We started OpenAI seven years ago because we felt something really interesting was happening in AI and we wanted to help steer in a positive direction. It’s honestly just really amazing to see how far whole field has come since then. And it’s really gratifying to hear from people like Raymond who are the technology we are building, and others, for so many things. We hear from people who are excited, we hear from people are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. Above all, it feels we’re entering an historic period right now where we as a are going to define a technology that will be so important for our society going forward. I believe that we can manage this for good.
So today, want 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 to you is what it’s like to build a tool for an AI rather than building it for human. So we have a new DALL-E model, which generates images, and we exposing it as an app for ChatGPT to use on your behalf. And you do things like ask, you know, suggest a nice post-TED meal and draw a picture it.
(Laughter)
Now you get all of the, sort of, ideation and creative back-and-forth and taking care of details for you that you get out of ChatGPT. And here we go, it’s not just idea for the meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just images in this case — sorry, it doesn’t generate text, also generates an image. And that is something that really expands the power of what can do on your behalf in terms of carrying out your intent. And I’ll point out, this all a live demo. This is all generated by the AI we speak. So I actually don’t even know what we’re going see. This looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended ChatGPT with other too, for example, memory. You can say “save this for later.” And the interesting thing about 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, ChatGPT users, over upcoming months. And you can look under the hood see that what it actually did was write a prompt like a human could. And so you sort of this ability to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s saved for later, and let me show you it’s like to use that information and to integrate other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” And it a little tricky for the AI. “And tweet out for all the TED viewers out there.”
(Laughter)
So if do make this wonderful, wonderful meal, I definitely want know how it tastes.
But you can see that ChatGPT is selecting all these different tools without having to tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking 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 as you kind of the menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, AI is able to sort of take away all those details from you. So you don’t have to the one who spells out every single sort of little piece of what’s to happen.
And as I said, this is a live demo, so sometimes the will happen to us. But let’s take a look at Instacart shopping list while we’re at it. And you can we sent a list of ingredients to Instacart. Here’s you need. And the 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 the actual 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 we have a tweet that’s been drafted for our review, is also 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 after this talk, you will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back the slides. Now, the important thing about how we build this, it’s not just about building tools. It’s about teaching the AI how to use them. Like, what do we even want it to do we ask these very high-level questions? And to do this, use an old idea. If you go back to Turing’s 1950 paper on the Turing test, he says, you’ll never program an answer to this. Instead, you learn it. You could build a machine, like a human child, and then it through feedback. Have a human teacher who provides rewards and punishments as it tries things out does things that are either good or bad.
And this is how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine through unsupervised learning process. We just show it the whole world, whole internet and say, “Predict what comes next in text you’ve never before.” And this process imbues it with all sorts of wonderful skills. For example, you’re shown a math problem, the only way to actually 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 this, we provide feedback. We have the AI try multiple things, give us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not the specific thing that the AI said, but very importantly, whole process that the AI used to produce that answer. And this allows it to generalize. allows it to teach, to sort of infer your intent and it in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things have to teach the AI are not what you’d expect. For example, when first showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going to able to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Sal Khan himself was kind and offered 20 hours of his own time to feedback to the machine alongside our team. And over the course of a of 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 improvements to models this way. And when you push that thumbs down in ChatGPT, that actually is kind of like up a bat signal to our team to say, “Here’s an area of where you should gather feedback.” And so when you that, that’s one way that we really listen to users and make sure we’re building something that’s more for everyone.
Now, providing high-quality feedback is a hard thing. you think about asking a kid to clean their room, if 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 the sort of reasoning applies to AI. As we move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the 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 on. And let me show you what I mean.
For example, you ask GPT-4 a question like this, of how much time between these two foundational blogs on unsupervised learning and learning from human feedback. And the model says two passed. But is it true? Like, these models are not 100-percent reliable, although they’re getting better every we provide some feedback. But we can actually use the AI to fact-check. it can actually check its own work. You can say, fact-check this 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 click into web pages. And it actually writes out whole chain of thought as it does it. It says, I’m just going search for this and it actually does the search. It then it the publication date and the search results. It then is another search query. It’s going to click into the blog post. all of this you could do, but it’s a very task. It’s not a thing that humans really want do. It’s much more fun to be in the driver’s seat, be in this manager’s position where you can, if you want, triple-check the work. out come citations so you can actually go and very easily any piece 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 to the side. And so that’s so interesting to me about this 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 in order to produce for another AI to become more useful to a human. And I think this really shows shape of something that we should expect to be much more common the future, where we have humans and machines kind very carefully and delicately designed in how they fit into 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 a that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that over time, if we get this right, we will be able to solve impossible problems.
And to you a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every of how we interact with computers. For example, think spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve changed that much in that time. And here is a specific spreadsheet of all the papers on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the data right here. let me show you the ChatGPT take on how analyze a data set like this.
So we can ChatGPT access to yet another tool, this one a 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 about it. very helpfully, you know, it knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The only information is the name of the file, the column names like you saw then the actual data. And from that it’s able to infer what these actually mean. Like, that semantic information wasn’t in there. has 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 that these are integer values and so therefore it’s number of authors in the paper,” like all of that, that’s for a human to do, and the AI is happy help with it.
Now I don’t even know what want to ask. So fortunately, you can ask the machine, “Can you some exploratory graphs?” And once again, this is a 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. So a histogram of the number of per paper, time series of papers per year, word of the paper titles. All of that, I think, be pretty interesting to see. And the great thing is, it can do it. Here we go, a nice bell curve. You that three is kind of the most common. It’s going to then make this nice plot of the 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 is Python code, you can inspect. And then we’ll see word cloud. So you see all these wonderful things that appear in these titles.
But I’m pretty unhappy about this 2023 thing. It this year look really bad. Of course, the problem is the year is not over. So I’m going to push 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 date I believe. you use that to make a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel like there was more I out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of an for it to have sort of, inferred magically that this is what wanted. But I inject my intent, I provide this additional piece of, you know, guidance. And the 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 slide again. This slide a parable of how I think we … A vision of how we may end up using this in the future. A person brought his very sick dog the vet, and the veterinarian made a bad call say, “Let’s just wait and see.” And the dog would not here today had he listened. In the meanwhile, he provided blood test, like, the full medical records, to GPT-4, said, “I am not a vet, you need to talk a professional, here are some hypotheses.” He brought that information a second vet who used it to save the dog’s life. Now, these systems, they’re perfect. You cannot overly rely on them. But this story, I think, shows that a with a medical professional and with ChatGPT as a brainstorming was able to achieve an outcome that would not happened otherwise. I think this is something we should all reflect on, think as we consider how to integrate these systems into our world.
And one thing I believe deeply, is that getting AI right is going to require participation from everyone. And that’s for deciding we want it to slot in, that’s for setting the rules of road, for what an AI will and won’t do. And if there’s one thing to take from this talk, it’s that this technology just looks different. Just from anything people had anticipated. And so we all have to become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, I that we can 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 suspect that a large number of people viewing this, you look at that and you think, “Oh my goodness, much every single thing about the way I work, need to rethink.” Like, there’s just new possibilities there. Am I right? 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 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. Why it you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the algorithmic progress, the data progress, all of those really industry-wide. But I think within OpenAI, we made a lot of very choices from the early days. And the first one was just confront reality as it lays. And that we just thought really 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 that the most thing has been to get teams of people who 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 also just the fact that you saw something in these language models that meant that if you continue invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, I mean, honestly, I think the story there is illustrative, right? I think that high level, deep learning, like we always knew that what we wanted to be, was a deep learning lab, and exactly how to do it? think that in the early days, we didn’t know. We tried a lot of things, and person was working on training a model to predict the character in Amazon reviews, and he got a result — this is a syntactic process, you expect, you know, model will predict where the commas go, where the and verbs are. But he actually got a state-of-the-art analysis classifier out of it. This model could tell if a review was positive or negative. I mean, today we just like, come on, anyone can do that. But this was the first that you saw this emergence, this sort of semantics that emerged from this syntactic process. And there we knew, you’ve got to scale this thing, you’ve got see where it goes.
CA: So I think this helps explain the riddle that baffles everyone looking this, because these things are described as prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could come from prediction machine. Just the stuff you showed us just now. And the key idea of is that when you get more of a thing, suddenly different things emerge. happens all the time, ant colonies, single ants run around, you bring enough of them together, you get these colonies that show completely emergent, different behavior. Or a city where few houses together, it’s just houses together. But as you the number of houses, things 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, you can try this in ChatGPT, if you add 40-digit —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s learned an internal circuit for how to do it. And the really thing is actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. And so you can see that it’s really learning process, but it hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s atoms than there are in the universe. So it had to have learned general, but that it hasn’t really fully yet learned that, Oh, I can sort of generalize to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to scale and look at an incredible number of pieces of text. it is learning things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is predicting some these emergent capabilities. And to do that actually, one of the I think is very undersung in this field is sort of engineering quality. Like, we to rebuild our entire stack. When you think about building a rocket, every tolerance has to be tiny. Same is true in machine learning. You have get every single piece of the stack engineered properly, and then you can doing these predictions. There are all these incredibly smooth scaling curves. They tell you something deeply fundamental intelligence. If you look at our GPT-4 blog post, can see all of these curves in there. And now we’re starting be able to predict. So we were able to predict, for example, performance on coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s about this that is actually smooth scaling, even though it’s still 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 predict in some level confidence, but it’s capable of surprising you. Why isn’t there just a huge risk of something terrible emerging?
GB: Well, I think all of these are questions of degree and scale and timing. I think one thing people miss, too, is sort of integration with the world 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. And so I that what we kind of see right now, if you look at this talk, a of what I focus on is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to look at math problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, how you know if this book summary is any good? You have to read the whole book. No one to do that.
(Laughter) And so I think that the important thing be that we take this step by step. And that say, OK, as we move on to book summaries, we to supervise this task properly. We have to build up a record with these machines that they’re able to actually carry out our intent. I think we’re going to have to produce even better, more efficient, more ways of scaling this, sort of like making the machine aligned with 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 to know that it’s not generating errors, that it doesn’t have sense and so forth. Is it your belief, Greg, that is true at any one moment, but that the expansion of the and the human 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 OpenAI, I mean, the short answer is yes, I believe that is where we’re headed. And I that the OpenAI approach here has always been just like, let reality you in the face, right? It’s like this field is the of broken promises, of all these experts saying X is to happen, Y is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t been yet. They might be right maybe 70 years plus one something like that is what you need. But I think that our approach has always been, you’ve got push to the limits of this technology to really it in action, because that tells you then, oh, here’s how can move on to a new paradigm. And we haven’t exhausted the fruit here.
CA: I mean, it’s quite a stance you’ve taken, that the right way to do is to put it out there in public and then harness all this, know, instead of just your team giving feedback, the is now giving feedback. But … If, you know, bad things are going to emerge, is out there. So, you know, the original story I heard on OpenAI when you were founded as a nonprofit, well you were there the great sort of check on the big companies their unknown, possibly evil thing with AI. And you were to build models that sort of, you know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that Google and Meta and so forth are all scrambling catch up. And some of their criticisms have been, you are forcing us to put out here without proper guardrails or we die. You know, how you, like, make the case that what you have done is here and not reckless.
GB: Yeah, we think about questions all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But one thing I think has been incredibly important, the very beginning, when we were thinking about how to build artificial general intelligence, have it benefit all of humanity, like, how are you to do that, right? And that default plan of being, well, you build in secret, you get this powerful thing, and then you figure out the safety of it and then 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 this alternative approach is the only other path 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 have the ability to them in action. And we’ve seen it from GPT-3, right? GPT-3, we really were afraid that the number one people were going to do with it was generate misinformation, try tip elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam bad, but there are things that are much worse. Here’s a experiment for you. Suppose you’re sitting in a room, there’s a box on table. You believe that in that box is something that, there’s a 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 world. Do you open that box?
GB: Well, so, 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 after we started OpenAI, I remember I was in Rico for an AI conference. I’m sitting in the hotel room looking out over this wonderful water, all these people having good time. And you think about it for a moment, if you could choose for basically that Pandora’s 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 better to have it 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 course you do the 500 years. My brother in the military at the time and like, he puts his life the line in a much more real way than any us typing things in computers and developing this technology at the time. so, yeah, I’m really sold on the you’ve got to this right. But I don’t think that’s quite playing field as it truly lies. Like, if you look at the whole 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 sort 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 them together, you get an overhang, which that if someone does, or the moment that someone does manage to connect to the circuit, you suddenly have this very powerful thing, no one’s any time to adjust, who knows what kind of precautions you get. And so I think that one thing I away is like, even you think about development of other sort of technologies, think nuclear weapons, people talk about being like a zero to one, sort of, in what humans could do. But I actually think if you look at capability, it’s been quite smooth over time. And so the history, I think, every technology we’ve developed has been, you’ve got to do it incrementally and you’ve got figure out how to manage it for each moment you’re increasing it.
CA: So what I’m hearing is that you … model you want us to have is that we have birthed this extraordinary child that may superpowers that take humanity to a whole new place. It is collective responsibility to provide the guardrails for this child to collectively teach it to be wise not to tear us all down. Is that basically model?
GB: I think it’s true. And I think it’s also important to say may shift, right? We’ve got to take each step as we it. And I think it’s incredibly important today that all do get literate in this technology, figure out how to provide the feedback, decide what we from it. And my hope is that that will continue to be the best path, but it’s so we’re honestly having this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)