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