We started OpenAI seven years ago because we like something really interesting was happening in AI and we to help steer it in a positive direction. It’s honestly just really to see how far this whole field has come since then. And it’s really gratifying to hear from like Raymond who are using the technology we are building, and others, so many wonderful things. We hear from people who are excited, we hear people who are concerned, we hear from people who feel those emotions at once. And honestly, that’s how we feel. Above all, it feels like we’re an historic period right now where we as a are going to define a technology that will be so for our society going forward. And I believe that we can 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 first I’m going to show you is what it’s like build a tool for an AI rather than building it a human. So we have a new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest a post-TED meal and draw a picture of it.
(Laughter)
Now get all of the, sort of, ideation and creative back-and-forth and taking care the details for you that you get out of ChatGPT. here we go, it’s not just the idea for meal, but a very, very detailed spread. So let’s see we’re going to get. But ChatGPT doesn’t just generate images in this case — sorry, it doesn’t text, it also generates an image. And that is something that really the power of what it can do on your behalf in of carrying out your intent. And I’ll point out, this all a live 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 looking at it.
Now we’ve ChatGPT with other tools too, for example, memory. You say “save this for later.” And the interesting thing 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, is coming to you, all ChatGPT users, over upcoming months. you can look under the hood and see that what it actually did was a prompt just like a human could. And so you sort of have this ability to inspect how machine is using these tools, which allows us to feedback to them.
Now it’s saved for later, and let show you what it’s like to use that information and integrate with other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” 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 it tastes.
But you can that ChatGPT is selecting all these different tools without me having tell it explicitly which ones to use in any situation. And this, I think, shows a new of thinking about the user interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste them, and usually it’s a great experience within an app long as you kind of know the menus and know all options. Yes, I would like you to. Yes, please. Always to be polite.
(Laughter)
And by having this unified language interface top of tools, the AI is able to sort of take away all details from you. So you don’t have to be one who spells out every single sort of little piece of what’s to happen.
And as I said, this is a live demo, so sometimes the unexpected will happen us. But let’s take a look at the Instacart shopping list while we’re it. And you can see we sent a list of ingredients 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 click it and sort of modify the actual quantities. And that’s something that I think shows they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we have a tweet that’s drafted for our review, which is also a very thing. We can click “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of the AI we want to. And 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, important thing about how we build this, it’s not about building these tools. It’s about teaching the AI how to use them. Like, what we even want it to do when we ask very high-level questions? And to do this, we use an old idea. If 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. You build a machine, like a human child, and then it through feedback. Have a human teacher who provides rewards and as it tries things out and does things that are either good or bad.
And this exactly how we train ChatGPT. It’s a two-step process. First, we produce what Turing would have called a machine through an unsupervised learning process. We just show it the whole world, the internet and say, “Predict what comes next in text you’ve seen before.” And this process imbues it with all of wonderful skills. For example, if you’re shown a math problem, only way to actually complete that math problem, to what comes next, that green nine up there, is to actually the math problem.
But we actually have to do a second step, too, which is to teach AI what to do with those skills. And for this, we 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 not just the specific thing that the AI said, very importantly, the whole process that the AI used to produce answer. And this allows it to generalize. It allows to teach, to sort of infer your intent and apply in scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach the AI not what you’d expect. For example, when we first showed GPT-4 to Khan Academy, 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 bad math in there, it will pretend that one plus one equals three and run with it.” So we had to collect some data. Sal Khan himself was very kind and offered 20 hours of his time to provide feedback to the machine alongside our team. And over the course of a of months we were able to teach the AI that, “Hey, really should push back on humans in this specific of scenario.” And we’ve actually made lots and lots of to the models this way. And when you push that down in ChatGPT, that actually is kind of like sending up bat signal to our team to say, “Here’s an of weakness where you should gather feedback.” And so 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 teaching them to all the toys 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 to harder tasks, we will have scale our ability to provide high-quality feedback. But for this, the AI is happy to help. It’s happy to help us provide even feedback and to scale our ability to supervise the machine as time goes on. And let me you what I mean.
For example, you can ask GPT-4 a question this, of how much time passed between these two blogs on unsupervised learning and learning from human feedback. And the model says two months passed. But it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. But can actually use the AI to fact-check. And it can actually check its work. You can say, fact-check this for me.
Now, in this case, I’ve given the AI a new tool. This one is a browsing tool where model can issue 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 the publication date and the search results. It then is issuing search query. It’s going to click into the blog post. all of this you could do, but it’s a tedious task. It’s not a thing that humans really want do. It’s much more fun to be in the driver’s seat, be in this manager’s position where you can, if want, triple-check the work. And out come citations so you can actually go and very easily verify piece of this whole chain of reasoning. And it turns out two months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. so thing that’s so interesting to me about this process is that it’s this many-step collaboration between a human and AI. Because a human, using this fact-checking tool is doing it in order to produce for another AI to become more useful to a human. And I this really shows the shape of something that we should to be much more common in the future, where we have humans and machines of very carefully and delicately designed in how they fit into a problem and we want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and the machines are operating in a way that’s and trustworthy. And together we’re able to actually create even more trustworthy machines. And I think over time, if we get this process right, we will able 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 of 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 time. And here is a specific spreadsheet of all the AI papers the arXiv for the past 30 years. There’s about 167,000 of them. And you can see there the data here. But let me show you 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 to run code, just like a data scientist would. And so you can just literally upload a file ask questions about it. And very helpfully, you know, knows the name of the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for 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 information wasn’t in there. It has to sort of, put together its world knowledge knowing that, “Oh yeah, arXiv is a site that submit papers and therefore that’s what these things are and these are integer values and so therefore it’s a number authors in the paper,” like all of that, that’s work a human to do, and the AI is happy to help with it.
Now I don’t even what I want to ask. So fortunately, you can ask machine, “Can you make some exploratory graphs?” And once again, is a super high-level instruction with lots of intent it. But I don’t even know what I want. the AI kind of has to infer what I might be interested in. And so it comes up some good ideas, I think. So a histogram of the number authors per paper, time series of papers per year, cloud of the paper titles. All of that, I think, will be interesting to see. And 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 nice plot of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and dropped off the cliff. What could be going on there? the way, all this is Python code, you can inspect. And then we’ll word cloud. So you can see all these wonderful that appear in these titles.
But I’m pretty unhappy about this 2023 thing. It makes year look really bad. Of course, the problem is that the year is over. So I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 even posted by April 13?] So April 13 was cut-off date I believe. Can you use that to make a projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like there was more I wanted out the machine here. I really wanted it to notice thing, maybe it’s a little bit of an overreach for it to have of, inferred magically that this is what I wanted. I inject my intent, I provide this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so if you want 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 it know what I want.
Now we’ll cut to the slide again. This slide shows a parable how I think we … A vision of how we end up using this technology in the future. A person brought his 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 the blood test, like, the full medical records, to GPT-4, said, “I am not a vet, you need to talk to a professional, here 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 rely on them. But story, I think, shows that a human with a medical and with ChatGPT as a brainstorming partner was able to achieve outcome that would not have happened otherwise. I think this is we should all reflect on, think about as we consider how to integrate these systems our world.
And one thing I believe really deeply, is that getting AI right going to require participation from everyone. And that’s for deciding we want it to slot in, that’s for setting the rules the road, for what an AI will and won’t do. And if there’s one thing to take away this talk, it’s that this technology just looks different. Just different anything people had anticipated. And so we all have become literate. And that’s, honestly, one of the reasons released ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … suspect that within every mind out here there’s a of reeling. Like, I suspect that a very large of people viewing this, you look at that and you think, “Oh my goodness, much every single thing about the way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to rethink the way that 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 just how hell have you done 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, the is, we’re all building on shoulders of giants, right, there’s no question. 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 a of very deliberate choices from the early days. And first one was just to confront reality as it lays. that we just thought really hard about like: What it going to take to make progress here? We tried a lot of things didn’t work, so you only see the things that did. And I that the most important thing has been to get teams of people who are different from each other to work together harmoniously.
CA: Can we have 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 just about the fact that you saw something in these language models that meant that if you to invest in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, I think the there is pretty illustrative, right? I think that high level, deep learning, like we always that was what we wanted to be, was a learning lab, and exactly how to do it? I think in the early days, we 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 syntactic process, you expect, you know, the model will predict where commas go, where the nouns and verbs are. But he actually a state-of-the-art sentiment analysis classifier out of it. This model tell you if a review was positive or negative. I mean, today we are just like, come on, can do that. But this was the first time you saw this emergence, this sort of semantics that emerged from this underlying 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 that baffles everyone looking at this, because these things are described prediction machines. And yet, what we’re seeing out of them feels … it just feels impossible that could come from a prediction machine. Just the stuff you us just now. And the key idea of emergence that when you get more of a thing, suddenly things emerge. It happens all the time, ant colonies, single ants around, when you bring enough of them together, you get these colonies that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But as you grow the number houses, things emerge, like suburbs and cultural centers and jams. Give me one moment for you when you just something pop that just blew your mind that you just did see coming.
GB: Yeah, well, so you can try this ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned an internal circuit for how do it. And the really interesting thing is actually, if you have it add like 40-digit number plus a 35-digit number, it’ll often get it wrong. And so can see that it’s really learning the process, but hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit table, that’s more atoms than there are in the universe. So it had to have learned something general, that it hasn’t really fully 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 is learning things that you didn’t know that it was going to capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science we’re starting to really get good at is predicting of these emergent capabilities. And to do that actually, one of the I think is very undersung in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think building a rocket, every tolerance has to be incredibly tiny. Same true in machine learning. You have to get every single piece of the stack engineered properly, then you can start doing these predictions. There are these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these in there. And now we’re starting to be able to predict. So we were able predict, for example, the performance on coding problems. We basically look at some models are 10,000 times or 1,000 times smaller. And so there’s something about this that is actually smooth scaling, though it’s still early 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, emerge that you can maybe predict in some level confidence, but it’s capable of surprising you. Why isn’t there just a huge risk something truly terrible emerging?
GB: Well, I think all of are questions of degree and scale and timing. And I think one people miss, too, is sort of the integration with the world is this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s so important to deploy incrementally. And so I that what we kind of see right now, if you look this talk, a lot of what I focus on is providing really high-quality feedback. Today, the tasks we do, you can inspect them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if this 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. And that we say, OK, we move on to book summaries, we have to supervise this task properly. We have to build up track record with these machines that they’re able to actually out our intent. And I think we’re going to have produce even better, more efficient, more reliable ways of scaling this, sort of making the 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, the system going to always — we’re never going to know that it’s generating errors, that it doesn’t have common sense and so forth. it your belief, Greg, that it is true at one moment, but that the expansion of the scale and the feedback that you talked about is basically going to take on that journey of actually getting to things like truth and wisdom and so forth, with a high of confidence. Can you be sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe that is where we’re headed. And I that the OpenAI approach here has always been just like, let reality hit in the face, right? It’s like this field is the field 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 is what you need. I think that our approach has always been, you’ve got push to the limits of this technology to really see it in action, because that you then, oh, here’s how we can move on to a paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s quite a stance you’ve taken, that the right way to do is to put it out there in public and harness all this, you know, instead of just your team giving feedback, world is now giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, the original story I heard on OpenAI when you were founded as a nonprofit, well were there as the great sort of check on big companies doing their unknown, possibly evil thing with AI. And were going to build models that sort of, you know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through the tech world that Google and Meta and so forth are all scrambling to catch up. And some of criticisms have been, you are forcing us to put this out here without proper or we die. You know, how do you, like, make the that what you have done is responsible here and not reckless.
GB: Yeah, think about these questions 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 incredibly important, the very beginning, when we were thinking about how to build artificial general intelligence, actually have it benefit of humanity, like, how are you supposed to do that, right? And that plan of being, well, you build in secret, you get this super powerful thing, then you figure out the safety of it and then you “go,” and you hope you got it right. I don’t know how to that plan. Maybe someone else does. But for me, was always terrifying, it didn’t feel right. And so I that this alternative approach is the only other path that see, which is that you do let reality hit you in the face. And I think you do 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 action. And we’ve seen it from GPT-3, right? GPT-3, we really were that the number one thing people were going to do 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 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 strong chance it’s something absolutely glorious that’s going to beautiful gifts to your family and to everyone. But there’s actually also a one percent in the small print there that says: “Pandora.” And there’s a chance that this could unleash unimaginable evils on the world. Do you that 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 told before, which is that 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 a good time. And you think about it for a moment, if you choose for basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On the hand you’re like, well, maybe for you personally, it’s better to have it be five years away. if it gets to be 500 years away and people get more time to get it right, which 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 the time and like, he puts his life on the line in a much more way than any of us typing things in computers and this technology at the time. And so, yeah, I’m really sold on the you’ve got approach this right. But I don’t think that’s quite playing the field as truly lies. Like, if you look at the whole history 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. the more that you sort of, don’t put together pieces that are there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. And 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 suddenly have this powerful thing, no one’s had any time to adjust, who knows what kind safety precautions you get. And so I think that one thing I away is like, even you think about development of other sort technologies, think about nuclear weapons, people talk about being a zero to one, sort of, change in what humans could do. But I actually think if you 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 to manage it for each moment that you’re increasing it.
CA: So what I’m hearing that you … the model you want us to have that we have birthed this extraordinary child that may have superpowers that take humanity to a whole place. It 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. And I think it’s important to say this may shift, right? We’ve got to take step as we encounter it. And I think it’s important today that we all do get literate in this technology, figure out how provide the feedback, decide what we want from it. And my hope that that will continue to be the best path, but it’s so we’re honestly having this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for coming to TED blowing our minds.
(Applause)