We started seven years ago because we felt like something really interesting was happening AI and we wanted to help steer it in a positive direction. It’s honestly just amazing to see how far this whole field has come since then. And it’s really gratifying hear from people like Raymond who are using the 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 right now where we as a world are going to a technology that will be so important for our society going forward. I believe that we can manage this for good.
So today, I want to show you the current state that technology and some of the underlying design principles we hold dear.
So the first thing I’m going show you is what it’s like to build a tool for an AI rather than building it a human. So we have a new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on your behalf. And you do things like ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, sort of, ideation and creative back-and-forth and taking of the details for you that you get out of ChatGPT. And we go, it’s not just the idea for the meal, but a very, very detailed spread. let’s see what we’re going to get. But ChatGPT doesn’t just generate in this case — sorry, it doesn’t generate text, it also generates an image. And is something that really expands the power of what it can do your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. This all generated by the AI as we speak. So I actually don’t 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, example, memory. You can say “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this little pop up here that “use the DALL-E app.” And by the way, this is coming to you, ChatGPT users, over upcoming months. And you can look under hood and see that what it actually did was write 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 provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use that information and to 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 the AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So you do make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT is selecting all different tools without me having to tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s a great experience within an as long as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, the AI is able to sort of away all those details from you. So you don’t 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 the unexpected will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And can see we sent a list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional UI is very valuable, right? If you look at this, you still can click through 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 have a new, augmented way to build them. And now have a tweet that’s been 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 we want to. And so this talk, you will be able to access this yourself. there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut 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 how use them. Like, what do we even want it do when we ask these very high-level questions? And to do this, we use old idea. If you go back to Alan Turing’s 1950 paper on the test, he says, you’ll never program an answer to this. Instead, you can learn it. You build a machine, like a human child, and then teach it feedback. Have a human teacher who provides rewards and as it tries things out and does things that either good or bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine through an unsupervised learning process. We just show it whole world, the whole internet and say, “Predict what comes in text you’ve never seen before.” And this process imbues it with all sorts of wonderful skills. example, if you’re shown a math problem, the only way to actually complete that math problem, to what comes next, that green nine up there, is to solve the math problem.
But we actually have to a 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 suggestions, and then a human rates them, says “This one’s better than that one.” And this reinforces just the specific thing that the AI said, but very importantly, the process that the AI used to produce that answer. And this allows 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 are not what you’d expect. For example, when we first showed GPT-4 to Khan Academy, 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. If there’s some bad math in there, will happily pretend that one plus one equals three and run with it.” So we had collect some feedback data. Sal Khan himself was very kind and offered 20 hours of his time to provide feedback to the machine alongside our team. over the course of a couple of months we were able teach the AI that, “Hey, you really should push back humans in this specific kind of scenario.” And we’ve actually made lots lots of improvements to the models this way. And when push that thumbs down in ChatGPT, that actually is kind like sending up a bat signal to our team to say, “Here’s an area of weakness where should gather feedback.” And so when you do that, that’s way 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 clean their room, if all you’re doing is inspecting floor, you don’t know if you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As we to harder tasks, we will have to scale our 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 me show you what I mean.
For example, you can GPT-4 a question like this, of how much time between these two foundational blogs on unsupervised learning and from human feedback. And the model says two months passed. But it true? Like, these models are not 100-percent reliable, although they’re getting better every time we some feedback. But we can actually use the AI to fact-check. it can actually check its own work. You can say, fact-check for me.
Now, in this case, I’ve actually given the AI a tool. This one is a browsing tool where the model can issue search queries click into web pages. And it actually writes out its whole 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 and the results. It then is issuing another search query. It’s to click into the blog post. And all of you could do, but it’s a very tedious task. It’s not a thing humans really want to do. It’s much more fun be in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. And out come citations you can actually go and very easily verify any piece of this whole chain of reasoning. it 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 so interesting to me about whole process is that it’s this many-step collaboration between human and an AI. Because a human, using this fact-checking is doing it in order to produce data for another AI to become more useful to human. And I think this really shows the shape of something that we should expect to be more common in the future, where we have humans and machines of very carefully and delicately designed in how they fit into problem and how we want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re able to actually create even more trustworthy machines. I think that over time, if we get this process right, we will able to solve impossible problems.
And to give you a sense of just impossible I’m talking, I think we’re going to be to rethink almost every aspect of how we interact with computers. For example, think about spreadsheets. They’ve been in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much in time. And here is a specific spreadsheet of all the AI papers on arXiv for the past 30 years. There’s about 167,000 of them. And can see there the data right here. But let me show the ChatGPT take on how to analyze a data like this.
So we can give ChatGPT access to yet another tool, this one a Python interpreter, it’s able to run code, just like a data would. And so you can just literally upload a file and ask about it. And very helpfully, you know, it knows the name the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it you.” The only information here is the name of the file, column names like you saw and then 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, together its world knowledge of knowing that, “Oh yeah, arXiv a site that people submit papers and therefore that’s what these things are that these are integer values and so therefore it’s a number authors in the paper,” like all of that, that’s work a human to do, and the AI is happy help with it.
Now I don’t even know what I want to ask. So fortunately, can ask the machine, “Can you make some exploratory graphs?” And once again, is a super high-level instruction with lots of intent behind it. I don’t even know what I want. And the AI kind of has to what I might be interested in. And so it up with some good ideas, I think. So a histogram the number of authors per paper, time series of papers per year, word cloud the paper titles. All of that, I think, will be pretty interesting to see. And the great is, it can actually do it. Here we go, a nice bell curve. You see three is kind of the most common. It’s going then make this nice plot of the papers per year. Something crazy happening in 2023, though. Looks like we were on an and it dropped off the cliff. What could be going on there? By the way, all this Python code, you can inspect. And then we’ll see word cloud. So can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem is the year is not over. So I’m going to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were even by April 13?] So April 13 was the cut-off date believe. Can you use that to make a fair projection? we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I 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. I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing again, so if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … vision of how we may end up using this in the future. A person brought his very sick dog to the vet, and the veterinarian a bad call to say, “Let’s just wait and see.” And dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you need talk to a professional, here are some hypotheses.” He brought information 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 a brainstorming partner was able achieve an outcome that would not have happened otherwise. I think this is something we all reflect on, think about as we consider how integrate these systems into our world.
And one thing believe really deeply, is that getting AI right is going require participation from everyone. And that’s for deciding how we want it slot in, that’s for setting the rules of the road, for what an AI will and won’t do. And there’s one thing to take away from this talk, it’s that this technology just looks different. Just from anything people had anticipated. And so we all to become literate. And that’s, honestly, one of the reasons we ChatGPT.
Together, I believe that we can achieve the OpenAI of ensuring that artificial general intelligence benefits all 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 people viewing this, you look at that and you think, “Oh my goodness, pretty 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, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually is how the hell have you done this?
(Laughter)
OpenAI has few hundred employees. Google has thousands of employees working on artificial intelligence. Why it you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re all on shoulders of giants, right, there’s no question. If you at the compute progress, the algorithmic progress, the data progress, all of those are industry-wide. But I think within OpenAI, we made a lot of very choices from the early days. And the first one was just to confront reality as lays. And that we just thought really hard about like: is it going to take to make progress here? tried a lot of things that didn’t work, so only see the things that did. And I think that the most important thing has been get teams of people who are very different from each other to work together harmoniously.
CA: Can 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 something also just about the fact that you saw something in language models that meant that if you continue to in them and grow them, that something at some point might emerge?
GB: Yes. And I think that, mean, honestly, I think the story there is pretty illustrative, right? I think that high level, deep learning, like we knew that was what we wanted to be, was a deep lab, and exactly how to do it? I think that in the days, we didn’t know. We tried a lot of things, and one person was working on training model to predict the next character in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model will predict the commas go, where the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model could tell you if a was positive or negative. I mean, today we are just like, come on, anyone do that. But this was the first time that saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve to scale this thing, you’ve got to see where it goes.
CA: So I this helps explain the riddle that baffles everyone looking at this, because things are described 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 key idea emergence is that when you get more of a thing, suddenly things emerge. It happens all the time, ant colonies, single run around, when you bring enough of them together, you get these ant colonies show completely emergent, different behavior. Or a city where a few houses together, it’s just houses together. But you grow the number of houses, things emerge, like suburbs and centers and traffic jams. Give me one moment for you when you just something pop that just blew your mind that just did not see coming.
GB: Yeah, well, so can try this in ChatGPT, if you add 40-digit —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s really learned internal circuit for how to do it. And the really interesting is actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. And you can see that it’s really learning the process, but hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit table, that’s more atoms than there are in the universe. it had to have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize this adding arbitrary numbers of arbitrary lengths.
CA: So what’s here is that you’ve allowed it to scale up look at an incredible number of pieces of text. And is learning things that you didn’t know that it was going to be of learning.
GB Well, yeah, and it’s more nuanced, too. So one science we’re starting to really get good at is predicting of these emergent capabilities. And to do that actually, one of things I think is very undersung in this field sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, every tolerance has be incredibly tiny. Same is true in machine learning. 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. tell you something deeply fundamental about intelligence. If you look our GPT-4 blog post, you can see all of these curves in there. And we’re starting to be able to predict. So we were able to predict, example, the performance on coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s about this that is actually smooth scaling, even though it’s still early days.
CA: here is, one of the big fears then, that from this. If it’s fundamental to what’s happening here, that as scale up, things emerge that you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just huge risk of something truly terrible emerging?
GB: Well, I think of 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. And so I that what we kind of see right now, if you look this talk, a lot of what I focus on providing really high-quality feedback. Today, the tasks that we do, can inspect them, right? It’s very easy to look that math problem and be like, no, no, no, machine, was the correct answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, how you know if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) And so I think that the important will be that we take this step by step. And we say, OK, as we move on to book summaries, 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 think we’re going to have to produce even better, more efficient, more ways of scaling this, sort of like making the machine be aligned with you.
CA: we’re going to hear later in this session, there critics who say that, you know, there’s no real inside, the system is going to always — we’re going to know that it’s not generating errors, that it doesn’t have sense and so forth. Is it your belief, Greg, that it is true at any one moment, that the expansion of the scale and the human feedback that talked about is basically going to take it on that journey of getting to things like truth and wisdom and so forth, with a degree of confidence. Can you be sure of that?
GB: Yeah, well, I think that the OpenAI, mean, the short answer is yes, I believe that is where we’re headed. And I think the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like this field is the field broken promises, of all these experts saying X is going happen, Y is how it works. People have been saying nets aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years plus one something like that is what you need. But I think that our approach has always been, you’ve got push to the limits of this technology to really see it action, because that tells you then, oh, here’s how can move on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a stance you’ve taken, that the right way to do this to put it out there in public and then all this, you know, instead of just your team giving feedback, the is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, you know, the original story that heard on OpenAI when you were founded as a nonprofit, well you were there as the great sort check on the big companies doing their unknown, possibly evil thing with AI. you were going to build models that sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, is the opposite. That your of GPT, especially ChatGPT, sent such shockwaves through the tech world that Google and Meta and so forth are all scrambling catch up. And some of their criticisms have been, you are us to put this out here without proper guardrails we 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 all time. Like, seriously all the time. And I don’t think we’re going to get it right. But one thing I has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, actually it benefit all of humanity, like, how are you supposed to that, right? And that default plan of being, well, 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 plan. Maybe someone else does. But for me, that was always terrifying, it didn’t feel right. And so think that this alternative approach is the only other path that I see, is that you do let reality hit you in 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, really were afraid that the number one thing people going to do with it was generate misinformation, try to tip elections. Instead, the number one was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but are things that are much worse. Here’s a thought experiment 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 strong chance it’s something absolutely glorious that’s going to beautiful gifts to your family and to everyone. But there’s actually also a one percent thing in the print there that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils the world. Do you open that box?
GB: Well, so, absolutely not. I 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 after we started OpenAI, remember I was in Puerto Rico for an AI conference. I’m in the hotel room just looking out over this water, all these people having a good time. And you think about for a moment, if you could choose for basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On the one hand you’re like, well, maybe you personally, it’s better to have it be five away. But if it gets to be 500 years and people get more time to get it right, which you pick? And you know, I just really felt 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 much more real way 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 don’t think that’s quite playing the field as it truly lies. Like, if you at the whole history of computing, I really mean it when I say that is an industry-wide or even just almost like a human-development- of-technology-wide shift. And more that you sort of, don’t put together the pieces that are there, right, we’re still faster computers, we’re still improving the algorithms, all of 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 does manage to to the circuit, then you suddenly have this very 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 is like, even you think about development of other sort of technologies, think about nuclear weapons, people talk being like a zero to one, sort of, change in humans could do. But I actually think that if you at capability, it’s been quite smooth over time. And so the history, I think, of technology we’ve developed has been, you’ve got to do it incrementally you’ve got to figure out how to manage it for moment that you’re increasing it.
CA: So what I’m hearing is that you … the model want us to have is that we have birthed this extraordinary child that may have superpowers that humanity to a whole new place. It is our collective responsibility to provide guardrails for this child to collectively teach it to 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 this may shift, right? We’ve to take each step as we encounter it. And I think it’s incredibly important today we all do get literate in this technology, figure how to provide the feedback, decide what we want from it. my hope is that that will continue to be best path, but it’s so good we’re honestly having this 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)