We OpenAI seven years ago because we felt like something really was happening in AI and we wanted to help it in a positive direction. It’s honestly just really amazing to how far this whole field has come since then. And it’s really gratifying hear from people like Raymond who are using the technology we building, and 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, it feels we’re entering an historic period right now where we as a world are going define a technology that will be so important for our society forward. And I believe that we can manage this for good.
So today, I want to you the current state of that technology and some of the underlying design that we hold dear.
So the first thing I’m going to show you what it’s like to build a tool for an AI rather than building it for a human. So have a new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on behalf. And you can do things like ask, you know, suggest nice post-TED meal and draw a picture of it.
(Laughter)
Now you all of the, sort of, ideation and creative back-and-forth and care of the details for you that you get out of ChatGPT. And here we go, it’s just the idea for the meal, but a very, detailed spread. So let’s see what we’re going to get. But ChatGPT doesn’t generate images in this case — sorry, it doesn’t generate text, it also generates an image. And is something that really expands the power of what it do on your behalf in terms of carrying out your intent. And I’ll point out, this all a live demo. This is all generated by AI as we speak. So I actually don’t even what 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 these tools is they’re very inspectable. So you get this little pop up that says “use the DALL-E app.” And by the way, is coming to you, all ChatGPT users, over upcoming months. And you can under the hood and see that what it actually did was write a prompt like a human could. And so you sort of have this ability to how the machine is using these tools, which allows us to provide to them.
Now it’s saved for later, and let me show you what it’s like to use that and to integrate with other applications too. You can say, “Now a shopping list for the tasty thing I was earlier.” And make it a little tricky for the AI. “And tweet out for all the TED viewers out there.”
(Laughter)
So if do make this wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting these different tools without me having to tell it which ones to use in any situation. And this, I think, shows new way of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great experience within an app as as you kind of know the menus and know all the options. Yes, I like you to. Yes, please. Always good to be polite.
(Laughter)
And by having this unified language interface on top 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 supposed to happen.
And I said, this is a live demo, so sometimes the unexpected happen to us. But let’s take a look at the Instacart shopping 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 the traditional is still very valuable, right? If you look at this, you can click through 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 a new, augmented way to build them. And now we have 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 to inspect, we’re able to change the work of AI if we want to. And so after this talk, you will able to access this yourself. And there we go. Cool. you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important thing about we build this, it’s not just about building these tools. It’s about the AI how to use them. Like, what do we even want it to do when we ask very high-level questions? And 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 this. Instead, you can learn it. You could build a machine, like a human child, and then it through feedback. Have a human teacher who provides rewards punishments as it tries things out and does things that are either good or bad.
And is exactly how we train ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised learning process. We just it the whole world, the whole internet and say, “Predict what next in text you’ve never seen before.” And this process imbues it with 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 there, is to actually solve the math problem.
But we actually have to do second step, too, which is to teach the AI to do with those skills. And for this, we provide feedback. We have the try out multiple things, give us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not just the thing that the AI said, but very importantly, the whole process the AI used to produce that answer. And this allows to generalize. It allows it to teach, to sort of infer intent and apply it in scenarios that it hasn’t seen before, it hasn’t received feedback.
Now, sometimes the things we have to teach the AI 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 students wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily that one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan himself was very kind offered 20 hours of his own time to provide feedback to the machine alongside our team. And the course of a couple of months we were able teach the AI that, “Hey, you really should push back on humans this specific kind of scenario.” And we’ve actually made lots and lots of improvements the models this way. And when you push that down in ChatGPT, that actually is kind of like sending up a bat signal our team to say, “Here’s an area of weakness where you should gather feedback.” And so when do that, that’s one way that we really listen our users 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 kid to clean their room, if all you’re doing is inspecting the floor, you don’t know if you’re teaching them to stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same sort of reasoning applies to AI. As move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the AI itself is happy to help. It’s happy to us provide even better feedback and to scale our ability supervise the machine as time goes on. And let me show what I mean.
For example, you can ask GPT-4 a like this, of how much time passed between these two foundational blogs on unsupervised learning and learning from 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. we can actually use the AI to fact-check. And it actually check its own 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 the model issue search queries and click into web pages. And actually writes out its whole chain of thought as does it. It says, I’m just going to search for this and it does the search. It then it finds the publication date and the search results. then is issuing another search query. It’s going to click the blog post. And all of this you could do, but it’s a very tedious task. It’s not thing that humans really want to do. It’s much fun to be in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. out come citations so you can actually go and easily verify 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 back to side. And so thing that’s so interesting to me about this whole process that it’s this many-step collaboration between a human and AI. Because a human, using this fact-checking tool is it in order to produce data for another AI become more useful to a human. And I think this really the shape of something that we should expect to be more common in the future, where we have humans and machines kind of very carefully delicately designed in how they fit into a problem and how we want to that problem. We make sure that the humans are providing management, the oversight, the feedback, and the machines are operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. And I think that over time, if get this process right, we will be able to solve impossible problems.
And to 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 spreadsheets. They’ve been around in some form since, we’ll say, 40 ago with VisiCalc. I don’t think they’ve really changed that much in that time. here is a specific spreadsheet of all the AI papers on the for 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 analyze a set like this.
So we can give ChatGPT access yet another tool, this one a Python interpreter, so it’s able to run code, just like data scientist would. And so you can just literally upload a and ask questions about it. And 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 it for you.” The only information is the name of the file, the column names you saw and then the actual data. And from it’s able to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, put together its knowledge of knowing that, “Oh yeah, arXiv is a site that people submit papers and that’s what these things are and that these are integer values and so therefore it’s a number authors in the paper,” like all of that, that’s for a human to do, and the AI is to help with it.
Now I don’t even know what 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 lots of intent behind it. But I don’t even know what I want. And the AI kind has to infer what I might be interested in. And so comes up with some good ideas, I think. So a of the number of authors per paper, time series of papers per year, word cloud of paper titles. All of that, I think, will be pretty interesting to see. And the great is, it can actually do it. Here we go, a nice curve. You see that three is kind of the common. It’s going to then make this nice plot of the 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, this is Python code, you can inspect. And then we’ll see cloud. So you can see all these wonderful things that appear these titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem that the year is not 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 were posted by April 13?] So April 13 was the cut-off date I believe. Can use that to make a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel like there was more I out of the machine here. I really wanted it to this thing, maybe it’s a little bit of an overreach for it have sort of, inferred magically that this is 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 want to 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 that, but it know what I want.
Now we’ll cut back the slide again. This slide shows a parable of 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 would not be here today had he listened. In meanwhile, he provided the blood test, like, the full medical records, 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 vet who used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot rely on them. But this story, I think, shows that a human a medical professional and with ChatGPT as a brainstorming partner able to achieve an outcome that would not have happened otherwise. think this is something we should all reflect on, think about as we consider to integrate these systems into our world.
And one thing I believe really deeply, is getting AI right is going to require participation from everyone. And that’s for deciding how we 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 from this talk, it’s that this technology just looks different. Just different from anything had anticipated. And so we all have to become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, believe that we can achieve the OpenAI mission of ensuring that artificial general benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I … I suspect that within every mind out here there’s a of reeling. Like, I suspect that a very large number of people this, you look at that and you think, “Oh my goodness, pretty much every single thing the way I work, I need to rethink.” Like, there’s just new possibilities there. I right? Who thinks that they’re having to rethink the that we do things? Yeah, I mean, it’s amazing, but it’s also really scary. let’s talk, Greg, let’s talk.
I mean, I guess my first question actually is just the hell have you done this?
(Laughter)
OpenAI has a hundred employees. Google has thousands of employees working on artificial intelligence. is it you who’s come up with this technology that the world?
Greg Brockman: I mean, the truth is, we’re all on shoulders of giants, right, there’s no question. If you look at the progress, the algorithmic progress, the data progress, all of those are really industry-wide. I think within OpenAI, we made a lot of very deliberate from the early days. And the first one was to confront reality as it lays. And that we just really hard about like: What is it going to take to make progress here? We tried lot of things that didn’t work, so you only see the things that did. I think that the most important thing has been to get of people who are very different from each other to work together harmoniously.
CA: Can we have 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 the 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 the there is pretty illustrative, right? I think that high level, 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 working on training a model to predict the next in Amazon reviews, and he got a result where — this is a syntactic process, expect, you know, the model will predict where the commas go, the nouns and verbs are. But he actually got a state-of-the-art sentiment classifier out of it. This model could tell you a review was positive or negative. I mean, today we just like, come on, anyone can do that. But this the first time that you saw this emergence, this sort of semantics that emerged from this underlying process. And there we knew, you’ve got to scale this thing, you’ve 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, we’re seeing out of them feels … it just feels impossible that that could come from a machine. Just the stuff you showed us just now. And the key idea emergence is that when you get more of a thing, suddenly different emerge. It happens all the time, ant colonies, single run around, when you bring enough of them together, you get these ant colonies that show emergent, different behavior. Or a city where a few houses together, it’s houses together. But as you grow the number of houses, things emerge, like and cultural centers and traffic jams. Give me one moment for you you saw just something pop that just blew your that you just did not see coming.
GB: Yeah, well, so you try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which it’s really learned an internal circuit for how to it. And the really interesting thing is actually, if have it add like a 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see that it’s really the process, but it hasn’t fully generalized, right? It’s you can’t memorize the 40-digit addition table, that’s more atoms there are in the universe. So it had to learned something general, but that it hasn’t really fully learned that, Oh, I can sort of generalize this to arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it scale up and look at an incredible number of pieces text. And it is learning things that you didn’t know that it was going to be of learning.
GB Well, yeah, and it’s more nuanced, too. one science that we’re starting to really get good at is predicting some of these emergent capabilities. And do that actually, one of the things I think is very in this field is sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, tolerance has to be incredibly tiny. Same is true in learning. You have to get every single piece of stack engineered properly, and then you can start doing these predictions. There all 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 predict. So we were able to predict, for example, performance on coding problems. We basically look at some models are 10,000 times or 1,000 times smaller. And so there’s something this that is actually smooth scaling, even though it’s still days.
CA: So here is, one of the big fears then, arises from this. If it’s fundamental to what’s happening here, that you scale up, things emerge that you can maybe predict in level of confidence, but it’s capable of surprising you. Why isn’t there a huge risk of something truly terrible emerging?
GB: Well, think all of these are questions of degree and and timing. And I think one thing people miss, too, is of the integration with the world is also this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s so important deploy incrementally. And so I think that what we kind of see right now, you look at this talk, a lot of what I focus is providing really high-quality feedback. Today, the tasks that we do, you can them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was the answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, how do know if this book summary is any good? You have to read the book. No one wants to do that.
(Laughter) And so I think that the important thing be that we take this step by step. And that we say, OK, as we move on 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 carry our intent. And I think we’re going to have to produce even better, more efficient, more ways of scaling this, sort of like making the be aligned with you.
CA: So we’re going to later in this session, there are critics who say that, you know, there’s real understanding inside, the system is going to always — we’re never going to know 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 moment, but that the expansion of the scale and human feedback that you talked about is basically going to it on that journey of actually getting to things like truth and wisdom so forth, with a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the answer is yes, I believe that is where we’re headed. And I that the OpenAI approach here has always been just like, let reality hit you the face, right? It’s like this field is the field of broken promises, 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 yet. They might be right maybe 70 years plus one something like that is what you need. But I think that our approach has been, you’ve got to push to the limits of technology to really see it in action, because that you then, oh, here’s how we can move on to a new paradigm. And we haven’t exhausted the fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the right to do this is to put it out there in public then harness all this, you know, instead of just your team giving feedback, world is now giving feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the original story that I heard on OpenAI when you founded as a nonprofit, well you were there as the great sort of check the big companies doing their unknown, possibly evil thing with AI. And you were to build models that sort of, you know, somehow held accountable and was capable of slowing the field down, need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. That your of GPT, especially ChatGPT, sent such shockwaves through the tech world that Google and Meta and so forth are all scrambling catch up. And some of their criticisms have been, you are forcing to put this out here without proper guardrails or die. You know, how do you, like, make the case that you have done is responsible here and not reckless.
GB: Yeah, we think about these questions all the time. Like, all the time. And I don’t think we’re always going to get it right. one thing I think has been incredibly important, from the very beginning, we were thinking about how to build artificial general intelligence, actually have it all of humanity, like, how are you supposed to that, right? And that default plan of being, well, you build in secret, you this super powerful thing, and then you figure out the safety of it then you push “go,” and you hope you got it right. I don’t how to execute that plan. Maybe someone else does. But for me, that was always terrifying, didn’t feel right. And so I think that this alternative approach is the only path that I see, which is that you do reality hit you in the face. And I think you do give people time to give input. You have, before these machines are perfect, before they are powerful, that you actually have the ability to see in action. And we’ve seen it from GPT-3, right? GPT-3, we 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: 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. You believe that in that box something that, there’s a very 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 that this actually could unleash unimaginable evils on the world. Do open that box?
GB: Well, so, absolutely not. I you don’t do it that way. And honestly, like, I’ll 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 looking over this wonderful water, all these people having a good time. And you think it for a moment, if you could choose for basically that Pandora’s box to be five years or 500 years away, which would you pick, right? On the one hand you’re like, well, maybe you personally, it’s better to have it be five years away. But it gets to be 500 years away and people get more time get it right, which do you pick? And you know, I just really felt it the moment. I was like, of course you do the 500 years. My brother in the military at the time and like, he his life on the line in a much more way than any of us typing things in computers developing this technology at the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t think that’s quite playing the field it truly lies. Like, if you look at the whole history computing, I really mean it when I say that this is an industry-wide even just almost like a human-development- of-technology-wide shift. And the more that you sort of, don’t put the pieces that are there, right, we’re still making computers, we’re still improving the algorithms, all of these things, they happening. And if you don’t put them together, you get an overhang, means that if someone does, or the moment that someone does to connect to the circuit, then you suddenly have this powerful thing, no one’s had any time to adjust, who what kind of safety precautions you get. And so I think that one thing I take away like, even you think about development of other sort of technologies, think about nuclear weapons, people talk being like a zero to one, sort of, change what humans could do. But I actually think that if look at capability, it’s been quite smooth over time. so the history, I think, of every technology we’ve has been, you’ve got 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 is that you … the model you want us to have is that we birthed this extraordinary child that may have superpowers that take to a whole new place. It is our collective to provide the guardrails 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 encounter it. And I think it’s incredibly important today that we all do get literate this technology, figure out how to provide the feedback, decide what we from it. And my hope is that that will continue be the best path, but it’s so good we’re honestly having this debate we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, you so much for coming to TED and blowing minds.
(Applause)