We OpenAI seven years ago 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 see how far this whole field has come since then. it’s really gratifying to hear from people like Raymond who are using technology we are building, and others, for so many things. We hear from people who are excited, we from people who are concerned, we hear from people feel both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an historic period now where we as a world are going to define a technology will be so important for our society going forward. And believe that we can manage this for good.
So today, want to show you the current state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m going to show you is it’s like to build a tool for an AI rather than building it for human. So we have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to 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 get all of the, of, ideation and creative back-and-forth and taking care of the details you that you get out of ChatGPT. And here we go, it’s just the idea for the meal, but a very, very spread. So let’s see what we’re going to get. But doesn’t just generate images in this case — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the power of what it can do your behalf in terms of carrying out your intent. And I’ll point out, is all a live demo. This is all generated the AI as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You say “save this for later.” And the interesting thing about these tools they’re very inspectable. So you get this little pop up here that “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 prompt just like a human could. so you sort of have this ability to inspect how the machine is using tools, which allows us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like to use that information and to with other 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 for the TED viewers out there.”
(Laughter)
So if you do make this wonderful, meal, I definitely want to know how it tastes.
But you can see that ChatGPT selecting all these different tools without me having to tell it which ones to use in any situation. And this, I think, shows a new way thinking about the user interface. Like, we are so used thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great experience within an as long as you kind of know the menus and know all the options. Yes, would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on top of tools, AI is able to sort of take away all those details from you. So don’t have to be the one who spells out every single sort little piece of what’s supposed to happen.
And as I said, this a live demo, so sometimes the unexpected will happen us. But let’s take a look at the Instacart shopping list 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 that the traditional UI is still very valuable, right? you look at this, you still can click through it and sort of the actual quantities. And that’s something that I think that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And we have a tweet that’s been drafted for our review, which is also a very important thing. can click “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. so after this talk, you will be able to access yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important about how we build this, it’s not just about building these tools. It’s about 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 idea. If you go back to Alan Turing’s 1950 paper the Turing test, he says, you’ll never program an to this. Instead, you can learn it. You could build a machine, like a human child, and teach it through feedback. Have a human teacher who rewards and punishments as it tries things out and 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 an unsupervised learning process. We just show it the whole world, whole internet and say, “Predict what comes next in 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 problem, to say what comes next, that green nine there, is to actually solve the math problem.
But we actually have to do a second step, too, is to teach the AI what to do with skills. And for this, we provide feedback. We have the try out multiple things, give us multiple suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the thing that the AI said, but very importantly, the process that the AI used to produce that answer. And this allows it to generalize. It allows it teach, to sort of infer your intent and apply it in that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have to teach AI are not what you’d expect. For example, when we showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going to be able teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math in there, it happily pretend that one plus one equals three and run it.” So we had to collect some feedback data. Sal himself was very kind and offered 20 hours of his own time to provide to the machine alongside our team. And over the of a couple of months we were able to the 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 way. And when you push that thumbs down in ChatGPT, actually is kind of like sending up a bat to our team to say, “Here’s an area of where you should gather feedback.” And so when you that, that’s one way that we really listen to our and make sure we’re building something that’s more useful for everyone.
Now, high-quality feedback is a hard thing. If you think about asking a to clean their room, if all you’re doing is the floor, you don’t know if you’re just teaching to stuff all the toys in the closet. This is a nice DALL-E-generated image, the 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 happy to help. It’s happy help us provide even better feedback and to scale ability to supervise the machine as time goes on. And let me show you 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 better every time we provide some feedback. But we can actually use the AI to fact-check. And can actually check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the AI new tool. This one is a browsing tool where the model can issue queries and click into web pages. And it actually writes out its chain of thought as it does it. It says, I’m going to search for this and it actually does search. It then it finds the publication date and search results. It then is issuing another search query. It’s going to click into blog post. And all of this you could do, but it’s a very task. It’s not a thing that humans really want to do. It’s much more fun to be the driver’s seat, to be in this manager’s position where can, if you want, triple-check the work. And out come citations so you can go and very easily verify any piece of this whole chain of reasoning. And it turns out two months was wrong. Two months and one week, that was correct.
(Applause)
And we’ll back to the side. And so thing that’s so to me about this whole process is that it’s this many-step collaboration between a human an 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 much more in the future, where we have humans and machines kind of carefully and delicately designed in how they fit into problem and how we want to solve that problem. We sure that the humans are providing the management, the oversight, the feedback, the machines are operating in a way that’s inspectable and trustworthy. And together we’re to actually create even more trustworthy machines. And I think over 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 to be able to rethink almost every aspect of 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 is a specific 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 let show you the ChatGPT take on how to analyze a set like this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able to run code, just like a data scientist would. so you can just literally upload a file and ask questions about it. very helpfully, you know, it knows the name of the file it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse for you.” The only information here is the name of the file, the column names like you saw then the actual data. And from that it’s able to what these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people papers and therefore that’s what these things are and that these are integer values so therefore it’s a number of authors in the paper,” like all that, that’s work for a human to do, and the AI is happy to help with it.
Now don’t even know what I want to ask. So fortunately, you can ask the machine, “Can make some exploratory graphs?” And once again, this is a super high-level instruction with lots of intent it. But I don’t even know what I want. And the AI kind of to infer what I might be interested in. And so it comes up with good ideas, I think. So a histogram of the of authors per paper, time series of papers per year, word cloud of the titles. All of that, I think, will be pretty interesting see. And the great thing is, it can actually it. Here we go, a nice bell curve. You see that three kind of the most common. It’s going to then make this plot of the papers per year. Something crazy is happening in 2023, though. Looks like were on an exponential and it dropped off the cliff. could be going on there? By the way, all this is Python code, can 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 makes this look really bad. Of course, the problem is that the is not over. So I’m going to push back 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, this the kind of ambitious one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted it to notice this thing, it’s a little bit of an overreach for it have sort of, inferred magically that this is what wanted. But I inject my intent, I provide this additional of, you know, guidance. And under the hood, the AI is just writing code again, so you want to inspect what it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If noticed, it even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back the slide again. This slide shows a parable of how I think we … A vision of how may end up using this technology in the future. A brought his very sick dog to the vet, and the veterinarian made bad call to say, “Let’s just wait and see.” And dog would not be here today had he listened. In the meanwhile, he the blood test, like, the 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 used it to 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 partner was able to an outcome that would not have happened otherwise. I think is something we should all reflect on, think about as consider how to integrate these systems into our world.
And one thing I believe really deeply, is that AI right is going to require participation from everyone. And that’s for deciding how we want to slot in, that’s for setting the rules of the road, what an AI will and won’t do. And if there’s one thing take away from this talk, it’s that this technology looks different. Just different from anything people had anticipated. And so all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we can achieve the OpenAI mission ensuring that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out there’s a feeling of reeling. Like, I suspect that a very large number of people viewing this, you at that and you think, “Oh my goodness, pretty much single thing about the way I work, I need rethink.” Like, there’s just new possibilities there. Am I right? thinks that they’re having 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, I guess first question actually is just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working on intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on of giants, right, there’s no question. If you look at the compute progress, the algorithmic progress, data progress, all of those are really industry-wide. But I think within OpenAI, we made lot of very deliberate choices from the early days. And the first one was just to reality as it lays. And that we just thought really hard about like: What is it going take to make progress here? We tried a lot things that didn’t work, so you only see the things that did. I think that the most important thing has been to teams of people who are very different from each other to work together harmoniously.
CA: Can have 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 about the fact that you saw something in these models that meant that if you continue to invest in them and grow them, something at some point might emerge?
GB: Yes. And I think that, I mean, honestly, think the story there is pretty illustrative, right? I think high level, deep learning, like we always knew that was what we to be, was a deep learning lab, and exactly how to do it? I think that in the days, we didn’t know. We tried a lot of things, and one person was on training a model to predict the next character Amazon reviews, and he got a result where — this a syntactic process, you expect, you know, the model will predict where the go, where the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model tell you if a review was positive or negative. mean, today we are just like, come on, anyone do that. But this was the first time that you saw this emergence, this sort of that emerged from this underlying syntactic process. And there we knew, you’ve got scale this thing, you’ve got to see where it goes.
CA: So I think this explain the riddle that baffles everyone looking at this, these things are described as prediction machines. And yet, what we’re seeing out of them feels … it feels impossible that that could come from a prediction machine. Just the stuff you showed just now. And the key idea of emergence is that you get more of a thing, suddenly different things emerge. It happens all the time, colonies, single ants run around, when you bring enough of them together, you these ant colonies that show completely emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow the of houses, things emerge, like suburbs and cultural centers and traffic jams. Give me one for you when you saw just something pop that just your mind that you 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 will do it, which means it’s really learned an circuit for how to do it. And the really thing is actually, if you have it add like 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see that it’s really learning the process, it hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s atoms than there are in the universe. So it to have learned something general, but that it hasn’t really yet learned 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 up and look at an incredible number of pieces of text. And it learning things that you didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. So science that we’re starting to really get good at is some 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 to get every single piece of the stack engineered properly, and then you start doing these predictions. There are all these incredibly smooth curves. They tell you something deeply fundamental about intelligence. If you at our GPT-4 blog post, you can see all of these curves there. And now we’re starting to be able to predict. we were able to predict, for example, the performance on problems. We basically look at some models that are 10,000 times or 1,000 smaller. And so there’s something about this that is actually smooth scaling, even though it’s still days.
CA: So here is, one of the big fears then, that from this. If it’s fundamental to what’s happening here, that as you scale up, things emerge that you maybe predict in some level of confidence, but it’s capable of surprising you. isn’t there just a huge risk of something truly emerging?
GB: Well, I think all of these are questions of degree and scale timing. And I think one thing people miss, too, is of the integration with the world is also this incredibly emergent, sort of, powerful thing too. And so that’s one of the reasons that we it’s so important to deploy incrementally. And so I that what we kind of see right now, if look at this talk, a lot of what I focus is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy look at that math problem and be like, no, no, no, machine, was the correct answer. But even summarizing a book, like, that’s a hard to supervise. Like, how do you know if this book summary is any good? You have to the whole book. No one wants to do that.
(Laughter) And I think that the important thing will be that we take step by step. And that we say, OK, as we move on to summaries, we have to supervise this task properly. We have to build up a record with these machines that they’re able to actually carry out our intent. And I we’re going to have to produce even better, more efficient, reliable ways of scaling this, sort of like making machine be aligned with you.
CA: So we’re going hear later in this session, there are critics who that, you know, there’s no real understanding inside, the system is going to — we’re never 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 that the expansion of the and the human feedback that you talked about is going to take it on that journey of actually to things like truth and wisdom and so forth, with high degree of confidence. Can you be sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, short answer is yes, I believe that is where we’re headed. And I think 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, is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t been right yet. They might be right 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 limits this technology to really see it in action, because tells you then, oh, here’s how we can move on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a stance you’ve taken, that the right way to do this to put it out there in public and then all this, you know, instead of just your team giving feedback, the world is now feedback. But … If, you know, bad things are going to emerge, is out there. So, you know, the original story that heard on OpenAI when you were founded as a nonprofit, well you were there the great sort of check on the big companies doing their unknown, possibly evil thing AI. And you were going to build models that sort of, know, somehow held them accountable and was capable of slowing the field down, if be. Or at 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 world that now Google and Meta so forth are all scrambling to catch up. And of their criticisms have been, you are forcing us put this out here without proper guardrails or we die. know, how do you, like, make the case that what have done is responsible here and not reckless.
GB: Yeah, think about these questions all the time. Like, seriously the time. And I don’t think we’re always going to it right. But one thing I think has been incredibly important, from the beginning, when we were thinking about how to build general intelligence, actually have it benefit all of humanity, like, how are supposed to do that, right? And that default plan being, well, you build in secret, you get this super powerful thing, then 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 else does. But for me, that was always terrifying, it didn’t feel right. And so I think this alternative approach is the only other path that see, which is that you do let reality hit you in the face. And I you do give people time to give input. You do have, before these machines are perfect, before they super powerful, that you actually 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 people were going do with it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: Viagra spam is bad, but 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 the table. You that in that box is something that, there’s a very strong chance it’s absolutely glorious that’s going to give beautiful gifts to your family and everyone. But there’s actually also a one percent thing in small print there that says: “Pandora.” And there’s a chance that this could unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t do it that way. 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 was in 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 you think about for a moment, if you could choose for basically Pandora’s box to be five years away or 500 away, which would you pick, right? On the one hand you’re like, well, maybe for personally, it’s better to 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 really felt it in the moment. I was like, of you do the 500 years. My brother was in military at the time and like, he puts his on the line in a much more real way than any of us typing things computers and developing this technology at the time. And so, yeah, I’m really sold on the you’ve got to this right. But I don’t think that’s quite playing the field as it lies. Like, if you look at the whole history 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 more that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re still improving algorithms, all of these things, they are happening. And if don’t put them together, you get an overhang, which means that if someone does, or the moment that does manage to connect to the circuit, then you suddenly have this powerful thing, no one’s had any time to adjust, who knows kind of safety precautions you get. And so I that one thing I take away is like, even think about development of other sort of technologies, think about nuclear weapons, talk about being like a zero to one, sort of, change what humans could do. But I actually think that if look at capability, it’s been quite smooth over time. And so the history, I think, of every we’ve developed has been, you’ve got to do it incrementally and you’ve got to out how to manage it for each moment that you’re it.
CA: So what I’m hearing is that you … the model want us to have is that we have birthed this extraordinary child that may have superpowers take humanity to a whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it to wise and not to tear us all down. Is that basically the model?
GB: I it’s true. And I think it’s also important to this may shift, right? We’ve got to take each as we encounter it. And I think it’s incredibly important today that we all do literate 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 having this debate because we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED and blowing minds.
(Applause)