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