We OpenAI seven years ago because we felt like something really was happening in AI and we wanted to help steer 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 Raymond who using the 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 who feel both those emotions at once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now where we as world are going to define a technology that will be so important our society going forward. And I believe that we can manage this good.
So today, I want to show you the current state of that technology some of the underlying design principles that we hold dear.
So the first I’m going to show 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, which generates images, we are exposing it as an app for ChatGPT use on your behalf. And you can do things ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, sort of, ideation creative back-and-forth and taking care of the details for you that you get out of ChatGPT. And we go, it’s not just the idea for the meal, 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 image. And that is something that really expands the of what it can do on your behalf in terms of carrying out intent. And I’ll point out, this is all a demo. This is all generated by the AI as speak. So I actually don’t even know what we’re going see. This looks wonderful.
(Applause)
I’m getting hungry just at 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 is they’re very inspectable. So you get this little pop up here that “use the DALL-E app.” And by the way, this is coming to you, all users, over upcoming months. And you can look under the hood and see that what actually did was write a prompt just like a human could. And so you sort of have this to inspect how the machine is using these tools, which allows us to feedback to them.
Now it’s saved for later, and me show you what it’s like to use that information and integrate with other applications too. You can say, “Now make a shopping list the tasty thing I was suggesting earlier.” And make it a little tricky for the AI. “And it out for all the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how it tastes.
But you can see that is selecting all these different tools without me having to tell it explicitly which to use in any situation. And this, I think, shows a new way thinking about the user interface. Like, we are so used to of, well, we have these apps, we click between them, we copy/paste them, and usually it’s a great experience within an app as as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this unified language interface top of tools, the AI is able to sort of take away all those details from you. you don’t have to be the one who spells out every single sort of little piece what’s supposed to happen.
And as I said, this is live demo, so sometimes the unexpected will happen to us. But let’s take look at the Instacart shopping list while we’re at it. And you can we sent a list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional UI 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 have a new, augmented way to build them. And now have a tweet that’s been drafted for our review, which is also very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able to change the work the AI if 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 cut to the 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, do we even want it to do when we these very high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 on the Turing test, he says, you’ll never program answer to this. Instead, you can learn it. You build a machine, like a human child, and then it through feedback. Have a human teacher who provides and 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 what Turing would have called a child machine 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 imbues 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 nine up there, to actually solve the math problem.
But we actually 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 out multiple things, give us multiple suggestions, and then a human rates them, says “This one’s better than one.” And this 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 allows to generalize. It allows it to teach, to sort of infer your and apply it in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes things we have to teach the AI are not what you’d expect. For example, when first showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going to be able teach students wonderful 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 we had to collect some data. Sal Khan himself was very kind and offered 20 hours of his own time to provide to the machine alongside our team. And over the course of a couple of months we able to teach the AI that, “Hey, you really should 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 is kind of like sending up a bat signal our team to say, “Here’s an area of weakness where you should gather feedback.” And when you do that, that’s one way that we really listen to users and make sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is hard thing. If you think about asking a kid to clean room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by way. And the same sort of reasoning applies to AI. As move to harder tasks, we will have to scale our to provide high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even better and to scale our ability to supervise the machine as time on. And let me show you what I mean.
For example, you ask GPT-4 a question like this, of 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 are not 100-percent reliable, although they’re better every time we provide some feedback. But we can actually use AI to fact-check. And it can actually check its own work. You can say, fact-check 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 queries and click into web pages. And it actually writes out its whole chain of thought as it it. It says, I’m just going to search for this and it actually does the search. It it finds the publication date and the search results. It is issuing another search query. It’s going to click into blog post. And all of this you could do, but it’s a very tedious task. It’s a thing that humans really want to do. It’s much fun to be in the driver’s seat, to be in manager’s position where you can, if you want, triple-check the work. And out come citations you can actually go and very easily verify any piece of this whole chain reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting me about this whole process is that it’s this many-step collaboration between a and an AI. Because a human, using this fact-checking tool is doing it order to produce data for another AI to become useful to a human. And I think this really the shape of something that we should expect to be much more common in future, where we have humans and machines kind of very carefully delicately designed in how they fit into a problem and how we want to solve that problem. We sure that the humans are providing the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And together we’re able to actually create even trustworthy machines. And I think that over time, if get this process right, we will be able to 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 with computers. For example, think spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve changed that much in that time. And here is a spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. And you see there the data right here. But let me show you ChatGPT take on how to analyze a data set like this.
So we can give ChatGPT to yet another tool, this one a Python interpreter, it’s able to run code, just like a data scientist would. And so can just literally upload a file and ask questions about it. And very helpfully, you know, knows the name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” The only here is 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, semantic information wasn’t in there. It has to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s these things are and that these are integer values so therefore it’s a number of authors in the paper,” like all of that, that’s work for a to do, and the AI is happy to help with it.
Now I don’t even know what want to ask. So fortunately, you can ask the machine, “Can make some exploratory graphs?” And once again, this is a super high-level instruction with lots of intent it. But I don’t even know what I want. the AI kind of has to infer what I be interested in. And so it comes up with good ideas, I think. So a histogram of the number authors per paper, time series of papers per year, word cloud the paper titles. All of that, I think, will be pretty interesting see. And the great thing is, it can actually do it. Here we go, a bell curve. You 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 it dropped off cliff. What could be going on there? By the way, this is Python code, you can inspect. And then we’ll word cloud. So you can see all these wonderful things that in these titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem is that the year not over. So I’m going to push back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were even by April 13?] So April 13 was the cut-off date I believe. Can 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 there was more I wanted out the machine here. I really wanted it to notice this thing, it’s a little bit of an overreach for it to have sort of, inferred magically this is what I wanted. But I inject my intent, I provide 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 the title. I didn’t ask for that, but it know what want.
Now we’ll cut back to the slide again. This slide a parable of how I think we … A vision of how may end up using this technology in the future. person brought his very sick dog to the vet, and the veterinarian made bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. In the meanwhile, he provided the test, like, the full medical records, to GPT-4, which said, “I am not a vet, need to talk to a professional, here are some hypotheses.” brought that information to a second vet who used it save the dog’s life. Now, these systems, they’re not perfect. You cannot overly on them. But this story, I think, shows that a human with a medical professional and with as a brainstorming partner was able to achieve an outcome that not have happened otherwise. I think this is something we should all reflect on, think as we consider how to integrate these systems into world.
And one thing I believe really deeply, is getting AI right is going to require participation from everyone. And that’s deciding how we want it to slot in, that’s for setting the rules of the road, what 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 different from people had anticipated. And so we all have to become literate. And that’s, honestly, one of the we released ChatGPT.
Together, I believe that we can achieve the OpenAI of ensuring that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look at that 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 way 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 my first question actually is just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working on artificial intelligence. is it you who’s come up with this technology shocked the world?
Greg Brockman: I mean, the truth is, we’re all on shoulders of giants, right, there’s no question. If look at the compute progress, the algorithmic progress, the data progress, all of are really industry-wide. But I think within OpenAI, we made a lot of very choices from the early days. And the first one was just confront reality as it lays. And that we just really hard about like: What is it going to to make progress here? We tried a lot of things didn’t work, so you only see the things that did. And think that the most important thing has been to get teams people who are very different from each other to together harmoniously.
CA: Can we have the water, by way, just brought here? I think we’re going to need it, it’s a dry-mouth topic. But isn’t something also just about the fact that you saw something in these models that meant that if you continue to invest in them and them, that something at 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 to be, was deep learning lab, and exactly how to do it? think that in the early days, we didn’t know. We tried a lot things, and one person was working on training a model to the next character 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 out of it. This model could tell you if a review positive or negative. I mean, today we are just like, on, anyone can do that. But this was the first that you saw this emergence, this sort of semantics that emerged from this syntactic process. And there we knew, you’ve got to scale this thing, you’ve got to see where goes.
CA: So I 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 them … it just 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 you get more of a thing, suddenly different things emerge. It happens all the time, ant colonies, ants run around, when you bring enough of them together, you get these colonies that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But as you grow the number of houses, emerge, like suburbs and cultural centers and traffic jams. Give me one moment for you you saw just something pop that just blew your mind that 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 do it. And the really interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often get it wrong. And you can see that it’s really learning the process, but it hasn’t generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s more than there are in the universe. So it had to learned something 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: what’s happened here is that you’ve allowed it to scale up and look at an incredible number of of text. And it is learning things that you didn’t know that it was going to capable of learning.
GB Well, yeah, and it’s more nuanced, too. So science that we’re starting to really get good at is predicting some of these emergent capabilities. And to that actually, one of the things I think is very undersung in field is sort of engineering quality. Like, we had to rebuild our entire stack. you think about building a rocket, every tolerance has be incredibly tiny. Same is true in machine learning. You have to every single piece of the stack engineered properly, and then can start doing these predictions. There are all these smooth scaling curves. They tell you something deeply fundamental about intelligence. you look at our GPT-4 blog post, you can see all of curves in there. And now we’re starting to be able to predict. So were able to predict, for example, the performance on problems. We basically look at some models that are 10,000 or 1,000 times smaller. And so there’s something about that is actually smooth scaling, even though it’s still days.
CA: So here is, one of the big fears then, arises from this. If it’s fundamental to what’s happening here, that 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 huge risk of something truly terrible emerging?
GB: Well, I think all of are questions of degree and scale and timing. And think one 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 think it’s so important to deploy incrementally. And so I that what we kind of see right now, if you look at this talk, a of what I focus on is providing really high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at that problem 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. No one to do that.
(Laughter) And so I think that the important 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 to actually carry out our intent. I think we’re going to have to produce even better, more efficient, more reliable of scaling this, sort of like making the machine be aligned with you.
CA: we’re going to hear later in this session, there are critics who say that, you know, there’s real understanding inside, the system is going to always — we’re never to know that it’s not generating errors, that it doesn’t have common and so forth. Is it your belief, Greg, that it is true at any one moment, that the expansion of the scale and the human feedback that you talked about is basically going take it on that journey of actually getting to things like and wisdom and so forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe that 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 these experts saying X is going 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 might right maybe 70 years plus one or something like that what you need. But I think that our approach has always been, you’ve got to push to the of this technology to really see it in action, because that tells you then, oh, here’s how can move on to a new paradigm. And we haven’t exhausted the fruit here.
CA: I mean, it’s quite a stance you’ve taken, that the right way to do this is to put out there in public and then harness all this, you know, of just your team giving feedback, the world is now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded as a nonprofit, well you were as the great sort of check on the big companies their unknown, possibly evil thing with AI. And you going to build models that sort of, you know, somehow held them accountable was 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 the opposite. That your release GPT, especially ChatGPT, sent such shockwaves through the tech world that now Google and Meta and so forth all scrambling to catch up. And some of their criticisms have been, you are forcing us to this out here without proper guardrails or we die. You know, do you, like, make the case that what you done is responsible here and not reckless.
GB: Yeah, we think about these all the time. Like, seriously all the time. And I don’t think we’re always going get it right. But one thing I think has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, actually have it benefit all humanity, like, how are you supposed to do that, right? And that default plan being, well, you build in secret, you get this super powerful thing, and then figure out the safety of it and then you push “go,” you hope you got it right. I don’t know to execute that plan. Maybe someone else does. But for me, that always terrifying, it didn’t feel right. And so I think this alternative approach is the only other path that I see, is that you do let reality hit you in the face. I think you do give people time to give input. You do have, before these machines perfect, before they are super powerful, that you actually have the ability to see them action. And we’ve seen it from GPT-3, right? GPT-3, we really were afraid that 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 are things that are much worse. Here’s a thought experiment for you. Suppose you’re sitting a room, there’s a box on the table. You believe that in box is something that, there’s a very strong chance it’s something absolutely that’s going to give beautiful gifts to your family and everyone. But there’s actually also a one percent thing in small print there that says: “Pandora.” And there’s a chance this actually could unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a story I haven’t actually told before, which is that shortly we started OpenAI, I remember I was in Puerto for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these people having a good time. you think about it for a moment, if you could choose basically that Pandora’s box to 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 to have it be five years away. But if gets to be 500 years away and people get more to get it right, which do you pick? And you know, I just really felt it in moment. I was like, of course you do the 500 years. My brother in the military at the time and like, he puts his life on the line in a more real way than any of us typing things in and developing this technology at the time. And so, yeah, I’m sold on the 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 mean it when I say that this is an industry-wide or just almost like a human-development- of-technology-wide shift. And the more that you sort of, don’t put the pieces that are there, right, we’re still making computers, we’re still improving the algorithms, all of these things, they happening. And if you don’t put them together, you get an overhang, means that 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 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 about of other sort of technologies, think about nuclear weapons, talk 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 technology we’ve developed has 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 I’m hearing is that you … the model you want us to have is that we have birthed extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility to provide the guardrails this child to collectively teach it to be wise and not to tear us all down. that basically the model?
GB: I think it’s true. And I think it’s also important to say may shift, right? We’ve got to take each step as we encounter it. I think it’s incredibly important today that we all do get literate in technology, figure out how to provide the feedback, decide what want from it. And my hope is that that will continue be the best path, but it’s so good we’re honestly having this debate we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, you so much for coming to TED and blowing minds.
(Applause)