We started OpenAI seven ago because we felt like something really interesting was in AI and we wanted to help steer it in positive direction. It’s honestly just really amazing to see how this whole field has come since then. And it’s gratifying to hear from people like Raymond who are using the technology we are building, and others, for many wonderful things. We hear from people who are excited, hear from people who are concerned, we hear from who feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering historic period right now where we as a world are going to a technology that will be so important for our society going forward. I believe that we can manage this for good.
So today, I want to show you the current of that technology and some of the underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s like to build tool for an AI rather than building it for a human. So have a new DALL-E model, which generates images, and we are exposing it an app for ChatGPT to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal and draw a of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of the details for you that you get of ChatGPT. And here we go, it’s not just idea for the meal, but a very, very detailed spread. So let’s see what we’re going get. But ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And is something that really expands the power of what can do on your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. This is all by the AI as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just looking 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 is they’re very inspectable. So get this little pop up here that says “use the DALL-E app.” And by the way, this coming to you, all ChatGPT users, over upcoming months. And you 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 to inspect how the machine is using these tools, which allows us to provide to them.
Now it’s saved for later, and let show you what it’s like to use that information to integrate with other applications too. You can say, “Now make a shopping list for the tasty thing was suggesting earlier.” And make it a little tricky the AI. “And tweet it out for all the viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want know how it tastes.
But you can see that is selecting all these different tools without me having to it explicitly which ones to use in any situation. And this, I think, shows new way of thinking about the user interface. Like, are so used to thinking of, well, we have these apps, click between them, we copy/paste between them, and usually it’s great experience within an app as long as you kind know the menus and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And by having this language interface on top of tools, the AI is able to sort of away all those details from you. So you don’t to be the one who spells out every single of little piece of what’s supposed to happen.
And as I said, this is a live demo, so the unexpected will happen to us. But let’s take look at the Instacart shopping list while we’re at it. you can see we sent a list of ingredients 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 still can click through it and sort modify the actual quantities. And that’s something that I shows that they’re not going away, traditional UIs. It’s just we a new, augmented way to build them. And now we have a tweet that’s drafted for our review, which is also a very important thing. We can click “run,” and we are, we’re the manager, we’re able to inspect, we’re able to change the work the AI if we want to. And so after this talk, you will be to access this yourself. And 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 teaching the how to use them. Like, what do we even it to do when we ask these very high-level questions? And to do this, we use old idea. If you go back to Alan Turing’s 1950 paper the Turing test, he says, you’ll never program an answer to this. Instead, you can it. You could build a machine, like a human child, and then teach it through feedback. a human teacher who provides rewards and punishments as it tries things out and does things that are good or bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, we what Turing would have called a child machine through an 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 all sorts of wonderful skills. For example, if you’re a math problem, the only way to actually complete that math problem, to say what comes next, that nine up there, is to actually solve the math problem.
But we actually have to do a step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. We have the AI out multiple things, give us multiple suggestions, and then a human them, says “This one’s better than that one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the used to produce that 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 received feedback.
Now, the things we have to teach the AI are what you’d expect. For example, when we first showed GPT-4 to Khan Academy, they said, “Wow, is so great, We’re going to be able to teach wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Khan himself was very kind and offered 20 hours of his own time to provide feedback to the alongside our team. And over the course of a of months we were able to teach the AI that, “Hey, you really should push back on humans this specific kind of scenario.” And we’ve actually made and lots of improvements to the models this way. And you push that thumbs down in ChatGPT, that actually kind of like sending up a bat signal to our team to say, “Here’s area of weakness where you should gather feedback.” And so when you do that, that’s one that we really listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. If you think about a kid to clean their room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff the toys in the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As we move to tasks, we will have to scale our ability to provide high-quality feedback. But for this, AI itself is happy to help. It’s happy to help us provide better feedback and to scale our ability to supervise machine as time goes on. And let me show what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between these two blogs on unsupervised learning and learning from human feedback. the model says two months passed. But is it true? Like, models are not 100-percent reliable, although they’re getting better every time we provide feedback. But we 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 case, I’ve actually given the AI a new tool. This one a browsing tool where the model can issue search queries click into web pages. And it actually writes out its whole chain thought as it 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 results. It then is issuing another search query. It’s to click into the blog post. And all of this you could do, it’s a very tedious task. It’s not a thing that humans really to do. It’s much more fun to be in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. And out come citations so you can actually go very 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 to the side. And so thing that’s so interesting me about this whole process is that it’s this many-step between a human and an AI. Because a human, using this fact-checking tool is doing in order to produce data for another AI to more useful to a human. And I think this shows the shape of something that we should expect to much more common in the future, where we have humans and machines kind of carefully and delicately designed in how they fit into a and how we want to solve that problem. We sure that the humans are providing the management, the oversight, feedback, and the machines are operating in a way that’s inspectable trustworthy. And together we’re able to actually create even more machines. And I think that over time, if we get process right, we will be able to solve impossible problems.
And to give you a sense of just how I’m talking, I think we’re going to be able to rethink every aspect of how we interact with computers. For example, think about spreadsheets. They’ve been around in some since, we’ll say, 40 years ago with VisiCalc. I don’t they’ve really changed that much in that time. And here is specific spreadsheet of all the AI papers on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the data right here. But let show you the ChatGPT take on how to analyze data set like this.
So we can give ChatGPT access to yet another tool, this one Python interpreter, so it’s able to run code, just a data scientist would. And so you can just upload a file and ask questions about it. And very helpfully, you know, it the name of the file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information here is name of the file, the column names like you saw and 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 knowing that, “Oh yeah, arXiv a site that people submit papers and therefore that’s what these things are and that these integer values and so therefore it’s a number of authors in paper,” like all of that, that’s work for a human to do, and the AI happy to help with it.
Now I don’t even what I want to ask. So fortunately, you can ask the machine, “Can you make exploratory graphs?” And once again, this is a super high-level instruction with lots intent behind it. But I don’t even know what I want. And the AI kind of has to what I might be interested in. And so it up with some good ideas, I think. So a histogram of the number of authors paper, time series of papers per year, word cloud the paper titles. All of that, I think, will be pretty interesting to see. And the thing is, it can actually do it. Here we go, nice bell curve. You see that three is kind of the common. It’s going to then make this nice plot of the papers year. Something crazy is happening in 2023, though. Looks we were on an exponential and it dropped off cliff. What could be going on there? By the way, all is Python code, you can inspect. And then we’ll see word cloud. you can see all these wonderful things that appear in these titles.
But I’m pretty unhappy this 2023 thing. It makes this year look really bad. Of course, the is that the year is not over. So I’m going to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by 13?] So April 13 was the cut-off date I believe. Can you use 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 wanted out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of overreach for it to have sort of, inferred magically this is what I wanted. But I inject my intent, provide this additional piece of, you know, guidance. And under the hood, the is just writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does the projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask for that, but it what I want.
Now we’ll cut back to the slide again. This slide shows a parable of how think we … A vision of how we may end up using technology in the future. A person brought his very sick dog the vet, and the veterinarian made a bad call to say, “Let’s just wait and see.” And the would not 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 not vet, you need to talk to a professional, here are some hypotheses.” He brought that to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You cannot overly rely on them. But this story, think, shows that a human with a medical professional with ChatGPT as a brainstorming partner was able to achieve an outcome 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 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 road, for what an AI will and won’t do. And if there’s one to take away from this talk, it’s that this technology just different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, of the reasons we released ChatGPT.
Together, I believe 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 … suspect that within every mind out here there’s a feeling of reeling. Like, I that 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 need to rethink.” Like, there’s just 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 also scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the have you done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why 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 the compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think within OpenAI, made a lot of very deliberate choices from the early days. And the first one was just to reality as it lays. And that we just thought hard about like: What is it going to take 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 of people who are very different from each other to work harmoniously.
CA: Can we have the water, by the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there also just about the fact that you saw something in language models that meant that if you continue to invest in them and grow them, that 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 wanted to be, was deep learning lab, and exactly how to do it? I that in the early days, we didn’t know. We tried lot of things, and one person was working on training a model predict the next character in Amazon reviews, and he a result where — this 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 out of it. This could tell you if a review was positive or negative. I mean, today we are just like, on, anyone can do that. But this was the first time that you this emergence, this sort of semantics that emerged from underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps explain the riddle that everyone looking at this, because these things are described as prediction machines. And yet, what we’re seeing 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 is that when you more of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you bring enough them together, you get these ant colonies that show completely emergent, different behavior. Or a city where few houses together, it’s just houses together. But as grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment for you when you saw just something that just blew your mind that you just did not 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 interesting thing is actually, you 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 learning the process, but it hasn’t generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s more atoms than 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 numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve it to scale up and look at an incredible number of pieces of text. it 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 that we’re starting to really good at is predicting some of these emergent capabilities. And do that actually, one of the things I think is very undersung in this field sort of engineering quality. Like, we had to rebuild our entire stack. When you think about building rocket, every tolerance has to be incredibly tiny. Same is in machine learning. You have to get every single piece of stack engineered properly, and then you can start doing these predictions. There are all these incredibly smooth curves. They tell you something deeply fundamental about intelligence. If you look our GPT-4 blog post, you can see all of these curves in there. And now we’re starting be able to predict. So we were able to predict, for example, the on coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s something about this that is actually scaling, even 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, as you scale up, things emerge that you can maybe in some level of confidence, but it’s capable of surprising you. Why isn’t there just a risk of something truly terrible emerging?
GB: Well, I 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 emergent, sort of, very powerful thing too. And so that’s one of 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 at this talk, a lot of what 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 math problem be like, no, no, no, machine, seven was the correct answer. But summarizing a book, like, that’s a hard thing to supervise. Like, how you 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 will 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 up a track record with these machines that they’re able to actually carry out 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 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 real inside, the system is 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 any one moment, but that the expansion the scale and the human feedback that you talked is basically going to take it on that journey of actually getting to like truth and wisdom and so forth, with a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the short is yes, I believe that is where we’re headed. And I think that OpenAI approach here has always been just like, let hit you in the face, right? It’s like this is the field of broken promises, of all these experts saying X is to happen, Y is how it works. People have saying neural nets aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years plus one or something like that is what need. But I think that our approach has always been, you’ve got to to the limits of this technology to really see it in action, that tells you then, oh, here’s how we can 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 harness this, you know, instead of just your team giving feedback, the world now giving feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the story that I heard on OpenAI when you were as a nonprofit, well you were there as the great sort check on the big companies doing their unknown, possibly evil thing with AI. And you were going 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 I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, sent shockwaves through the tech world that now Google and Meta and so are 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. know, how do you, like, make the case that what you done is responsible here and not reckless.
GB: Yeah, think about these questions all the time. Like, seriously all time. And I don’t think we’re always going to get it right. But one I think has been incredibly important, from the very beginning, we were thinking about how to build artificial general intelligence, 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 thing, and then you figure out the safety of it then you push “go,” and you hope you got right. I don’t know how to execute that plan. someone else does. But for me, that was always terrifying, it didn’t right. And so I think that this alternative approach is only other path that I see, which is that you do let reality hit you in the face. 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 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, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but are things that are much worse. Here’s a thought experiment for you. Suppose you’re sitting in room, there’s a box on the table. You believe in that box is something that, there’s a very strong chance it’s absolutely glorious that’s going to give beautiful gifts to family and to everyone. But there’s actually also a one thing in the small print there that says: “Pandora.” And there’s chance that this actually could unleash unimaginable evils on the world. you open that box?
GB: Well, so, absolutely not. I you don’t do it that way. And honestly, like, I’ll tell you a story that haven’t actually told before, which is that shortly after we started OpenAI, I remember was in Puerto Rico for an AI conference. I’m in the hotel room just looking out over this wonderful water, all these having a good time. And you think about it a moment, if you could choose for basically that Pandora’s to be five years away or 500 years away, which would pick, right? On the one hand you’re like, well, maybe for you personally, it’s to have it be five years away. But if it gets to be 500 away and people get more time to get it right, which do pick? And you know, I just really felt it in the moment. I like, of course you do the 500 years. My brother was in the military the time and like, he puts his life on the line a much more real way than any of us typing things in computers and developing 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 quite the field as it truly lies. Like, if you at the whole history of computing, I really mean when I say that this is an industry-wide or 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 overhang, which means that if someone does, or the moment that someone manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time to adjust, who knows what kind safety precautions you get. And so I think that thing I take away is like, even you think about of other sort of technologies, think about nuclear weapons, talk about being like a zero to one, sort of, change in humans could do. But I actually think that if you look at capability, it’s been smooth over time. And so the history, I think, of every technology we’ve developed has been, you’ve got do it incrementally and you’ve got to figure out how to it for each moment that you’re increasing it.
CA: So what I’m is that you … the model you want us to have that we have birthed this extraordinary child that may have superpowers that take to a whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it to be wise and not tear us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important to say this may shift, right? We’ve got to each step as we encounter it. And I think it’s incredibly important that we all do get literate in this technology, 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 because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you much for coming to TED and blowing our minds.
(Applause)