We started OpenAI seven years because we felt like something really interesting was happening in AI we wanted to help steer it in a positive direction. It’s honestly just really amazing to see how this whole field has come since then. And it’s really gratifying hear from people like Raymond who are using the technology we are building, others, for so many wonderful things. We hear from people who are excited, we hear from who are concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now we as a world are going to define a technology that will be so for our society going forward. And I believe that we can manage this 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 an AI rather than building it for a human. we have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest nice post-TED meal and 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 that you get of ChatGPT. And here we go, it’s not just the idea the meal, but a very, very detailed spread. So let’s see what we’re to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also an image. And that is something that really expands power of what it can do on your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. This is 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 just looking at it.
Now we’ve extended ChatGPT with tools too, for example, memory. You can say “save this later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here that says “use the DALL-E app.” by the way, this is coming to you, all ChatGPT users, over 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 this ability to inspect how the machine is using these tools, which us to provide feedback to them.
Now it’s saved for later, and let me show you it’s like to use that information and to integrate with other applications too. You can say, “Now a shopping list 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 you make this wonderful, wonderful meal, I definitely want to how it 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, shows a way of thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a experience within an app as long as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on top of tools, the AI is to sort of take away all those details from you. So don’t have to be the one who spells out every sort of little 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 take a look the Instacart shopping list while we’re at it. And you 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 the actual quantities. And 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 now we a tweet that’s been drafted for our review, which is also very important thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the of the AI if we want to. And so this talk, you will be able to access this yourself. 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 want it to do when we ask these very high-level questions? to do this, we use an old idea. If you go back to Turing’s 1950 paper on the Turing test, he says, you’ll never program an answer to this. Instead, can learn it. You could build a machine, like human child, and then teach it through feedback. Have a teacher who provides rewards and punishments as it tries out 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 a child machine through an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes next in text you’ve never seen before.” And process imbues it with all sorts of wonderful skills. example, if you’re shown a math problem, the only to actually complete that math problem, to say what comes next, that green nine up there, is actually solve the math problem.
But we actually have to do 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 things, give us multiple suggestions, and then a human rates them, “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 hasn’t received feedback.
Now, sometimes the things we have teach the AI 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 able to teach students wonderful things. one problem, it doesn’t double-check students’ math. If there’s some math in there, it will happily pretend that one plus one equals three and run with it.” So had to collect some feedback data. Sal Khan himself was very and offered 20 hours of his own time to feedback to the machine alongside our team. And over the of a couple of months we were able to teach AI that, “Hey, you really should push back on humans in this specific of scenario.” And we’ve actually made lots and 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 an area of weakness where should gather feedback.” And so when you do that, that’s way that we really listen to our users and make sure we’re building something that’s useful for everyone.
Now, providing high-quality feedback is a hard thing. If you think asking a kid to clean their room, if all you’re doing inspecting the floor, you don’t know if you’re just teaching them to stuff all the toys in closet. This is a nice DALL-E-generated image, by the way. 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 happy help. It’s happy to help us provide even better feedback and to scale our to supervise the machine as time goes on. And let me you what I mean.
For example, you can ask GPT-4 a like this, of how much time passed between these foundational 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, 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 me.
Now, in this case, I’ve actually given the a new tool. This one is a 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 and actually does the search. It then it finds the date and the search results. It then is issuing another query. It’s going to click into the blog post. all of this 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 in the driver’s seat, be in this manager’s position where you can, if you want, triple-check the work. out come citations so you can actually go and very easily any piece of this whole chain of reasoning. And it actually turns out two 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 me about this whole process is that it’s this many-step collaboration a human and an AI. Because a human, using this fact-checking is doing 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 much more common in the future, where we humans and machines kind of very carefully and delicately designed in they fit into a problem and how we want to solve problem. We make sure that the humans are providing the management, oversight, the feedback, and the machines are operating in way that’s inspectable and trustworthy. And together we’re able to actually create even more trustworthy machines. And I that over time, if we get this process right, we will be to solve impossible problems.
And to give you a sense of just impossible I’m talking, I think we’re going to be able to almost every aspect of how we interact with computers. For example, about spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really 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. you can see there the data right here. But me show you the ChatGPT take on how to a data set 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 data scientist would. And so you can literally upload a file and ask questions about it. And very helpfully, know, it knows the name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” only information here is the name of the file, the 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 has to of, put together its world knowledge of knowing that, “Oh yeah, arXiv is site that people submit papers and therefore that’s what these things are and that these are integer and so therefore it’s a number of authors in 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 I want to ask. So fortunately, you can ask the machine, “Can you make some exploratory graphs?” once again, this is a super high-level instruction with lots of intent it. But I don’t even know what I want. And AI kind of has to infer what I might be in. And so it comes up with some good ideas, I think. So a histogram of the number of per paper, time series of papers per year, word of the paper titles. All of that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here we go, a bell curve. You see that three is kind of the common. It’s going to then make this nice plot of the papers per year. crazy is happening in 2023, though. Looks like we were an exponential and it dropped off the cliff. What be going on there? By the way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all wonderful things that appear in these titles.
But I’m pretty about this 2023 thing. It makes this year look really bad. Of course, the problem is the year is not over. So I’m going to push 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 was cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel like there was I wanted out of the machine here. I really wanted it to this thing, maybe it’s a little bit of an overreach for it to have sort of, inferred that this is what I wanted. But I inject my intent, provide this additional piece of, you 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 correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … A vision how we may end up using this technology in future. A person brought his very sick dog to the vet, and the veterinarian made a 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, full medical records, to GPT-4, which said, “I am a vet, you need to talk to a professional, here are hypotheses.” He brought that information to a second vet who it to save the dog’s life. Now, these systems, they’re not perfect. 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. think this is something we should all reflect on, about as we consider how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI right is going require participation from everyone. And that’s for deciding how want it to slot in, that’s for setting the rules of the road, for what an AI and won’t do. And if there’s one thing to take away from talk, it’s that this technology just looks different. Just 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 that we can achieve the OpenAI mission of ensuring that general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect within every mind out here there’s a feeling of reeling. Like, I suspect a very large number of people viewing this, you at that and you think, “Oh my goodness, pretty every single thing about the way I work, I to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re having to rethink 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 done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working artificial intelligence. Why is it you who’s come up with this technology that shocked the world?
Greg Brockman: mean, the truth is, we’re all building on shoulders of giants, right, there’s no question. If look at the compute progress, the algorithmic progress, the progress, all of those are really industry-wide. But I think within OpenAI, we a lot of very deliberate choices from the early days. And first one was just to confront reality as it lays. And that just thought really hard about like: What is it going to take to make progress here? We a lot of things that didn’t work, so you see the things that did. And I think that the most important thing has been get teams of people who are very different from each other to work together harmoniously.
CA: Can we the water, by the way, just brought here? I think we’re going to 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 that 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, I think story there is pretty illustrative, right? I think that level, deep learning, like we always knew that was what wanted to be, was a deep learning lab, and exactly how to do it? I think that in early days, we didn’t know. We tried a lot of things, and one person was working on training model to predict the next character in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the will predict where the commas go, where the nouns verbs are. But he actually got a state-of-the-art sentiment analysis classifier 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 emerged from this underlying syntactic process. And there we knew, you’ve got to scale thing, you’ve got to see where it goes.
CA: So I think this helps explain the riddle that everyone looking at this, because these things are described prediction machines. And yet, what we’re seeing out of them feels … it just impossible that that could come from a prediction machine. Just the stuff you us just now. And the key idea of emergence is 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 of together, you get these ant colonies that show completely emergent, different behavior. a city where a few houses together, it’s just houses together. But you grow the number of houses, things emerge, like and cultural centers and traffic jams. Give me one moment for you when you saw just something pop just blew your mind that you just did not see coming.
GB: Yeah, well, you can try this in ChatGPT, if you add 40-digit —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s learned an internal circuit for how to do it. And really interesting thing is actually, if you have it add like a 40-digit number plus 35-digit number, it’ll often get it 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 are in the universe. So it had to have something general, but that it hasn’t really fully yet learned that, Oh, I can sort of generalize to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened is that you’ve allowed it to scale up and look at an incredible of pieces of text. And 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 get good at is some of these emergent capabilities. And to do that actually, one of the things I is very undersung in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think about building a rocket, every tolerance has to be tiny. Same is true in machine learning. You have get every single piece of the stack engineered properly, and you can start doing these predictions. There are all incredibly smooth scaling curves. They tell you something deeply fundamental intelligence. If you look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting to able to predict. So we were able to predict, for example, the performance on coding problems. We look at some models that are 10,000 times or 1,000 times smaller. so there’s something about this that is actually smooth scaling, even 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 you up, things emerge that you can maybe predict in level of confidence, but it’s capable of surprising you. Why isn’t just a huge risk of something truly terrible emerging?
GB: Well, I think all of these are of degree and scale and timing. And I think one thing people miss, too, is sort of integration with the world is also this incredibly emergent, of, very powerful thing too. And so that’s one the reasons that we think it’s so important to deploy incrementally. And so I think what we kind of see right now, if you look at this talk, a lot of I focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s easy to look at 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 book summary is any good? You have to read the whole book. No one to do that.
(Laughter) And so I think that important thing will be that we take this step by step. 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 these that they’re able to actually carry out our intent. And I we’re going to have to produce even better, more efficient, more reliable ways scaling this, sort of like making the machine be aligned you.
CA: So we’re going to hear later in this session, are critics who say that, you know, there’s no understanding inside, the system is going to always — we’re going to 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 the expansion of the scale and the human feedback that talked about is basically going to take it on journey of actually getting to things like truth and wisdom and forth, with a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, mean, the short 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 you in the face, right? It’s this field is the field of broken promises, of all these experts X is going to happen, Y is how it works. People have been neural nets aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years plus one something like that is what you need. But I think our approach has always been, you’ve got to push the limits of this technology to really see it in action, because that tells you then, oh, here’s we can move on to a new paradigm. And just haven’t exhausted the fruit here.
CA: I mean, it’s quite controversial stance you’ve taken, that the right way to do this is to put it out in public and then harness all this, you know, of just your team giving feedback, the world is now feedback. But … If, you know, bad things are going to emerge, it is there. So, you know, the original story that I heard on OpenAI when you were as a nonprofit, well you were there as the sort of check on the big companies doing their unknown, evil thing with AI. And you were going to build models sort of, you know, somehow held them accountable and was capable of slowing the down, if need be. Or at least that’s kind of I heard. And yet, what’s happened, arguably, is the opposite. That release of GPT, especially ChatGPT, sent such shockwaves through tech world that now Google and Meta and so forth all scrambling to catch up. And some of their criticisms have been, you 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 done is here and not reckless.
GB: Yeah, we think about these all the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing I think has been important, from the very beginning, when we were thinking about how to build artificial intelligence, actually have it benefit all of humanity, like, how you supposed to do that, right? And that default plan being, well, you build in secret, you get this powerful thing, and then you figure out the safety it and then you push “go,” and you hope you got right. I don’t know how to execute that plan. Maybe someone else does. But for me, that always terrifying, it didn’t feel right. And so I that this alternative approach is the only other path that I see, which is you do let reality hit you in the face. And I think you give people time to give input. You do have, before these machines perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve it from GPT-3, right? GPT-3, we really were afraid that number one thing people were going to do with it generate misinformation, try to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there things that are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on the table. believe that in that box is something that, there’s a very strong it’s something absolutely 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 print there says: “Pandora.” And there’s a chance that this actually could unleash unimaginable 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 you a story that I haven’t actually told before, which is shortly after we started OpenAI, I remember I was in Puerto for an AI conference. I’m sitting in the hotel room looking out over this wonderful water, all these people having a good time. And you about it for a moment, if you could choose for basically that Pandora’s box to be five away or 500 years away, which would you pick, right? On the hand you’re like, well, maybe for you personally, it’s better have it be five years away. But if it to be 500 years away and people get more time to get right, which do you pick? And you know, I just really felt 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 than any of us typing in computers and developing this technology at the time. And so, yeah, I’m really sold on the you’ve to approach this right. But I don’t think that’s playing the field as it truly lies. Like, if you look at the history of computing, I really mean it when I say that is an industry-wide or even just almost like a human-development- of-technology-wide shift. And the more you sort of, don’t put together the pieces that there, right, we’re still making faster computers, we’re still improving the algorithms, of 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 someone does manage to to the circuit, then you suddenly have this very powerful thing, one’s had any time to adjust, who knows what of safety precautions you get. And so I think one thing I take away is like, even you think about development other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, change what humans could do. But I actually think that if look at capability, it’s been quite smooth over time. 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 for moment that you’re increasing it.
CA: So what I’m hearing that you … the model you want us to have is that have birthed this extraordinary child that may have superpowers that take humanity to a whole place. It is our collective responsibility to provide the for this child to collectively teach it to be and not to tear us all down. Is that basically model?
GB: I think it’s true. And I think it’s also important to say this shift, right? We’ve got to take each step as we encounter it. And I think it’s incredibly important that we all do get literate in this technology, figure out how to provide the feedback, what we want from it. And my hope is that that will continue be the best path, but it’s so good we’re having this debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, you so much for coming to TED and blowing minds.
(Applause)