We started OpenAI seven years because we felt like something really interesting was happening in AI we wanted to help steer it in a positive direction. It’s honestly just really amazing to how far this whole field has come since then. And it’s really gratifying to from people like Raymond who are using the technology are building, and others, for so many wonderful things. We hear from people who are excited, we from people who are concerned, we hear from people who feel those emotions at once. And honestly, that’s how we feel. Above all, it like we’re entering an historic period right now where we as a world are to define a technology that will be so important for our society going forward. And believe that we can manage this for good.
So today, I to show you the current state of that technology and some of the underlying design principles we hold dear.
So the first thing I’m going to show you is what it’s like build a tool for an AI rather than building it for a human. we have a new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on 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 all of the, sort of, ideation and back-and-forth and taking care of the details for you that you get of ChatGPT. And here we go, it’s not just the idea for the meal, but very, very detailed spread. So let’s see what we’re going get. But ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And that is something that expands the power of what it can do on your behalf in terms of out your intent. And 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.
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I’m getting hungry just looking at it.
Now we’ve extended ChatGPT other tools too, for example, memory. You can say “save this for later.” And the interesting about these 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, over months. And you can look under the hood and see that what it actually did was write prompt just like a human could. And so you sort of have ability to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like use that information and to integrate with other applications too. You can say, “Now make shopping list for the tasty thing I was suggesting earlier.” And it a little tricky for the AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, definitely 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 to use in any situation. And this, I think, shows a new way of thinking about the 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 like you to. Yes, please. good to be polite.
(Laughter)
And by having this unified language on top of tools, the AI is able to sort take away all those details from you. So you don’t have to be one who spells out every single sort of little piece of what’s supposed happen.
And as I said, this is a live demo, so the unexpected will happen to us. But let’s take a look at the Instacart shopping while we’re at it. And you can see we sent a list of ingredients to Instacart. Here’s everything need. And the thing that’s really interesting is that the UI is still very valuable, right? If you look at this, still can click through it and sort of modify 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 build them. And now we have a tweet that’s been for our review, which is also a very important thing. We can click “run,” there we are, we’re the manager, we’re able to inspect, we’re able change the work of the AI if we want to. And so after this talk, will be able to access this yourself. And there go. Cool. Thank you, everyone.
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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 to do when we ask these very high-level questions? And do this, we use an old idea. If you go back to 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 through feedback. Have a teacher who provides rewards and punishments as it tries things out and does things are either good or bad.
And this is exactly we train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an unsupervised learning process. We just show the whole world, the whole internet and say, “Predict what comes in text you’ve never seen before.” And this process imbues it with sorts of wonderful skills. For example, if you’re shown a math problem, only way to actually complete that math problem, to what comes next, that green nine up there, is to actually the math problem.
But we actually have to do second step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. have the AI try out multiple things, give us multiple suggestions, and then human rates them, says “This one’s better than that one.” And this not just the specific thing that the AI said, but importantly, the whole process that the AI used to produce that answer. And this it to generalize. It allows it to teach, to sort infer your intent and apply it in scenarios that it hasn’t seen before, that 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 so great, We’re going be able to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily that one plus one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was very and offered 20 hours of his own time to provide feedback to machine alongside our team. And over the course of a of months we were able to teach the AI that, “Hey, you really push back on humans in this specific kind of scenario.” we’ve actually made lots and lots of improvements to the this way. And when you push that thumbs down in ChatGPT, actually is kind of like sending up a bat signal to our team to say, “Here’s area of weakness where you should gather feedback.” And so you do that, that’s one way that we really listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback is a thing. If you think about asking a kid to clean room, if all you’re doing is inspecting the floor, you don’t if you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by the way. And same sort of reasoning applies to AI. As we move to harder tasks, we will have to our ability to provide high-quality feedback. But for this, the AI itself is happy help. It’s happy to help us provide even better feedback to scale our ability to supervise the machine as time goes on. let me show you what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between these two foundational blogs on unsupervised learning and learning human feedback. And the model says two months passed. But is it true? Like, these models not 100-percent reliable, although they’re getting better every time we provide some feedback. we can actually use the AI to fact-check. And it can actually check its own work. You say, fact-check this for me.
Now, in this case, I’ve actually given the AI a new tool. one is a browsing tool where the model can search queries and click into web pages. And it writes out its whole chain of thought as it does it. It says, I’m just to search for this and it actually does the search. It then it finds the publication date the search results. It then is issuing another search query. It’s going to click into the blog post. And all this you could do, but it’s a very tedious task. It’s not a thing that really want to do. It’s much more fun to be the driver’s seat, to be in this manager’s position you can, if you want, triple-check the work. And out citations so you can actually go and very easily any piece of this whole chain of reasoning. And actually turns out two months was wrong. Two months and one week, that 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 many-step collaboration between a human and an AI. Because human, using this fact-checking tool is doing it in order produce data for another AI to become more useful to a human. And I think this really the shape of something that we should expect to much more common in the future, where we have humans machines kind of very carefully and delicately designed in how they fit into a problem how we want to solve that problem. We make sure that the humans are the management, the oversight, the feedback, and the machines are operating in a way that’s inspectable and trustworthy. together we’re able to actually create even more trustworthy machines. I think that over time, if we get this process right, we be able to solve impossible problems.
And to give you a sense just how impossible I’m talking, I think we’re going be able to rethink almost every aspect of how we interact with computers. For example, about spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really that much in that time. And here is a specific spreadsheet all the AI papers on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the data right here. But let me you the ChatGPT take on how to analyze a data like this.
So we can give ChatGPT access to yet another tool, one a Python interpreter, so it’s able to run code, 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 file and it’s like, “Oh, is CSV,” comma-separated value file, “I’ll parse it for you.” The only here 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 knowing that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s what these are and that these are integer values and so therefore it’s a 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 I to ask. So fortunately, you can ask the machine, “Can you make some exploratory graphs?” And once again, this a super 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 I might be interested in. And so it comes with some good ideas, I think. So a histogram of the number authors per paper, time series of papers per year, word of the paper titles. All of that, I think, will pretty interesting to see. And the great thing is, can actually do it. Here we go, a nice bell curve. You see three is kind of the most common. It’s going to then this nice plot of the papers per year. Something crazy happening in 2023, though. Looks like we were on an exponential it dropped off the cliff. What could be going on there? the way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all these wonderful that appear in these titles.
But I’m pretty unhappy this 2023 thing. It makes this year look really bad. Of course, the problem is that the year not over. So I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 even posted by April 13?] So April 13 was the cut-off I believe. Can you use that to make a projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I wanted 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 provide this additional piece of, know, guidance. And under the hood, the AI is just code again, so if you want to inspect what it’s doing, it’s very possible. now, it does the correct projection.
(Applause)
If you noticed, it even the title. I didn’t ask for that, but it know what I want.
Now we’ll cut to the slide again. This slide shows a parable of how think we … A vision of how we may up using this technology in the future. A person brought his sick dog to the vet, and the veterinarian made a bad call say, “Let’s just wait and see.” And the dog would not be here had he listened. In the meanwhile, he provided the blood test, like, full medical records, to GPT-4, which said, “I am not a vet, you need to to a professional, here are some hypotheses.” He brought that to a second vet who used it to save the dog’s life. Now, systems, they’re not perfect. You cannot overly rely on them. But this story, think, shows that a human with a medical professional and with ChatGPT as brainstorming partner was able to achieve an outcome that would have happened otherwise. I think this is something we should all reflect on, about as we consider how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI is going to require participation from everyone. And that’s for deciding how we want to slot in, that’s for setting the rules of road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s that technology just looks different. Just different from anything people anticipated. And so we all have to become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, believe that we can achieve the OpenAI mission of ensuring that artificial general intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every mind here there’s a feeling of reeling. Like, I suspect a very large number of people viewing this, you look that and you think, “Oh my goodness, pretty much every single about the way I work, I need to rethink.” Like, there’s just new possibilities there. I right? Who thinks that they’re having to rethink the way we do things? Yeah, I mean, it’s amazing, but it’s also really scary. let’s talk, Greg, let’s talk.
I mean, I guess my first question is just how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why is it you who’s come up with this that shocked the world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. If you look at the compute progress, the progress, the data progress, all of those are really industry-wide. I think within OpenAI, we made a lot of deliberate choices from the early days. And the first was just to confront reality as it lays. And that we just thought really hard about like: is it going to take to make progress here? We tried a lot of that didn’t work, so you only see the things that did. I think that the most important thing has been to get of people who are very different from each other to work harmoniously.
CA: Can we have the water, by the way, brought here? I think we’re going to need it, it’s a dry-mouth topic. isn’t there something also just about the fact that you something in these language models that meant that if you continue to invest in them and them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the story is pretty illustrative, right? I think that high level, deep learning, like always knew that was what we wanted to be, was a learning lab, and exactly how to do it? I 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 in Amazon reviews, and he got a result where — this is syntactic process, you expect, you know, the model will where the commas go, where the nouns and verbs are. But he got a state-of-the-art sentiment analysis classifier out of it. This model could tell you a review was positive or negative. I mean, today we are like, come on, anyone can do that. But this was the time that you saw this emergence, this sort of semantics that emerged from underlying syntactic process. And there we knew, you’ve got to this thing, you’ve got to see where it goes.
CA: So think this helps explain the riddle that baffles everyone at this, because these things are described as prediction machines. And yet, what we’re seeing of them feels … it just feels impossible that could come from a prediction machine. Just the stuff showed us just now. And the key idea of emergence is that 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 together, you get these ant colonies that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But as you grow the number houses, things emerge, like suburbs and cultural centers and traffic jams. Give me moment for you when you saw just something pop that just blew your mind you just did not see coming.
GB: Yeah, well, so you can try this ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model do it, which means it’s really learned an internal circuit for how to it. And the really interesting thing is actually, if you it add like a 40-digit number plus a 35-digit number, it’ll get it wrong. And so you can see that it’s learning the 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 something general, but that it hasn’t really fully yet learned that, Oh, can sort 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 that you didn’t know that it was to be capable 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, of the things I think is very undersung in field is sort of engineering quality. Like, we had rebuild our entire stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in learning. You have to get every single piece of stack engineered properly, and 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, you can see all of curves in there. And now we’re starting to be able to predict. So we were able predict, for example, the performance on coding problems. We look at some models that are 10,000 times or 1,000 times smaller. And so there’s something about that is actually smooth scaling, even though it’s still early days.
CA: So here is, one of the fears then, that arises from this. If it’s fundamental what’s happening here, that as you scale up, things emerge you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just a risk of something truly terrible emerging?
GB: Well, I think all these are questions of degree and scale and timing. And I think one thing people miss, too, is of the 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 think that what we kind of see right now, if look at this talk, a lot of what I focus on is providing high-quality feedback. Today, the tasks that we do, you can inspect them, right? It’s very easy to at that math problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know this book summary is any good? You have to read whole book. No one wants to do that.
(Laughter) so I think that the important thing will be that we take this by step. And that we say, OK, as we move to book summaries, we have to supervise this task properly. We have build up a track record with these machines that they’re able actually carry out our intent. And I think we’re to have to produce even better, more efficient, more reliable of scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later in this session, there are critics say that, you know, there’s no real understanding inside, the system is going to always — we’re going to know that it’s not generating errors, that doesn’t have common sense and so forth. Is it your belief, Greg, that it is true any one moment, but that the expansion of the scale and human feedback that you talked about is basically going to take it that journey of actually 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 that the OpenAI, I mean, the short answer is yes, I believe that is we’re headed. And I think that the OpenAI approach here has always been like, let reality hit you in the face, right? It’s like this field is the field broken promises, of all these experts saying X is going happen, Y is how it 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 plus one or something like that is what you need. But I think that our approach always been, you’ve got to push to the limits of this technology really see it in action, because that tells you then, oh, here’s how we can on to a new paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s quite controversial stance you’ve taken, that the right way to this is to put it out there in public and then harness this, you know, instead of just your team giving feedback, the 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 on OpenAI when you were founded as a nonprofit, well you were there as great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were to build models that sort of, you know, somehow held 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 tech world that now Google and Meta and so forth are all to catch up. And some of their criticisms have been, you forcing us to put this out here without proper guardrails or die. You know, how do you, like, make the case that what you have done is responsible and not reckless.
GB: Yeah, we think about these questions the time. Like, seriously all the time. And I don’t think we’re always going get it right. But one thing I think has been important, from the very beginning, when we were thinking about how to artificial general intelligence, actually have it benefit all of humanity, like, how you supposed to do that, right? And that default of being, well, you build in secret, you get super powerful thing, and then you figure out the safety of it and then you “go,” and you hope you got it right. I don’t know to execute that plan. Maybe someone else does. But for me, that was terrifying, it didn’t feel right. And so I think that this alternative approach is the only other path I see, which is that you do let reality you in the face. And I think you do give people time to give input. do have, before these machines are perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number one thing people going to do with it was generate misinformation, try to elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: Viagra spam is bad, but there are things that much worse. Here’s a thought experiment for you. Suppose you’re in a room, there’s a box on the table. believe that in that box is something that, there’s a very strong chance it’s something glorious that’s going to give beautiful gifts to your family and to everyone. But there’s also a one percent thing in the small print 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, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a story that I haven’t actually before, which is that shortly after we started OpenAI, remember I was in Puerto Rico for an AI conference. I’m sitting in hotel room just looking out over this wonderful water, all these people a good time. And you think about it for a moment, you could choose for basically that Pandora’s box to be five years away or 500 away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to have be five years away. But if it gets to 500 years away and people get more time to get it right, which do pick? And you know, I just really felt it the moment. I was like, of course you do 500 years. My brother was in the military at the time and like, puts his life on the line in a much more real than any of us typing things in computers and developing technology at the time. And so, yeah, I’m really on the 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 of 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 the more that sort of, don’t put together the pieces that are there, right, we’re still making faster computers, we’re still 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 someone does manage to connect to the circuit, then you have this very powerful thing, no one’s had any time to adjust, who knows what kind of precautions you get. And so I think that one thing take away is like, even you think about development other sort of technologies, think about nuclear weapons, people talk being like a zero to one, sort of, change what humans could do. But I actually think that if you look at capability, it’s quite smooth 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 figure out how to manage it for each moment you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have is we have birthed this extraordinary child that may have that take humanity to a whole new place. It our collective responsibility to provide the guardrails for this child collectively teach it to be wise and not to us all down. Is that basically the model?
GB: I it’s true. And I think it’s also important to say this shift, right? We’ve got to take each step as we encounter it. And think it’s incredibly important today that we all do get in this technology, figure out how to provide the feedback, decide what we want from it. my hope is that 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)