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