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