We started seven years ago because we felt like something really was happening in AI and we wanted to help steer in a positive direction. It’s honestly just really amazing to see how far whole field has come since then. And it’s really gratifying to hear from people like who are using the technology we are building, and others, for so many wonderful things. hear from people who are excited, we hear from who are concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now where we as a are going to define a technology that will be so important our society going forward. And I believe that we manage this for 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 rather than building it for a human. So we a new DALL-E model, which generates images, and we are exposing it an app for ChatGPT to use on your behalf. And you can do things ask, you know, suggest a nice post-TED meal and a picture of it.
(Laughter)
Now you get all the, sort of, ideation and creative back-and-forth and taking care the details for you that you get out of ChatGPT. here we 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. ChatGPT doesn’t just generate images in this case — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the power of what it can do on your in terms of carrying out your intent. And I’ll out, this is all a live demo. This is all generated by AI as we speak. So I actually don’t even 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, example, memory. You can say “save this for later.” And the thing about these tools is they’re very inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, this is coming to you, ChatGPT users, over upcoming months. And you can look under the hood and that what it actually did was write a prompt like a human could. And so you sort of this ability to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s saved for later, and let me you what it’s like to use that information and to integrate with other applications too. can say, “Now make a shopping list for the thing I was suggesting earlier.” And make it a tricky for the AI. “And tweet it out for the TED viewers out there.”
(Laughter)
So if you do make this wonderful, meal, I definitely want to know how it tastes.
But you see that ChatGPT is selecting all these different tools me having to tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking the user interface. Like, we are so used to thinking of, well, we these apps, we click between them, we copy/paste between them, and usually it’s a great experience an app as long as you kind of know the menus and all the options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this language interface on top of tools, the AI is able to of take away all those details from you. So don’t have to be the one who spells out every single sort of piece of what’s supposed to happen.
And as I said, this is a live demo, so sometimes the will happen to us. But let’s take a look at the shopping list while we’re at it. And you can see sent a list of ingredients to Instacart. Here’s everything you need. And the thing that’s interesting is that the traditional UI is still very valuable, right? If you look at this, you still can through it and sort of modify the actual quantities. And that’s something that I shows that they’re not going away, traditional UIs. It’s we have a new, augmented way to build them. now we have a tweet that’s been drafted for our review, which is also very important thing. We can click “run,” and there we are, we’re the manager, we’re able inspect, we’re able to change the work of the if we want to. And so after this talk, will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut to the slides. Now, the important thing about how build this, it’s not just about building these tools. It’s teaching the AI how to use them. Like, what do we even want it do when we ask these very high-level questions? And do this, we use an old idea. If you go back to Alan Turing’s 1950 on the Turing test, he says, you’ll never program an answer to this. Instead, can learn it. You could build a machine, like a human child, and then teach it through feedback. a human teacher who provides rewards and punishments as tries things out and does things that are either good or bad.
And this is exactly we train ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised 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 with all sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that math problem, to say what next, that green nine up there, is to actually the math problem.
But we actually have to do a second step, too, which is to teach the what to do with those skills. And for this, we feedback. We have the AI try out multiple things, give us suggestions, and then a human rates them, says “This one’s better than one.” And this reinforces not just the specific thing that the AI said, but importantly, the whole process that the AI used to produce that answer. And allows it to generalize. It allows it to teach, to of infer your intent and apply it in scenarios it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have to teach AI are 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 to be able to teach wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily pretend that one plus equals three and run with it.” So we had collect some feedback data. Sal Khan himself was very kind and 20 hours of his own time to provide feedback to the machine alongside our team. And over the of a couple of months we were able to teach the AI that, “Hey, you really should back on humans in this specific kind of scenario.” we’ve actually made lots and lots of improvements to models this way. And 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 way that we really listen to our users and make sure we’re building something that’s more for everyone.
Now, providing high-quality feedback is a hard thing. If you think about asking a kid clean their room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff all the toys in closet. This is a nice DALL-E-generated image, by the way. And the same sort of reasoning to AI. As we move to harder tasks, we have to scale our ability to provide high-quality feedback. But for this, the AI 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 like this, of how much time passed between these foundational blogs on unsupervised learning and learning from human feedback. the model says two months passed. But is it true? Like, models are not 100-percent reliable, although they’re getting better time we provide some feedback. But we can actually use the AI fact-check. And it can actually check its own work. can say, fact-check this for me.
Now, in this case, I’ve given the AI a new tool. This one is browsing tool where the model can issue search queries click into web pages. And it actually writes out whole chain of thought as it does it. It says, I’m just going to for this and it actually does the search. It then finds the publication date and the search results. It is issuing another search query. It’s going to click the blog post. And all of this you could do, but it’s very tedious task. It’s not a thing that humans really want to do. It’s much more fun be in the driver’s seat, to be in this manager’s position where can, if you want, triple-check the work. And out come citations so can actually go and very easily verify any piece this whole chain of reasoning. And it actually turns out two months was wrong. Two and one week, that was correct.
(Applause)
And we’ll back to the side. And so thing that’s so interesting to me this whole process is that it’s this many-step collaboration a human and an AI. Because a human, using this fact-checking tool is doing in order to produce data for another AI to become useful to a human. And I think this really shows shape of something that we should expect to be much more common in the future, we have humans and machines kind of very carefully and delicately designed in how they fit into a and how we want to solve that problem. We make sure the humans are providing the management, the oversight, the feedback, and the are operating in a way that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that over time, we get this process right, we will be able solve impossible problems.
And to give you a sense of how impossible I’m talking, I think we’re going to be able to rethink almost every aspect of how interact with computers. For example, think about spreadsheets. They’ve been around in form since, we’ll say, 40 years ago with VisiCalc. don’t think they’ve really changed that much in that time. And here a specific spreadsheet of all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you can see there the right here. But let me show you the ChatGPT take on how to analyze data set like this.
So we can give ChatGPT access to yet another tool, this one Python interpreter, so it’s able to run code, just like a scientist would. And so you can just literally upload a file and ask questions about it. And helpfully, you know, it knows the name of the file it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” only information here is the name of the file, the column like you saw and then the actual data. And from that it’s able to infer what these 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 a site that people submit papers and therefore that’s these things are and that these are integer values and so therefore it’s a number of authors in paper,” like all of that, that’s work for a human to do, the AI is happy to help with it.
Now I don’t even know what I want to ask. fortunately, you can ask the machine, “Can you make exploratory graphs?” And once again, this is a super high-level instruction with lots of behind it. But I don’t even know what I want. And the kind of has to infer what I might be interested in. And so comes up with some good ideas, I think. So a histogram of the of authors per paper, time series of papers per year, cloud of 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. You that three is kind of the most common. It’s going to then make nice plot of the papers per year. Something crazy is in 2023, though. Looks like we were on an exponential it dropped off the cliff. What could be going on there? By the way, all this is 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 about this 2023 thing. It makes this year look really bad. 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 to make a fair projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I wanted out of the machine here. I wanted it to notice this thing, maybe it’s a little bit 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 writing code again, so if you want to inspect what it’s doing, it’s very possible. And now, it 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 back to 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 very sick dog to vet, and the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be here had he listened. In the meanwhile, he provided the test, like, the full medical records, to GPT-4, which said, “I am not vet, you need to talk to a professional, here are 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 rely them. But this story, I think, shows that a with a medical professional and with ChatGPT as a partner was able to achieve an outcome that would not have happened otherwise. I think this is something should all reflect on, think about as we consider how to integrate systems into our world.
And one thing I believe really deeply, 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 the rules of the road, for what an AI and won’t do. And if there’s one thing to away from this talk, it’s that this technology just different. Just different from anything people had anticipated. And 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 ensuring that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … suspect that within every mind out here there’s a of reeling. Like, I suspect that a very large number people viewing this, you look at that and you think, “Oh my goodness, pretty much single thing about the way I work, I need rethink.” Like, there’s just new possibilities there. Am I right? thinks that they’re having to rethink the way that 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 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 it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re all building shoulders of giants, right, there’s no question. If you at the compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think within OpenAI, we made a lot of very choices from the early days. And the first one was to confront reality as it lays. And that we just thought hard about like: What is it going to take to make here? We tried a lot of things that didn’t work, so you only the things that did. And I think that the most important thing been to get teams of people who are very different from each other to work together harmoniously.
CA: we have the water, by the way, just brought here? I we’re going 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 meant that if you continue to invest 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 wanted to be, was a deep learning lab, and exactly to do it? I think that in the early days, we didn’t know. We a lot of things, and one person was working training a model to predict the next character in reviews, and he got a result where — this a syntactic process, you expect, you know, the model will predict the commas go, where the nouns and verbs are. But he actually got a state-of-the-art 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 first time that you saw this emergence, this sort semantics that emerged from this underlying syntactic process. And there knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps explain the riddle that baffles looking at this, because these things are described as machines. And yet, what we’re seeing out of them feels … it just feels impossible that could come from a prediction machine. Just the stuff you showed us just now. And the key of emergence is that when you get more of a thing, suddenly different emerge. It happens all the time, ant colonies, single ants around, when you bring enough of them together, you these ant colonies that show completely emergent, different behavior. a city where a few houses together, it’s just houses together. But as grow the number of houses, things emerge, like suburbs and cultural centers and traffic jams. Give me one for you when you saw just something pop that blew your mind that you just did not see coming.
GB: Yeah, well, you can try this in ChatGPT, if you add 40-digit —
CA: 40-digit?
GB: 40-digit numbers, the model will it, which means it’s really learned an internal circuit how to do it. And the really interesting thing actually, if you have it add like a 40-digit number a 35-digit number, it’ll often get it wrong. And so you 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 to have learned general, but that it hasn’t really fully yet learned that, Oh, I can sort of generalize to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened is that you’ve allowed it to scale up and look at an number of pieces of text. And it is learning things that didn’t know that it was going to be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. to do that actually, one of the things I think is very in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think building a rocket, every tolerance has to be incredibly tiny. Same is in machine learning. You have to get every single piece of the stack properly, and then you can start doing these predictions. There are all these incredibly smooth scaling curves. They you something deeply fundamental about intelligence. If you look our GPT-4 blog post, you can see all of these curves in there. And now we’re to be able to predict. So we were able to predict, for example, the on coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s something this that is actually smooth scaling, even though it’s still early days.
CA: So here is, one the big 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 one thing people miss, too, is sort of the integration the world is also this incredibly emergent, sort of, very powerful thing too. And so that’s of the reasons that we think it’s so important to deploy incrementally. so I think that what we kind of see right now, if look at this talk, a lot of what I focus on is providing really high-quality feedback. Today, the 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. But summarizing a book, like, that’s a hard thing to supervise. Like, do you know if this book summary is any good? have to read the whole book. No one wants to do that.
(Laughter) And I think that the important thing will be that we take this by step. And that we say, OK, as we move on book summaries, we have to supervise this task properly. We to build up a track record with these machines that they’re able to actually carry out intent. And I think we’re going to have to even better, more efficient, more reliable ways of scaling this, sort of like the machine be aligned with you.
CA: So we’re going to hear later in this session, there critics who say that, you know, there’s no real understanding inside, the system is to always — we’re never going to know that it’s not generating errors, that it doesn’t have common sense so forth. Is it your belief, Greg, that it true at any one moment, but that the expansion the scale and the human feedback that you talked about is going to take it on that journey of actually getting things like truth and wisdom and so forth, with a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI approach here always been just like, let reality hit you in the face, right? It’s this field is the field of broken promises, of all these saying X is going to happen, Y is how it works. People been saying neural nets aren’t going to work for 70 years. haven’t been right yet. They might be right maybe 70 plus one or something like that is what you need. I think that our approach has always been, you’ve got to to the limits of this technology to really see in action, because that tells you then, oh, here’s how we move on to a new paradigm. And we just haven’t exhausted the here.
CA: I 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 harness all this, you know, instead of just your 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 I heard on OpenAI when you were founded as a nonprofit, you were there as the great sort of check on the companies doing their unknown, possibly evil thing with AI. And you were going to build models sort of, you know, somehow held them accountable and was capable slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, the opposite. That your 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 this out here without proper guardrails or we die. know, how do you, like, make the case that what you done is responsible here and not reckless.
GB: Yeah, we think about these questions all the time. Like, all the time. And I don’t think we’re always going to get it right. But one thing I has been incredibly important, from the very beginning, when we were about how to build artificial general intelligence, actually have 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 it and then push “go,” and you hope you got it right. don’t know how to execute that plan. Maybe someone else does. for me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the only other path I see, which is that you do let reality hit you in face. And I think you do give people time to input. You do have, before these machines are perfect, before are super powerful, that you actually have the ability to them in action. And we’ve seen it from GPT-3, right? GPT-3, really were afraid that the number one thing people going to do with it was generate misinformation, try to tip elections. Instead, the one thing was generating Viagra spam.
(Laughter)
CA: So spam is bad, but there are things that are much worse. Here’s a thought for you. Suppose you’re sitting in a room, there’s a box on the table. You believe that in box is something that, there’s a very strong chance it’s something absolutely glorious that’s going to give gifts to your family and to everyone. But there’s actually also a one percent thing in small print there that says: “Pandora.” And there’s a chance that this actually could unleash evils on the world. Do you open that box?
GB: Well, so, absolutely not. I think 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 after 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 time. And you think about it for a moment, if could choose for basically that Pandora’s box to be years away or 500 years 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 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 computers and this technology at the time. And so, yeah, I’m sold on the you’ve got to approach this right. But don’t think that’s quite playing the field as it truly lies. Like, if look at the whole history of computing, I really mean it when say that this is an industry-wide or even just almost a human-development- of-technology-wide shift. And the more that you sort of, don’t together the pieces that are there, right, we’re still making faster computers, we’re still improving the algorithms, all these things, they are happening. And if you don’t put them together, you get an overhang, means that if someone does, or the moment that someone manage to connect to the circuit, then you suddenly have very powerful thing, no one’s had any time to adjust, who knows what of safety precautions you get. And so I think that one thing I take away is like, you think about development of other sort of technologies, about nuclear weapons, people talk about being like a zero one, sort of, change in what humans could do. I actually think that if you look at capability, it’s been quite smooth time. And so the history, I think, of every technology we’ve developed has been, you’ve to do it incrementally and you’ve got to figure out to manage it for each moment that you’re increasing it.
CA: So I’m hearing is that you … the model you want us have is that we have birthed this extraordinary child that have superpowers that take humanity to a whole new place. It is our responsibility to provide the guardrails for this child to collectively it to be wise and not to tear us down. Is that basically the model?
GB: I think it’s true. And think it’s also important to say this may shift, right? We’ve got to each step as we encounter it. And I think it’s important today that we 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 be 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 so much for coming to TED and blowing our minds.
(Applause)