We OpenAI seven years ago because we felt like something really interesting was happening in AI and wanted to help steer it in a positive direction. It’s honestly just really amazing see how far this 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, so many wonderful things. We hear from people who are excited, we hear from people are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period right now where we as a world are going define a technology that will be so important for our going forward. And I believe that we can manage this good.
So today, I want to show you the current state of technology and some of the underlying design principles that hold dear.
So the first thing I’m going to show 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, which generates images, we are exposing it as an app for ChatGPT use on your behalf. And you can do things like ask, know, suggest a nice post-TED meal and draw a picture of it.
(Laughter)
Now you all 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 go, it’s not just the idea for the meal, a very, very detailed spread. So let’s see what we’re going get. But ChatGPT doesn’t just generate images in this case — sorry, it doesn’t generate text, 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 live demo. This is all generated by the AI we speak. So I actually don’t even know what we’re going to see. This wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended with other tools too, for example, memory. You can say “save this for later.” And interesting 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, all users, over upcoming months. And you can look under the hood see that what it actually did was write a prompt just a human could. And so you sort of have this ability inspect how the machine is using these tools, which allows us to provide feedback them.
Now it’s saved for later, and let me show what it’s like to use that information and to integrate other applications too. You can say, “Now make a shopping list the tasty thing I was suggesting earlier.” And make it little tricky for the AI. “And tweet it out for all the TED out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, definitely want to know how it tastes.
But you can that ChatGPT is selecting all these different tools without me having to tell it which ones to use in any situation. And this, I think, a new way of thinking about the user interface. Like, we are so used thinking of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great experience within an app as as you kind of know the menus and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And by having unified language interface on top of tools, the AI able to sort of take away all those details you. So you don’t have to be the one who spells out single sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes the unexpected will to us. But let’s take a look at the Instacart shopping list while we’re at it. you can see we sent a list of ingredients 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 can click through it and sort of modify the actual quantities. And that’s something that I think that they’re not going away, traditional UIs. It’s just we a new, augmented way to build them. And now have a tweet that’s been drafted for our review, which 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 AI we want to. And so after this talk, you be able to access this yourself. And there we go. Cool. you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important thing how we build this, it’s not just about building these tools. It’s about teaching the AI how use them. Like, what do we even want it to do when we ask these high-level questions? And to do this, we use an old idea. If go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never an answer to this. Instead, you can learn it. You could build a machine, like a human child, then teach it through feedback. Have a human teacher who rewards and punishments as it tries things out and does things are either good or bad.
And this is exactly how train ChatGPT. It’s a two-step process. First, we produce what Turing would called a child machine through an unsupervised learning process. We just show it the whole world, the whole and say, “Predict what comes next in text you’ve never seen before.” And process imbues it with all sorts of wonderful skills. For example, you’re shown a math problem, the only way to actually complete that math problem, say what comes 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 provide feedback. We have AI try out multiple things, give us multiple suggestions, and a human rates them, says “This one’s better than that one.” And this not just the specific thing that the AI said, but very importantly, the whole process 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, 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 so great, We’re going to be able to students wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily pretend one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan was very kind and offered 20 hours of his own time to provide to the machine alongside our team. And over the course a couple of months we were able to teach AI that, “Hey, you really should push back on in this specific kind of scenario.” And we’ve actually made lots and lots of to the models this way. And when you push thumbs down in ChatGPT, that actually is kind of like sending a bat signal to our team to say, “Here’s area of weakness where you should gather feedback.” And so when you do that, that’s one way that really listen to our users and make sure we’re something that’s more useful for everyone.
Now, providing high-quality is a hard thing. If you think about asking a to clean their room, if all you’re doing is inspecting floor, you don’t know if you’re just teaching them to all the toys in the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As we move harder tasks, we will have to scale our ability provide high-quality feedback. But for this, the AI itself is happy to help. It’s to help us provide even better feedback and to our ability to supervise the machine as time goes on. And me show you what I mean.
For example, you ask GPT-4 a question like this, of how much passed between these two foundational blogs on unsupervised learning and learning from human feedback. And the model says months passed. But is it true? Like, these models are not 100-percent reliable, although they’re getting every time we provide some feedback. But we can use the AI to fact-check. And it can actually check its own work. can say, fact-check this 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 and into web pages. And it actually writes out its whole chain of as it does it. It says, I’m just going to search 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. all of this you could do, but it’s a very task. It’s not a thing that humans really want to do. It’s more fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check the work. out come citations so you can actually go and very verify any piece of this 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 back to the side. And so thing that’s interesting to me about this whole process is that it’s this many-step between a human and an AI. Because a human, using this fact-checking tool is doing it order to produce data for another AI to become more useful to a human. And I think really shows the shape of something that we should expect to be much more common in future, where we have humans and machines kind of very carefully and designed in how they fit into a problem and we want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and machines are operating in a way that’s inspectable and trustworthy. And together we’re able to actually create more trustworthy machines. And I think that over time, if we this process right, we will be able to solve impossible problems.
And to give you sense of just how impossible I’m talking, I think we’re going to be to rethink almost every aspect of how we interact computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago with VisiCalc. don’t think they’ve really changed that much in that time. And is a specific spreadsheet of all the AI papers on the arXiv for past 30 years. There’s about 167,000 of them. And can see there the data right here. But let me show you the ChatGPT take on how to a data set like this.
So we can give ChatGPT access to another tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And so you can just literally upload a and ask 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 the of the file, the column names like you saw 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 to sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv a site that people submit papers and therefore that’s what these things and that these are integer values and so therefore it’s a number of authors in the paper,” like of that, that’s work for a human to do, and the AI is happy to 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, this is a high-level instruction with lots of intent behind it. But I don’t even know what I want. And the kind of has to infer what I might be in. And so it comes up with some good ideas, I think. So a histogram of the number of per paper, time series of papers per year, word cloud of the paper titles. All of that, think, will be pretty interesting to see. And the thing is, it 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 of papers per year. Something crazy is happening in 2023, though. Looks like were on an exponential and it dropped off the cliff. could be going on there? By the way, all this Python code, you can inspect. And then we’ll see cloud. So you can see all these wonderful things appear in these 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 push back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What of papers in 2022 were 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 one.
(Laughter)
So you know, again, I feel like was more I wanted out 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 is I wanted. But I inject my intent, I provide this additional of, you 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. now, it does the correct projection.
(Applause)
If you noticed, it even updates 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 I think we … A 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.” the dog would not be here today had he listened. In the meanwhile, provided the blood test, like, the full medical records, to GPT-4, which said, “I not a vet, you need to talk to a professional, are some hypotheses.” He brought that information to a second vet who used to save the dog’s life. Now, these systems, they’re not perfect. You overly rely on them. But this story, I think, that a human with a medical professional and with as a brainstorming partner was able to achieve an outcome that would have happened otherwise. I think this is something we should reflect on, think about as we consider how to integrate these systems our world.
And one thing I believe really deeply, is that getting AI right is going to participation from everyone. And that’s for deciding how we want to slot in, that’s for setting the rules of the road, for what an AI and won’t do. And if there’s one thing to take from this talk, it’s that this technology just looks different. Just different anything people had anticipated. And so we all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we can the OpenAI mission of ensuring that artificial general intelligence benefits all 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 this, you look at that and you think, “Oh my goodness, pretty much every single thing about way I work, I need to rethink.” Like, there’s new 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 also really scary. 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 a few hundred employees. has 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. If you look 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 deliberate choices the early days. And the first one was just to confront reality as it lays. And that we thought really hard about like: What is it going to take to progress here? We tried a lot of things that didn’t work, so you only see the that did. And I think that the most important thing been to get teams of people who are very from each other to work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there something just about the fact that you saw something in language models that meant that if you continue to invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, I mean, honestly, I the story there is pretty illustrative, right? I think that high level, deep learning, we always knew that was what we wanted to be, a deep learning lab, and exactly how to do it? I that in the early days, we didn’t know. We tried a lot of things, and one 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 predict where the commas go, the nouns and verbs are. But he actually got a state-of-the-art sentiment analysis classifier out it. This model could tell you if a review was positive or negative. I mean, today are just like, come on, anyone can do that. But was the first time that you saw this emergence, sort of semantics that emerged from this underlying syntactic process. there we knew, you’ve got to scale this thing, you’ve got to see where goes.
CA: So I think this helps explain the riddle that everyone looking at this, because these things are described as prediction machines. And yet, what we’re seeing out them 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 a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you bring enough of them together, get these ant colonies that show completely emergent, different behavior. Or city where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, like suburbs and centers and traffic jams. Give me one moment for you you saw just something pop that just blew your mind you just did not see coming.
GB: Yeah, well, you can try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the will do it, which means it’s really learned an internal circuit for how to it. And the really interesting thing is actually, if you have it add like 40-digit number plus a 35-digit number, it’ll often get it wrong. And so 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 addition table, that’s more atoms than there in the universe. So it had to have learned something general, but that it hasn’t really fully learned that, Oh, I 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 at an incredible number of pieces of text. And it is learning things that you didn’t that it was going to be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science we’re starting to really get good at is predicting some of these emergent capabilities. And to do actually, one of the things I think is very undersung in this field is sort of quality. Like, we had to rebuild our entire stack. When you think about building a rocket, tolerance has to be incredibly tiny. Same is true in machine learning. You have to get every piece of the stack engineered properly, and then you can start doing these predictions. There all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these curves there. And now we’re starting to be able to predict. we were able to predict, for example, the performance on problems. We basically look at some models that are 10,000 or 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, one the big fears then, that arises from this. If it’s fundamental to what’s happening here, that as you up, things emerge that you can maybe predict in level of confidence, but it’s capable of surprising you. Why isn’t there just a huge of something truly terrible emerging?
GB: Well, I think all of these questions of degree and scale and timing. And I think one thing people miss, too, is sort the integration with the world is also this incredibly emergent, of, very powerful thing too. And so that’s one of the reasons that we it’s so important to deploy incrementally. And so I think what we kind of see right now, if you look at this talk, a of what I focus on is providing really high-quality feedback. Today, tasks that we do, you can inspect them, right? It’s easy to look at that math problem and be like, no, no, no, machine, seven was the correct answer. even summarizing a book, like, that’s a hard thing to supervise. Like, how do you if this book summary is any good? You have to read the whole book. No one wants do that.
(Laughter) And so I think that the important thing will be that take this step by step. And that we say, OK, we move on to book summaries, we have to supervise task properly. We have to build up a track record with these machines they’re able to actually carry out our intent. And think we’re going to have to produce even better, efficient, more reliable ways of scaling this, sort of like making the machine aligned with you.
CA: So we’re going to hear in this session, there are critics who say that, you know, there’s no understanding inside, the system is going to always — we’re never going know that it’s not generating errors, that it doesn’t have common and so forth. Is it your belief, Greg, that is true at any one moment, but that the of the scale and the human feedback that you about is basically going to take it on that journey actually 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, I mean, the short is yes, I believe that is where we’re headed. And I think the OpenAI approach here has always been just like, let reality hit you the face, right? It’s like this field is the field of broken promises, of all these experts saying is going to happen, Y is how it works. People have been saying neural nets aren’t to work for 70 years. They haven’t been right yet. might be right maybe 70 years plus one or something like that is what need. But I think that our approach has always been, you’ve to push to the limits of this technology to see it in action, because that tells you then, oh, here’s how we can move on to new paradigm. And we just haven’t exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that the way to do this is to put it out there public 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, original story that I heard on OpenAI when you were founded a nonprofit, well you were there as the great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held them accountable was capable of slowing the field down, if need be. Or 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 the tech world that now and Meta and so forth are all scrambling to catch up. And some of their criticisms have been, are forcing us to put this out here without proper guardrails or die. You know, how do you, like, make the that what you have done is responsible here and not reckless.
GB: Yeah, we think these questions all the time. Like, seriously all the time. I don’t think we’re always going to get it right. But thing I think has been incredibly important, from the very beginning, when we thinking about how to build artificial general intelligence, actually it benefit all of 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, then you figure out the safety of it and you push “go,” and you hope you got it right. don’t know how to execute that plan. Maybe someone else does. But for me, that always terrifying, it didn’t feel right. And so I think that this alternative approach is the only other that I see, which is that you do let reality hit you in the face. And I you do give people time to give input. You do have, before machines are perfect, before they are super powerful, that actually have the ability to see them in action. we’ve seen it from GPT-3, right? GPT-3, we really were afraid that number one thing people were going to do with was generate misinformation, try to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on the table. You believe that that box is something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful gifts to your family to everyone. But there’s actually also a one percent thing in small print there that says: “Pandora.” And there’s a chance this actually could unleash unimaginable evils on the world. Do you that box?
GB: Well, so, absolutely not. I think you don’t do it that way. honestly, like, I’ll tell you a story that I haven’t told before, which is that shortly after we started OpenAI, I remember 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 think about it for a moment, if you could for basically that Pandora’s box to be five years or 500 years away, which would you pick, right? On the one hand you’re like, well, for 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 do pick? And you know, I just really felt it in the moment. I was like, of course do the 500 years. My brother was in the at the time and 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 sold on the you’ve to approach this right. But I don’t think that’s 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 or even just like a human-development- of-technology-wide shift. And the more that you sort of, don’t put together 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 together, you get an overhang, which means that if does, or the moment that someone does manage to connect to circuit, then you suddenly have this very powerful thing, no one’s had any time to adjust, who what kind of safety precautions you get. And so I think that one thing take away is like, even you think about development of other sort of technologies, think 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 quite smooth over time. And so the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve got to figure out how to it for each moment that you’re increasing it.
CA: So what I’m hearing is that … the model you want us to have is that we have birthed this extraordinary child that have superpowers that take humanity to a whole new place. It is our collective to provide the guardrails for this child to collectively it to be wise and not to tear us all down. Is 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 think it’s incredibly today that we all do get literate in this technology, out how to provide the feedback, decide what we want from it. And my hope that that will continue to be the 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 you much for coming to TED and blowing our minds.
(Applause)