We started OpenAI seven years ago we felt like something really interesting was happening in AI and we wanted to steer it in a positive direction. It’s honestly just really to see how far this whole field has come since then. it’s really gratifying to hear from people like Raymond who are using technology we are building, and others, for so many wonderful things. We hear from people are excited, we hear from people who are concerned, we hear from people who feel both those at once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now where as a world are going to define a technology that be so important for our society going forward. And I believe that can manage this for good.
So today, I want to you the current state of that technology and some of the underlying design principles we hold dear.
So the first thing I’m going to you is what it’s like to build a tool for an rather than building it for a human. So we have a new DALL-E model, generates images, and we are exposing it as an for ChatGPT to use on your behalf. And you can do things like ask, know, suggest a nice post-TED meal and draw a of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of the details for you that you out of ChatGPT. And here we go, it’s not just the idea for meal, but a very, very detailed spread. So let’s see what we’re going get. But ChatGPT doesn’t just generate images in this — sorry, it doesn’t generate text, it also generates an image. And that something that really expands the power of what it do 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 what we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT other tools too, for example, memory. You can say “save for later.” And the interesting thing about these tools is they’re very inspectable. you get this little pop up here that says “use the DALL-E app.” 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 like a human could. And so you of have this ability to inspect how the machine is these 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 other too. You can say, “Now make a shopping list for the tasty thing I was earlier.” And make it a little tricky for the AI. “And tweet it out all the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how it tastes.
But can see that ChatGPT is selecting all these different tools me having to tell it explicitly which ones to use in any situation. And this, I think, a new way of thinking about the user interface. Like, we are used to 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 long as you kind of the menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And by having this language interface on top of tools, the AI is to sort of take away all those details from you. So you don’t have to be the one who out every single sort of little piece of what’s to happen.
And as I said, this is a live demo, so sometimes the will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And you see we sent a list of ingredients to Instacart. Here’s everything need. And the thing that’s really interesting is that the traditional is still very valuable, right? If you look at this, you still can through it and sort of modify the actual quantities. And that’s something that think shows that they’re not going away, traditional UIs. It’s just we have a new, way to build them. And now we have a tweet that’s been drafted our review, which is also a very important thing. can click “run,” and there we are, we’re the manager, we’re able to inspect, we’re able to the work of the AI if we want to. And after this talk, you will be able to access this yourself. there we go. Cool. Thank you, everyone.
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So we’ll cut back to 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 do we want it to do when we ask these very high-level questions? And to do this, we use an old idea. you go back to Alan Turing’s 1950 paper on the test, he says, you’ll never program an answer to this. Instead, can learn it. You could build a machine, like human child, and then teach it through feedback. Have human teacher who provides rewards and punishments as it tries things out does 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 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 this process it with all sorts of wonderful skills. For example, if you’re a math problem, the only way to actually complete math problem, to say what comes next, that green up there, is to actually solve the math problem.
But we have to do a second step, too, which is teach the AI what to do with those skills. for this, we provide feedback. We have the AI try out multiple things, give us multiple suggestions, then a human rates them, says “This one’s better than that one.” And this reinforces just the specific thing that the AI said, but importantly, the whole process that the AI used to produce 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 before, that it hasn’t received feedback.
Now, sometimes the things we to teach the AI are not what you’d expect. For example, when we showed GPT-4 to Khan Academy, they said, “Wow, this is great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. If there’s some bad in 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 was very and offered 20 hours of his own time to provide feedback the machine alongside our team. And over the course of a couple of months we were to teach the AI that, “Hey, you really should push back humans in this specific kind of scenario.” And we’ve actually made lots lots of improvements to the models this way. And when you push that 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 when you do that, that’s one way that we really to our users and make sure we’re building something that’s useful for everyone.
Now, providing high-quality feedback is a thing. If you think about asking a kid 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 DALL-E-generated image, by the way. And the same sort reasoning 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 is happy to help. It’s happy to help us provide better feedback and to scale our ability to supervise machine as time goes on. And let me show you what I mean.
For example, can ask GPT-4 a question like this, of how much time passed between two foundational blogs on unsupervised learning and learning from human feedback. And the model says two passed. But is it true? Like, these models are 100-percent reliable, although they’re getting better every time we some feedback. But we can actually use the AI fact-check. And it can actually check its own work. You say, fact-check this for me.
Now, in this case, I’ve actually given the AI a new tool. one is a browsing tool where the model can issue search queries and into web pages. And it actually writes out its chain of thought as it does it. It says, I’m just going search for this and it actually does the search. It then it the publication date and the search results. It then is issuing another query. It’s going to click into the blog post. all of this you could do, but it’s a tedious task. It’s not a thing that humans really 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 very easily verify any piece of this whole chain of reasoning. And actually turns out two months was wrong. Two months one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting to about this whole process is that it’s this many-step collaboration between a human and an AI. Because human, using this fact-checking tool is doing it in order to produce data another AI to become more useful to a human. And I think really shows the shape of something that we should expect be much more common in the future, where we have and machines kind of very carefully and delicately designed how they fit into a problem and how we want solve that problem. We make sure that the humans are providing the management, the oversight, the feedback, the machines are operating in a way that’s inspectable and trustworthy. And together we’re able to actually even more trustworthy machines. And I think that over time, if we get this process right, will be able to solve impossible problems.
And to give a sense of just how impossible I’m talking, I think we’re going to able to rethink almost every aspect of how we with computers. For example, think about spreadsheets. They’ve been around in some form since, we’ll say, 40 ago with VisiCalc. I don’t think they’ve really changed that in that time. And here is a specific spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. you can see there the data right here. But me show you the ChatGPT take on how to a data set like this.
So we can give access to yet another tool, this one a Python interpreter, so it’s able to code, just like a data scientist would. And so you can just literally upload a file ask questions about it. And very helpfully, you know, it knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the name of the file, the names like you saw and then the actual data. And that it’s able to infer what these columns actually mean. Like, that semantic wasn’t in there. It has to sort of, put its world knowledge of knowing that, “Oh yeah, arXiv is a site that people submit papers therefore that’s what these things are and that these are integer and so therefore it’s a number of authors in the paper,” like all of that, that’s work for human to do, and the AI is happy to help it.
Now I don’t even know what I want to ask. fortunately, you can ask the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots of intent behind it. But I don’t even what I want. And the AI kind of has to infer what might be interested in. And so it comes up with some good ideas, think. So a histogram of the number of authors per paper, time of papers per year, word cloud of the paper titles. All of that, think, will be pretty interesting to see. And the great thing is, it actually do it. Here we go, a nice bell curve. see that three is kind of the most common. It’s going to then make nice plot of the papers per year. Something crazy is in 2023, though. Looks like we were on an exponential and dropped off the cliff. What could be going on there? By the way, all this Python code, you can inspect. And then we’ll see word cloud. So can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. It makes year look really bad. Of course, the problem is that year is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted April 13?] So April 13 was the cut-off date I believe. Can you use that make a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I like there was more I wanted out of the here. I really wanted it to notice this thing, maybe it’s a little bit of an overreach for to have sort of, inferred magically that this is what I wanted. But inject my intent, I provide this additional piece of, you know, guidance. And under the hood, AI is just writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If you noticed, it even the title. I didn’t ask for that, but it what I want.
Now we’ll cut back to the slide again. This shows a parable of how I think we … A vision of how may end up using this technology in the future. A person brought very sick dog to the vet, and the veterinarian made bad call to say, “Let’s just wait and see.” And the dog would not here today had he listened. In the meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” He brought that to a second vet who used it to save the dog’s life. Now, these systems, they’re perfect. You cannot overly rely on them. But this story, think, shows that a human with a medical professional and with ChatGPT as a partner was able to achieve an outcome that would have happened otherwise. I think this is something we should all reflect on, think about we consider how to integrate these systems into our world.
And one thing I believe really deeply, that getting AI right is going to require participation from everyone. that’s for deciding how we want it to slot in, that’s for the rules of the road, for what an AI will and won’t do. And if there’s one thing take away from this talk, it’s that this technology looks different. Just different from anything people had anticipated. And so all have 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 that within mind out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, 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 just new there. Am I right? Who thinks that they’re having to rethink the way we do things? Yeah, I mean, it’s amazing, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the hell 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 the world?
Greg Brockman: mean, the truth is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the progress, the data progress, all of those are really industry-wide. I think within OpenAI, we made a lot of very choices from the early days. And the first one was just to confront reality it lays. And that we just thought really hard like: What is it going to take to make progress here? We tried a of things that didn’t work, so you only see the things that did. And I think that most important thing has been to get teams of who are very different from each other to work 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 there also 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 something some point might emerge?
GB: Yes. And I think that, I mean, honestly, think the story there is pretty illustrative, right? I think high level, deep learning, like we always knew that was what we wanted be, was a deep learning lab, and exactly how to do it? I that in the early days, we didn’t know. We tried a of things, and one person was working on training a model to predict the next 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 are. But he actually 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, we are just like, come on, anyone can do that. this was the first time that you saw this emergence, sort of semantics that emerged from this underlying syntactic process. And there we knew, you’ve got to scale thing, you’ve got to see where it goes.
CA: So I think helps explain the riddle that baffles everyone looking at this, because things are described as prediction machines. And yet, what we’re seeing out 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 of emergence is that when get more of a thing, suddenly different things emerge. happens all the time, ant colonies, single ants run around, when you enough of them together, you get these ant colonies that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But as grow the number of houses, things emerge, like suburbs and centers and traffic jams. Give me one moment for you when you saw just something that just blew your mind that you just did not see coming.
GB: Yeah, well, so you can 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 how to do it. And the really interesting thing is actually, if have 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 the 40-digit addition table, that’s more atoms than there in the universe. So it had to have learned general, but that it hasn’t really fully yet learned that, Oh, I can sort of this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed to scale up and look at an incredible number of pieces of text. And is learning things that you didn’t know that it was going to be of learning.
GB Well, yeah, and it’s more nuanced, too. So science that we’re starting to really get good at is predicting of these emergent capabilities. And to do that actually, one of the I think is very undersung in this field is sort engineering quality. Like, we had to rebuild our entire stack. When think about building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get single piece of the stack engineered properly, and then can start doing these predictions. There are all these incredibly smooth scaling curves. They tell you deeply fundamental about intelligence. If you look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting to be to predict. So we were able to predict, for example, the on coding problems. We basically look at some models that are 10,000 times or 1,000 times smaller. And there’s something about this that is actually smooth scaling, even it’s still early days.
CA: So here is, one of 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 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 of these are questions of degree and scale and timing. And think one thing people miss, too, is sort of the integration with world is also this incredibly emergent, sort of, very powerful thing too. And so that’s one of reasons that we think it’s so important to deploy incrementally. so I think that what we kind of see right now, 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 inspect them, right? It’s easy to look at that math problem and be like, no, no, no, machine, was the correct answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, do you know if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) And so think that the important thing will be that we take this step by step. And we say, OK, as we move on to book summaries, we have supervise this task properly. We have to build up a track record with these machines that they’re able actually carry out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways of scaling this, sort of like making the be aligned with you.
CA: So we’re going to hear later in this session, there are critics say that, you know, there’s no real understanding inside, the system is going to — we’re never going to know that it’s not generating errors, that it doesn’t have sense and so forth. Is it your belief, Greg, that it is true at one moment, but that the expansion of the scale and the human feedback that you talked is basically going to take it on that journey of actually getting to things like truth wisdom and so forth, with a high degree of confidence. Can 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 that the OpenAI approach 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 experts saying X is going to happen, Y is how it works. have been saying neural nets aren’t going to work 70 years. They haven’t been right yet. They might right maybe 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 the limits of this technology to really see it action, because that tells you then, oh, here’s how we can on to a new paradigm. And we just haven’t exhausted the here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the right to do this is to put it out there in and then harness all this, you know, instead of just your team giving feedback, the world is now feedback. But … If, you know, bad things are going to emerge, it out there. So, you know, the original story that I heard on when you were founded as a nonprofit, well you were as the great sort of check on the big companies doing their unknown, evil 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. Or at least that’s of what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent shockwaves through the tech world that now Google and Meta so forth are all scrambling to catch up. And some of their criticisms have been, you are us to put this out here without proper guardrails or 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 going to get it right. But one thing I has 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 supposed to 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 it and then you push “go,” and you hope you it right. I don’t know how to execute that plan. Maybe else does. But for me, that was always terrifying, didn’t feel right. And so I think that this alternative approach is the only other path I see, which is that you do let reality hit you in the face. I think you do give people time to give input. You have, before these machines are perfect, before they are super powerful, that you actually have the ability to them in action. And we’ve seen it from GPT-3, right? GPT-3, we really afraid that the 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, but there are things that are much worse. Here’s thought experiment for you. Suppose you’re sitting in a room, there’s a on the table. You believe that in that box something that, there’s a very strong chance it’s something absolutely that’s going to give beautiful gifts to your family to everyone. But there’s actually also a one percent thing in the small print there that says: “Pandora.” there’s a chance that this actually could unleash unimaginable evils on the world. Do open that box?
GB: Well, so, absolutely not. I think 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 started OpenAI, I remember I was in Puerto Rico for an conference. I’m sitting in the hotel room just looking out over this wonderful water, these people having a good time. And you think 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 one hand you’re like, well, maybe for you personally, it’s better to have it five years away. But if it gets to 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, of course you do the 500 years. brother was in the military at the time and like, he puts his life on line in a much more real way than any of us typing things in computers and this technology at the time. And so, yeah, I’m sold on the you’ve got to approach this right. I don’t think that’s quite playing the field as it truly lies. Like, if you at the whole history of computing, I really mean 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 pieces that are there, right, we’re still making faster computers, we’re still improving algorithms, all of these things, they are happening. And if don’t put them together, you get an overhang, which means if someone does, or the moment that someone does manage connect to the circuit, then you suddenly have this very powerful thing, no one’s had any time adjust, who knows what kind of safety precautions you get. And so I that one thing I take away is like, even you think about development other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, change what humans could do. But I actually think that if you look at capability, it’s been smooth over time. And so the history, I think, of every technology we’ve developed has been, you’ve 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 you … model you want us to have is that we have birthed this child that may have superpowers that take humanity to whole new place. It is our collective responsibility to provide the guardrails for this child to collectively it to be wise and not to tear us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important say this may shift, right? We’ve got to take step as we encounter it. And I think it’s incredibly important today that we do get literate in this technology, figure out how to provide feedback, decide what we want from it. And my 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)