We OpenAI seven years ago because we felt like something really interesting was happening in AI and we 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 gratifying to hear from people like Raymond who are using the technology are building, and others, for so many wonderful things. We from people who are excited, we hear from people are concerned, we hear from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels like we’re entering an period right now where we as a world are going define a technology that will be so important for our society going forward. And believe that we can manage this for good.
So today, want to show you the current state of that and some of the underlying design principles that we dear.
So the first thing I’m going to show is what it’s like to build a tool for an AI rather than it for a human. So we have a new DALL-E model, generates images, and we are exposing it as an for ChatGPT to use on your behalf. And you can things like ask, you know, suggest a nice post-TED meal and draw picture 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 go, it’s not just the idea for the meal, but a very, very spread. So let’s see what we’re going to get. But ChatGPT doesn’t just images in this case — sorry, it doesn’t generate text, it also generates an image. And that something that really expands the power of what it can on your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. is all generated by the AI as we speak. So actually don’t even know what we’re going to see. This wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. can say “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this pop up here that says “use the DALL-E app.” by the way, this is coming to you, all ChatGPT users, upcoming months. And you can look under the hood and see that it actually did was write a prompt just like a human could. so you sort of have this ability to inspect how the machine is these tools, which allows us to provide feedback to them.
Now it’s saved for later, and let show you what it’s like to use that information and to integrate with other too. You can say, “Now make a shopping list the tasty thing I was suggesting earlier.” And make it a little tricky for the AI. “And it out for all the TED viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I want to know how it tastes.
But you can see that ChatGPT is selecting all different tools without me having to tell it explicitly which ones to use in situation. And this, I think, shows a new way of about the user interface. Like, we are so used thinking of, well, we have these apps, we click between them, we copy/paste between them, and usually it’s great experience within an app as long as you kind of know the menus and all the options. Yes, I would like you to. Yes, please. Always good to be polite.
(Laughter)
And by this unified 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 the one who spells out every single sort of little of what’s supposed to happen.
And as I said, is a live demo, so sometimes the unexpected will to us. But let’s take a look at the Instacart list while we’re at it. And you can see we a list of ingredients to Instacart. Here’s everything you need. And thing that’s really interesting is that the traditional UI still very 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 that they’re not going away, traditional UIs. It’s just we have a new, way to build them. And now we have a that’s been drafted for our review, which is also very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able change the work of the AI if we want to. so after 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, the thing about 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 these very high-level questions? And do this, we use an old idea. If you 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 could a machine, like a human child, and then teach it through feedback. Have human teacher who provides rewards and punishments as it tries out and does things that are either good or bad.
And this exactly how we train ChatGPT. It’s a two-step process. First, we what Turing would have called a child machine through unsupervised learning process. We just show it the whole world, the whole and say, “Predict what comes next in text you’ve seen 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 nine 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, provide feedback. We have the AI try out multiple things, give us suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the AI used to produce that answer. And allows it to generalize. It allows it to teach, to sort of infer your intent apply 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 we showed GPT-4 to Khan Academy, they said, “Wow, this is 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 there, it will happily pretend that one plus one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was very kind and 20 hours of his own time to provide feedback to the machine our team. And over the course of a couple months we were able to teach the AI that, “Hey, you really should push on humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to the models this way. when you push that thumbs down in ChatGPT, that is kind of like sending up a bat signal to our to say, “Here’s an area of weakness where you gather feedback.” And so when you do that, that’s one way that we listen to our users and make sure we’re building something that’s more useful for everyone.
Now, providing high-quality is a hard thing. If you think about asking kid to clean their room, if all you’re doing is the floor, you don’t know if you’re just teaching to stuff all the toys in the closet. This is a nice DALL-E-generated image, the way. And the same sort of reasoning applies AI. As we move to 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 happy to help us even better feedback and to scale 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 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 every time we provide some feedback. But we can use the AI to fact-check. And it can actually its own work. You can say, fact-check this for me.
Now, in this case, I’ve actually given AI a new tool. This one is a browsing where the model can issue search queries and click into pages. And it actually writes out its whole chain of thought it does it. It says, I’m just going to search for and it actually does the search. It then it finds the publication date and search results. It then is issuing another search query. It’s going to click the blog post. And all of this you could do, but it’s a very tedious task. It’s not a that humans really want to 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 verify any piece this whole chain of reasoning. And it actually turns out two was wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut back to the side. And so thing that’s 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 in order to produce data for another AI to more useful to a human. And I think this shows the shape of something that we should expect to much more common in the future, where we have humans and machines kind very carefully and delicately designed in how they fit a problem and how we want to solve that problem. We make sure that the are providing the management, the oversight, the feedback, and the are operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. I think that over time, if we get this 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 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 is a specific spreadsheet of all the AI papers on the for the past 30 years. There’s about 167,000 of them. And can see there the data right here. But let me you the ChatGPT take on how to analyze a data set like this.
So we give ChatGPT access to yet another tool, this one a interpreter, so it’s able to run code, just like a scientist would. And so you can just literally upload file and ask questions about it. And very helpfully, know, it knows the name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” The only here is the name of the file, the column names you saw and then the actual data. And from that it’s able infer what these columns actually mean. Like, that semantic wasn’t in there. It has to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people submit papers and that’s what these things are and that these are integer values and therefore it’s a number of authors in the paper,” like all of that, that’s work for a to do, and the AI is happy to help with it.
Now I don’t even know I want to ask. So fortunately, you can ask the machine, “Can you make some graphs?” And once again, this is a super high-level instruction with lots intent behind it. But I don’t even know what I want. And AI kind of has to infer what I might be interested in. And so comes up with some good ideas, I think. So histogram of the number of authors per paper, time series of papers per year, word cloud the paper titles. All of that, I think, will be interesting to see. And the great thing is, it actually do it. Here we go, a nice bell curve. see that three is kind of the most common. It’s going to then make this plot of the papers per year. Something crazy is in 2023, though. Looks 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 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 is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage papers in 2022 were even posted by April 13?] April 13 was the cut-off date I believe. Can you use that to make fair projection? So we’ll see, this is the kind ambitious one.
(Laughter)
So you know, again, I feel there was more I wanted out of the machine here. I really wanted it to this thing, maybe it’s a little bit of an for it to have sort of, inferred magically that this what I wanted. But I inject my intent, I this additional piece of, you know, guidance. And under the hood, the is just writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does correct projection.
(Applause)
If you noticed, it even updates 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 of how I think we … A vision of how we may end up using this technology the future. A person brought his very sick dog to the vet, the veterinarian made a bad call to say, “Let’s just and see.” And the dog would not be 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 to professional, here are some hypotheses.” He brought that information to a second vet used it to save 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 medical professional and with ChatGPT as a brainstorming partner able to achieve an outcome that would not have happened otherwise. think this is something we should all reflect on, think about as we consider to integrate these systems into our world.
And one thing I really deeply, is that getting AI right is going to require participation from everyone. And that’s deciding how we want it to slot in, that’s for setting the rules of the road, for an AI will and won’t do. And if there’s one thing to take away from talk, it’s that this technology just looks different. Just 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 that can achieve the OpenAI mission of ensuring that artificial intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s a of reeling. Like, I suspect that a very large number of people viewing this, you at that and you think, “Oh my goodness, pretty much every single thing about the way work, I need to rethink.” Like, there’s just new possibilities there. Am right? Who thinks that they’re having to rethink the that we do things? Yeah, I mean, it’s amazing, it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually is just how hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands employees working on artificial intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s no question. If you look at 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 from the early days. And the first one was just confront reality as it lays. And that we just really hard about like: What is it going to take to make progress here? tried a lot of things that didn’t work, so you only see the that did. And I think that the most important has been to get teams of people who are different from each other to work together harmoniously.
CA: we have the water, by the way, just brought here? I think we’re to need it, it’s a dry-mouth topic. But isn’t there something also just about fact that you saw something in these language models that that if you continue to invest in them and grow them, that at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, we always knew that was what we wanted to be, was a deep learning lab, and exactly how to it? I think that in the early days, we didn’t know. We tried a lot of things, one person was working on training a model to predict the next character in Amazon reviews, and got a result where — this is a syntactic process, expect, you know, the model will predict where the commas go, the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model could you if a review was positive or negative. I mean, today we are just like, come on, can do that. But this was the first time you saw this emergence, this sort of semantics that emerged from underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve got see where it goes.
CA: So I think this helps the riddle that baffles everyone looking at this, because these things are described as machines. And yet, what we’re seeing out of them … it just feels impossible that that could come from a prediction machine. Just stuff you showed us just now. And the key of emergence is that when you get more of a thing, suddenly things emerge. It happens all the time, ant colonies, ants run around, when you bring enough of them together, you these ant colonies that show completely emergent, different behavior. Or a where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, like suburbs and cultural and traffic jams. Give me one moment for you when saw just something pop that just blew your mind that you just not see coming.
GB: Yeah, well, so you can try in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which it’s really learned an internal circuit for how to it. And the really interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often it wrong. And so you can see that it’s really 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 in universe. So it had to have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize this to arbitrary numbers of arbitrary lengths.
CA: So what’s happened here that you’ve allowed it to scale up and look at an incredible of pieces of text. And it is learning things that you didn’t know that it was to be capable of learning.
GB Well, yeah, and it’s more nuanced, too. one science that we’re starting to really get good at predicting some of these emergent capabilities. And to do that actually, of the things I think is very undersung in field is sort of engineering quality. Like, we had to rebuild 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 you can start doing these predictions. There are all these incredibly smooth scaling curves. tell you something deeply fundamental about intelligence. If you look at our GPT-4 blog post, can see all of these curves in there. And now we’re starting to be able predict. So we were able to predict, for example, the performance coding problems. We basically look at some models that are 10,000 times or 1,000 smaller. And so there’s something about this that is actually smooth scaling, even though it’s still days.
CA: So here is, one of the big fears then, that arises from this. If it’s to what’s happening here, that as you scale up, things that you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just huge risk of something truly terrible emerging?
GB: Well, think all of these are questions of degree and scale timing. And I think one thing people miss, too, sort of the integration with the world is also incredibly emergent, sort of, very powerful thing too. And so that’s one of the reasons we think it’s so important to deploy incrementally. And I think that what we kind of see right now, you look at this talk, a lot of what 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 that math problem be like, no, no, no, machine, seven was the correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if this book is any good? You have to read the whole book. No wants to do that.
(Laughter) And so I think that the important thing will be that we take step by step. And that we say, OK, as we move to book summaries, we have to supervise this task properly. We to build up a track record with these machines they’re able to actually carry out our intent. And I we’re going to have to produce even better, more efficient, more reliable ways of scaling this, sort like making the machine be aligned with you.
CA: So we’re going to hear in 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 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 scale and the human feedback that you talked about basically going to take it on that journey of getting to things like truth and wisdom and so forth, with a high of confidence. Can you be sure of that?
GB: Yeah, well, I think the OpenAI, I mean, the short answer is yes, believe that is where we’re headed. And I think the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like this field is field of broken promises, of all these experts saying X is going to happen, Y is it works. People have been saying neural nets aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years plus one or something like that is what need. But I think that our approach has always been, you’ve to push to the limits of this technology to really it in action, because that tells you then, oh, here’s how we can move to a new paradigm. And we just haven’t exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that the right way to this is to put it out there in public and then all this, you know, instead of just your team giving feedback, the world is giving feedback. But … If, you know, bad things are going to emerge, it out there. So, you know, the original story that I heard on OpenAI you were founded as a nonprofit, well you were there as the great sort check on the big companies doing their unknown, possibly 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, if be. Or at least that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. That release of GPT, especially ChatGPT, sent such shockwaves through the tech that now Google and Meta and so forth are all scrambling to catch up. some of their criticisms have been, you are forcing us to put this out here without proper or we die. You know, how do you, like, make the case that what you have is responsible here and not reckless.
GB: Yeah, we about these questions all the time. Like, seriously all time. And I don’t think we’re always going to get it right. one thing I think has been incredibly important, from very beginning, when we were thinking about how to build artificial intelligence, actually have it benefit all of humanity, like, how are you supposed do that, right? And that default plan of being, well, you build secret, you get this super powerful thing, and then you figure out the safety of and then you push “go,” and you hope you got it right. I don’t know to execute that plan. Maybe someone else does. But for me, was always terrifying, it didn’t feel right. And so think that this alternative approach is the only other that I see, which is that you do let reality hit you in the face. And think you do give people time to give input. do have, before these machines are perfect, before they are super powerful, that you have the ability to see them in action. And we’ve it from GPT-3, right? GPT-3, we really were afraid the number one thing people were going to do it was generate misinformation, try to tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but there are things 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 is something that, there’s a very strong chance it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually also a percent thing in the small print there that says: “Pandora.” And there’s a chance that this could unleash unimaginable evils on the world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. And honestly, like, I’ll tell you a that I haven’t actually told before, which is that after we started OpenAI, I remember I was in Puerto Rico for an conference. I’m sitting in the hotel room just looking out over this wonderful water, all people having a good time. And you think about it for a moment, if you choose for basically that Pandora’s box to be five away or 500 years away, which would you pick, right? On the hand you’re like, well, maybe for you personally, it’s to have it be five years away. But if it gets be 500 years away and people get more time to get it right, do you pick? And you know, I just really it in the moment. I was like, of course do the 500 years. My brother was in the military at the and like, he puts his life on the line a much more real way than any of us typing in computers and developing this technology at the time. And so, yeah, I’m sold on the you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, you look at the whole history of computing, I really mean it when I say that this is industry-wide or even just almost like a human-development- of-technology-wide shift. the more that you sort of, don’t put together the pieces that there, right, we’re still making faster computers, we’re still improving the algorithms, all of things, they are happening. And if you don’t put them together, you get overhang, which means that if someone does, or the moment that someone manage to connect to the circuit, then you suddenly have this very thing, no one’s had any time to adjust, who knows kind of safety precautions you get. And so I think that one I take away is like, even you think about of other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, in what humans could do. But I actually think that if 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 to do it incrementally and you’ve got to figure how to manage it for each moment that you’re 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 have superpowers take humanity to a whole new place. It is collective responsibility to provide the guardrails for this child to collectively teach it to wise and not to tear us all down. Is that the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve to take each step as we encounter it. And I think it’s important today that we all do get literate in this technology, out how to provide the feedback, decide what we want it. And my hope is that that will continue to the best path, but it’s so good we’re honestly having debate because we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED blowing our minds.
(Applause)