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 amazing to see far this whole field has come since then. And it’s really gratifying to hear from people like who are using the technology we are building, and others, for so many things. We hear from people who are excited, we hear from who are concerned, we hear from people who feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering an historic period now where we as a world are going to define a technology that will be so for our society going forward. And I believe that we can manage this for good.
So today, I to show you the current state of that technology and some of the design principles that we hold dear.
So the first thing I’m going to you is what it’s like to build a tool for an AI than building it for a human. So we have new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, sort of, ideation and back-and-forth and taking care of the details for you that you get out ChatGPT. And here we go, it’s not just the idea for the meal, but a very, detailed spread. So let’s see what we’re going to get. But ChatGPT doesn’t just images in this case — sorry, it doesn’t generate text, it also generates an image. And that is that really expands the power of what it can do your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. This is all 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 hungry just looking it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You say “save this for later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here that “use the DALL-E app.” And by the way, this 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 for later, and let me show you what it’s like use that information and to integrate with other applications too. can say, “Now make a shopping list for the tasty thing I was suggesting earlier.” make it a little tricky for the AI. “And tweet out for all the TED viewers 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 any situation. 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, usually it’s a great experience within an app as as you kind of know the menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And by having this unified language on top of tools, the AI is able to sort of take away all those from you. So you don’t have to be the one spells out every single sort of little piece of what’s supposed to happen.
And 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 we’re at it. And you can see we sent a list ingredients to Instacart. Here’s everything you need. And the thing that’s really is that the traditional UI is still very valuable, right? If you look 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 going away, traditional UIs. It’s we have a new, augmented way to build them. And now have a tweet that’s been drafted for our review, which also a very important thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the work of the if we want to. And so after this talk, you will be able to access this yourself. there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important thing how we build this, it’s not just about building these tools. It’s about teaching AI how to use them. Like, what do we want it to do when we ask these very high-level questions? And to this, we use an old idea. If you go to Alan Turing’s 1950 paper on the Turing test, he says, you’ll program an answer to this. Instead, you can learn it. You could build a machine, a human child, and then teach it through feedback. Have a human teacher who provides rewards punishments as it tries things out and does things that either good or bad.
And this is exactly how train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an unsupervised process. We just show it the whole world, the internet and say, “Predict what comes next in text you’ve seen before.” And this process imbues it with all sorts of skills. For example, if you’re shown a math problem, the way to actually complete that math problem, to say what next, that green nine up there, is to actually solve the math problem.
But we actually have do a second step, too, which is to teach the AI what to do with skills. And for this, we provide feedback. We have the AI out multiple things, give us multiple suggestions, and then a rates them, says “This one’s better than that one.” this reinforces not just the specific thing that the said, but very importantly, the whole process that the used to produce that answer. And this allows it to generalize. allows it to teach, to sort of infer your intent and apply it in scenarios that hasn’t seen before, that it hasn’t received feedback.
Now, the things we have to teach the AI are not what you’d expect. For example, when 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 there, it will happily pretend that one plus one equals and run with it.” So we had to collect some feedback data. Khan himself was very kind and offered 20 hours of his own time provide feedback to the machine alongside our team. And over the of a couple of months we were able to teach the AI that, “Hey, you really should back on 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 thumbs down in ChatGPT, that actually is kind of like sending up a bat to our team to say, “Here’s an area of weakness you should 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 everyone.
Now, providing high-quality feedback is a hard thing. If you think about asking kid to clean their room, if all you’re doing is inspecting floor, you don’t know if 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 to scale our ability to high-quality feedback. But for this, the AI itself is happy to help. It’s happy help us provide even better feedback and to scale our ability to supervise the machine time goes on. And let me show you what 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 learning human feedback. And the model says two months passed. is it true? Like, these models are not 100-percent reliable, although they’re getting every time we provide some feedback. But we can use the AI to fact-check. And it can actually check its work. You can say, fact-check this for me.
Now, in this case, I’ve actually given AI a new tool. This one is a browsing tool where the model can issue search queries click into web pages. And it actually writes out its whole chain of thought it does it. It says, I’m just going to search for this and 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 blog post. all of this you could do, but it’s a tedious task. It’s not a thing that humans really want to do. It’s much more fun be in the driver’s seat, to be in this manager’s position where you can, if 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 turns out two months was wrong. Two months and week, that was correct.
(Applause)
And we’ll cut back to side. And so thing that’s so interesting to me about whole process is that it’s this many-step collaboration between a and an AI. Because a human, using this fact-checking tool is it in order to produce data for another AI become more useful to a human. And I think really shows the shape of something that we should to be much more common in the future, where we have humans and machines of very carefully and delicately designed in how they fit into a problem how we want to solve that problem. We make sure that the humans are providing the management, oversight, the feedback, and the machines are operating in a way that’s inspectable and trustworthy. And together we’re to 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 just how impossible I’m talking, I think we’re going to be to rethink almost every aspect 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 think they’ve really that much in that time. And here is a specific spreadsheet of all the AI papers on the for the 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 data like this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And so you just literally upload a file and ask questions about it. And very helpfully, you know, it the name of the file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information here the name of the file, the column names like you saw and then the actual data. from that it’s able to infer what these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its world knowledge knowing that, “Oh yeah, arXiv is a site that people submit and therefore that’s what these things are and that these are integer values and therefore it’s a number of authors in the paper,” all of that, that’s work for a human to do, the AI is happy to help with it.
Now don’t even know what I want to ask. So fortunately, you can the machine, “Can you make some exploratory graphs?” And once again, this is super high-level instruction with lots of intent behind it. I don’t even know what I want. And the AI kind of to infer what I might be interested in. And so it comes up with good ideas, I think. So a histogram of the number of authors per paper, series of papers per year, word cloud of the paper titles. All that, I think, will be pretty interesting to see. And the great thing is, it can actually it. Here we go, a nice bell curve. You that three is kind of the most common. It’s going then make this nice plot of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and it dropped off cliff. What could be going on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you can all these wonderful things that appear in these titles.
But I’m 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 posted by 13?] So April 13 was the cut-off date I believe. you use that to make a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. really wanted it to notice this thing, maybe it’s little bit of an overreach for it to have of, inferred magically that this is what I wanted. But I my intent, I provide this additional piece of, you know, guidance. And under the hood, the AI just writing code again, so if you want to what it’s doing, it’s very possible. And now, it the correct projection.
(Applause)
If you noticed, it even updates the title. didn’t ask for that, but it know what I want.
Now we’ll cut back to slide again. This slide shows a parable of how think we … A vision of how we may end up this technology in the future. A person brought his sick dog to the vet, and the veterinarian made bad call to say, “Let’s just wait and see.” And the dog would not be here today he listened. In the meanwhile, he provided the blood test, like, the full medical records, to GPT-4, said, “I am not a vet, you need to talk a professional, here are some hypotheses.” He brought that information to a second who used it to save the dog’s life. Now, systems, they’re not perfect. You cannot overly rely on them. But this story, I think, shows a human with a medical professional and with ChatGPT as a brainstorming partner was able to achieve outcome that would not have happened otherwise. I think this something we should all reflect on, think about as we consider how to integrate these into our world.
And one thing I believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding how we want it to in, that’s for setting the rules of the road, for what AI will and won’t do. And if there’s one to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. so we 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 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 suspect that very large number of people viewing this, you look at that and 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 that we do things? Yeah, mean, it’s amazing, but it’s also 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 a hundred employees. Google has thousands of employees working on artificial intelligence. Why is it who’s come up with this technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re all building shoulders of giants, right, there’s no question. If you look at compute progress, the algorithmic progress, the data progress, all of those really industry-wide. But I think within OpenAI, we made a lot of very deliberate choices from early days. And the first one was just to confront reality as it lays. that we just thought really hard about like: What is it going to take make progress here? We tried a lot of things that didn’t work, so you only see the things did. And I think that the most important thing has to get teams of people who are very different each other to work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something also just about fact that you saw something in these language models meant that if you continue to invest in them and them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the there is pretty illustrative, right? I think that high level, learning, like we always knew that was what we wanted be, was a 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 Amazon reviews, and he got result where — this is a syntactic process, you expect, know, the model will 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, today we are like, come on, anyone can do that. But this was the first that you saw this emergence, this sort of semantics that emerged from this underlying syntactic process. there we knew, you’ve got to scale this thing, you’ve got to see where it goes.
CA: So think this helps explain the riddle that baffles everyone looking at this, because these are described as prediction machines. And yet, what we’re seeing out of 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 you get more of thing, suddenly different things emerge. It happens all the time, ant colonies, single ants run around, when bring enough of them together, you get these ant that show completely emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow the number of houses, emerge, like suburbs and cultural centers and traffic jams. Give one moment for you when you saw just something pop that just blew your mind that just did not see coming.
GB: Yeah, well, so can try this in ChatGPT, if you add 40-digit —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which it’s really learned an internal circuit for how to it. And the really interesting thing is actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. And so can see that it’s really learning the process, but hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are 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 generalize this adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened is that you’ve allowed it to scale up and look at an number of pieces of text. And it is learning things that you didn’t know that was going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting to really get at is predicting some of these emergent capabilities. And do that actually, one of the things I think is undersung in this field is sort of engineering quality. Like, we had to rebuild our entire stack. When think about building a rocket, every tolerance has to be incredibly tiny. Same true in machine learning. You have to get every single piece the stack engineered properly, and then you can start doing these predictions. There are these incredibly smooth scaling curves. They tell you something deeply about intelligence. If you look at our GPT-4 blog post, 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 coding problems. We basically at some models that are 10,000 times or 1,000 smaller. And so there’s something about this that is smooth scaling, even though it’s still early days.
CA: So is, one of the big fears then, that arises from this. it’s fundamental to what’s happening here, that as you up, things emerge that you can maybe predict in level of confidence, but it’s capable of surprising you. isn’t there just a huge risk of something truly emerging?
GB: Well, I think all of these are questions of degree and scale and timing. I think one thing people miss, too, is sort of the with the world is also this incredibly emergent, sort of, very thing too. And so that’s one of the reasons that we think it’s important to deploy incrementally. And so I think that what we kind of right now, if you look at this talk, a lot of what I focus on is really high-quality feedback. Today, the tasks that we do, can inspect them, right? It’s very easy to look that math problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know this book summary is any good? You have to read whole book. No one wants to do that.
(Laughter) And I think that the important thing will be that take this step by step. And that we say, OK, as we move to book summaries, we have to supervise this task properly. We to build up a track record with these machines they’re able to actually carry out our intent. And I think we’re to have to produce even better, more efficient, more reliable ways of scaling this, sort of 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 real understanding inside, the system going to always — we’re never going to know that it’s not generating errors, that doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, that the expansion of the scale and the human feedback that you about 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 you be of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short is yes, I believe that is where we’re headed. I think that the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like this field is the field broken promises, of all these experts saying X is going to happen, Y is how it works. have been saying neural nets aren’t going to work for 70 years. haven’t been right yet. They might be right maybe 70 years one or something like that is what you need. But I think that approach has always been, you’ve got to push to the limits of this to 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 to put it out there in public and then harness this, you know, instead of 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 on OpenAI you were founded as a nonprofit, well you were as the great sort of check on the big doing their unknown, possibly evil thing with AI. And you were going to build models that sort of, know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that now Google Meta and so forth are all scrambling to catch up. And some of criticisms have been, you are forcing us to put this here without proper guardrails or we die. You know, how you, like, make the case that what you have done responsible here and not reckless.
GB: Yeah, we think about questions all the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one I think has been incredibly important, from the very beginning, when we were thinking how to build artificial general intelligence, actually have it benefit all humanity, like, how are you supposed to do that, right? And that plan of being, well, you build 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 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. And I think that this alternative approach is the only other path that I see, is that you do let reality hit you in the face. And I think you do people time to give input. You do have, before these are perfect, before they are super powerful, that you actually have ability to see them in action. And we’ve seen it from GPT-3, right? GPT-3, we really were afraid the number one thing people were going to do 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 experiment for you. Suppose you’re in a room, there’s a box on the table. You that in that box is something that, there’s a strong chance it’s something absolutely glorious that’s going to give beautiful gifts to family and to everyone. But there’s actually also a one percent thing in the small there that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils on the world. you open that box?
GB: Well, so, absolutely not. I you don’t do it that way. And honestly, like, I’ll tell you a story that I haven’t told before, which is that shortly after we started OpenAI, I remember I was Puerto Rico for an AI conference. I’m sitting in the hotel room looking out over this wonderful water, all these people having a good time. And you think about for a moment, if you could choose for basically that Pandora’s box 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 have be five years away. But if it gets to 500 years away and people get more time to get right, which do you pick? And you know, I just really felt it in the moment. was like, of course you do the 500 years. My brother was in military at the time and like, he puts his life on the in a much more real way than any of us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on you’ve got to approach 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, I mean it when I say that this is an industry-wide or even just like a human-development- of-technology-wide shift. And the more that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re still improving the algorithms, all these things, they are happening. And if you don’t put them together, get an overhang, which means that if someone does, the moment that someone does manage to connect to circuit, then you suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety you get. And so I think that one thing I take away is like, even you about development of other sort of technologies, think about nuclear weapons, talk about being like a zero to one, sort of, change in what could do. But I actually think that if you look at capability, it’s been quite smooth time. And so the history, I 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 it for each moment you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have is that we birthed 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 to collectively teach it to be wise and not tear us all down. Is that basically the model?
GB: I think it’s true. And I think it’s also to 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 all do literate in this technology, figure out how to provide the feedback, decide what we from it. And my hope is that that will to be the best path, but it’s so good we’re honestly this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for coming to and blowing our minds.
(Applause)