We OpenAI seven years ago because we felt like something really interesting happening in AI and we wanted to help steer 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 the technology are building, and others, for so many wonderful things. We hear from people who excited, we hear from people who are concerned, we hear from who feel both those emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period right now where we as world are going to define a technology that will so important for our society going forward. And I believe 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 to you is what it’s like to build a tool for an rather than building it for a human. So we have new DALL-E model, which generates images, and we are exposing it as an app ChatGPT to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal draw a picture of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of the details for you that you get out ChatGPT. And here we go, it’s not just the idea 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, it generates an image. And that is something that really the power of what it can do on your behalf in of carrying out your intent. And I’ll point out, this is all a live demo. is all generated by the AI as we speak. I actually don’t even know what 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, for example, memory. You can “save this for later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here that “use the DALL-E app.” And by the way, this is 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 of have this to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s for later, and let me show you what it’s like to use that information to integrate with other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So if you do make wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting these different tools without me having to tell it which ones to use in any situation. And this, I think, shows a new of thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a experience within an app as long as you kind know the menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And by this unified language interface on top of tools, the AI able to sort of take away all those details you. So you don’t have to be the one spells out every single sort of little piece of what’s supposed happen.
And as I said, this is a live demo, sometimes the unexpected will happen to us. But let’s take look at the Instacart shopping list while we’re at it. And you can see we a list of ingredients to Instacart. Here’s everything you need. And the thing that’s really interesting is the traditional UI is still very valuable, right? If look at this, you still can click through it and of modify the actual quantities. And that’s something that think shows that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And now we have a tweet that’s drafted for our review, which is also a very important thing. We can “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 able to access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important 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 we even want it do when we ask these very high-level questions? And to this, we use an old idea. If you go to Alan Turing’s 1950 paper on the Turing test, he says, you’ll program an answer to this. Instead, you can learn it. could build a machine, like a human child, and then teach it feedback. Have a human teacher who provides rewards and punishments it tries things out and does things that are either or bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, we produce what Turing would called a child machine through an unsupervised learning process. We just show it whole world, the whole internet and say, “Predict what next in text you’ve never seen before.” And this 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 nine up there, is to actually solve the problem.
But we actually have to do a second step, too, which is to teach AI what to do with those skills. And for this, we provide feedback. have the AI try out multiple things, give us multiple suggestions, and then a human them, says “This one’s better than that one.” And this reinforces not just the specific that the AI said, but very importantly, the whole that the AI used to produce that answer. And this allows it to generalize. It allows it teach, to sort of infer your intent and apply it in scenarios it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes things we have to teach the AI are not you’d expect. For example, when we first showed GPT-4 to Academy, they said, “Wow, this is so great, We’re to be 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 plus one equals three and run with it.” So had to collect some 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 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 the models this way. when you push that thumbs down in ChatGPT, that actually kind of like sending up a bat signal to our team to say, “Here’s an area weakness where you should gather feedback.” And so when you do that, that’s one that we really listen to our users and make sure we’re building something that’s useful for everyone.
Now, providing high-quality feedback is a hard thing. you think about asking a kid to clean their room, if all you’re doing is inspecting floor, you don’t know if you’re just teaching them stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same sort reasoning applies to AI. As we move to harder tasks, will have to scale our ability to provide high-quality feedback. But 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 show what I mean.
For example, you can ask GPT-4 a question like this, of much time passed between these two foundational blogs on unsupervised learning and learning human feedback. And the model says two months passed. But it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. But we actually use the AI to fact-check. And it can actually its own work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This one is a browsing tool the model can issue search queries and click into pages. And it actually writes out its whole chain thought as it does it. It says, I’m just going to search for and it actually does the search. It then it finds publication date and the search results. It then is another search query. It’s going to click into the post. And all of this you could do, but it’s very tedious task. It’s not a thing that humans want to do. It’s much more fun to be in the driver’s seat, to in this manager’s position where you can, if you want, triple-check work. And out come citations so you can actually go and very easily verify any piece of whole chain of reasoning. And it actually turns out months was wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut back 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, using this fact-checking tool doing it 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 be much more common in the future, where we have humans and kind of very carefully and delicately designed in how they fit 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 a way that’s inspectable trustworthy. And together we’re able to actually create even more trustworthy machines. And I think that time, if we get this process right, we will be able to solve impossible problems.
And to you a sense of just how impossible I’m talking, think we’re going to be able to rethink almost every aspect how we 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 think they’ve changed that much in that time. And here is specific spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. And you can see there the data right here. let me show you the ChatGPT take on how to analyze a set like this.
So we can give ChatGPT access to another tool, this one a Python interpreter, so it’s to run code, just like a data scientist would. And so you can just upload a file and ask questions about it. And helpfully, you know, it knows the name of the 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 the actual data. And from that it’s able to infer what columns actually mean. Like, that semantic information wasn’t in there. It has to sort of, put together world knowledge of knowing that, “Oh yeah, arXiv is site that people submit papers and therefore that’s what these things are and that are integer values and so therefore it’s a number of in the paper,” like all of that, that’s work a human to do, and the AI is happy to help with it.
Now don’t even know what I want to ask. So fortunately, you can ask the machine, “Can you make exploratory graphs?” And once again, this is a super high-level instruction with lots of intent it. But I don’t even know what I want. the AI kind of has to infer what I might interested in. And so it comes up with some ideas, I think. So a histogram of the number of authors per paper, time of papers per year, word cloud of the paper titles. All of that, I think, be pretty interesting to see. And the great thing is, it can actually do it. Here we go, a bell curve. You see that three is kind of most common. It’s going to then make this nice plot of papers per 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 is 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 pretty unhappy about 2023 thing. It makes this year look really bad. Of course, the problem that the year is not over. So I’m going to push back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of in 2022 were even posted by April 13?] So April 13 the cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I like there was 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 it have sort of, inferred magically that this is what I wanted. But I inject my intent, I provide additional piece of, you know, guidance. And under the hood, the AI is just writing code again, so if want to inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it updates the title. I didn’t ask for that, but it what I want.
Now we’ll cut back to the slide again. This slide shows a parable of I think we … A vision of how we end up using this technology 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 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 a professional, here are some hypotheses.” He that information to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You cannot overly rely on them. this story, I think, shows that a human with a professional and with ChatGPT as a brainstorming partner was able to achieve an outcome that would have happened otherwise. I think this is something we should all on, think about as we consider how to integrate these systems into world.
And one thing I believe really deeply, is that getting AI 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 the road, for what an 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 different from anything people anticipated. And so we all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I … I suspect that within every mind out here there’s a feeling reeling. Like, I suspect that a very large number of viewing this, you look at that and you think, “Oh my goodness, pretty much every single thing about way I work, I need to rethink.” Like, there’s just new possibilities there. Am right? Who thinks that they’re having to rethink the way that do 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 question actually just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working artificial intelligence. Why 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. you look at the compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think within OpenAI, we a lot of very deliberate choices from the early days. And the one was just to confront reality as it lays. And that we just thought really hard like: What is it going to take to make progress here? tried a lot of things that didn’t work, so you only 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 the water, by the way, just brought here? I we’re going to need it, it’s a dry-mouth topic. isn’t there something also just about the fact that you saw something these language models that meant that if you continue invest in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, think the story there is pretty illustrative, right? I think high level, deep learning, like we always knew that was what we wanted be, was a deep learning lab, and exactly how to do it? I think that the early days, we didn’t know. We tried a of things, and one person was working on training a model predict the next character in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model predict where the commas go, where the nouns and verbs are. But he got a state-of-the-art sentiment analysis classifier out of it. model could tell you if a review was positive 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 this underlying syntactic process. 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 looking at this, because these things are described as prediction machines. yet, what we’re seeing out of them feels … it feels impossible that that could come from a prediction machine. the stuff you showed us just now. And the key idea of emergence is when you get more of a thing, suddenly different things emerge. It happens all the time, colonies, single ants run around, when you bring enough of them together, get these ant colonies that show completely emergent, different behavior. 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 when you saw just something pop that just blew your mind that just did not see coming.
GB: Yeah, well, so you try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the will do it, which means it’s really learned an internal for how to do it. And the really interesting thing is actually, you have it add like a 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see 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. So it had 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: what’s happened here is that you’ve allowed it to scale up and look at an number of pieces of text. And it is learning things you didn’t know that it was going to be of learning.
GB Well, yeah, and it’s more nuanced, too. So science that we’re starting to really get good at is predicting of these emergent capabilities. And to do that actually, one the things I think is very undersung in this field is sort engineering quality. Like, we had to rebuild our entire stack. When you think about building a rocket, every tolerance to be incredibly 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. They you something deeply fundamental about intelligence. If you 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. we were able to predict, for example, the performance coding problems. We basically look at some models that are 10,000 times 1,000 times smaller. And so there’s something about this is actually smooth scaling, even though it’s still early days.
CA: So is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that as you scale up, things emerge that can maybe predict in some level of confidence, but it’s capable of surprising you. isn’t there just a huge risk of something truly emerging?
GB: Well, I think all of these are questions of degree and scale and timing. And think one thing people miss, too, is sort of the integration with the world is this incredibly emergent, sort of, very powerful thing too. And so that’s one of the reasons that we it’s so important to deploy incrementally. And so I that what we kind of see right now, if look at this talk, a lot of what I focus on providing really high-quality feedback. Today, the tasks that we do, you can inspect them, right? It’s very to look at that math problem and be like, no, no, no, machine, seven was the correct answer. even summarizing a book, like, that’s a hard thing to supervise. Like, how do you if this book summary is any good? You have to read the whole book. one wants to do that.
(Laughter) And so I think that the important thing be that we take this step by step. And that we say, OK, as we move on to summaries, we have to supervise this task 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 going to have to 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, know, there’s no real understanding inside, the 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 any one moment, but the expansion of the scale and the human feedback that you talked is 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 that the OpenAI, I mean, short answer is yes, I believe that is where we’re headed. And I think that OpenAI approach here has always been just like, let reality hit you the face, right? It’s like this field is the field of broken promises, all these experts saying X is going to happen, Y is how works. People 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 one or like that is what you need. But I think that our approach has always been, you’ve to push to the limits of this technology to really see it in action, because tells you then, oh, here’s how we can move on to a new paradigm. And just haven’t exhausted the fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do this is to put it out there in public then harness all this, you know, instead of just your team feedback, the world is now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, you know, the original story that I heard on when you were founded as a nonprofit, well you were there as the great of check on the big companies doing their unknown, possibly thing with AI. And you were going to build models that sort of, you know, held them accountable and was capable of slowing the down, if need be. Or at least that’s kind of I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves through the tech world that now and Meta and so forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to this out here without proper guardrails or we die. know, how do you, like, make the case that what you have is responsible here and not reckless.
GB: Yeah, we about these questions all the time. Like, seriously all the time. And I don’t think we’re always to get it right. But one thing I think has been incredibly important, from the beginning, when we were thinking about how to build artificial general intelligence, have it 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 powerful thing, and then you figure out the safety of it and you push “go,” and you hope you got it right. I don’t know to execute that plan. Maybe someone else does. But me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the only path that I see, which is that you do let reality hit you the face. And I think you do give people time to give input. You do have, before machines are perfect, before they are super powerful, that you actually have the ability see them in action. And we’ve seen it from GPT-3, right? GPT-3, we really were afraid the number one thing people were going to do it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: 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 a box on table. You believe that in that box is something that, there’s a very strong chance it’s something absolutely glorious that’s to give beautiful gifts to your family and to everyone. there’s actually also a one percent thing in the small there that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils on the world. you open that box?
GB: Well, so, absolutely not. 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 shortly we started OpenAI, I remember I was in Puerto Rico for an AI conference. I’m sitting in the room just looking out over 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 or 500 years away, which would you pick, right? On the 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 away and people get more time to get it right, which do you pick? And know, I just really felt it in the moment. I was like, of course you do 500 years. My brother was in the military at the time like, he puts his life on the line in a more real way than any of us typing things in 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 think that’s quite playing the field it truly lies. Like, if you look at the whole of computing, I really mean it when I say that this is an industry-wide or even almost like a human-development- of-technology-wide shift. And 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 the algorithms, of these 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 does to connect to the circuit, then you suddenly have very powerful thing, no one’s had any time to adjust, knows what kind of safety precautions you get. And I think that one thing I take away is like, even you think about development other sort of technologies, think about nuclear weapons, people talk about being like zero to one, sort of, change in what humans could do. But actually think that if you look at capability, it’s been quite over time. And so the history, I think, of every we’ve developed has been, you’ve got to do it incrementally and you’ve got to out 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 our collective to provide the guardrails for this child to collectively it to be wise and not to tear us all down. Is that basically the model?
GB: 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 the feedback, decide what we want from 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: Greg Brockman, thank you so for coming to TED and blowing our minds.
(Applause)