We started OpenAI seven years ago because felt like something really interesting was happening in AI we wanted to help steer it in a positive direction. It’s honestly really amazing to see how far this whole field come since then. And it’s really gratifying to hear from people like Raymond who using the technology we are building, and others, for so wonderful things. We hear from people who are excited, we hear from people are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. Above all, it feels we’re entering an historic period right now where we as a world are going to define a that will be so important for our society going forward. And believe that we can manage this for good.
So today, I want show you the current state of that technology and some of the design principles that we hold dear.
So the first I’m going to show you is what it’s like to build a for an AI rather than building it for a human. So we a new DALL-E model, which generates images, and we exposing it as an app for ChatGPT to use your 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 of the, sort 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 the meal, but very, very detailed spread. So let’s see what we’re going to get. ChatGPT doesn’t just generate images in this case — sorry, it doesn’t generate text, it also an image. And that is something that really expands the power what it can do on your behalf in terms of carrying your intent. And I’ll point out, this is all a live demo. is all generated by the AI as we speak. So actually don’t even know what we’re going to see. This looks wonderful.
(Applause)
I’m getting just looking at it.
Now we’ve extended ChatGPT with tools too, for example, memory. You can say “save for later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, this is coming to you, all users, over upcoming months. And you can look under hood and see that what it actually did was write a prompt just like human could. And so you sort of have this ability to inspect how the machine is using tools, which allows us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like to that information and to integrate with other applications too. 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 for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT is selecting all these different tools without having to tell it explicitly which ones to use any situation. And this, I think, shows a new way of thinking about 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 experience within an app as long as you kind of know menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And by this unified language interface on top of tools, the AI is able to sort take away all those details from you. So you don’t have to the one who spells out every single sort of little piece what’s supposed to happen.
And as I said, this a live demo, so sometimes the unexpected will happen us. But let’s take a look at the Instacart list while we’re at it. And you can see sent a list of ingredients to Instacart. Here’s everything need. And the thing that’s really interesting is that the UI 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 something that I think shows they’re not going away, traditional UIs. It’s just we a new, augmented way to build them. And now have a tweet that’s been drafted for our review, is 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 AI we want to. And so after this talk, you will be able access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll back 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, he says, you’ll never program an to this. Instead, you can learn it. You could build a machine, like a human child, then teach it through feedback. Have a human teacher who provides rewards and as it tries things out and does things that are good or bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes next in text you’ve never before.” And this process imbues it with all sorts wonderful skills. For example, if you’re shown a math problem, the only 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 to a second step, too, which is to teach the AI to do with those skills. And for this, we provide feedback. have the AI try out multiple things, give us multiple suggestions, and a human rates them, says “This one’s better than that one.” And reinforces not just the specific thing that the AI said, but very importantly, the whole that the AI used to produce that answer. And this it to generalize. It allows it to teach, to sort infer your intent and apply it in scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach the AI not what you’d expect. For example, when we first GPT-4 to Khan Academy, they said, “Wow, this is great, We’re going to be able to teach students wonderful things. one problem, it doesn’t double-check students’ math. If there’s some math in there, it will happily pretend that one one equals three and run with it.” So we had to collect feedback data. Sal Khan himself was very kind and offered 20 hours his own time to provide feedback to the machine our team. And over the course of a couple of months we were to teach the AI that, “Hey, you really should back on humans in this specific kind of scenario.” we’ve actually made lots and lots of improvements to the models this way. when you push that thumbs down in ChatGPT, that actually is kind like sending up a bat signal to our team to say, “Here’s an area of where you should gather feedback.” And so when you do that, that’s way that we really listen to our users and sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback a hard thing. If you 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 is nice DALL-E-generated image, by the way. And the same sort of reasoning applies to AI. we move to harder tasks, we will have to our ability to provide high-quality feedback. But for this, AI itself is happy to help. It’s happy to help us provide even better and to scale our ability to supervise the machine as time goes on. let me show you what I mean.
For example, can ask GPT-4 a question like this, of how time passed between these two foundational blogs on unsupervised learning and from human feedback. And the model says two months passed. is it true? Like, these models are not 100-percent reliable, although they’re better every time we provide some feedback. But we can actually use the AI to fact-check. it can actually check its own work. You can say, fact-check this for me.
Now, in this case, I’ve given the AI a new tool. This one is a browsing tool where the model can issue search and click into web pages. And it actually writes out its whole of thought as it does it. It says, I’m just going search for this and it actually does the search. It then finds the publication date and the search results. It is issuing another search query. It’s going to click the blog post. And all of this you could do, but it’s a very task. It’s not a thing that humans really want 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 and very verify any piece of this whole chain of reasoning. it actually turns out two months was wrong. Two months and week, that was correct.
(Applause)
And we’ll cut back to the side. And so thing that’s interesting to me about this whole process is that it’s this many-step collaboration between a human and an AI. a human, using this fact-checking tool is doing it order to produce data for another AI to become more useful to human. And I think this really shows the shape of something that we should expect to be much common in the future, where we have humans and machines kind of very carefully delicately designed in how they fit into a problem and we want to solve that problem. We make sure the humans are providing the management, the oversight, the feedback, and the are operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. And think that over time, if we get this process right, we will be to solve impossible problems.
And to give you a sense just how impossible I’m talking, I think we’re going to be able to almost every aspect of how we interact with computers. For example, think 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 much in that time. here is a specific spreadsheet of all the AI papers on the arXiv for past 30 years. There’s about 167,000 of them. And you can there the data right here. But let me show the ChatGPT take on how to analyze a data set like this.
So we can give access to yet another tool, this one a Python interpreter, it’s able to run code, just like a data would. And so you can just literally upload a file and ask about it. And very helpfully, you know, it knows name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” only information here is the name of the file, the column names like saw and then the 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 papers and therefore that’s what these things are and that are integer values and so 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 I don’t even know I want to ask. So fortunately, you can ask the machine, “Can you make exploratory graphs?” And once again, this is a super high-level instruction with lots of behind it. But I don’t even know what I want. the AI kind of has to infer what I might be interested in. And so it comes with some good ideas, I think. So a histogram of the of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, will pretty interesting to see. And the great thing is, can actually do it. Here we go, a nice curve. You see that three is kind of the most common. It’s going to then this nice plot of the papers per year. Something crazy happening in 2023, though. Looks like we were on an exponential and dropped off the cliff. What could be going on there? the way, all this is Python code, you can inspect. And then we’ll word cloud. So you can see all these wonderful things that appear these titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem that the year is 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 April 13?] So April 13 was the cut-off date I believe. you use that to make a fair projection? So we’ll see, this the kind of ambitious one.
(Laughter)
So you know, again, I like there was more I wanted out of the machine here. I really wanted to notice this thing, maybe it’s a little bit an overreach for it to have sort of, inferred that this is what I wanted. But I inject my intent, I provide this piece of, you know, guidance. And under the hood, the AI is just code again, so if you want to inspect what it’s doing, it’s very possible. And now, it does the projection.
(Applause)
If you noticed, it even updates the title. didn’t ask for that, but it know what I want.
Now we’ll cut back to the slide again. This slide a parable of how I think we … A of how we may end up using this technology in future. A person brought his very sick dog to vet, and the veterinarian made a bad call to say, “Let’s just and see.” And the dog would not be here today he listened. In the meanwhile, he provided the blood test, like, full medical records, to GPT-4, which said, “I am a vet, you need to talk to a professional, here are some hypotheses.” He brought that information to 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, I think, shows that a human a medical professional and with ChatGPT as a brainstorming was able to achieve an outcome that would not have otherwise. I think this is something we should all reflect on, think as 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. And that’s for deciding we want it to slot in, that’s for setting the rules of the road, for an AI will and won’t do. And if there’s one thing to away from this talk, it’s that this technology just looks different. Just from anything people had anticipated. And so we all to become literate. And that’s, honestly, one of the we released ChatGPT.
Together, I believe that we can achieve OpenAI mission of ensuring that artificial general intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every mind here there’s a feeling of reeling. Like, I suspect that a very number of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about the way I work, I to rethink.” Like, there’s just new possibilities there. Am 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, I guess first question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google has of employees working on artificial intelligence. Why is it you who’s come up with this technology shocked the world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. you look at the compute progress, the algorithmic progress, the data progress, all of those are industry-wide. But I think within OpenAI, we made a lot of very deliberate choices the early days. And the first one was just confront reality as it lays. And that we just thought really about like: What is it going to take to make here? We tried a lot of things that didn’t work, so only see the things that did. And I think that the most important thing has been to teams of people who are very different from each other work together harmoniously.
CA: Can we have the water, by the way, just brought here? I 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 that if you to invest in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, I think the story is pretty illustrative, right? I think that high level, deep learning, like always knew that was what we wanted to be, was a deep learning lab, and exactly how do it? I think that in the early days, didn’t know. We tried a lot of things, and one was working on training a model to predict the next character in Amazon reviews, he got a result where — this is a syntactic process, you expect, you know, the will predict where the commas go, where the nouns and verbs are. But he got a state-of-the-art sentiment analysis classifier out of it. model could tell you if a review was positive or negative. I mean, today are just like, come on, anyone can do that. But this was the first time that saw this emergence, this sort of semantics that emerged from this syntactic process. And there we knew, you’ve got to this thing, you’ve got to see where it goes.
CA: So I think this explain the riddle that baffles everyone looking at this, because these things are described as prediction machines. yet, what we’re seeing out of them feels … it feels 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 a thing, suddenly different things emerge. It happens all time, ant colonies, single ants run around, when you bring enough of them together, get these ant colonies that show completely emergent, different behavior. Or a where a few houses together, it’s just houses together. But as you grow number of houses, things emerge, like suburbs and cultural centers and jams. Give me one moment for you when you saw just something pop just blew your mind that you just did not coming.
GB: Yeah, well, so you can try this ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s learned an internal circuit for how to do it. And the interesting thing is actually, if you have it add like 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, 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 universe. So it had to have learned something general, but that it hasn’t fully yet learned 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 that you didn’t know that it was going to be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really get good is predicting some of these emergent capabilities. And to do that actually, one of the things I is very undersung in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think about a rocket, every tolerance has to be incredibly tiny. Same is true machine learning. You have to get every single piece of the stack engineered properly, then you can start doing these predictions. There are all incredibly smooth scaling curves. They tell you something deeply about intelligence. If you look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting be able to predict. So 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 times smaller. And so there’s something about this that actually smooth scaling, even though it’s still early days.
CA: here is, one of the big fears then, that from this. If it’s fundamental to what’s happening here, that as scale 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 terrible emerging?
GB: Well, I all of these are questions of degree and scale and timing. And I think one thing miss, too, is sort of the integration with the world is also incredibly emergent, sort of, very powerful thing too. And so that’s one of the that we think it’s so important to deploy incrementally. And so I think that what kind of see right now, if you look at talk, a lot of what I focus on is really 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. But even summarizing book, like, that’s a hard thing to supervise. Like, how do you know if this summary is any good? You have to read the whole book. one wants to do that.
(Laughter) And so I think that important thing will be that we take this step by step. And that say, OK, as we move on to book summaries, have to supervise this task properly. We have to build a track record with these machines that they’re able 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 like making machine be aligned with you.
CA: So we’re going to hear later in this session, there are who say that, you know, there’s no real understanding inside, the system is to always — we’re never going to know that it’s generating errors, that it 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 that you talked about is basically going to take it on that journey of actually getting things like truth and wisdom and so forth, with a high degree of confidence. you be sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, short answer is yes, I believe that is where we’re headed. And I that the OpenAI approach here has always been just like, let reality hit in the face, right? It’s like this field is field of broken promises, of all these experts saying X is to happen, Y is how it works. People have been saying nets aren’t going to work for 70 years. They haven’t been right yet. They be right maybe 70 years plus one or something that is what you need. But I think that our has always been, you’ve got to push to the of this technology to really see it in action, because tells you then, oh, here’s how we can move on 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 way to this is to put it out there in public and then harness this, you know, instead of just your team giving feedback, the world is giving feedback. But … If, you know, bad things are going to emerge, it is out there. So, know, the original story that I heard on OpenAI when you were founded as a nonprofit, you were there as the great sort of check on big companies doing their unknown, possibly evil thing with AI. And you were going to build models sort of, you 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 of GPT, especially ChatGPT, sent such shockwaves through the tech world that now Google and and so forth are all scrambling to catch up. some of their criticisms have been, you are forcing us to put this here without proper guardrails or we die. You know, how do you, like, make the case that you have done is responsible here and not reckless.
GB: Yeah, we about these questions all the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing I think been incredibly important, from the very beginning, when we were thinking how to build artificial general intelligence, actually have it benefit of humanity, like, how are you supposed to do that, right? And that plan of being, well, you build in secret, you get super powerful thing, and then you figure out the safety of it then you push “go,” and you hope you got it right. I don’t how to execute that plan. Maybe someone else does. But for me, that was always terrifying, it didn’t right. And so I think that this alternative approach is the other path that I see, which is that you do let reality hit in the face. And I think you do give people time to give input. You do have, these machines are perfect, before they are super powerful, that actually have the ability to see them in action. 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 things that are much worse. Here’s a thought experiment you. Suppose you’re sitting in a room, there’s a on the table. You believe that in that box is something that, there’s very strong chance it’s something absolutely glorious that’s going to beautiful gifts to your family and to everyone. But there’s also a one percent thing in the small print there that says: “Pandora.” And there’s a chance this actually could unleash unimaginable evils on the world. Do open that box?
GB: Well, so, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a that I haven’t actually told before, which is that after we started OpenAI, I remember I was in Puerto Rico an AI conference. I’m sitting in the hotel room just looking out this wonderful water, all these people having a good time. And you think it for a moment, if you could choose for basically that Pandora’s box to be years away or 500 years away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to it be five years away. But if it gets be 500 years away and people get more time to get right, which do you pick? And you know, I really felt it in the moment. I was like, course you do the 500 years. My brother was the military at the time and like, he puts life on the line in a much more real way 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 the field it truly lies. Like, if you look at the whole history of computing, I really 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 together the pieces that are there, right, we’re still making faster computers, we’re improving 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, the moment that someone does manage to connect to the circuit, you suddenly have this very powerful thing, no one’s had any to adjust, who knows what kind of safety precautions you get. And I think that one thing I take away is like, even you think development of other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, change in what humans do. But I actually think that if you look capability, it’s been quite smooth over time. And so 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 manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you want to have is that we have birthed this extraordinary child that may have that take humanity to a whole new place. It 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 it’s true. And I think it’s also important to say this may shift, right? We’ve to take each step as we encounter it. And I think it’s incredibly important that we all do get literate in this technology, figure out how to provide the feedback, decide what want from it. And my hope is that that will continue to the best path, but it’s so good we’re honestly having debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you much for coming to TED and blowing our minds.
(Applause)