We started OpenAI seven years ago because felt like something really interesting was happening in AI 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 to hear from people like Raymond who are using technology we are building, and others, for so many wonderful things. We hear people who are excited, we hear from people who are concerned, we hear from people who both those emotions at once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now where we as a world are to define a technology that will be so important our society going forward. And I believe that we can manage this good.
So today, I want to show you the current state of technology and some of the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s like build a tool for an AI rather than building it for a human. So have a new DALL-E model, which generates images, and we exposing it as an app for ChatGPT to use your behalf. And you can do things like ask, know, suggest a nice post-TED meal and draw a of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of the for you that you get out of ChatGPT. And here we go, it’s just the idea for the meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just generate in this case — sorry, it doesn’t generate text, also generates an image. And that is something that really expands the of what it can do on your behalf in of carrying out your intent. And I’ll point out, this is a live demo. This is all generated by the as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry looking at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And the thing about these tools is they’re very inspectable. So you get this little pop up here that “use the DALL-E app.” And by the way, this is coming to you, all ChatGPT users, upcoming months. And you can look under the hood and see what it actually did was write a prompt just like a human could. And so you sort have this ability to inspect how the machine is using these tools, which allows us to provide to them.
Now it’s saved for later, and let me show what it’s like to use that information and to integrate with other applications too. can say, “Now make a shopping list for the tasty thing I suggesting earlier.” And make it a little tricky for the AI. “And tweet out for all the TED viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, definitely want to know how it tastes.
But you see that ChatGPT is selecting all these different tools without having to tell it explicitly which ones to use in any situation. And this, think, shows a new way of thinking about the user interface. Like, are so used to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great experience an app as long as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, the is able to sort of take away all those details 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 I said, this is a live demo, so sometimes the unexpected will to us. But let’s take a look at the Instacart shopping list while we’re it. And you can see we sent a list of to Instacart. Here’s everything you need. And the thing that’s really interesting 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. And that’s 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 have a tweet that’s been drafted for our review, which is a very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able to the work of the AI if we want to. And so this talk, you will be able to access this yourself. there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, important thing about how we build this, it’s not about building these 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 old idea. you go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program answer to this. Instead, you can learn it. You could build machine, like a human child, and then teach it through feedback. Have a human who provides rewards and punishments as it tries things out and does things that are good or bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, we what Turing would have called a child machine through an unsupervised process. We just show it the whole world, the whole internet say, “Predict what comes next in text you’ve never seen before.” And process imbues it with all sorts of wonderful skills. For example, if you’re a math problem, the only way to actually complete that math problem, to what comes next, that green nine up there, is to actually the math problem.
But we actually have to do a second step, too, is to teach the AI what to do with skills. And for this, we provide feedback. We have the AI try out multiple things, us multiple suggestions, and then a human rates them, says “This one’s better that one.” And this reinforces not just the specific thing that the AI said, very importantly, the whole process that the AI used to that answer. And this allows it to generalize. It allows it to teach, sort of infer your 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, this so great, We’re going to be able to teach students things. Only one problem, it 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 with it.” So we had collect some feedback data. Sal Khan himself was very and offered 20 hours of his own time to provide feedback to the alongside our team. And over the course of a couple of months were able to teach the AI that, “Hey, you really should push back on humans in specific kind of scenario.” And we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up bat signal 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 we listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback is hard thing. If you think about asking a kid clean their room, if all you’re doing is 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 reasoning applies 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 provide even better feedback and to scale our ability supervise the machine as time goes on. And let me show you what mean.
For example, you can ask GPT-4 a question this, of how much time passed between these two foundational blogs on learning and learning from human feedback. And the model two months passed. But is it true? Like, these models not 100-percent reliable, although they’re getting better every time we provide feedback. But we can actually use the AI to fact-check. it can actually check its own work. You can say, fact-check for me.
Now, in this case, I’ve actually given the a new tool. This one is a browsing tool where model can issue search queries and click into web pages. And it actually writes its whole chain of thought as it does it. says, I’m just going to search for this and it actually does the search. then it finds the publication date and the search results. It then is another search query. It’s going to click into the 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 more to be in the driver’s seat, to be in this manager’s position where you can, you want, triple-check the work. And out come citations so you can go and very easily verify any piece of this whole chain of reasoning. And actually turns out two months was wrong. Two months and one week, was correct.
(Applause)
And we’ll cut back to the side. And so that’s so interesting to me about this whole process that it’s this many-step collaboration between a human and an AI. a human, using this fact-checking tool is doing it in to produce data for another AI to become more useful to a human. And I think this shows the shape of something that we should expect to be much common in the future, where we have humans and machines of very carefully and delicately designed in how they into a problem and how we want to solve that problem. We make sure that the humans providing the management, the oversight, the feedback, and the machines are operating in way that’s inspectable and trustworthy. And together we’re able to actually create even more trustworthy machines. And I that over time, if we get this process right, we will be able solve impossible problems.
And to give you a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every aspect of how interact with computers. For example, think about spreadsheets. They’ve around 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 a spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. you can see there the data right here. But let show you the ChatGPT take on how to analyze a data set like this.
So can give ChatGPT access to yet another tool, this one Python interpreter, so it’s able to run code, just like a data scientist would. And you can just literally upload a file and ask questions about it. 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 for you.” The only information here is the name the file, the column names like you saw and then the actual data. from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It has to of, put together its world knowledge of knowing that, “Oh yeah, arXiv is site that people submit papers and therefore that’s what things are and that these are integer values and so therefore it’s a number of authors in paper,” like all of that, that’s work for a human to do, and the is happy to help with it.
Now I don’t even know what I want to ask. So fortunately, can ask the machine, “Can you make some exploratory graphs?” And once again, this is a super high-level with lots of intent behind it. But I don’t even know I want. And the AI kind of has to infer I might be interested in. And so it comes up with good ideas, I think. So a histogram of the number of authors paper, time series of papers per year, word cloud of the paper titles. All of that, think, will be pretty interesting to see. And the thing is, it can actually do it. Here we go, a nice curve. You see that three is kind of the common. It’s going to then make this nice plot of papers per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped off cliff. What could be going on there? By the way, all this is code, you can inspect. And then we’ll see word cloud. you can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. It this year look really bad. Of course, the problem is that the year is not over. So I’m to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were even by April 13?] So April 13 was the cut-off I believe. Can you use that to make a fair projection? we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I out of the machine here. I really wanted it to notice thing, maybe it’s a little bit of an overreach for it to have sort of, inferred magically this is what I wanted. But I inject my intent, I provide this 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 does 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 slide again. This slide shows a parable of how think we … A vision of how we may end up using this in the future. A person brought his very sick to the vet, and the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be here today had listened. In the meanwhile, he 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 vet who used to save the dog’s life. Now, these systems, they’re not perfect. cannot overly rely on them. But this story, I think, that a human with a medical professional and with ChatGPT a brainstorming partner was able to achieve an outcome that would not happened otherwise. I think this is something we should reflect on, think about as we consider how to integrate these systems 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 it to slot in, that’s for setting the rules of road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s that this technology just looks different. different from anything people had anticipated. And so we have to become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe we can achieve the OpenAI mission of ensuring that artificial intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within mind out here there’s a feeling of reeling. Like, I suspect that a very large number people viewing this, you look at that and you think, “Oh my goodness, pretty much every single thing about the I work, I need to rethink.” Like, there’s just possibilities there. Am I right? Who thinks that they’re having to rethink the way that 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 actually is just how the hell have you done this?
(Laughter)
OpenAI has a few employees. Google has thousands of employees working on artificial intelligence. is it you who’s come up with this technology that shocked the world?
Greg Brockman: mean, the truth is, we’re all building on shoulders of giants, right, there’s no question. If you look the 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 choices the early days. And the first one was just to confront reality it lays. And that we just thought really hard about like: What is it going to take to progress here? We tried a lot of things that didn’t work, you only see the things that did. And I think the most important thing has been to get teams of people who are very from each other to work together harmoniously.
CA: Can we have water, by the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there something just about the fact that you saw something in these language 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, think the story there is pretty illustrative, right? I think that high level, learning, like we always knew that was what we wanted to be, a deep learning lab, and exactly how to do it? think that in the early days, we didn’t know. We tried a of things, and one person was working on training a model to predict the character in Amazon reviews, and he got a result where — this is a syntactic process, expect, you know, the model will predict where the go, where the nouns and verbs are. But he actually got a state-of-the-art analysis classifier out of it. This model could tell you if a was positive or negative. I mean, today we are like, come on, anyone can do that. But this the first time that you saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve got scale this thing, you’ve got to see where it goes.
CA: So think this helps explain the riddle 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 that that could come from a prediction machine. Just the stuff showed us just now. And the key idea of emergence is that when you get more a thing, suddenly different things emerge. It happens all the time, colonies, single ants run around, when you bring enough of them together, you get these colonies that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and cultural centers and jams. Give me one moment for you when you saw just 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 model will it, which means it’s really learned an internal circuit for how to do it. And really interesting thing is actually, if you have it add like a 40-digit number a 35-digit number, it’ll often get it wrong. And you can see that it’s really learning the process, it hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are in the universe. So it to have learned something general, but that it hasn’t really fully yet learned that, Oh, I can of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed to scale up and look at an incredible number of of text. And it is learning things that you didn’t know it was going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting really get good at is predicting some of these emergent capabilities. And do that actually, one of the things I think is undersung in this field is sort of engineering quality. Like, we had to our entire stack. When you think about building a rocket, every tolerance has to be tiny. Same is true in machine learning. You have to get every single piece of the stack properly, and then you can start doing these predictions. There are all these incredibly scaling curves. They tell you something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these in there. And now we’re starting to be able to predict. So we were able to predict, example, the performance on coding problems. We basically look some models that are 10,000 times or 1,000 times smaller. And so there’s something about this that is smooth scaling, even though it’s still early days.
CA: So here is, of the big fears then, that arises from this. If it’s fundamental to what’s happening here, that you scale up, things emerge that you can maybe predict some level of confidence, but it’s capable of surprising you. isn’t there just a huge risk of something truly terrible emerging?
GB: Well, I think of these are questions of degree and scale and timing. And think one thing people miss, too, is sort of integration with the world is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that we think it’s so important to incrementally. And so I think that what we kind of see right now, if you look this talk, a lot of what I focus on is providing really high-quality feedback. Today, the tasks we do, you can inspect them, right? It’s very easy to look at math problem and be like, no, no, no, machine, seven was the correct answer. But even a book, like, that’s a hard thing to supervise. Like, how you know if this book summary is any good? You to read the whole book. No one wants to that.
(Laughter) And so I think that the important will be that we take this step by step. And that we say, OK, we move on to book summaries, we have to this task properly. We have to build up a track record with these that they’re able to actually carry out our intent. I think we’re going to have to produce even better, efficient, more reliable ways of scaling this, sort of like making the be aligned with you.
CA: So we’re going to hear later in this session, there are critics say that, you know, there’s no real understanding inside, system is going to always — we’re never going know that it’s not generating errors, that it doesn’t have common sense and forth. Is it your belief, Greg, that it is true at one moment, but that the expansion of the scale the human feedback that you talked about is basically going to take it on that journey of actually to things like truth and wisdom and so forth, with a high degree of confidence. Can you be of that?
GB: Yeah, well, I think that the OpenAI, I mean, short answer is yes, I believe that is where we’re headed. I think that the OpenAI approach here has always been just like, let reality you in the face, right? It’s like this field is field of broken promises, of all these experts saying is going to happen, Y is how it works. have been saying neural nets aren’t going to work 70 years. They haven’t been right yet. They might be right maybe 70 years plus or something like that is what you need. But I think that our has always been, you’ve got to push to the limits of this technology really see it in action, because that tells you 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 a controversial stance you’ve taken, the right way to do this is to put it out there public and then harness all this, you know, instead of just your team giving feedback, the world now giving feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded a nonprofit, well you were there as the great of check on the big companies doing their unknown, evil thing with AI. And you were going to build that sort of, you know, somehow held them accountable and was capable of slowing the field down, need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. That your of GPT, especially ChatGPT, sent such shockwaves through the world that now Google and Meta and so forth are all scrambling catch up. And some 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, the case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions all the time. Like, seriously all the time. I don’t think we’re always going to get it right. one thing I think has been incredibly important, from the very beginning, when we were thinking about how build artificial general intelligence, actually have it benefit all humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, you get this super powerful thing, then you figure out the safety of it and you push “go,” and you hope you got it right. don’t know how to execute that plan. Maybe someone else does. But for me, was always terrifying, it didn’t feel right. And so I think that this alternative approach is only other path that I see, which is that do let reality hit you in the face. And think you do give people time to give input. You do have, these machines are perfect, before they are super powerful, that you actually have the to see them in 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 thing was generating Viagra spam.
(Laughter)
CA: So Viagra is bad, but there are things that are much worse. Here’s thought experiment for you. Suppose you’re sitting in a room, there’s box on the table. You believe that in that box is something that, there’s very strong chance it’s something absolutely glorious that’s going to beautiful gifts to your family and to everyone. But there’s actually also a one percent in the small print there that says: “Pandora.” And there’s chance that this actually could unleash unimaginable evils on the world. Do open that box?
GB: Well, so, absolutely not. I think don’t do it that 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 looking out over this wonderful water, these people having a good time. And you think about it for a moment, if you could choose basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On one hand you’re like, well, maybe for you personally, it’s better to have it five years away. But if it gets to be 500 years away and people get more time to get right, which do you pick? And you know, I just really felt it in the moment. was like, of course you do the 500 years. My brother was the military at the time and like, he puts life on the line in a much more real than any of us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on the you’ve to approach this right. But I don’t think that’s playing the field as it truly lies. Like, if you look at the whole history of computing, really mean it when I say that this is industry-wide or even just almost like a human-development- of-technology-wide shift. the more that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re still improving algorithms, all of these things, they are happening. And if don’t put them together, you get an overhang, which that if someone does, or the moment that someone manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time adjust, who knows what kind of safety precautions you get. so I think 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, in what humans could do. But I actually think that you look 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 do it incrementally and you’ve got to figure out how manage it for each moment that you’re increasing it.
CA: So what I’m hearing that you … the model you want us to have that we have birthed this extraordinary child that may have that take humanity to a whole new place. It is our collective responsibility provide the guardrails for this child to collectively teach to be wise and not to tear us all down. that basically the model?
GB: I think it’s true. I think it’s also important to say this may shift, right? We’ve to take each step as we encounter it. And think it’s incredibly important today that we all do get literate in this technology, figure out to provide the feedback, decide what we want from it. And my hope is that will continue to be the 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 blowing our minds.
(Applause)