We started OpenAI seven years ago because felt like something really interesting was happening in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to see how this whole field has come since then. And it’s really to hear from people like Raymond who are using the technology we are building, others, for so many wonderful things. We hear from who are excited, we hear from people who are concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, it feels we’re entering an historic period right now where we a world are going to define a technology that be so important for our society going forward. And I believe we can manage this for good.
So today, I want to show you the current state of that and some of the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s like to build a tool an AI rather than building it for a human. we have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to use your behalf. And you can do things like ask, you know, suggest nice post-TED meal and draw a picture of it.
(Laughter)
Now you get of the, sort of, ideation and creative back-and-forth and taking care of the for you that you get out of ChatGPT. And here we go, it’s not just the for the meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just generate in this case — sorry, it doesn’t generate text, also generates an image. And that is something that really expands the power of what can do on your behalf in terms of carrying out intent. And I’ll point out, this is all a live demo. This is generated by the AI as we speak. So I actually don’t even know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry just at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You can “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, is coming to you, all ChatGPT users, over upcoming months. you can look under the hood and see that what it actually did was a prompt just like a human could. And so sort of have this ability to inspect how the machine using these tools, which allows us to provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use that information and to integrate other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So if you make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT selecting all these different tools without me having to tell it explicitly which ones to use any situation. And this, I think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, we these apps, we click between them, we copy/paste between them, and it’s a great experience within an app as long as you kind know 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 of take away all those details from you. So you don’t to be the one who spells out every single sort of little piece of what’s 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 you can we sent a list of ingredients to Instacart. Here’s everything you need. And the thing that’s really is that the traditional UI is still very valuable, right? you look at this, you still can click through and sort of modify the actual quantities. And that’s that I think shows that they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we have a tweet that’s been for our review, which is also a very important thing. We click “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of AI if we want to. And so after this talk, you will able to access this yourself. And 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 to use them. Like, what we even want it to do when we ask these high-level questions? And to do this, we use an idea. If you 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. could build a machine, like a human child, and then it through feedback. Have a human teacher who provides rewards and punishments as it tries things out and things that 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 the whole world, the whole internet and say, “Predict comes next in text you’ve never seen before.” And process imbues it with all sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that problem, to say what comes next, that green nine up there, is actually solve the math problem.
But we actually have to a second step, too, which is to teach the AI what do with those skills. And for this, we provide feedback. We have the try out multiple things, give us multiple suggestions, and then a human them, says “This one’s better than that one.” And this reinforces not the specific thing that the AI said, but very importantly, the process that the AI used to produce that answer. this 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, the things we have to teach the AI are 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 teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that one plus one equals three run with it.” So we had to collect some feedback data. Sal Khan himself was kind and offered 20 hours of his own time to provide feedback to machine alongside our team. And over the course of a of months we were able to teach the AI that, “Hey, really should 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, that actually is of like sending up a bat signal to our team say, “Here’s an area of weakness where you should gather feedback.” so when you do that, that’s one way that we really listen to our and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. If you think about a kid to clean their room, if all you’re doing is inspecting the floor, you don’t know 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 applies AI. As we move to harder tasks, we will have scale our ability to 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 let me show you what mean.
For example, you can ask GPT-4 a question like this, of how time passed between these two foundational blogs on unsupervised learning learning from human feedback. And the model says two months passed. But it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. But can actually use the AI to fact-check. And it can actually check own work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This one is a browsing where the model can issue search queries and click into web pages. it actually writes out its whole chain of thought it does it. It says, I’m just going to search for this it actually does the search. It then it finds the publication date and the search results. It then issuing another search query. It’s going to click into the post. And all of this you could do, but it’s a very tedious task. It’s not thing that humans really want to do. It’s much more fun to be in driver’s seat, to be in this manager’s position where you can, you want, triple-check the work. And out come citations so you can actually go and very easily any piece of this whole chain of reasoning. And it actually turns two months was wrong. Two months and one week, was correct.
(Applause)
And we’ll cut back to the side. And so thing that’s so interesting me about this whole process is that it’s this many-step between a human and an AI. Because a human, this fact-checking tool is doing it in order to data for another AI to become more useful to a human. And I think this really shows the of something that we should expect to be much more common in future, where we have humans and machines kind of very carefully and delicately in how they fit into a problem and how want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and the machines are operating in a way that’s and trustworthy. And together we’re able to actually create even more trustworthy machines. And I that over time, if we get this process right, we will be able to impossible problems.
And to give you a sense of just how impossible I’m talking, I think we’re to be able to rethink almost every aspect of how interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years with VisiCalc. I 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 you the ChatGPT take on how to analyze a set like this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able to code, just like a data scientist would. And so you just literally upload a file and ask questions about it. very helpfully, you know, it knows the name of file and 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 names like you saw and then actual data. And from that it’s able to infer what these columns actually mean. Like, that semantic 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 and therefore that’s what these things are and that are integer values and so therefore it’s a number of in the paper,” like all of that, that’s work for a human to do, and the is happy to help with it.
Now I don’t know what I want to ask. So fortunately, you can ask machine, “Can you make some exploratory graphs?” And once again, this is a super high-level instruction with of intent behind it. But I don’t even know what I want. And AI kind of has to infer what I might interested in. And so it comes up with some good ideas, think. So a histogram of the number of authors per paper, time series papers per year, word cloud of the paper titles. All of that, I think, will be pretty to see. And the great thing is, it can actually do it. Here we go, a bell curve. You see that three is kind of the common. It’s going to then make this nice plot of the per year. Something crazy is happening in 2023, though. Looks we were on an exponential and it dropped off the cliff. What could be going there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you can see all these wonderful things that in these titles.
But I’m pretty unhappy about this 2023 thing. It makes this year look bad. Of course, the problem is that the year is not over. So I’m going push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by 13?] So April 13 was the cut-off date I believe. Can you use that to make a projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, I feel there was more I wanted out of the machine here. I really 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 additional piece of, you know, guidance. And under the hood, the is just writing code 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, but know what I want.
Now we’ll cut back to the slide again. This slide shows parable of how I think we … A vision of how may end up using this technology in the future. A person brought his very sick dog the vet, and the veterinarian made a bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. In the meanwhile, he provided blood test, like, the full medical records, to GPT-4, said, “I am not a vet, you need to to a professional, here are some hypotheses.” He brought that information to 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 a brainstorming partner was able to achieve an outcome that would not have happened otherwise. I this is something we should all reflect on, think as we consider how to integrate these systems into world.
And one thing I believe really deeply, is that getting right is going to require participation from everyone. And that’s for how we want it to slot in, that’s for setting the of the road, for what an AI will 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 so we all to become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I that we can achieve the OpenAI mission of ensuring that general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect within every mind out here there’s a feeling of reeling. Like, I suspect a very large number of people viewing this, you look at that and you think, “Oh my goodness, much every single thing about the way I work, I need to rethink.” Like, there’s new possibilities there. Am I right? Who thinks that they’re to rethink 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 question actually is just how the hell have you done this?
(Laughter)
OpenAI has a few employees. Google has thousands of employees working on artificial intelligence. is it you who’s come up with this technology shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on shoulders giants, right, there’s no question. If you look at compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the first was just to confront reality as it lays. And that we just thought really hard like: What is it going to take to make progress here? tried a lot of things that didn’t work, so you only the things that did. And I think that the important thing has been to get teams of people who are very from each other to work together harmoniously.
CA: Can we have the water, by the way, brought here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about the that you saw something in these language models that meant if you continue to invest in them and grow them, something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story there is 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, one person was working on training a model to the next character in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model predict 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 if a review was positive or negative. I mean, today we are just like, on, anyone can do that. But this was the first time that you saw emergence, this sort of semantics that emerged from this underlying syntactic process. And there knew, you’ve got to scale this thing, you’ve got see where it goes.
CA: So I think this helps the riddle that baffles everyone looking at this, because these are described as prediction machines. And yet, what we’re seeing of them feels … it just feels impossible that that could come from a prediction machine. Just the you showed us just now. And the key idea emergence is that when you get more of a thing, suddenly different things emerge. happens all 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. a city where a few houses together, it’s just together. But as you grow the number of houses, things emerge, like and cultural centers and traffic jams. Give me one moment for 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 in ChatGPT, if you 40-digit numbers —
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 thing is actually, if you have it add like 40-digit number plus a 35-digit number, it’ll often get wrong. And so 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 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 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 incredible number of pieces of text. And it is learning that you didn’t know that it was going to 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 emergent capabilities. And to do that actually, one of things I think is very undersung in this field is sort of engineering quality. Like, we 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 to get every single piece of the stack engineered properly, and then you start doing these predictions. There are all these incredibly scaling curves. They tell you something deeply fundamental about intelligence. If you look at our GPT-4 post, you can see all of these curves in 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 times or 1,000 times smaller. And so there’s something this that is actually smooth scaling, even though it’s early days.
CA: So here is, one of the big fears then, arises from this. If it’s fundamental to what’s happening here, as you scale up, things emerge that you can maybe predict in some level of confidence, but it’s of surprising you. Why isn’t there just a huge of something truly terrible emerging?
GB: Well, I think all of these are questions degree and scale and timing. And I think one thing people miss, too, is sort of integration with the world is also this incredibly emergent, sort of, very thing too. And so that’s one of the reasons we think it’s so important to deploy incrementally. And so I think that we kind of see right now, if you look at this talk, a lot of what I on is providing really high-quality feedback. Today, the tasks that we do, you can them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven was the correct answer. But even summarizing 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 thing will be that we take this step by step. And that we say, OK, we move on to book summaries, we have to supervise this task properly. We have to build up track record with these machines that they’re able to carry out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways of scaling this, sort like making the machine be aligned with you.
CA: So we’re going to hear later in session, there are critics who say that, you know, there’s no understanding inside, the system is going to always — we’re never going to that it’s not generating errors, that it doesn’t have common sense and so forth. Is your belief, Greg, that it is true at any one moment, but that the expansion of scale and the human feedback that you talked about is basically going to it on that journey of actually getting to things truth and wisdom and so forth, with a high degree of confidence. Can be sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I that is where we’re headed. And I think that OpenAI approach here has always been just like, let reality you in the face, right? It’s like this field the field of broken promises, of all these experts saying X is to happen, Y is how it works. People have 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. But I think that our has 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 we can move on to a new paradigm. And we just haven’t exhausted fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do 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, things are going to emerge, it is out there. So, you know, the original story that I heard OpenAI when you were founded as a nonprofit, well were there as the great sort of check on big 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 of slowing the field down, need be. Or at least that’s kind of what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent such through the tech world that now Google and Meta so forth are all scrambling to catch up. And of their criticisms have been, you are forcing us to put this out without proper guardrails or we die. You know, how do you, like, make case that what you have done is responsible here and reckless.
GB: Yeah, we think about these questions all the time. Like, seriously the time. And I don’t think we’re always going get it right. But one thing I think has incredibly important, from the very beginning, when we were thinking about how to build artificial general intelligence, have it 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 you push “go,” and you hope you got it right. I don’t know how to execute that plan. Maybe else does. But for me, that was always terrifying, it didn’t feel right. so I 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 input. You do have, before these machines are perfect, they are super powerful, that you actually have the to see them in action. And we’ve seen it from GPT-3, right? GPT-3, we were afraid that the number one thing people were going to do with it was misinformation, try to tip 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 sitting in room, there’s a box on the table. You believe that in that is something that, there’s a very strong chance it’s absolutely glorious that’s going to give beautiful gifts to family and to everyone. But there’s actually also a one percent in the small 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, not. I think you don’t do it that way. And honestly, like, I’ll tell a story that I haven’t actually told before, which is shortly after we started OpenAI, I remember I was in Puerto Rico for an AI conference. I’m sitting the hotel room just looking out over this wonderful water, all people having a good time. And you think about it for a moment, if you could choose for that Pandora’s box to be five years away or 500 years away, which would pick, right? On the one hand you’re like, well, maybe for you personally, it’s better to it be five years away. But if it gets to 500 years away and people get more time to get it right, which you pick? And you know, I just really felt it in the moment. I like, of course you do the 500 years. My was in the military at the time and like, puts his life on the line in a much real way than any of us typing things in computers and developing this at the time. And so, yeah, I’m really sold on the you’ve got approach this right. But I don’t think that’s quite playing the as it truly lies. Like, if you look at the whole history computing, I really mean it when I say that this is an industry-wide or 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 making faster computers, we’re still improving the algorithms, all these things, they are happening. And if you don’t them together, you get an overhang, which means that if someone does, the moment that someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety precautions you get. And so I that one thing I take away is like, even you about development of other sort of technologies, think about nuclear weapons, people talk about being like a to one, sort of, change in what humans could do. But I actually think that you look at capability, it’s been quite smooth over time. And so the history, think, of every technology we’ve developed has been, you’ve got to it incrementally and you’ve got to figure out how to manage for each moment that you’re increasing it.
CA: So I’m hearing is that you … the model you want us to have is that we have this extraordinary child that may have superpowers that take humanity to a new place. It is our collective responsibility to provide the guardrails for this child to collectively teach it be wise and not to tear us all down. that basically the model?
GB: I think it’s true. And 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 important today that we all do get literate in this technology, figure out how to provide the feedback, what we want from it. And my hope is that will continue to be the best path, but it’s good we’re honestly having this debate because we wouldn’t otherwise if weren’t out there.
CA: Greg Brockman, thank you so much for coming to TED and blowing minds.
(Applause)