We started OpenAI seven ago because we felt like something really interesting was happening AI and we wanted to help steer it in a positive direction. It’s honestly just really amazing to how far this whole field has come since then. it’s really gratifying to hear from people like Raymond who are the technology we are building, and others, for so many wonderful things. hear from people who are excited, we hear from people 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 right now where we a world are going to define a technology that will be important for our society going forward. And I believe that we can manage for good.
So today, I want to show you the current of that technology and some of the underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s like to build a tool for an AI rather building it for a human. So we have a new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on behalf. And you can do things like ask, you know, a nice post-TED meal and draw a picture of it.
(Laughter)
Now you get all the, sort of, ideation and creative back-and-forth and taking care of details for you that you get out of ChatGPT. And here we go, it’s not just the idea the meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also generates an image. And that is something really expands the power of what it can do on your behalf in of carrying out your intent. And I’ll point out, is all a live demo. This is all generated by the as we speak. So I actually don’t even know what we’re going to see. looks wonderful.
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I’m getting hungry just looking at it.
Now we’ve extended with other tools too, for example, memory. You can “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this pop up here that says “use the DALL-E app.” And the way, this is coming to you, all ChatGPT users, over months. And you can look under the hood and that what it actually did was write a prompt just a human could. And so you sort of have this to inspect how the machine is using these tools, which allows us to provide to them.
Now it’s saved for later, and let me show you what it’s like to that information and to integrate with other applications too. You say, “Now make a shopping list for the tasty thing I was suggesting earlier.” And make it a tricky for the AI. “And tweet it out for the TED viewers out there.”
(Laughter)
So if you make this wonderful, wonderful meal, I definitely want to know how it tastes.
But you can that ChatGPT is selecting all these different tools without me having to tell it explicitly ones to use in any situation. And this, I think, a new way of thinking about the user interface. Like, are so used to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s a great experience within an app as long you kind of know the menus and know all options. Yes, I would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on top of tools, the is able to sort of take away all those details you. So you don’t have to be the one who out every single sort of little piece of what’s supposed happen.
And as I said, this is a live demo, sometimes the unexpected will happen to us. But let’s a look at the Instacart shopping list while we’re it. And you can 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 click through and sort of modify the actual quantities. And that’s that I think shows that they’re not going away, traditional UIs. It’s just we a new, augmented way to build them. And now we have tweet that’s been drafted for our review, which is a very important thing. We can click “run,” and we are, we’re the manager, we’re able to inspect, we’re to change the work of the AI if we want to. And so after talk, you will be able to access this yourself. And there go. Cool. Thank you, everyone.
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So we’ll cut to the slides. Now, the important thing about how build this, it’s not just about building these tools. It’s about teaching the AI to use them. Like, what do we even want it to do when ask these very 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, you can it. You could build a machine, like a human child, then teach it through feedback. Have a human teacher who provides and punishments as it tries things out and does that are either good or bad.
And this is how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine through an unsupervised learning process. We show it the whole world, the whole internet and say, “Predict what comes in text you’ve never seen before.” And this process imbues it all sorts of wonderful skills. For example, if you’re a math problem, the only way to actually complete that math problem, to say what next, that green 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 to do those skills. And for this, we provide feedback. We have the AI try out multiple things, us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not just specific thing that the AI said, but very importantly, the whole process the AI used to produce that answer. And this allows it to generalize. allows it to teach, to sort of infer your and apply it in scenarios that it hasn’t seen before, that hasn’t received feedback.
Now, sometimes the things we have teach the AI are not what you’d expect. For example, when first showed GPT-4 to Khan Academy, they said, “Wow, is so 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 math in there, it happily pretend that one plus one equals three and run with it.” So had to collect some feedback data. Sal Khan himself was kind and offered 20 hours of his own time to provide feedback to machine alongside our team. And over the course of couple of months we were able to teach the that, “Hey, you really should push back on humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to the models this way. And when push that 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 where you should gather feedback.” so when you do that, that’s one way that we really listen to our and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. If think about asking a kid to clean their room, all 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 a nice DALL-E-generated image, by the way. And the same sort of reasoning applies to AI. As move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the AI itself is happy help. It’s happy to help us provide even better feedback 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 unsupervised learning and learning from human feedback. And the model two months passed. But is it true? Like, these models not 100-percent reliable, although they’re getting better every time we 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 a new tool. This one is a browsing tool the model can issue search queries and click into web pages. it actually writes out its whole chain of thought it does it. It says, I’m just going to search for this and it actually the search. It then it finds the publication date the search results. It then is issuing another search query. It’s to click into the blog post. And all of you could do, but it’s a very tedious task. It’s a thing that humans really want to do. It’s more fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check the work. And out come so you can actually go and very easily verify any piece of this whole chain reasoning. And it actually turns out two months was wrong. Two and one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting to me this whole process is that it’s this many-step collaboration between a human an AI. Because a human, using this fact-checking tool is doing it in order to data for another AI to become more useful to a human. And I this really shows the shape of something that we expect to be much more common in the future, where we humans and machines kind of very carefully and delicately designed in how they fit a problem and how we want to solve that problem. make sure that the humans are providing the management, the oversight, the feedback, and machines are operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. I think that over time, if we get this process right, will be able to solve impossible problems.
And to give you a sense of just impossible I’m talking, I think we’re going to be to rethink almost every aspect of how we interact computers. For example, think about spreadsheets. They’ve been around in some form since, we’ll say, 40 years with VisiCalc. I don’t think they’ve really changed that in that time. And here is a specific spreadsheet of all the 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 show you the ChatGPT take on how to analyze data set like this.
So we can give ChatGPT to yet another tool, this one a Python interpreter, so it’s able to run code, just like data scientist would. And so you can just literally upload a and ask questions 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 name of file, the column names like you saw and then the data. And from that it’s able to infer what columns actually mean. Like, that semantic information wasn’t in there. It has to sort of, together its world knowledge of 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 so therefore it’s number of authors in the paper,” like all of that, that’s for a human to do, and the AI is happy help with it.
Now I don’t even know what I want ask. So fortunately, you can ask the machine, “Can you make exploratory graphs?” And once again, this is a super high-level with lots of intent behind it. But I don’t even what I want. And the AI kind of has to infer what I might interested in. And so it comes up with some good ideas, think. So a histogram of the number of authors per paper, time series of papers year, word cloud of the paper titles. All of that, I think, will be pretty interesting see. And the great thing is, it can actually do it. we go, a nice bell curve. You see that three is of the most common. It’s going to then make this nice plot the papers per year. Something crazy is happening in 2023, though. Looks like we were on exponential and it dropped 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 appear in these titles.
But I’m pretty about 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?] April 13 was the cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I wanted out of the machine here. I wanted it to notice this thing, maybe it’s a little of an overreach for it to have sort of, inferred magically that this is what I wanted. But 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 the correct projection.
(Applause)
If you noticed, it even the title. I didn’t ask for that, but it know I want.
Now we’ll cut back to the slide again. This shows a parable of how I think we … vision of how we may end up using this in the future. A person brought his very sick dog to the vet, and the made a bad call to say, “Let’s just wait and see.” And the dog not be here today had he listened. In the meanwhile, he the 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 save dog’s life. Now, these systems, they’re not perfect. You cannot overly rely them. But this story, I think, shows that a human a medical professional and with ChatGPT as a brainstorming partner was to achieve an outcome that would not have happened otherwise. I think this is something we all reflect on, think about as we consider how to integrate 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 setting the rules 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 just looks different. Just different anything people had anticipated. And so we all have to literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, believe that we can achieve the OpenAI mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I … I suspect that within every mind out here there’s a feeling reeling. Like, I suspect that a very large number of people viewing this, look at that and you think, “Oh my goodness, pretty every single thing about the way I work, I need to rethink.” Like, there’s new possibilities there. Am I right? Who thinks that they’re having to rethink the that we do things? Yeah, I mean, it’s amazing, it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my question actually is just how the hell have you done this?
(Laughter)
OpenAI has a hundred employees. Google has thousands of employees working on artificial intelligence. Why is it you who’s up with this technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on of giants, right, there’s no question. If you look at compute progress, the algorithmic progress, the data progress, all of those are really industry-wide. I think within OpenAI, we made a lot of deliberate choices from the early days. And the first one was just to confront reality as it lays. that we just thought really hard about like: What is going to take to make progress here? We tried a lot of that didn’t work, so you only see the things that did. I think that the most important thing has been to get teams of people who are very from each other to work together harmoniously.
CA: Can we have water, by the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there something also just about the fact you saw something in these language models that 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 story is pretty illustrative, right? I think that high level, deep learning, like we always knew that what we wanted to be, was a deep learning lab, and exactly how do it? I 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, he got a result where — this is a process, you expect, you know, the model will predict where the commas go, where the nouns verbs are. But he actually got a state-of-the-art sentiment analysis out of it. This model could tell you if review was positive or negative. I mean, today we are just like, on, anyone can do that. But this was the first time you saw this emergence, this sort of semantics that from this underlying syntactic process. And there we knew, you’ve to scale this thing, you’ve got to see where it goes.
CA: So I think this helps explain riddle that baffles everyone looking at this, because these things are as prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could come a prediction machine. Just the stuff you showed us just now. And the key of emergence is that when you get more of thing, suddenly different things emerge. It happens all the time, ant colonies, single run around, when you bring enough of them together, get these ant colonies that show completely emergent, different behavior. Or city where a few houses together, it’s just houses together. But as you grow the number houses, things emerge, like suburbs and cultural centers and traffic jams. Give me 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 try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned an internal circuit for how to do it. 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. so you can see that it’s really learning the process, but it hasn’t fully generalized, right? It’s you can’t memorize the 40-digit addition table, that’s more atoms 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 this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s here is that you’ve allowed it to scale up look at an incredible number of pieces of text. And is learning things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really get good at is some of these emergent capabilities. And to do that actually, one of the I think is very undersung in this field is sort of engineering quality. Like, we had rebuild our entire stack. When you think about building a rocket, every tolerance has be incredibly tiny. Same is true in machine learning. You to get every single piece of the stack engineered properly, and then you can start doing these predictions. There all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If you look our GPT-4 blog post, you can see all of curves in there. And now we’re starting to be to predict. So we were able to predict, for example, performance on coding problems. We basically look at some that are 10,000 times or 1,000 times smaller. And there’s something about this that is actually smooth scaling, even though it’s still days.
CA: So here is, one of the big then, that arises from this. If it’s fundamental to what’s here, that as you scale up, things emerge that you can maybe predict some level of confidence, but it’s capable of surprising you. Why isn’t there just huge risk of something truly terrible emerging?
GB: Well, I think all of are questions of degree and scale and timing. And I think thing people miss, too, is sort of the integration the world is also this incredibly emergent, sort of, very powerful 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 of see right now, if you look at this talk, a lot of what I focus on is providing high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was the correct answer. even summarizing a book, like, that’s a hard thing to supervise. Like, how do you know this book summary is any good? You have to read the whole book. No wants to do that.
(Laughter) And so I think the important thing will be that we take this step by step. And we say, OK, as we move on to book summaries, have to supervise this task properly. We have to build up a track record with these that they’re able to actually carry out our intent. And think we’re going to have to produce even better, more efficient, more reliable ways scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later this 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, it doesn’t have common 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 feedback that you talked about is basically going to it on that journey of actually getting to things like truth and and so forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that the OpenAI, mean, the short answer is yes, I believe that is where we’re headed. And I think that OpenAI approach here has always been just like, let reality hit you in the face, right? It’s this field is the field of broken promises, of all these experts saying X going to happen, Y is how it works. People have been saying nets aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years plus one or something that is what you need. But I think that approach has always been, you’ve got to push to limits of this technology to really see it in action, because that tells then, oh, here’s how we can move on to a new paradigm. we just haven’t exhausted the fruit here.
CA: I mean, it’s a controversial stance you’ve taken, that the right way to this is to put it out there in public and then all this, you know, instead of just your team feedback, the world is now giving feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, the original story I heard on OpenAI when you were founded as a nonprofit, you were there as the great sort of check on the companies doing their unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held them accountable and was capable of slowing field down, if need be. Or at least that’s kind what I heard. And yet, what’s happened, arguably, is the opposite. That release of GPT, especially 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 we die. You know, how do you, like, make the case that what you have done is here and not reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing think has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, have it benefit all of humanity, like, how are you supposed to that, right? And that default plan of being, well, you build secret, you get this super powerful thing, and then figure out the safety of it and then you push “go,” and you you got it right. I don’t know how to execute that plan. Maybe someone does. But for me, that was always terrifying, it didn’t right. And so I think that this alternative approach is the only other path I see, which is that you do let reality hit in the face. And I think you do give people time to 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 from GPT-3, right? GPT-3, we were afraid that the number one thing people were to do with it was generate misinformation, try to elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra is bad, but there are things that are much worse. Here’s a experiment 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 chance it’s something glorious that’s going to give beautiful gifts to your family and everyone. But there’s actually also a one percent thing in the small print there says: “Pandora.” And there’s a chance that this actually could unleash evils on the world. Do you open that box?
GB: Well, so, absolutely not. I think you don’t it that way. And honestly, like, I’ll tell you a story I haven’t actually told before, which is that shortly after we started OpenAI, I I was in Puerto Rico for an AI conference. I’m sitting the hotel room just looking out over this wonderful water, these people having a good time. And you think about for a moment, if you could choose for basically that Pandora’s 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 to have it be five years away. But if it gets be 500 years away and people get more time to get it right, which do pick? And you know, I just really felt it the moment. I was like, of course you do the 500 years. My brother was the military at the time and like, he puts his life on the line a much more real way than any of us typing in computers and developing this technology at the time. And so, yeah, I’m really sold on the you’ve to approach this right. But I don’t think that’s quite playing the field as truly lies. Like, if you look at the whole of computing, I really mean it when I say that this an industry-wide or even just almost like a human-development- of-technology-wide shift. the more that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. if you don’t put them together, you get an overhang, which means that if someone does, or the moment someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had any to adjust, who knows what kind of safety precautions you get. And so I think that one I 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 in what humans could do. But 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 been, you’ve got to do it incrementally and you’ve got to out how to manage it for each moment that you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have that we have birthed this extraordinary child that may have superpowers that humanity to a whole new place. It is our collective responsibility to provide guardrails for this child to collectively teach it to wise and not to tear us all down. Is that 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 that we all do get literate in this technology, figure out how provide the feedback, decide what we 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 it weren’t out there.
CA: Greg Brockman, thank you so much for coming to and blowing our minds.
(Applause)