We started OpenAI years ago because we felt like something really interesting happening in AI and we wanted to help steer in a positive direction. It’s honestly just really amazing to see how far this whole field come since then. And it’s really gratifying to hear from people like Raymond who are the technology we are building, and others, for so wonderful things. We hear from people who are excited, we hear from people who concerned, we hear from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels like we’re an historic period right now where we as a world are going to define technology that will be so important for our society going forward. And I that we can manage this for good.
So today, I want to show you the current state of technology and some of the underlying design principles that we hold dear.
So the first thing I’m to show you is what it’s like to build a for an AI rather than building it for a human. So have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest 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 for you that you get out of ChatGPT. And here we go, it’s not just the idea for meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just generate images this case — sorry, it doesn’t generate text, it also an image. And that is something that really expands power of what it can do on your behalf in of carrying out your intent. And I’ll point out, this all a live demo. This is all generated by AI as we speak. So I actually don’t even what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve ChatGPT with other tools too, for 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 up here that says “use DALL-E app.” And by the way, this is coming to you, all ChatGPT users, over months. And you can look under the hood and see that it actually did was write a prompt just like a human could. And so you sort of this ability to inspect how the machine is using tools, which allows us to provide feedback to them.
Now it’s saved for later, and let show you what it’s like to use that information and to integrate with applications too. You can say, “Now make a shopping list for the tasty thing I suggesting earlier.” And make it a little tricky for the AI. “And tweet it out for all the viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want know 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 any situation. And this, I think, shows a new of thinking about the user interface. Like, we are so used to thinking of, well, we these apps, we click between them, we copy/paste between them, and usually it’s a great within an app as long as you kind of know the menus and know all the options. Yes, would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on 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 little piece what’s supposed to happen.
And as I said, this is a live demo, so sometimes unexpected will happen to us. But let’s take a look at the Instacart list while we’re at it. And you can see we sent list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional UI is still very valuable, right? If you at this, you still can click through it and sort of the actual quantities. And that’s something that I think shows that they’re not away, traditional UIs. It’s just we have a new, way to build them. And now we have a that’s been drafted for our review, which is also a important thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the of the AI if we want to. And so this talk, you will be able to access this yourself. And we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, important thing about how we build this, it’s not 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 old idea. If you back to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, can learn it. You could build a machine, like a human child, and then it through feedback. Have a human teacher who provides rewards and punishments as tries things out and does things that are either or bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called a child through an unsupervised learning process. We just show it 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. example, if you’re shown a math problem, the only way to actually complete that problem, to say what comes next, that green nine up there, is to actually solve math problem.
But we actually have to do a step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. We have the AI try multiple things, give us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the AI used produce that answer. And this allows it to generalize. It allows it to teach, to sort of your intent and apply it in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes things we 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 to able to teach students wonderful things. Only one problem, doesn’t double-check students’ math. If there’s some bad math there, it will happily pretend that one plus one equals and run with it.” So we had to collect some feedback data. Sal Khan himself very kind and offered 20 hours of his own time to provide to the machine alongside our team. And over the course of a couple of months we able to teach the AI that, “Hey, you really should push back on humans in this specific kind scenario.” And we’ve actually made lots and lots of improvements the models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up a bat signal to our to say, “Here’s an area of weakness where you gather feedback.” And so when you do that, that’s one way that we really listen to our and make sure we’re building something that’s more useful for everyone.
Now, providing high-quality is a hard thing. If you think about asking a kid to clean their room, if you’re doing is inspecting the floor, you don’t know if you’re teaching them to stuff all the toys in the closet. This is a nice DALL-E-generated image, the way. And the same sort of reasoning applies to AI. we move 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 ask GPT-4 a question like this, of how much time passed between these two blogs on unsupervised learning and learning from human feedback. And the model says months passed. But is it true? Like, these models not 100-percent reliable, although they’re getting better every time provide some feedback. But we can actually use the AI to fact-check. And it actually check its own work. You can say, fact-check for me.
Now, in this case, I’ve actually given the AI a new tool. This one a browsing tool 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 for this and it actually does the search. It it finds the publication date and the search results. then is issuing another search query. It’s going to click into the blog post. And all of 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 in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. out come citations so you can actually go and very easily verify any piece 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 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 a human an AI. Because a human, using this fact-checking tool doing it in order to produce data for another to become more useful to a human. And I this really shows the shape of something that we should to be much more common in the future, where we have humans and machines kind of very and delicately designed in how they fit into a and how we want to solve that problem. We make sure that the humans are the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. And I think that over time, we get this process right, we will be able to solve impossible problems.
And to you a sense of just how impossible I’m talking, think we’re going to be able to rethink almost every of how we interact with computers. For example, think about spreadsheets. They’ve been around in form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really that much in that time. And here is a spreadsheet of all the AI papers on the arXiv for past 30 years. There’s about 167,000 of them. And you can see the data right here. But let me show you the ChatGPT take on how analyze a data set like this.
So we can give ChatGPT access yet another tool, this one a Python interpreter, so it’s to run code, just like a data scientist would. And 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 file, “I’ll parse it for you.” The only information here is the of the file, the column names like you saw and then actual data. And from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s what things are and that these are integer values and so therefore it’s a number authors in the paper,” like all of that, that’s work for a human do, and the AI 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 a super high-level instruction with lots of intent behind it. I don’t even know what I want. And the AI kind has to infer what I might be interested in. And so comes 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 thing is, it can actually do it. Here we go, a nice bell curve. see 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 and it off the cliff. What could be going on there? By the way, this is Python code, you can inspect. And then we’ll 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 really bad. Of course, the problem is that the year not over. So I’m going to push back on 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 I believe. Can you use that to a fair projection? So we’ll see, this is the 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 of an for it to have sort of, inferred magically that this what 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 back to the slide again. This slide shows a parable of how I think we … vision of how we may end up using this technology in the future. A person 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 meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, need to talk to a professional, here are some hypotheses.” brought that information to a second vet who used it to the dog’s life. Now, these systems, they’re not perfect. You overly rely on them. But this story, I think, shows that a human with a professional and with ChatGPT as a brainstorming partner was able to an outcome that would not have happened otherwise. I think this something we should all reflect on, think about as consider how to integrate these systems into our world.
And one thing I believe really deeply, is getting AI right is going to require participation from everyone. And that’s deciding 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 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 have become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe that we achieve the OpenAI mission of ensuring that artificial general benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s feeling of reeling. Like, I suspect that a very number of people viewing this, you look at that you think, “Oh my goodness, pretty much every single 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 first question actually just 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 building on shoulders giants, right, there’s no question. If you look at the compute progress, algorithmic progress, the data progress, all of those are really industry-wide. But think within OpenAI, we made a lot of very choices from the early days. And the first one just to confront reality as it lays. And that just thought really hard about like: What is it going to take make progress here? We tried a lot of things didn’t work, so you only see the things that did. I think that the most important thing has been to teams of people who are very different from each other to work together harmoniously.
CA: we have the water, by the way, just brought here? think we’re going to need it, it’s a dry-mouth topic. But isn’t there something just about the fact that you saw something in these language models that meant if you continue to invest in them and grow them, that at some point might emerge?
GB: Yes. And I think that, I mean, honestly, I think the there is pretty illustrative, right? I think that high level, deep learning, we always knew that was what we wanted to be, was deep learning lab, and exactly how to do it? think that in the early days, we didn’t know. tried a lot of things, and one person was on training a model to predict the next character in reviews, and he got a result where — this a syntactic process, you expect, you know, the model predict 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 you if a review was or negative. I mean, today we are just like, come on, can do that. But this was the first time that you saw this emergence, this sort semantics that emerged from this underlying syntactic process. And there knew, you’ve got to scale this thing, you’ve got to see where it goes.
CA: So think this helps explain the riddle that baffles everyone at this, because these things are described as prediction machines. And yet, we’re seeing out of them feels … it just impossible that that could come from a prediction machine. Just the stuff you showed just now. And the key idea of 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 these ant colonies that show completely emergent, different behavior. Or a city where few houses together, it’s just houses together. But as you grow the of houses, things emerge, like suburbs and cultural centers and traffic jams. Give me moment for you when you saw just something pop that blew your mind that you just did not see coming.
GB: Yeah, well, so you try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model do it, which means it’s really learned an internal circuit for to do it. And the really interesting thing is actually, if you have add like a 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see that it’s really the process, but it hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are in the universe. it had to have learned something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to adding numbers of arbitrary lengths.
CA: So what’s happened here that you’ve allowed it to scale up and look at incredible number of pieces of text. And it is learning things you didn’t know that it was going to be capable 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 of the things I think is very in this field is sort of engineering quality. Like, we to rebuild our entire stack. When you think about building a rocket, every tolerance has to incredibly tiny. Same is true in machine learning. You have get every single piece of the stack engineered properly, and you can start doing these predictions. There are all these incredibly smooth curves. They tell you something deeply fundamental about intelligence. If you at our GPT-4 blog post, you can see all of curves in there. And now we’re starting to be able predict. So 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 is smooth scaling, even though it’s still early days.
CA: So here is, one of the big then, that arises from this. If it’s fundamental to what’s happening here, that as you scale up, things that you can maybe predict in some level of confidence, but it’s capable of surprising you. Why isn’t just a huge risk of something truly terrible emerging?
GB: Well, I think all of these are of degree and scale and timing. And I think one thing people miss, too, is sort of the with the world is also this incredibly emergent, sort of, powerful thing too. And so that’s one of the reasons that think it’s so important to deploy incrementally. And so I think what we kind of see right now, if you look at this talk, lot of what I focus on is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to at that math problem and be like, no, no, no, machine, seven was the answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, how do know if this book summary is any good? You have to read the book. No one wants to do that.
(Laughter) And so I think that the important thing will be we take this step by step. And that we say, OK, we move on to book summaries, we have to this task properly. We have to build up a record with these machines that they’re able to actually carry our intent. And I think we’re going to have to produce better, more efficient, more reliable ways of scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later in this session, there are critics who that, you know, there’s no real understanding inside, the system is going always — we’re never going to 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 expansion of the scale and the human that you talked about is basically going to take it that journey of actually getting to things like truth and 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 hit you in the face, right? It’s like this field is the field of broken promises, all these experts saying X is going to happen, Y is how works. People have been saying neural nets aren’t going work for 70 years. They haven’t been right yet. They might be right 70 years plus one or something like that is what you need. I think that our approach 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 how can move on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that right way to do this is to put it out there in public and then harness this, you know, instead of just your team giving feedback, world is now giving feedback. But … If, you know, bad things are going to emerge, it out there. So, you know, the original story that I heard on OpenAI when you were founded a nonprofit, well you were there as the great sort check on the big companies doing their unknown, possibly thing with AI. And you were going to build models sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. at least that’s kind of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, such shockwaves through the tech world that now Google and Meta and so forth all scrambling to catch up. And some of their criticisms have been, you forcing us to put this out here without proper guardrails we die. You know, how do you, like, make the case that what you have is responsible here and not reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t think we’re always to get it right. But one thing I think been incredibly important, from the very beginning, when we were thinking about how build artificial general intelligence, actually have it benefit all of humanity, like, how are you supposed do that, right? And that default plan of being, well, you in secret, you get this super powerful thing, and then you figure the safety of it and then you push “go,” and you hope you got it right. I don’t how to execute that plan. Maybe someone else does. But me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the other path that I see, which is that you do let reality hit you the face. And I think you do give people time to give input. You do have, before these are perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve it from GPT-3, right? GPT-3, we really were afraid that the number one thing people were going do with it was generate misinformation, try to tip elections. Instead, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam bad, but there are things that are much worse. Here’s a thought for you. Suppose you’re sitting in a room, there’s a box on the table. You believe in that box is something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful gifts to family and to everyone. But there’s actually also a one thing in the small print there that says: “Pandora.” And there’s a chance that actually could unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely 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 that shortly after we started OpenAI, remember I was in Puerto Rico for an AI conference. I’m sitting in hotel room just looking out over this wonderful water, all people having a good time. And you think about for a moment, if you could choose for basically 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 have it be five years away. if it gets to be 500 years away and people get more to get it right, which do you pick? And know, I just really felt it in the moment. I like, of course you do the 500 years. My brother in the military at the time and like, he puts his on the line in a much more real way than any us typing things in computers and developing this technology at time. And so, yeah, I’m really sold on the you’ve got to this right. But I don’t think that’s quite playing field as it truly lies. Like, if you look at the whole history of computing, really mean it when I say that this is an industry-wide or even just almost like human-development- of-technology-wide shift. And the more that you sort of, don’t put together the pieces that there, right, we’re still making faster computers, we’re still improving algorithms, all of these 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 connect to the circuit, then you suddenly have this powerful thing, no one’s had any time to adjust, who knows what kind of safety you get. And so I think that one thing take away is like, even you think about development other sort of technologies, think about nuclear weapons, people talk being like a zero to one, sort of, change what humans could do. But I actually think that if you look capability, it’s been quite smooth over time. And so the history, think, of every technology we’ve developed has been, you’ve got do it incrementally and you’ve got to figure out how to manage for each moment that you’re increasing it.
CA: So what I’m hearing is that you … the you want us to have is that we have birthed extraordinary child that may have superpowers that take humanity to a new place. It is our collective responsibility to provide the guardrails for child to collectively teach it to be wise and not to us all down. Is that basically the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve got to take 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 it. And my hope is that that will continue to be the best path, it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much for coming to and blowing our minds.
(Applause)