. So we have a new DALL-E model, which images, and we are exposing it as an app for ChatGPT use on your behalf. And you can do things like ask, you know, a nice post-TED meal and draw a picture of it.
(Laughter)
Now you all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you out of ChatGPT. And here we go, it’s not just the idea for the meal, a very, very detailed spread. So let’s see what we’re going to get. But ChatGPT doesn’t generate images in this case — sorry, it doesn’t generate text, it generates an image. And that is something that really expands the of what it can do on your behalf in of carrying out your intent. And I’ll point out, is all a live demo. This is all generated by AI as we speak. So I actually don’t even know what we’re going to see. looks wonderful.
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I’m getting hungry just looking at it.
Now we’ve extended ChatGPT with tools too, for example, memory. You can say “save for later.” And the interesting thing about these tools is they’re very inspectable. So you this little pop up here that says “use the DALL-E app.” And by the way, this coming to you, all ChatGPT users, over upcoming months. And can look under the hood and see that what it actually did was a prompt just like a human could. And so you sort of have this to inspect how the machine is using these tools, which allows us provide feedback to them.
Now it’s saved for later, and let me show you it’s like to use that information and to integrate with other applications too. You can say, “Now make shopping list for the tasty thing I was suggesting earlier.” And it a little tricky for the AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting all different tools without me having to tell it explicitly which to use in any situation. And this, I think, shows a new way thinking about the user interface. Like, we are so used thinking of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great experience an app as long as you kind of know the and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And by having this language interface on top of tools, the AI is able to sort of take away those details from you. So you don’t have to be the one who spells every single sort of little piece of what’s supposed to happen.
And as I said, is a live demo, so sometimes the unexpected will happen to us. But let’s take a look at Instacart shopping list while we’re at it. And you can we sent a list of ingredients to Instacart. Here’s you need. And the thing that’s really interesting is the traditional UI is still very valuable, right? If you look at this, still can click through it and sort of modify actual quantities. And that’s something that I think shows they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we a tweet that’s been drafted for our review, which is also a important thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the work of the AI if we to. And so after this talk, you will be able to access yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important thing about we build this, it’s not just about building these tools. It’s about teaching the AI how to use them. Like, do we even want it to do when we ask these high-level questions? And to do this, we use an old idea. you go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll program an answer to this. Instead, you can learn it. You could build a machine, like a child, and then teach it through feedback. Have a human teacher who provides rewards and punishments as it things out and does things that are either good or bad.
And this is how we train ChatGPT. It’s a two-step process. First, we produce what Turing have called a child machine through an unsupervised learning process. We show it the whole world, the whole internet and say, “Predict comes next in text you’ve never seen before.” And this imbues it with all sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that math problem, to say what comes next, green nine up there, is to actually solve the problem.
But we actually have to do a second step, too, which is to teach the what to do with those skills. And for this, we provide feedback. We have the AI try out things, give us multiple suggestions, and then a human rates them, says “This one’s better that one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the used to produce that answer. And this allows it generalize. It allows it to teach, to sort of infer your intent apply it in scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach the are not what you’d expect. For example, when we first showed GPT-4 to Khan Academy, said, “Wow, this is so great, We’re going to be able to teach wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that one one equals three and run with it.” So we had collect some feedback data. Sal Khan himself was very and offered 20 hours of his own time to provide to the machine alongside our team. And over the of a couple of months we were able to the AI that, “Hey, you really should push back on humans this specific kind of scenario.” And we’ve actually made lots lots of improvements to the models this way. And you push that thumbs down in ChatGPT, that actually is of like sending up a bat signal to our to say, “Here’s an area of weakness where you should feedback.” And so when you do that, that’s one way that we really listen to users and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. you think about asking a kid to clean their room, if all you’re doing inspecting the floor, you don’t know if you’re just them to stuff all the toys in the closet. is a nice DALL-E-generated image, by the way. And same sort of reasoning applies to AI. As we to harder tasks, we will have to scale our to provide high-quality feedback. But for this, the AI itself happy to help. It’s happy to help us provide better feedback and to scale our ability to supervise the machine as time goes on. And let show you what I mean.
For example, you can GPT-4 a question like this, of how much time between these two foundational blogs on unsupervised learning and learning human feedback. And the model says two months passed. But is true? Like, these models are not 100-percent reliable, although they’re better every time we provide some feedback. But we can actually the AI to fact-check. And it can actually check its work. You can say, fact-check this for me.
Now, in this case, I’ve actually given AI a new tool. This one is a browsing where the model can issue search queries and click into web pages. And it actually writes out its chain of thought as it does it. It says, I’m just going to search for and it actually does the search. It then it the publication date and the search results. It then is issuing another search query. It’s to click into the blog post. And all of this you do, but it’s a very tedious task. It’s not a that humans really want to do. It’s much more fun to be the driver’s seat, to be in this manager’s position where you can, if want, triple-check the work. And out come citations so you can actually go and easily verify any piece of this whole chain of reasoning. it actually turns out two months was wrong. Two and one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting to me about this whole process that it’s this many-step collaboration between a human and AI. Because a human, using this fact-checking tool is doing in order to produce data for another AI to become more useful to human. And I think this really shows the shape of something that we expect to be much more common in the future, where have humans and machines kind of very carefully and delicately designed in how they fit a problem and how we want to solve that problem. We make sure that the are providing the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. And think that over time, if we get this process right, we be able to solve impossible problems.
And to give you a sense of just how I’m talking, I think we’re going to be able to rethink almost every aspect of how interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years with VisiCalc. I don’t think they’ve really changed that much that time. And here is a specific spreadsheet of all AI papers on the arXiv for the past 30 years. There’s about 167,000 of them. And you can see there data right here. But let me show you the ChatGPT take on how to analyze a data set this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And so you can just literally upload file and ask questions about it. And very helpfully, know, it knows the name of the file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information here is name of the file, the column names like you and then the actual data. And from that it’s able to infer what these actually mean. Like, that semantic information wasn’t in there. It has to sort of, put together its world of knowing that, “Oh yeah, arXiv is a site people submit papers and therefore that’s what these things and that these are integer values and so therefore it’s a number authors in the paper,” like all of that, that’s work a human to do, and the AI is happy to help it.
Now I don’t even know what I want to ask. So fortunately, can ask the machine, “Can you make some exploratory graphs?” And once again, is a super high-level instruction with lots of intent behind it. But don’t even know what I want. And the AI kind of has to infer I might be interested in. And so it comes with some good ideas, I think. So a histogram the number of authors per paper, time series of papers year, word cloud of the paper titles. All of that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here go, a nice bell curve. You see that three is of the most common. It’s going to then make this nice plot of the per year. Something crazy is happening in 2023, though. Looks like we were on an exponential and it off the cliff. What could be going on there? By the way, all is Python code, you can inspect. And then we’ll see word cloud. So you can see all these things that appear in these titles.
But I’m pretty about this 2023 thing. It makes this year look bad. Of course, the problem is that the year is over. So I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the kind ambitious one.
(Laughter)
So you know, again, I feel like there was I wanted out of the machine here. I really it to notice this thing, maybe it’s a little of an overreach for it to have sort of, inferred magically that this is I wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing code again, if you want to inspect what it’s doing, it’s very possible. now, it does the correct projection.
(Applause)
If you noticed, it updates the title. I didn’t ask for that, but it know what want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … A vision of we may end up using this technology in the future. A person brought his very sick to the vet, and the veterinarian made a bad to say, “Let’s just wait and see.” And the would not be here today had he listened. In meanwhile, he provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” He brought that information to a second who used it to save the dog’s life. Now, these systems, they’re perfect. You cannot overly rely on them. But this story, think, shows that a human with a medical professional and with ChatGPT as a partner was able to achieve an outcome that would have happened otherwise. I think this is something we should all reflect on, think about as we consider to integrate these systems into our world.
And one I believe really deeply, is that getting AI right going to require participation from everyone. And that’s for deciding how we it to slot in, that’s for setting the rules the road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s that this technology looks different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, of the reasons we released ChatGPT.
Together, I believe that we can achieve the OpenAI of ensuring that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every out here there’s a feeling of reeling. Like, I suspect a very large number of people viewing this, you at that and you think, “Oh my goodness, pretty much single thing about the way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to rethink the that we do things? Yeah, I mean, it’s amazing, it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the have you done this?
(Laughter)
OpenAI has a few hundred employees. Google thousands of employees working on artificial intelligence. Why is it you who’s come up with this technology that the world?
Greg Brockman: I mean, the truth is, we’re all building shoulders of giants, right, there’s no question. If you look at the compute progress, algorithmic progress, the data progress, all of those are really industry-wide. I think within OpenAI, we made a lot of very deliberate choices from the days. And the first one was just to confront as it lays. And that we just thought really hard about like: is it going to take to make progress here? tried a lot of things that didn’t work, so you see the things that did. And I think that the most thing has been to get teams of people who very different from each other to work together harmoniously.
CA: Can we have water, by the way, just brought here? I think we’re going to need it, it’s a dry-mouth topic. isn’t there something also just about the fact that you saw something in these language models meant that if you continue to invest in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, I think story there is pretty illustrative, right? I think that high level, deep learning, like always knew that was what we wanted to be, was a deep learning lab, and how to do it? I think that in the early days, we didn’t know. We tried lot of things, and one person was working on training a to predict the next character in Amazon reviews, and he got result where — this is a syntactic process, you expect, you know, the model will predict where commas go, where the nouns and verbs are. But he actually got a state-of-the-art sentiment analysis classifier out it. This model could tell you if a review was positive negative. I mean, today we are just like, come on, anyone can that. But this was the first time that you saw this emergence, this sort of that emerged from this underlying syntactic process. And there we knew, you’ve got to this thing, you’ve got to see where it goes.
CA: So I think this explain the riddle that baffles everyone looking at this, these things are described as prediction machines. And yet, what we’re seeing of them feels … it just feels impossible that could come from a prediction machine. Just the stuff showed us just now. And the key idea of emergence is that when you more of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you bring enough of together, you get these ant colonies that show completely emergent, different behavior. a city where a few houses together, it’s just together. But as you grow the number of houses, emerge, like suburbs and cultural centers and traffic jams. Give me one for you when you saw just something pop that just blew your mind that you just not see coming.
GB: Yeah, well, so you can try this in ChatGPT, if you 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s really an internal circuit for how to do it. And the interesting thing is actually, if you have it add like 40-digit number plus a 35-digit number, it’ll often get it wrong. so you can see that it’s really learning the process, it hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit table, that’s more atoms than there are in the universe. it had to have learned something general, but that hasn’t really fully yet learned that, Oh, I can sort of generalize this adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it scale up and look at an incredible number of pieces text. And it is learning things that you didn’t that it was going to be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science we’re starting to really get good at is predicting some of emergent capabilities. And to do that actually, one of things I think is very undersung in this field is of engineering quality. Like, we had to rebuild our stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in machine learning. have to get every single piece of the stack engineered properly, then you can start doing these predictions. There are all incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If look at our GPT-4 blog post, you can see all of these curves there. And now we’re starting to be able to predict. we were able to predict, for example, the performance on coding problems. We basically at some models that are 10,000 times or 1,000 smaller. And so there’s something about this that is smooth scaling, even though it’s still early days.
CA: here is, one of the big fears then, that from this. If it’s fundamental to what’s happening here, that as you scale up, things emerge you can maybe predict in some level of confidence, but it’s capable surprising you. Why isn’t there just a huge risk of something truly terrible emerging?
GB: Well, think all of these are questions of degree and scale and timing. And think one thing people miss, too, is sort of the integration with the is also this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s so to deploy incrementally. And so I think that what we kind of see now, if you look at this talk, a lot of what focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven was the correct answer. even summarizing a book, like, that’s a hard thing supervise. Like, how do you know if this book summary any good? You have to read the whole book. No one to do that.
(Laughter) And so I think that the important will be that we take this step by step. that we say, OK, as we move on to book summaries, we have supervise this task properly. We have to build up a record with these machines that they’re able to actually out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways of scaling this, sort of making the machine be aligned with you.
CA: So we’re to hear later in this session, there are critics who that, you know, there’s no real understanding inside, the is going to always — we’re never going to that it’s not generating errors, that it doesn’t have sense and so forth. Is it your belief, Greg, it is true at any one moment, but that the expansion of the and the human feedback that you talked about is basically to take it on that journey of actually getting to things like truth and and so forth, with a high degree of confidence. Can be sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And I that the OpenAI approach here has always been just like, let reality hit you in face, right? It’s like this field is the field of promises, of all these experts saying X is going happen, Y is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like that what you need. But I think that our approach always been, you’ve got to push to the limits of this technology really see it in action, because that tells you then, oh, here’s how we can on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the right way to this is to put it out there in public then harness all this, you know, instead of just your team giving feedback, the world is giving feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the original story that I heard on when you were founded as a nonprofit, well you were there the great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were to build models that sort of, you know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through the tech world now Google and Meta and so forth are all to catch up. And some of their criticisms have been, you are forcing us to put this here without proper guardrails or we die. You know, how do you, like, make the that what you have done is responsible here and not reckless.
GB: Yeah, we think about questions all the time. Like, seriously all the time. I don’t think we’re always going to get it right. But one I think has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, actually have benefit all of humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, you get this super powerful thing, then you figure out the safety of it and then you “go,” and you hope you got it right. I don’t know how to execute that plan. Maybe someone does. But for me, that was always terrifying, it didn’t feel right. And so I think this alternative approach is the only other path that I see, which is that you do reality hit you in the face. And I think you give people time to give input. You do have, these machines are perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number one thing people were going to do it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are things are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on the table. believe that in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually also a one thing in the small print there that says: “Pandora.” And there’s a chance this actually could unleash unimaginable evils on the world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. And honestly, like, I’ll you a story that I haven’t actually told before, which is that shortly after we OpenAI, I remember I was in Puerto Rico for AI conference. I’m sitting in the hotel room just looking out over this wonderful water, these people having a good time. And you think about for a moment, if you could choose for basically that Pandora’s box to be five years or 500 years away, which would you pick, right? On one hand you’re like, well, maybe for you personally, it’s better to have it be five away. But if it gets to be 500 years and people get more time to get it right, which do you pick? And you know, just really felt it in the moment. I was like, course you do the 500 years. My brother was in the military at the and like, he puts his life on the line in much more real way than any of us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on the you’ve got to approach this right. But don’t think that’s quite playing the field as it truly lies. Like, if look at the whole history of computing, I really it when I say that this is an industry-wide or just almost like a human-development- of-technology-wide shift. And the more that you sort of, don’t together the pieces that are there, right, we’re still making faster computers, we’re still improving algorithms, all of these things, they are happening. And if you don’t put them together, you get overhang, which means that if someone does, or the that someone does manage to connect to the circuit, you suddenly have this very powerful thing, no one’s any time to adjust, who knows what kind of safety you get. And so I think that one thing I take away is like, even think about development of other sort of technologies, think about weapons, people talk about being like a zero to one, of, change in what humans could do. But I actually that if you look at capability, it’s been quite smooth time. And so the history, I think, of every we’ve developed has been, you’ve got to do it and you’ve got to figure out how to manage it for each moment you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have is that have birthed this extraordinary child that may have superpowers take humanity to a whole new place. It is collective responsibility to provide the guardrails for this child to collectively teach it be wise and not to tear us all down. that basically the model?
GB: I think it’s true. And I think it’s also important to say may shift, right? We’ve got to take each step as encounter it. And I think it’s incredibly important today that we all do get in this technology, figure out how to provide the feedback, decide what we want from it. And my hope that that will continue to be the best path, it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much for coming to TED blowing our minds.
(Applause)