We started OpenAI seven years ago because we felt something really interesting was happening in AI and we wanted to help it in a positive direction. It’s honestly just really to see how far this whole field has come since then. And it’s gratifying to hear from people like Raymond who are the 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 feel. Above all, it feels like we’re entering an historic period right now where we a world are going to define a technology that will be so for our society going forward. And I believe that we can manage this good.
So today, I want to show you the state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m to show you is what it’s like to build a tool for an AI rather than building 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 do things like ask, you know, suggest a post-TED meal and draw a picture of it.
(Laughter)
Now you get of the, sort of, ideation and creative back-and-forth and taking care of the details you that you get out of ChatGPT. And here we go, it’s not just the 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, it also generates an image. And that something that really expands the power of what it can do on your behalf terms of carrying out your intent. And I’ll point out, this is a live demo. This 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 just looking at it.
Now we’ve extended ChatGPT with other too, for example, memory. You can say “save this 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 coming to you, all ChatGPT users, over upcoming months. you can look under the hood and see that what it actually did was a prompt just like a human could. And so you sort of this ability to inspect how the machine is using these tools, which allows us to provide feedback them.
Now it’s saved for later, and let me you what it’s like to use that information and integrate with other applications too. You can say, “Now make a shopping list the tasty thing I was suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, meal, I definitely want to know how it tastes.
But can see that ChatGPT is selecting all these different without me having to tell it explicitly which ones use in any situation. And this, I think, shows a new way thinking about the user interface. Like, we are so used to thinking of, well, we 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 know the menus know all the options. Yes, I would like you to. Yes, please. Always to be polite.
(Laughter)
And by having this unified language on top of tools, the AI is able to of take away all those details from you. So you don’t have to be the who spells out every single sort of little piece what’s supposed to happen.
And as I said, this a live demo, so sometimes the unexpected will happen to us. let’s take a look at the Instacart shopping list while we’re it. And you can see we sent a list of ingredients to Instacart. Here’s you need. And the thing that’s really interesting is that 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 something I think shows that they’re not going away, traditional UIs. It’s just we have 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 click “run,” and we are, we’re the manager, we’re able to inspect, we’re able to change the work of the if we want to. And so after this talk, you will be able to access 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 just about building these tools. It’s about teaching the AI to use them. Like, what do we even want it to when we ask these very high-level questions? And to do this, use an old idea. If you go back to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, you can learn it. could build a machine, like a human child, and then teach through feedback. Have a human teacher who provides rewards and punishments as tries things out and does things that are either or bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called a child machine through unsupervised learning process. We just show it the whole world, the whole internet and say, “Predict what comes next text you’ve never seen before.” And this process imbues it all sorts of wonderful skills. For example, if you’re shown a math problem, the only way actually complete that math problem, to say what comes next, that green nine up there, is to actually the math problem.
But we actually have to do second step, too, which is to 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 them, says “This one’s better than that one.” And this reinforces just the specific thing that the AI said, but very importantly, the whole process that the 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 that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have to teach the AI not what you’d expect. For example, when we first GPT-4 to Khan Academy, they said, “Wow, this is great, We’re going to be able to teach students wonderful things. Only problem, it doesn’t double-check students’ math. If there’s some bad in there, it will happily pretend that one plus equals three and run with it.” So we had to collect some feedback data. Sal Khan himself was kind and offered 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 back humans in this specific kind of scenario.” And we’ve actually made and lots of improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind like sending up a bat signal to our team say, “Here’s an area of weakness where you should feedback.” And so when you do that, that’s one that we really listen to our users and make we’re building something that’s more useful for everyone.
Now, high-quality feedback is a hard thing. If 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 teaching them to stuff all the toys in the closet. This a nice DALL-E-generated image, by the way. And the same sort of reasoning applies AI. As we move to harder tasks, we will have scale our ability to provide high-quality feedback. But for this, the itself is happy to help. It’s happy to help us provide better feedback and to scale our ability to supervise the machine 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 unsupervised learning learning from human feedback. And the model says two months passed. But is it true? Like, 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. And it 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. This one is a tool where the model can issue search queries and click web pages. And it actually writes out its whole chain thought as it does it. It says, I’m just going to search for this and it actually does search. It then it finds the publication date and the search results. It is issuing another search query. It’s going to click into blog post. And all of this you could do, but it’s very tedious task. It’s not a thing that humans really to do. It’s much more fun to be in driver’s seat, to be in this manager’s position where you can, if want, triple-check the work. And out come citations so you actually go and very easily verify any piece of whole chain of reasoning. And it actually turns out two months wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting me about this whole process is that it’s this many-step between a human and an AI. Because a human, this fact-checking tool is doing it in order to produce data for another AI to become more to a human. And I think this really shows the shape something that we should expect to be much more in the future, where we have humans and machines kind of very and delicately designed in how they fit into a problem and how we want solve that problem. We make sure that the humans are providing the management, the oversight, the feedback, the machines are operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. And I think that over time, if get this process right, we will be able to solve impossible problems.
And to give you a of just how impossible I’m talking, I think we’re to be able to rethink almost every aspect of we interact with computers. For example, think about spreadsheets. They’ve been in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much in that time. And here is a specific spreadsheet all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you can see the data right here. But let me show you the ChatGPT take on to analyze a data set like this.
So we can ChatGPT access to yet another tool, this one a Python interpreter, so it’s able to run code, like a data scientist would. And so you can just literally upload a file and questions about it. And very helpfully, you know, it knows the of the file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information here is name of the file, the column names like you saw and then the actual data. from that it’s able to infer what these columns actually mean. Like, semantic information wasn’t in there. It has to sort of, put its world knowledge of knowing that, “Oh yeah, arXiv a site that people submit papers and therefore that’s these things are and that these are integer values so therefore it’s a number of authors in the paper,” like all of that, that’s work for 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 make some exploratory graphs?” And once again, this is a super high-level instruction 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 per paper, series of papers per year, word cloud of the paper titles. All of that, think, will be pretty interesting to see. And the great thing is, can actually 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 the papers per year. Something crazy is happening in 2023, though. Looks like we on an exponential and it dropped off the cliff. 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 see all wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year really bad. Of course, the problem is that the year is not over. I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you use that make a fair projection? So we’ll see, this is the kind ambitious one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted it to notice this thing, maybe it’s a 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 additional piece of, know, guidance. And under the hood, the AI is just writing code again, so 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 title. I didn’t ask for that, but it know I 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 technology in the future. A person brought his very sick dog to the vet, the veterinarian made a bad call to say, “Let’s just wait and see.” And dog would not be here today had he listened. In the meanwhile, he provided the test, like, the full medical records, to GPT-4, which said, “I am not a vet, you to talk to a 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 perfect. You cannot overly rely on them. But this story, think, shows that a human with a medical professional with ChatGPT as a brainstorming partner was able to achieve an outcome would not have happened otherwise. I think this is something we all reflect on, think about as we consider how to integrate these systems our world.
And one thing I believe really deeply, is that getting right is going to require participation from everyone. And that’s for deciding how we want it to in, that’s for setting the rules of the road, for what AI will and won’t do. And if there’s one thing to take away this talk, it’s that this technology just looks different. Just different from anything had anticipated. And so we all have to become literate. And that’s, honestly, one the reasons we released ChatGPT.
Together, I believe that we can the OpenAI mission of ensuring that artificial general intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out there’s a feeling of reeling. Like, I suspect that a very large number of people viewing this, look at that and you think, “Oh my goodness, much every single thing about the way I work, I need to rethink.” Like, there’s just possibilities there. Am I right? Who thinks that they’re having to the way 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 first question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google thousands of employees working on artificial intelligence. Why is you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. you look at the compute progress, the algorithmic progress, the data progress, all of are really industry-wide. But I think within OpenAI, we a lot of very deliberate choices from the early days. And the first one was just to confront reality it lays. And that we just thought really hard about like: is it going to take to make progress here? We a lot of things that didn’t work, so you only see the things that did. And think that the most important thing has been to get teams of people who are very different from other to work together harmoniously.
CA: Can we have 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 about the fact that you saw something in these language models meant that if you continue to invest in them grow them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, like always knew that was what we wanted to be, was deep learning lab, and exactly how to do it? think that in the early days, we didn’t know. tried a lot of things, and one person was working on a model to predict the next character in Amazon reviews, and he a result where — this is a syntactic process, expect, you know, the 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 you if a review was positive negative. I mean, today we are just like, come on, anyone can do that. But was the first time 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 to where it goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things are 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 idea of emergence is that when you get more of 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 ant that show completely emergent, different behavior. Or a city where a houses together, it’s just houses together. But as you grow 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 just blew your mind that you just did 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 do it. And the 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 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 number of of text. And it is learning things that you didn’t that 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 capabilities. And to do that actually, one of the things think is very undersung in this field is sort of quality. Like, we had to rebuild our entire stack. When 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, then you can start doing these predictions. There are these incredibly smooth scaling curves. They tell you something deeply 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 to predict. So were able to predict, for example, the performance on coding problems. basically look at some models that are 10,000 times 1,000 times smaller. And so there’s something about this that actually 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, as you scale up, things emerge that you can maybe predict in level of confidence, but it’s capable of surprising you. Why isn’t just a huge risk of something truly terrible emerging?
GB: Well, I think all of these questions of degree and scale and timing. And I think one thing miss, too, is sort of the integration with the is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that we it’s so important to deploy incrementally. And so I think that what we kind of right now, if you look at this talk, a of what I focus on is providing really high-quality feedback. Today, the tasks that we do, you 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 summarizing a book, like, that’s a hard thing to supervise. Like, how do you know if this summary is any good? You have to read the whole book. one wants to do that.
(Laughter) And so I think that the important will be that we take this step by step. And that we say, OK, as we move to book summaries, we have to supervise this task properly. We have to build up a track with these machines that they’re able to actually carry out our intent. And I think we’re going have to produce even better, more efficient, more reliable of scaling this, sort of like making the machine aligned with you.
CA: So we’re going to hear later in session, there are critics who say that, you know, there’s no understanding inside, the system is going to always — we’re never going to know that it’s generating errors, that it doesn’t have common sense and forth. Is it your belief, Greg, that it is true any 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, a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the answer is yes, I believe that is where we’re headed. I think that the OpenAI approach here has always been like, let reality hit you in the face, right? It’s like field is the field of broken promises, of all these saying X is going to happen, Y is how it works. People have saying neural nets aren’t going to work for 70 years. They haven’t been yet. They might be right maybe 70 years plus one or something like that what you need. But I think that our approach always been, you’ve got to push to the limits of this to really see it in action, because that tells then, oh, here’s how we can move on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the right to do this is to put it out there in and then harness all this, you know, instead of your team giving feedback, the world is now giving feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, the original story that I on OpenAI when you were founded as a nonprofit, well you were there as great sort of check on the big companies doing their unknown, possibly evil thing AI. And you were going to build models that sort of, know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s kind of 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 scrambling to catch up. And some of their criticisms have been, you are forcing us to put this here without proper guardrails or we die. You know, how do you, like, make the that what you have done is responsible here and reckless.
GB: Yeah, we think about these questions all the time. Like, seriously all time. And I don’t think we’re always going to get it right. But one thing think has been incredibly important, from the very beginning, we were thinking about how to build artificial general intelligence, actually have it benefit all humanity, like, how are you supposed to do that, right? And that default plan being, well, you build in secret, you get this super powerful thing, and you figure out the safety of it and then you push “go,” and you you got it right. I don’t know how to execute that plan. Maybe someone else does. But me, that was always terrifying, it didn’t feel right. And so I think this alternative approach is the only other path that I see, which that you do let reality hit 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 have the ability to see them in action. And we’ve it from GPT-3, right? GPT-3, we really were afraid that the one thing people were going to do with it was generate misinformation, to tip elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: Viagra spam is bad, but there are things that are worse. Here’s a thought experiment for you. Suppose you’re sitting in a room, there’s box on the table. You believe that in that box 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. But there’s actually also one percent thing in the small print there that says: “Pandora.” And there’s a chance that this actually could unimaginable evils on the world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. honestly, like, I’ll tell you a story that I haven’t actually before, which is that shortly 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 could choose basically that Pandora’s box to be five years away or 500 years away, which you pick, right? On the one hand you’re like, well, for you personally, it’s better to have it be years away. But if it gets to be 500 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 you do the 500 years. brother was in the military at the time and like, he puts his life the line in a much more real way than of us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, if you look at the whole history computing, I really mean it when I say that this is an industry-wide even just almost like a human-development- of-technology-wide shift. And more that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re improving the algorithms, all of these things, they are happening. And if you don’t put them together, get an overhang, which means 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 to adjust, knows what kind of safety precautions you get. And I think that one thing I take away is like, 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 that 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 to figure out to manage it for each moment that you’re increasing it.
CA: So what I’m is that you … the model you want us to have is that we birthed this extraordinary child that may have superpowers that take to a whole new place. It is our collective responsibility to provide the guardrails this child to collectively teach it to be wise and not to tear us all down. that basically 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 it’s incredibly important today that we all do get in this technology, figure out how to provide the feedback, what we want from it. And my hope is that will continue to be the best path, but it’s so good we’re having this debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much coming to TED and blowing our minds.
(Applause)