We started OpenAI seven years ago because felt like something really interesting was happening in AI and we wanted help steer it in a positive direction. It’s honestly just really to see how far this whole field has come since then. it’s really gratifying to hear from people like Raymond are using the technology we are building, and others, for so wonderful things. We hear from people who are excited, we hear from people who are concerned, hear from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels we’re entering an historic period right now where we a world are going to define a technology that be so important for our society going forward. And believe that we can manage this for good.
So today, I want to 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 build a tool for an AI rather than building it for human. So we have a new DALL-E model, which images, and we are exposing it as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest a nice post-TED meal draw a 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 you get 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. But ChatGPT doesn’t just images in this case — sorry, it doesn’t generate text, it also generates an image. that is something that really expands the power of what it do on your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. This is all by the AI as we speak. So I actually don’t even know 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, example, memory. You can say “save this for later.” And the thing about these tools is they’re very inspectable. So you get this little up here that says “use the DALL-E app.” And by the way, this is to you, all ChatGPT users, over upcoming months. And you can look under the hood see that what it actually did was write a just like a human could. And so you sort of have ability to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use that information and to integrate with other too. You can say, “Now make a shopping list for the tasty thing 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 do make this wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting these different tools without me having to tell it which ones to use in any situation. And this, I think, a new way of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, click between them, we copy/paste between them, and usually it’s a great experience within an app long as you kind of know the menus and know all the options. Yes, would like you to. Yes, please. Always good to be polite.
(Laughter)
And by 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 spells out every single sort of piece of what’s supposed to happen.
And as I said, this is a demo, so sometimes the unexpected will happen to us. But let’s a 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 need. And the thing that’s really interesting is that the traditional UI is still valuable, right? If you look at this, you still can 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 build them. And now we have a tweet that’s been drafted our review, which is also a very important thing. We can “run,” and there we are, we’re the manager, we’re able to inspect, we’re able change the work of the AI if we want to. And so after this talk, will be able to 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 the AI how to use them. Like, what do we even it to do when we ask these very high-level questions? And to do this, we use old idea. If you go back to Alan Turing’s 1950 paper on Turing test, he says, you’ll never program an answer to this. Instead, you can it. You could build a machine, like a human child, and then teach it feedback. Have a human teacher who provides rewards and as it tries things out and does things that are either good or bad.
And is exactly how we train ChatGPT. It’s a two-step process. First, we produce what Turing would have a child machine through an unsupervised learning process. We just show the whole world, the whole internet and say, “Predict what next in text you’ve never seen before.” And this process imbues it with all sorts of wonderful skills. example, if you’re shown a math problem, the only way to actually complete that math problem, to say comes next, that green nine up there, is to actually the math problem.
But we actually have to do second step, too, which is to teach the AI what to do with those skills. for this, we provide feedback. We 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 this reinforces just the specific thing that the AI said, but very importantly, the process that the AI used to produce that answer. And this allows to generalize. It allows it to teach, to sort of infer your intent and it in scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach the are not what you’d expect. For example, when we first showed GPT-4 Khan Academy, they 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. there’s some bad math in there, it will happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Sal Khan himself was very kind and offered 20 of his own time to provide feedback to the machine alongside our team. And over course of a couple of months we were able to teach the that, “Hey, you really should push back on humans in this specific of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you push that thumbs down in ChatGPT, actually is kind of like sending up a bat signal to 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 users and sure we’re building something that’s more useful for everyone.
Now, high-quality feedback is a hard thing. If you think about a kid to clean their room, if all you’re doing inspecting the floor, you don’t know if you’re just them to stuff all the toys in the closet. This a nice DALL-E-generated image, by the way. And the same sort reasoning applies to AI. As we move to harder tasks, will have to scale 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 feedback and scale our 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, of how much time between these two foundational blogs on unsupervised learning and learning from human feedback. And the model says months passed. But is it true? Like, these models are 100-percent reliable, although they’re getting better every time we provide some feedback. But we actually use the AI to fact-check. And it can check its own work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This one is a tool where the model can issue search queries and click into pages. And it actually writes out its whole chain of thought as it does it. It says, I’m going to search for this and it actually does the search. It then finds the publication date and the search results. It then is another search query. It’s going to click into the blog post. And 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 be in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. out come citations 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 months and week, that was correct.
(Applause)
And we’ll cut back to side. And so thing that’s so interesting to me about this whole process that it’s this many-step collaboration between a human and AI. Because a human, using this fact-checking tool is it in order to produce data for another AI to more useful to a human. And I think this really the shape of something that we should expect to be more common in the future, where we have humans and machines kind 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, oversight, the feedback, and the 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, we will be to solve impossible problems.
And to give you a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every aspect how we interact with computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really changed much in that time. And here is a specific spreadsheet of all the AI papers on the for the past 30 years. There’s about 167,000 of them. And you can see the data right here. But let me show you ChatGPT take on how to analyze a data set like this.
So we give ChatGPT access to yet another tool, this one a Python interpreter, so it’s able to run code, like a data scientist would. And so you can just upload a file and ask questions about it. And helpfully, you know, it knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The only information here the name of the file, the column names like you saw and then the actual data. And that it’s able to infer what these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its world of knowing that, “Oh yeah, arXiv is a site people submit papers and therefore that’s what these things are that these are integer values and so therefore it’s a of authors in the paper,” like all of that, that’s work a human to do, and the AI is happy to help 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 instruction with lots of intent behind it. I don’t even know what I want. And the kind of has to infer what I might be interested in. And it comes up with some good ideas, I think. So a histogram of the number of authors paper, time series of papers per year, word cloud of the paper titles. All that, I think, will be pretty interesting to see. And the thing is, it can actually do it. Here we go, a bell curve. You see that three is kind of most common. It’s going to then make this nice plot of the papers year. Something crazy is happening in 2023, though. Looks like we were on an exponential it dropped off the cliff. What could be going on there? the way, all this is Python code, you can inspect. then we’ll see word cloud. So you can see all these wonderful things that appear these titles.
But I’m pretty unhappy about this 2023 thing. It this year look really bad. Of course, the problem that the year is not over. So I’m going push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What of papers in 2022 were even posted by April 13?] So April 13 the cut-off date I believe. Can you use that to make fair projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I out of the machine here. I really wanted it to notice this thing, maybe it’s a little bit an overreach for it to have sort of, inferred magically that this is what wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back to the slide again. This slide shows a of how I think we … A vision of how may end up using this technology in the future. A person brought his very dog to the vet, and the veterinarian made a bad to say, “Let’s just wait and see.” And the dog would be here today had he listened. In the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not a vet, you need to talk to professional, here are some hypotheses.” He brought that information to second vet who used it to save the dog’s life. Now, systems, they’re not perfect. You cannot overly rely on them. But this story, I think, that a human with a medical professional and with ChatGPT as a brainstorming was able to achieve an outcome that would not have happened otherwise. I think this is something should all reflect on, think about as we consider how to integrate these systems into world.
And one thing I believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding how we want it slot in, that’s for setting the rules of the road, for what 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. different 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 the OpenAI mission of ensuring that artificial general benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within mind out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, you look at that 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 they’re having to rethink the way that 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 my question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands employees working on artificial intelligence. Why is it you who’s up with this technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think within OpenAI, we made a lot of very choices from the early days. And the first one just to confront reality as it lays. And that just thought really hard about like: What is it going to take to progress 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 thing has to get teams of people who are very different from each other work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going 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 you continue to invest in them and grow them, something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story there is pretty illustrative, right? I that high level, deep learning, like we always knew that was what we to be, was a deep learning lab, and exactly to 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 Amazon reviews, and he got a result where — this is a syntactic process, you expect, you know, model will predict where the commas go, where the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model could tell you if a review was or negative. I mean, today we are just like, on, anyone can do that. But this was the time that you saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve got see where it goes.
CA: So I think this helps explain the riddle that baffles looking at this, because these things are described as prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could come from a prediction machine. Just the stuff showed us just now. And the key idea of emergence is that when get more of a thing, suddenly different things emerge. 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 a 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 blew your mind that just did not see coming.
GB: Yeah, well, so you can try this in ChatGPT, if you 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s really an internal circuit for how to do it. And really interesting thing is actually, if you have it like a 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see that it’s really the process, but it hasn’t fully generalized, right? It’s like can’t memorize the 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 really yet learned that, Oh, I can sort of generalize this to arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is you’ve allowed it to scale up and look at incredible number of pieces of text. And it is learning things you didn’t know that it was going to be of learning.
GB Well, yeah, and it’s more nuanced, too. one science that we’re starting to really get good at is predicting of these emergent capabilities. And to do that actually, one of things I think is very undersung in this field is of engineering quality. Like, we had to rebuild our stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in machine learning. You have to get every single of the stack engineered properly, and then you can start doing these predictions. There are all these smooth scaling curves. They tell you something deeply fundamental intelligence. If you look at our GPT-4 blog post, you can all of these curves in there. And now we’re to be able to predict. So we were able 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 about this that is actually smooth scaling, even though it’s still early days.
CA: So here is, one of 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, it’s capable of surprising 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 think thing people miss, too, is sort of the integration with world is also this incredibly emergent, sort of, very powerful thing too. And so that’s one the reasons that we think it’s so important to deploy incrementally. And so think that what we kind of see right now, if you at this talk, a lot of what I focus is providing really high-quality feedback. Today, the tasks that we do, can inspect them, right? It’s very easy to look at math problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, how do you if this book summary is any good? You have to read the book. No one wants to do that.
(Laughter) And so I that the important thing will be that we take step by step. And that we say, OK, as we move on book summaries, we have to supervise this task properly. We to build up a track record with these machines that they’re able to carry out our intent. And I think we’re going to have to produce even better, 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, system is going to always — we’re never going know that it’s not generating errors, that it doesn’t have common sense and forth. Is it your belief, Greg, that it is true at any one moment, but that the expansion the scale and the human feedback that you talked about is basically going to take on that journey of actually getting to things like truth 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, the 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 in the face, right? It’s like this field is the of broken promises, of all these experts saying X is going happen, Y is how it works. People have been saying neural nets aren’t going to for 70 years. They haven’t been right yet. They might be right maybe 70 years plus or something like that is what you need. But I think our approach has always been, you’ve got to push to the of this technology to really see it in action, that tells you then, oh, here’s how we can move to a new paradigm. And we just haven’t exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that right way to do this is to put it out there in public then harness all this, you know, instead of just your team giving feedback, the is now giving feedback. But … If, you know, bad things are going to emerge, is out there. So, you know, the original story that I heard on OpenAI when you were founded a nonprofit, well you were there as the great of check on the big companies doing their unknown, possibly thing with AI. And you were going to build that sort of, you know, somehow held them accountable was capable of slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent shockwaves through the tech world that now Google and Meta and so forth are all to catch up. And some of their criticisms have been, are forcing us to put this out here without proper guardrails or we die. You know, do you, like, make the case that what you have is responsible here and not reckless.
GB: Yeah, we think about questions all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But one thing I 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 do that, right? And that default plan of being, well, you build in secret, get this super powerful thing, and then you figure out the safety it and then you push “go,” and you hope got it right. I don’t know how to execute plan. Maybe someone else does. But for me, that always terrifying, it didn’t feel right. And so I think that alternative approach is the only other path that I see, is that you do let reality hit you in the face. And think you do give people time to give input. You do have, before these are perfect, before they are super powerful, that you actually have the 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 to do with it was generate misinformation, try to tip elections. Instead, the one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam 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 that in that 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 to everyone. But there’s actually also a one thing in the small print there that says: “Pandora.” And there’s a chance that this actually unleash unimaginable evils on the world. Do you open that box?
GB: Well, so, absolutely not. think you don’t do it that way. And honestly, like, I’ll tell you a that 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 room just looking out over this wonderful water, all people having a good time. And you think about it for a moment, if you could for basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to have it be five years away. But if gets to be 500 years away and people get more time to get it right, which do pick? And you know, I just 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 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 the field as it truly lies. Like, if you look at the whole history of computing, I mean it when I say that this is an industry-wide or even just almost like human-development- of-technology-wide shift. And the more that you sort of, don’t put together the pieces that are there, right, we’re making faster computers, we’re still improving the algorithms, all these things, they are happening. And if you don’t put them together, you get an overhang, means that if someone does, or the moment that does manage to connect to the circuit, then you have this very powerful thing, no one’s had any time to adjust, who knows what kind of precautions you get. And so I think that one thing I take away is like, even you think development of other sort of technologies, think about nuclear weapons, people talk 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 quite smooth time. And so the 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 to manage it each moment that you’re increasing it.
CA: So what I’m hearing is that you … the you want us to have is that we have birthed this extraordinary child that have superpowers that take humanity to a whole new place. is our collective responsibility to provide the guardrails for this child to collectively teach it be wise and not to tear us all down. that basically the model?
GB: I think it’s true. 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 feedback, decide what we want from it. And my hope is that will continue to be the best path, but it’s good we’re honestly having this debate because we wouldn’t if it weren’t out there.
CA: Greg Brockman, thank you so much for coming to TED blowing our minds.
(Applause)