We OpenAI seven years ago because we felt like something interesting was happening in AI and we wanted to help steer 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 using the we are building, and others, for so many wonderful things. We hear from people are excited, we hear from people who are concerned, we hear from people who feel those emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period right where we as a world are going to define a technology will be so important for our society going forward. And I believe that we manage this for good.
So today, I want to show you the current state of technology and some of the underlying design principles that we dear.
So the first thing I’m going to show is what it’s like to build a tool for AI rather than building it for a human. So have a new DALL-E model, which generates images, and we exposing 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 of ChatGPT. And here we go, it’s not just the for 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 behalf in terms of carrying out your intent. And I’ll point out, this is all live demo. This 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 hungry just at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” the interesting thing about these tools is they’re very inspectable. So you get this pop up here that says “use the DALL-E app.” And by the way, is coming to you, all ChatGPT users, over upcoming 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 ability inspect how the machine is using these tools, which allows us to feedback to them.
Now it’s saved for later, and let me show you what it’s like use that information and to integrate with other applications too. You can say, “Now make a shopping list for tasty thing I was suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want know how it tastes.
But you can see that ChatGPT selecting all these different tools without me having to it explicitly which ones to use in any situation. this, I think, shows a new way of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great experience within an app as long as you of know the menus and know all the options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this language interface 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 who spells out every sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes unexpected will happen to us. But let’s take a look at the Instacart shopping while we’re at it. And you can see we sent a list of ingredients to Instacart. Here’s you need. And the thing that’s really interesting is the traditional UI is still very valuable, right? If you look this, you still can click through it and sort of modify the actual quantities. that’s something that I think shows that they’re not going away, UIs. It’s just we have a new, augmented way to build them. And we have a tweet that’s been drafted for our review, which is also a important thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change work of the AI if we want to. And so after this talk, you be able to access this yourself. And there we go. Cool. you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the thing about how we build this, it’s not just building these tools. It’s about teaching the AI how to use them. Like, what do even want it to do when we ask these high-level questions? And to do this, we use an old idea. you go back to Alan Turing’s 1950 paper on Turing test, he says, you’ll never program an answer this. Instead, you can learn it. You could build a machine, like a human child, and then teach through feedback. Have a human teacher who provides rewards and punishments as it tries things and does things that are either good or bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called a child machine through an unsupervised learning process. just show it the whole world, the whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it with all sorts wonderful skills. For example, if you’re shown a math problem, the only way actually complete that math problem, to say what comes next, that green nine up there, is to solve the math problem.
But we actually have to do a second step, too, which is to the AI what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, give us multiple suggestions, and then human rates them, says “This one’s better than that one.” And this not just the specific thing that the AI said, very importantly, the whole process that the AI used to produce answer. And this allows it to generalize. It allows it to teach, sort of infer your intent and apply it in scenarios that it hasn’t seen before, it hasn’t received feedback.
Now, sometimes the things we to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, said, “Wow, this is so great, We’re going to be to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math in there, it will pretend that one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan himself very kind and offered 20 hours of his own time to provide feedback the machine alongside our team. And over the course of a couple of months we were to teach the AI that, “Hey, you really should push on humans in this specific kind of scenario.” And we’ve actually made lots and lots of improvements the models this way. And when you push that down in ChatGPT, that actually is kind of like sending up a bat signal to our team say, “Here’s an area of weakness where you should gather feedback.” And so when you that, that’s one way that we really listen to our users and make sure we’re something that’s more useful for everyone.
Now, providing high-quality feedback is hard thing. If you think about asking a kid to their room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff the toys in the closet. This is a nice DALL-E-generated image, the way. And the same sort of reasoning applies to AI. we move to harder tasks, we will have to scale our ability provide high-quality feedback. But for this, the AI itself happy to help. It’s happy to help us provide even better feedback and to scale ability to supervise the machine as 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 unsupervised learning and learning from human feedback. And the says two months passed. But is it true? Like, these are not 100-percent reliable, although they’re getting better every time we provide some feedback. But we can use the AI to fact-check. And it can actually check its own work. You say, fact-check this for me.
Now, in this case, I’ve given the AI a new tool. This one is browsing tool where the model can issue search queries and click 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 it actually does the search. It then it finds the publication date and the 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 not a thing that really want to do. It’s much more fun to be the driver’s seat, to be in this manager’s position where you can, you want, triple-check the work. And out come citations so you can actually go very easily verify any piece of this whole chain of reasoning. And actually turns out two months was wrong. Two months and week, that was correct.
(Applause)
And we’ll cut back the side. And so thing that’s so interesting to me about this whole is that it’s this many-step collaboration between a human and an AI. Because a human, this fact-checking tool is doing it in order to produce data for another to become more 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 and delicately designed in how they fit into a problem and how we want to that problem. We make sure that the humans are the management, the oversight, the feedback, and the machines are operating in way that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that time, if we get this process right, we will be to solve impossible problems.
And to give you a of just how impossible I’m talking, I think we’re going to be able to rethink every aspect of how we interact with computers. For example, think about spreadsheets. They’ve been around in form since, we’ll say, 40 years ago with VisiCalc. I don’t they’ve really changed that much in that time. And here is specific spreadsheet of all the AI papers on the for the past 30 years. There’s about 167,000 of them. And you see there the data right here. But let me show the ChatGPT take on how to analyze a data like this.
So we can give ChatGPT access to yet another tool, this one a interpreter, so it’s able to run code, just like a scientist would. And so you can just literally upload a file and ask questions about it. very helpfully, you know, it knows the name of file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse 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 sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a site that people submit papers therefore that’s what these things are and that these are integer and so therefore it’s a number of authors in the paper,” all of that, that’s work for a human to do, and the AI is happy to with it.
Now I don’t even know what I want to ask. So fortunately, you ask the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots intent behind it. But I don’t even know what I want. And the AI kind has 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, time of papers per year, word cloud of the paper titles. All of that, I think, be pretty interesting to see. And the great thing is, it can actually do it. Here we go, nice bell curve. You see that three is kind of the common. It’s going to then make this nice plot the papers per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped off the cliff. What be going on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you can see all wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year look really bad. course, the problem is that the year is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were even by April 13?] So April 13 was the cut-off date believe. Can you use that to make a fair projection? So we’ll see, this is kind of ambitious one.
(Laughter)
So you know, again, I feel there was more I wanted out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of an for it to have sort of, inferred magically that is what I wanted. But I inject my intent, provide this additional piece of, you know, guidance. And under the hood, the is just writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask for that, but 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 using this in the future. A person brought his very sick dog to vet, and the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be here today had listened. In the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” He brought that information to a second vet used it to 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 with a medical professional and with ChatGPT as a partner was able to achieve an outcome that would not have happened otherwise. I think this is we should all reflect on, think about as we consider how to integrate these systems our world.
And one thing I believe really deeply, is that getting right is going 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 an AI will and won’t do. And there’s one thing to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. And so we all have become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe that we achieve the 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 mind out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, you look at and you think, “Oh my goodness, pretty much every single thing about the way work, I need to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re 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, guess my first question actually is just how the hell you done this?
(Laughter)
OpenAI has a few hundred employees. Google thousands of employees working on artificial intelligence. Why is it who’s come up with this technology that shocked the world?
Greg Brockman: mean, the truth is, we’re all building on shoulders giants, right, there’s no question. If you look at the progress, the algorithmic progress, the data progress, all of those are really industry-wide. I think within OpenAI, we made a lot of very choices from the early days. And the first one was just confront reality as it lays. And that we just thought really hard like: What is it going to take to make here? We tried a lot of things that didn’t work, so you only see things that did. And I think that the most important has been to get teams of people who are very different each other to work together harmoniously.
CA: Can we have water, by the way, just brought here? I think we’re going to need it, it’s a dry-mouth topic. isn’t there something also just about the fact that you saw 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. And I that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, like we always knew that was what wanted to 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 working on training model to predict the next character in Amazon reviews, and got a result where — this is a syntactic process, you expect, you know, the model will where the commas go, where the nouns and verbs are. But he actually got a state-of-the-art sentiment analysis classifier 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 time that you saw emergence, this sort of semantics that emerged from this underlying process. And there we knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps explain the riddle baffles everyone looking at this, because these things are described as prediction machines. yet, what we’re seeing out of them feels … just feels impossible that that could come from a prediction machine. the stuff you showed us just now. And the key idea of emergence that when you get more of a thing, suddenly different emerge. It happens all the time, ant colonies, single ants run around, when you bring enough them together, you get these ant colonies that show completely emergent, different behavior. Or a city where few houses together, it’s just houses together. But as you grow number of houses, things emerge, like suburbs and cultural and traffic jams. Give me one moment for you when you saw just something pop that just blew mind that you just did not see coming.
GB: Yeah, well, so you can this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will it, which means it’s really learned an internal circuit for how to do it. And really 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 the universe. So it had to have learned something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to up and look at an incredible number of pieces of text. it is learning things that you didn’t know that it going to be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. And to do actually, one of the things I think is very undersung this field is sort of engineering quality. Like, we had rebuild our entire stack. When you think about building a rocket, every tolerance to be incredibly tiny. Same is true in machine learning. have to get every single piece of the stack engineered properly, and then you can doing these predictions. There are all these incredibly smooth scaling curves. They tell you something fundamental about intelligence. If you look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting to be able to predict. So we were able predict, for example, the performance on coding problems. We basically look some models that are 10,000 times or 1,000 times smaller. And so there’s something about this is actually smooth scaling, even though it’s still early days.
CA: So here is, of the big fears then, that arises from this. If it’s fundamental to what’s happening here, that as scale up, things emerge that you can maybe predict in some level of confidence, but it’s capable of you. Why isn’t there just a huge risk of truly terrible emerging?
GB: Well, I think all of are questions of degree and scale and timing. And I one thing people miss, too, is sort of the integration with the world is also this emergent, sort of, very powerful thing too. And so that’s one of the reasons that we think it’s so to deploy incrementally. And so I think that what we kind of see right now, if you look this talk, a lot of what I focus on providing 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 was correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if this book is any good? You have to read the whole book. No one to do that.
(Laughter) And so I think that the important thing will be that take this step by step. And that we say, OK, as move on to book summaries, we have to supervise this task properly. We to build up a track record with these machines that they’re able to actually out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways of scaling this, sort of like making the machine 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 inside, the system is going to always — we’re 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, but the expansion of the scale and the human feedback that you talked about is basically going to take on that journey of actually getting to things like and wisdom and so forth, with a high degree confidence. Can you be sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, the answer is yes, I believe that is where we’re headed. And I that the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like this field is field of broken promises, of all these experts saying X is going to happen, Y how it works. People have been saying neural nets aren’t going to work 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like that is 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 you 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 quite a controversial stance you’ve taken, that right way to do this is to put it there in public and then harness all this, you know, instead of just team giving feedback, the world is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, you know, the original story that heard on OpenAI when you were founded as a nonprofit, well you were there as the great sort check on the big companies doing their unknown, possibly evil thing AI. And you were going to build models that sort of, you know, held them accountable and was capable of slowing the field down, if need be. Or least that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves through the tech that now Google and Meta and so forth are scrambling to catch up. And some of their criticisms have been, you are forcing to put this out here without proper guardrails or die. You know, how do you, like, make the case what you have done is responsible here and not reckless.
GB: Yeah, think about these questions all the time. Like, seriously all time. And I don’t think we’re always going to get 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 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 you figure out the safety it and then you push “go,” and 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 that this 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, these machines are perfect, before they are super powerful, you actually have the ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number one thing people were going to do with was generate misinformation, try to tip elections. Instead, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are things are much worse. Here’s a thought experiment for you. Suppose you’re in a room, there’s a box on the table. You believe that in that box 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 there that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils the world. Do you open that box?
GB: Well, so, absolutely not. I think don’t do it that way. And honestly, like, I’ll tell a story that I haven’t actually told before, which is that shortly we started OpenAI, I remember I was in Puerto Rico an AI conference. I’m sitting in the hotel room just out over this wonderful water, all these people having a good time. And think about it for a moment, if you could choose basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On the hand you’re like, well, maybe for you personally, it’s better to have it be five away. But if it gets to be 500 years away and people more time to get it right, which do you pick? you know, I just really felt it in the moment. was like, of course you do the 500 years. My was in the military at the time and like, he puts his life the line in a much more real way than any of us typing things in and developing this technology at the time. And so, yeah, I’m really sold on the you’ve got approach this right. But I don’t think that’s quite playing the field as truly lies. Like, if you look at the whole history of computing, I really mean when I say that this is an industry-wide or even almost like a human-development- of-technology-wide shift. And the more that you sort of, don’t put 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, or the that someone does manage to connect to the circuit, then you suddenly have this very powerful thing, one’s had any time to adjust, who knows what kind safety precautions you get. And so I think that one thing I away is like, even you think about development of other of technologies, think about nuclear weapons, people talk about being like a zero to one, of, change in what humans could do. But I actually think that if you at capability, it’s been quite smooth over time. And so the history, think, of every technology we’ve developed has been, you’ve got do it incrementally and you’ve got to figure out how manage it for each moment that you’re increasing it.
CA: So what I’m hearing that you … the model you want us to have is that we have birthed this child that may have superpowers that take humanity to a whole place. It is our collective responsibility to provide the guardrails for this child collectively teach it to be wise and not to tear all down. Is that basically the model?
GB: I think it’s true. I think it’s also important to say this may shift, right? We’ve got to take each step as encounter it. And I think it’s incredibly important today that we all do get in this technology, figure out how to provide the feedback, decide we want 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 out there.
CA: Greg Brockman, thank you so much coming to TED and blowing our minds.
(Applause)