We started OpenAI seven years ago because we felt something really interesting was happening in AI and we to help steer it in a positive direction. It’s honestly just really amazing to how far this whole field has come since then. And it’s gratifying to hear from people like Raymond who are the technology we are building, and others, for so many things. We hear from people who are excited, we hear people who are concerned, we hear from people who both those 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 technology will be so important for our society going forward. I believe that we can manage this for good.
So today, I want to show the current state of that technology and some of underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s like to build a tool for AI rather than building it for a human. So we have new DALL-E model, which generates images, and we are exposing it as an app for to use on 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 all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you out of ChatGPT. And here we go, it’s not just the idea the meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also generates an image. And is something that really expands the power of what can do on your behalf in terms of carrying your intent. And I’ll point out, this is all a demo. This is all generated by the AI as we speak. So I actually don’t 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.” And 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, this is coming you, all ChatGPT users, over upcoming months. And you can look under the and see that what it actually did was write prompt just like a human could. And so you of have this ability to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s for later, and let me show you what it’s like to use that information and to integrate with applications too. You can say, “Now make a shopping for the tasty thing I was suggesting earlier.” And it a little tricky for the AI. “And tweet it out all the TED viewers out there.”
(Laughter)
So if do make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT is selecting all these tools without me having to tell it explicitly which ones to use in any situation. And this, think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste between them, and usually it’s a great experience an app as long as you kind of know menus and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, AI is able to sort of take away all those details you. So you don’t have to be the one who out every single sort of little piece of what’s supposed happen.
And as I said, this is a live demo, so sometimes the unexpected happen to us. But let’s take a look at the shopping list while we’re at it. And you can see we sent a list of to Instacart. Here’s everything you need. And the thing that’s really interesting is that traditional UI is 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 we have a new, way to build them. And now we have a tweet that’s been drafted for our review, which is a 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 of the AI if we want to. And so after this talk, you will be 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 about building tools. It’s about teaching the AI how to use them. Like, what do we want it to do when we ask these very high-level questions? And to do this, we use an old idea. you go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll program an answer to this. Instead, you can learn it. could build a machine, like a human child, and then teach it feedback. Have a human teacher who provides rewards and punishments as it things out and does things that are either good bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have a child machine through an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes in text you’ve never seen before.” And this process it with all sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that 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 with those skills. And for this, we provide feedback. We have the AI try out multiple things, us multiple suggestions, and then a human rates them, says “This one’s better than one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process 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, it hasn’t received feedback.
Now, sometimes the things we have to the AI are not what you’d expect. For example, we first showed GPT-4 to Khan Academy, they said, “Wow, this so great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. If there’s some bad in there, it will happily pretend that one plus one equals and run with it.” So we had to collect feedback data. Sal Khan himself was very kind and offered 20 hours of his own time provide feedback to the machine alongside our team. And over the course of couple of months we were able to teach the that, “Hey, you really should push back on humans in specific kind of scenario.” And we’ve actually made lots lots of improvements to the models this way. And when push that thumbs down in ChatGPT, that actually is of like sending up a bat signal to our team to say, “Here’s 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 building that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. If think about asking a kid to clean their room, all you’re doing is inspecting the floor, you don’t if you’re just teaching them to stuff all the in the closet. This is a nice DALL-E-generated image, by way. And the same sort of reasoning applies to AI. As we move harder tasks, we will have to scale our ability to high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even feedback and to scale our ability to supervise the machine time goes on. And let me show you what I mean.
For example, you can ask GPT-4 a like this, of how much time passed between these two blogs on unsupervised learning and learning from human feedback. And the model says two passed. But is it true? Like, these models are not 100-percent reliable, although they’re getting better every we provide some feedback. But we can actually use 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 actually given the AI new tool. This one is a browsing tool where the model can search queries and click into web pages. And it actually out its whole chain of thought as it does it. It says, I’m just going search for this and it actually does the search. It then it finds the publication date and search results. It then is issuing another search query. It’s going to into the blog post. And all of this you do, but it’s a very tedious task. It’s not a that humans really want to do. It’s much more fun be in the driver’s seat, to be in this manager’s position where you can, if you want, triple-check work. And out come citations so you can actually go and very verify any piece of this whole chain of reasoning. And it actually turns two months was wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting to me about this whole process is it’s this many-step collaboration between a human and an AI. Because a human, using this fact-checking tool is it in order to produce data for another AI to become useful to a human. And I think this really shows the shape of something that we should to be much more common in the future, where we have humans and kind of very carefully and delicately designed in how fit into a problem and how we want to solve that problem. We make sure that the are providing the management, the oversight, the feedback, and the machines are operating in a 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 almost every aspect how we interact with computers. For example, think about spreadsheets. They’ve 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. And here is specific spreadsheet of all the AI papers on the arXiv for the past 30 years. There’s about 167,000 them. And you can 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 another tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And so you can just literally upload a and ask questions about it. And very helpfully, you know, it knows the of the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it you.” The only information here is the name of the file, column names like you saw and then the actual data. And from that it’s to infer what these columns actually 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 submit papers therefore that’s what these things are and that these integer values and so therefore it’s a number of in the paper,” like all of that, that’s work for a human do, and the AI is happy to help with it.
Now don’t even know what I want to ask. So fortunately, you ask the machine, “Can you make some exploratory graphs?” once again, this is a super high-level instruction with lots of behind it. But I don’t even know what I want. And the AI of has to infer what I might be interested in. And it comes up with some good ideas, I think. a histogram of the number of authors per paper, series of papers per year, word cloud of the paper titles. of that, I think, will be pretty interesting to see. the great thing is, it can actually do it. we go, a nice bell curve. You see that three is of the most common. It’s going to then make this nice of the papers per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped the cliff. What could 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 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 to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage papers in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you that to make a fair projection? So we’ll see, is 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 it notice this thing, maybe it’s a little bit of an overreach it to have sort of, inferred magically that this what I wanted. But I inject my intent, I this additional piece of, you know, guidance. And under the hood, AI is just writing code again, so if you to inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it updates the title. I didn’t ask for that, but know what I want.
Now we’ll cut back to the slide again. This slide a parable of how I think we … A vision of how we may end up using this in the future. A person brought his very sick dog to the vet, and the veterinarian made bad call 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 full records, to GPT-4, which said, “I am not a vet, you need to talk to a professional, 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 human with a medical and with ChatGPT as a brainstorming partner was able to achieve an outcome that would not have otherwise. I think this is something we should all reflect on, think about as we how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI right is going to require 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 won’t do. And if 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 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. I mean … I that within every mind out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, you look that and you think, “Oh my goodness, pretty much every single thing about the I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to rethink the 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 first question actually is how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google thousands of employees working on artificial intelligence. Why is it you who’s come up with this that shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on shoulders giants, right, there’s no question. If you look at the compute progress, algorithmic progress, the data progress, all of those are really industry-wide. But I think within OpenAI, made a lot of very deliberate choices from the early days. And the first one just to confront reality as it lays. And that we just really hard about like: What is it going to take to make progress here? tried a lot of things that didn’t work, so only see the things that did. And I think that the most thing has been to get 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 think we’re to need it, it’s a dry-mouth topic. But isn’t there something also just about fact that you saw something in these language models that meant if you continue to invest in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, think the story there is pretty illustrative, right? I think that level, deep learning, like we always knew that was what we wanted to be, a deep learning lab, and exactly how to do it? I think that the early days, we didn’t know. We tried a lot things, and one person was working on training a to predict the next character in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model will where the commas go, where the nouns and verbs are. But he actually a state-of-the-art sentiment analysis classifier out of it. This model could you if a review was positive or negative. I mean, we are just like, come on, anyone can do that. this was the first 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 riddle that baffles everyone looking at this, because these are described as prediction machines. And yet, what we’re out of them feels … it just feels impossible that that could come from a prediction machine. the stuff you showed us just now. And the key idea of emergence is that when get more of a thing, suddenly different things emerge. It all the time, ant colonies, single ants run around, you bring enough of them together, you get these ant colonies that completely emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow the number of houses, things emerge, suburbs and cultural centers and traffic jams. Give me one moment for when you saw just something pop that just blew your 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 will do it, which means it’s really learned an circuit for how to do it. And the really interesting is actually, if you have it add like a 40-digit number a 35-digit number, it’ll often get it wrong. And so you can that it’s really learning the process, but it hasn’t fully generalized, right? It’s you can’t memorize the 40-digit addition table, that’s more atoms than there in 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 adding arbitrary of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to scale and look at an incredible number of pieces of text. it is learning things that you didn’t know that it was to be capable of learning.
GB Well, yeah, and it’s more nuanced, too. one science that we’re starting to really get good at is some of these emergent capabilities. And to do that actually, one of the I think is very undersung in this field is sort engineering quality. Like, we had to rebuild our entire stack. When you think about a rocket, every tolerance has to be incredibly tiny. 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 incredibly scaling curves. They tell you something deeply fundamental about intelligence. you look at our GPT-4 blog post, you can all of these curves in there. And now we’re starting be able to predict. So we were able to predict, for example, performance on coding problems. We basically look at some that are 10,000 times or 1,000 times smaller. And there’s something about this that is actually smooth scaling, even it’s still early days.
CA: So here is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that as you scale up, emerge that you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just a huge of something truly terrible emerging?
GB: Well, I think all these are questions of degree and scale and timing. I think one thing people miss, too, is sort of the with 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 think that what we kind of see right now, if look at this talk, a lot of what I focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s very easy to look at math problem and be like, no, no, no, machine, seven was the correct answer. even summarizing a book, like, that’s a hard thing supervise. Like, how do you know if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) And I think that the important thing will be that we this step by step. And that we say, OK, as we move on to book summaries, we to supervise this task properly. We have to build up a track record with machines that they’re able to actually carry out our intent. And I think we’re to have to produce even better, more efficient, more reliable of scaling this, sort of like making the machine be aligned 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 not errors, that it doesn’t have common sense and so forth. Is your belief, Greg, that it is true at any moment, but that the expansion of the scale and the human feedback that you talked about is 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, the short 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 field is the field of broken promises, of all these experts saying X is going happen, Y is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t been yet. They might be 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 to the limits 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 fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do this is to it out there in public and then harness all this, you know, instead just your team giving feedback, the world is now feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the original story I heard on OpenAI when you were founded as a nonprofit, you were there as the great sort of check on the big companies doing their unknown, possibly evil 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 what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through the tech that now Google and Meta and so forth are all scrambling to catch up. And some 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 case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions all the time. Like, all the time. And I don’t think we’re always to get it right. But one thing I think has been incredibly important, the very beginning, when we were thinking about how build artificial general intelligence, actually have it benefit all of humanity, like, how are supposed to do that, right? And that default plan of being, well, build in secret, you get this super powerful thing, and you figure out the safety of it and then you push “go,” and you you got it right. I don’t know how to execute that plan. Maybe someone else does. for me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the only other path I see, which is that you do let reality hit you in the face. And I think do give people time to give input. You do have, before machines are perfect, before they are super powerful, that you have the ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the number one thing were going to do with it was generate misinformation, to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on table. You believe that in that box is something that, there’s a strong chance it’s something absolutely glorious that’s going to beautiful gifts to your family and to everyone. But there’s actually a one percent thing in the small print there that says: “Pandora.” there’s a chance that this actually could unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t it that way. And honestly, like, I’ll tell you a story that I haven’t actually told before, is that shortly after we started OpenAI, I remember I in Puerto Rico for 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 for basically that Pandora’s box be five years away or 500 years away, which would you pick, right? On one hand you’re like, well, maybe for you personally, it’s to have it be five years away. But if gets to be 500 years away and people get time to get it right, which do you pick? you know, I just really felt it in the moment. I was like, of course you the 500 years. My brother was in 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 the you’ve got approach this right. But I don’t think that’s quite playing the field as it lies. Like, if you look at the whole history of computing, I really mean it when I that this is an industry-wide or even just almost a 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 still improving algorithms, all of these things, they are happening. And if you don’t them together, you get an overhang, which means that someone does, or the moment that someone does manage to connect to circuit, then you suddenly have this very powerful thing, no one’s had any time to adjust, knows what kind of 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, sort of, change what humans could do. But I actually think that you look at capability, it’s been quite smooth over time. And 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 manage it for each moment that you’re increasing it.
CA: So I’m hearing is that you … the model you want to have is that we have birthed this extraordinary that may have superpowers that take humanity to a whole new place. It is collective responsibility to provide the guardrails for this child to collectively teach it to be wise and to tear us all down. Is that basically the model?
GB: I think it’s true. And think it’s also important to say this may shift, right? We’ve got to take each step as we it. And I think it’s incredibly important today that we all do get literate in this technology, figure how to provide the feedback, decide what we want from it. And my is that that will continue to be the best path, it’s so good we’re honestly having this debate because we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED blowing our minds.
(Applause)