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