We started OpenAI seven years ago because felt like something really interesting was happening in AI and we wanted to steer it in a positive direction. It’s honestly just really amazing to see how far this whole has come since then. And it’s really gratifying to hear people like Raymond who are using the technology we building, and others, for so many wonderful things. We hear from people who are excited, hear from people who are concerned, we hear from people feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering an historic period right now where as a world are going to define a technology that will so important for our society going forward. And I believe that we can manage this good.
So today, I want to show you the current state of that technology some of the underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s to build a tool for an AI rather than it for a human. So we have a new DALL-E model, which images, and we are exposing it as an app for to use on your behalf. And you can do things ask, you know, suggest a nice post-TED meal and draw a picture of it.
(Laughter)
Now get all of the, sort of, ideation and creative back-and-forth and taking care of the details for you you get out of ChatGPT. And here we go, it’s not 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 images in this case — sorry, doesn’t generate text, it also generates an image. And is something that really expands the power of what it can on your behalf in terms of carrying out your intent. 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 we’re going to see. This looks wonderful.
(Applause)
I’m getting just looking at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And the interesting about these tools is they’re very inspectable. So you get this little pop here that 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 and see that what it actually was write a prompt just like a human could. And so you sort have this ability to inspect how the machine is these tools, which allows us to provide feedback to them.
Now it’s for later, and let me show you what it’s like to use that information to integrate with other applications too. You can say, “Now make a shopping list for tasty thing I was suggesting earlier.” And make it a little tricky 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 see that ChatGPT is selecting all these different tools without having to tell it explicitly which ones to use in situation. And this, I think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s a experience within an app as long as you kind of know the menus and all the options. Yes, I would like you to. Yes, please. Always good to be polite.
(Laughter)
And by this unified language interface on top of tools, the AI is able sort of take away all those details from you. So you don’t have to 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 sometimes the unexpected will happen us. But let’s take a look at the Instacart shopping list while we’re it. And you can see we sent a list of ingredients Instacart. Here’s everything you need. And the thing that’s really interesting that the traditional UI is still very valuable, right? If you at this, you still can click through it and sort modify the actual quantities. And that’s something that I think shows that they’re going away, traditional UIs. It’s just we have a new, augmented to build them. And now we have a tweet that’s been drafted our review, which is also a very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able to the work of the AI if we want to. And so after this talk, you will be 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 not just 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 very high-level questions? to do this, we use an old idea. If you go back to Turing’s 1950 paper 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, then teach it through feedback. Have a human teacher who provides rewards punishments as it tries things out and does things are either good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what Turing would have a child machine through an unsupervised learning process. We show it the whole world, the whole internet and say, “Predict what comes next in text you’ve seen before.” And this process imbues it with all of wonderful skills. For example, if you’re shown a math problem, the only way to complete that math problem, to say what comes next, that nine up there, is to actually solve the math problem.
But actually have to do a second step, too, which is to teach the AI what do with those skills. And for this, we provide feedback. We have the AI out multiple things, give us multiple suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the specific thing that the said, but very importantly, the whole process that the AI used to produce answer. And this allows it to generalize. It allows it to teach, to sort infer your intent and apply it in scenarios that it hasn’t before, that it hasn’t received feedback.
Now, sometimes the things have to teach the AI are not what you’d expect. For example, we first showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going be able to teach students wonderful things. Only one problem, doesn’t double-check students’ math. If there’s some bad math in there, it will pretend that one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan himself was very kind and offered 20 hours his own time to provide feedback to the machine alongside team. And over the course of a couple of months were able to teach the AI that, “Hey, you really push back on humans in this specific kind of scenario.” we’ve actually made lots and lots of improvements to the models 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 feedback.” And so when you do that, that’s one way we really listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback a hard thing. If you think about asking a kid to their room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff the toys in the closet. This is a nice DALL-E-generated image, by the way. And the same sort of applies to AI. As we move to harder tasks, will have to scale our ability to provide high-quality feedback. But for this, the AI itself happy to help. It’s happy to help us provide even better and to scale our ability to supervise the machine as time goes on. And me show you what I mean.
For example, you can ask GPT-4 a question like this, how much time passed 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 better 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 given the AI a new tool. This one is browsing tool where the model can issue search queries and click web pages. And it actually writes out its whole chain thought as 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 search results. It then is issuing another search query. It’s to click into the blog post. And all of this you do, but it’s a very tedious task. It’s not a thing that humans really want do. It’s much more fun to be in the driver’s seat, to in this manager’s position where you can, if you want, triple-check the work. And out come citations so you actually go and very easily verify 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 thing that’s so interesting to me about this whole is that it’s this many-step collaboration between a human 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 the shape of something that we expect to be much more common in the future, where have humans and machines kind of very carefully and delicately in how they fit into a problem and how we want to solve problem. We make sure that the humans are providing management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re able actually create even more trustworthy machines. And I think over time, if we get this process right, we will able to solve impossible problems.
And to give you a sense of just how I’m talking, I think we’re going to be able to rethink almost every of how we interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years ago with VisiCalc. I don’t they’ve really changed that much in that time. And here is a specific spreadsheet all the AI papers on the arXiv for the past 30 years. There’s about 167,000 of them. And can see there the data right here. But let me show you the ChatGPT take how to analyze a data set like this.
So can give ChatGPT access to yet another tool, this one a interpreter, so it’s able to run code, just like a data scientist would. And so you just literally upload a file and ask questions about it. And helpfully, you know, it knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The information here is the name of the file, the column like you saw and then the actual data. And from that it’s to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, put together world knowledge of knowing that, “Oh yeah, arXiv is site that people submit papers and therefore that’s what these are and that these are integer values and so therefore it’s number of authors in the paper,” like all of that, that’s work for a human to do, and AI is happy to help with it.
Now I don’t know what I want to ask. So fortunately, you can ask the machine, “Can make some exploratory graphs?” And once again, this is super high-level instruction with lots of intent behind it. But I don’t know what I want. And the AI kind of has infer 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 per year, word cloud of the paper titles. All 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 that three is kind of the most common. It’s to then make this nice plot of the papers per year. Something is happening in 2023, though. Looks like we were on an exponential it dropped off the cliff. What could be going on there? the way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all these wonderful that appear in these titles.
But I’m pretty unhappy this 2023 thing. It makes this year look really bad. Of course, the is that the year is not over. So I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you that to make a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel like there was I wanted out of the machine here. I really wanted it to notice this thing, maybe it’s a bit of an overreach for it to have sort of, inferred magically that this what I wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing again, so if you want to inspect what it’s doing, it’s very possible. And now, it does the projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back to the slide again. This slide shows a of how I think we … A vision of how we may end up using this technology in future. A person brought his very sick dog to the vet, the veterinarian made a bad call to say, “Let’s just wait and see.” And dog would not be here today had he listened. In the meanwhile, he provided blood test, like, the full medical records, to GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” He that information to a second vet who used it to the dog’s life. Now, these systems, they’re not perfect. You cannot overly on them. But this story, I think, shows that a human a medical professional and with ChatGPT as a brainstorming partner was able to achieve an outcome that not have happened otherwise. I think this is something should all reflect on, think about as we consider how to these 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 will won’t do. And if there’s one thing to take away from talk, it’s that this technology just looks different. Just different from anything people had anticipated. And so all have to become literate. And that’s, honestly, one of the reasons released ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring that artificial general benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I that within every mind out here there’s a feeling of reeling. Like, I suspect that a large number of people viewing this, you look at that and you think, “Oh my goodness, pretty much single thing about the way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having rethink the way that we do things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, I my first question actually is just how the hell you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of employees on artificial intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, the is, we’re all building on shoulders of giants, right, there’s no question. If look at the compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think within OpenAI, we made lot of very deliberate choices from the early days. And the first one was just confront reality as it lays. And that we just really hard about like: What is it going to take to progress here? We tried a lot of things that didn’t work, you only see the things that did. And I think that most 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 a dry-mouth topic. But isn’t something also just about the fact that you saw something these language models that meant that if you continue to invest in and grow them, that something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story is pretty illustrative, right? I think that high level, deep learning, like always knew that was what we wanted to be, was a deep learning lab, exactly how to do it? I think that in early days, we didn’t know. We tried a lot of things, and one person was on training a model to predict the next character Amazon reviews, and he got a result where — this a syntactic process, you expect, you know, the model will where the commas go, where the nouns and verbs are. But he actually 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 this emergence, this sort of semantics that emerged from this syntactic process. And there we knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps explain riddle that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing out of them feels … it just feels that that could come from a prediction machine. Just stuff you showed us just now. And the key idea of emergence is that when you get more a thing, suddenly different things emerge. It happens all the time, ant colonies, single ants run around, you bring enough of them together, you get these ant colonies that show completely emergent, different behavior. Or 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 one moment for you when you saw just something pop that just your mind that you just did not see coming.
GB: Yeah, well, so you can try this ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will it, which means it’s really learned an internal circuit for to do it. And the really interesting thing is actually, if you it add like a 40-digit number plus a 35-digit number, it’ll often get it wrong. so you can see that it’s really learning the process, it hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s atoms than there are in the universe. So it had to have learned something general, but it hasn’t really fully yet learned that, Oh, I can sort of generalize this to adding numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to scale up and at an incredible number of pieces of text. And is learning things that you didn’t know that it was to be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is predicting of these emergent capabilities. And to do that actually, one the things I think is very undersung in this is sort of engineering quality. Like, we had to rebuild our entire stack. you think about building a rocket, every tolerance has be incredibly tiny. Same is true in machine learning. You have get every single piece of the stack engineered properly, and then you can start doing 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 in there. And now we’re starting to be able to predict. So were able to predict, for example, the performance on coding problems. We basically look at models that are 10,000 times or 1,000 times smaller. so there’s something about this that is actually smooth scaling, though it’s still early days.
CA: So here is, of the big fears then, that arises from this. If it’s to what’s happening here, that as you scale up, things emerge that you can maybe predict in some 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 of these are questions of degree and scale and timing. And I one thing people miss, too, is sort of the integration with the world also this incredibly emergent, sort of, very powerful thing too. And so that’s one of the that we think it’s so important to deploy incrementally. And so I think that what we kind see right now, if you look at this talk, a lot of I focus on is providing really high-quality feedback. Today, the 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. But summarizing a book, like, that’s a hard thing to supervise. Like, how do you know if this summary is any good? You have to read the book. No one wants to do that.
(Laughter) And so think that the important thing will be that we this step by step. And that we say, OK, as we on to book summaries, we have to supervise this task properly. We have build up a track record with these machines that they’re able to actually carry our intent. And I think we’re going to have to produce even better, more efficient, more reliable of scaling this, sort of like making the machine be aligned with you.
CA: we’re going to hear later in this session, there are critics who say that, you know, there’s real understanding inside, the system is going to always — we’re never going to know it’s not generating errors, that it doesn’t have common and so forth. Is it your belief, Greg, that it is true at any one moment, but the expansion of the scale and the human feedback that you about is basically going to take it on that of actually getting to things like truth and wisdom and 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 has always been just like, let reality hit you in the face, right? It’s like this is the field of broken promises, of all these experts saying X going to happen, Y is how it works. People have been saying neural nets aren’t going work for 70 years. They haven’t been right yet. They be right maybe 70 years plus one or something like is what you need. But I think that our has always been, you’ve got 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 a paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s quite controversial stance you’ve taken, that the right way to do this is put it out there in public and then harness all this, you know, instead just your team giving feedback, the world is now giving feedback. But … If, know, bad things are going to emerge, it is 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 the big companies their unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held accountable and was capable of slowing the field down, need be. Or at least that’s kind of what I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, 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 or we die. know, how do you, like, make the case that what you have done responsible here and not reckless.
GB: Yeah, we think about these questions the time. Like, seriously all the time. And I don’t think we’re always to get it right. But one thing I think has been incredibly important, from the beginning, when we were thinking about how to build artificial general intelligence, actually have it all of humanity, like, how are you supposed to that, right? And that default plan of being, well, you in secret, you get this super powerful thing, and then you figure out safety of it and then you push “go,” and you hope you it right. I don’t know how to execute that plan. Maybe someone else does. But for me, that was terrifying, it didn’t feel right. And so I think that this alternative approach is the only other that I see, which is that you do let reality hit you the face. And I think you do give people time give input. You do have, before these machines are perfect, before they are super powerful, you actually have the ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number one people were going to do with it was generate misinformation, try to tip elections. Instead, the one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are that are much worse. Here’s a thought experiment for you. 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 give beautiful to your family and to everyone. But there’s actually a one percent thing in the small print there says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils on world. Do you open that box?
GB: Well, so, absolutely not. I you don’t do it that way. And honestly, like, I’ll tell you story that I haven’t actually told before, which is that shortly we started OpenAI, I remember I was in Puerto for an AI conference. I’m sitting in the hotel room just out over this wonderful water, all these people having 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 you pick, right? On the hand you’re like, well, maybe for you personally, it’s better to it be five years away. But if it gets be 500 years away and people get more time get it right, which do you pick? And you know, just really felt it in the moment. I was like, course you do the 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 than any of typing things in computers and developing this technology at time. And so, yeah, I’m really sold on the you’ve got approach this right. But I don’t think that’s quite the field as it truly lies. Like, if you look at the whole history of computing, I mean it when I say that this is an industry-wide or even almost like a human-development- of-technology-wide shift. And the more that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re still improving the algorithms, all of things, they are happening. And if you don’t put them together, you get an overhang, which that if someone does, or the moment that someone does manage to connect to the circuit, then you have this very powerful thing, no one’s had any time to adjust, knows what kind of safety precautions you get. And so I think that thing I take away is like, even you think about of other sort of technologies, think about nuclear weapons, people talk about being like zero to one, sort of, change in what humans could do. But actually think that if you look at capability, it’s quite smooth over time. And so the history, I think, of every we’ve developed has been, you’ve got to do it incrementally and you’ve got to out how to manage it for each moment that you’re it.
CA: So what I’m hearing is that you … the model you want us to have that we have birthed this extraordinary child that may have superpowers that take to a whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it to wise and not to tear us all down. Is that basically the model?
GB: I it’s true. And I think it’s also important to this may shift, right? We’ve got to take each step we encounter it. And I think it’s incredibly important today that we all do get literate this technology, figure out how to provide the feedback, decide what want from it. And my hope is that that will continue to the best path, but it’s so 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 to TED and blowing our minds.
(Applause)