We started OpenAI seven years ago we felt like something really interesting was happening in AI and we to help steer it in a positive direction. It’s honestly just really amazing to see far this whole field has come since then. And it’s really gratifying hear from people like Raymond who are using the we are building, and others, for so many wonderful things. We hear people who are excited, we hear from people who concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, it feels like we’re entering an historic period right where we as a world are going to define technology that will be so important for our society going forward. And I that we can manage this for good.
So today, I want to show you the current state of technology and some of the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s like to build a tool for an rather than building it for a human. So we a new DALL-E model, which generates images, and we exposing it as an app for ChatGPT to use your behalf. And you can do things like ask, you know, suggest a nice post-TED meal draw a picture of it.
(Laughter)
Now you get 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 the meal, but a very, very detailed spread. let’s see what we’re going to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also generates an image. And that is something that really the power of what it can do on your behalf in terms of carrying your intent. And I’ll point out, this is all a demo. This is all generated by the AI as we speak. So I don’t even know what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry 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 tools is they’re very inspectable. So you get this little up here that says “use the DALL-E app.” And the way, this is coming to you, all ChatGPT users, upcoming months. And you can look under the hood and see that what it did was write a prompt just like a human could. And you sort of have this ability to inspect how the machine is using these tools, which allows us provide feedback to them.
Now it’s saved for later, and let me show you what it’s to use that information and to integrate with other too. You can say, “Now make a shopping list the tasty thing I was suggesting earlier.” And make it a little tricky for AI. “And tweet it 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 all these different tools without having to tell it explicitly which ones to use any situation. And this, I think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste between them, usually it’s a great experience within an app as long as you kind of know menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And by this unified language interface on top of tools, the AI is to sort of take away all those details from you. So don’t have to be the one who spells out every single sort little piece of what’s supposed to happen.
And as I said, this is live demo, so sometimes the unexpected will happen to us. But let’s a look at the Instacart shopping list while we’re it. And you can see we sent a list of ingredients to Instacart. Here’s everything you need. And thing that’s really interesting is that the traditional UI is still valuable, right? If you look at this, you still can click through it and sort of modify the quantities. And that’s something that I think shows that they’re not going away, traditional UIs. It’s we have a new, augmented way to build them. And now we have a that’s been 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 of the AI if we want to. And so after this talk, will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut to the slides. Now, the important thing about how build this, it’s not just about building these tools. It’s about teaching the AI how to use them. Like, do we even want it to do when we these 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 never program answer to this. Instead, you can learn it. You build a machine, like a human child, and then teach it through feedback. Have human teacher who provides rewards and punishments as it tries things out and 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 child machine through an unsupervised learning process. We just show it whole world, the whole internet and say, “Predict what next in text you’ve never seen before.” And this process it with all sorts of wonderful skills. For example, you’re shown a math problem, the only way to actually complete math problem, to say what comes next, that green up there, is to actually solve the math problem.
But we actually have do a second step, too, which is to teach the AI what to do with those skills. for this, we provide feedback. We have the AI try out multiple things, give multiple suggestions, and then a human rates them, says “This one’s better than that one.” this reinforces not just the specific thing that the AI said, but importantly, the whole process that the AI used to produce that answer. And allows it to generalize. It allows it to teach, to of infer your intent and apply it in scenarios that it hasn’t seen before, it hasn’t received feedback.
Now, sometimes the things we have teach the AI are not what you’d expect. For example, when we first GPT-4 to Khan Academy, they said, “Wow, this is 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 there, it will happily pretend that one plus one equals and run with it.” So we had to collect some data. Sal Khan himself was very kind and offered 20 of his own time to provide feedback to the machine our team. And over the course of a couple of months we were able to teach the that, “Hey, you really should push back on humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind like sending up a bat signal to our team say, “Here’s an area of weakness where you should gather feedback.” And so when you that, that’s one way that we really listen to our users and make we’re building something that’s more useful for everyone.
Now, providing high-quality feedback a hard thing. If you think about asking a to clean their room, if all you’re doing is inspecting floor, you don’t know if you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by the way. And same sort of reasoning applies to AI. As we to harder tasks, we will have to scale our ability to high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even better and to scale our ability to supervise the machine as time goes on. let me show you what I mean.
For example, can ask GPT-4 a question like this, of how much time passed 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, although they’re better every time we provide some feedback. But we actually use the AI to fact-check. And it can actually check its work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This one is browsing tool where the model can issue search queries and click into web pages. And it actually writes its whole chain of thought as it does it. It says, I’m just going to search this and it actually does the search. It then it finds the publication and the search results. It then is issuing another search query. It’s going to into the blog post. And all of this you could do, but it’s very tedious task. It’s not a thing that humans really want do. It’s much more fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check the work. out come citations so you can actually go and very easily verify any piece this whole chain of reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting to me 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 doing in order to produce data for another AI to become more useful to human. And I think this really shows the shape of something that we expect to be much more common in the future, we have humans and machines kind of very carefully delicately designed in how they fit into a problem and how we to solve that problem. We make sure that the humans are the management, the oversight, the 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 this process right, we will able to solve impossible problems.
And to give you a sense just how impossible I’m talking, I think we’re going be able to rethink almost every aspect of how interact with computers. For example, think about 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 in that time. And here is a specific spreadsheet all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you can see there data right here. But let me show you the ChatGPT on how to analyze a data set like this.
So we can give ChatGPT to yet 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 and ask questions about it. And very helpfully, you know, it knows the name 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 and then the actual data. And from that it’s able to infer these columns actually mean. Like, that semantic information wasn’t in there. It to sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a that people submit 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 of that, that’s work for a human do, and the AI is happy to help with it.
Now I don’t even know what I want ask. So fortunately, you can ask the machine, “Can you some exploratory graphs?” And once again, this is a super high-level instruction lots of intent behind it. But I don’t even know I want. And the AI kind of has to what I might be interested in. And so it comes up with some 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 is kind of the most common. It’s going to then make this nice plot of papers 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, all is Python code, you can inspect. And then we’ll see word cloud. So you can see these wonderful things that appear in these titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem is 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 April 13?] So April 13 was the cut-off 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 out of the machine here. I really wanted it to notice thing, maybe it’s a little bit of an overreach it to have sort of, inferred magically that this what I wanted. But I inject my intent, I provide additional piece of, you know, guidance. And under the hood, the AI is just code again, so if you want to inspect what it’s doing, it’s 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 to the slide again. This shows a parable of how I think we … A of how we may end up using this technology in the future. A person brought his very sick to the vet, and the veterinarian made a bad call say, “Let’s just wait and see.” And the dog would be here today had he listened. In the meanwhile, he provided the blood test, like, full medical records, to GPT-4, which said, “I am not a vet, you need to talk to professional, here 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 with a medical professional and with ChatGPT as a partner was able to achieve an outcome that would not have happened otherwise. think this is something we should all reflect on, think as we consider how to integrate these systems into our world.
And one thing I believe deeply, is that getting AI right is going to require participation everyone. And that’s for deciding how we want it 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 to take from 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 that artificial general benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every mind here there’s a feeling of reeling. Like, I suspect that a very large number of people this, you look at that and you think, “Oh my goodness, much every single thing about the way I work, I to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re 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 my first question is just how 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 building on shoulders of giants, right, there’s no question. If you at the compute progress, the algorithmic progress, the data progress, of those are really industry-wide. But I think within OpenAI, we a lot of very deliberate choices from the early days. the first one was just to confront reality as it lays. And that just thought really hard about like: What is it going to take make progress here? We tried a lot of things that didn’t work, so only see the things that did. And I think that the most important thing has been to teams of people who are very different from each to work together harmoniously.
CA: Can we have the water, by the way, just brought here? I think we’re to need it, it’s a dry-mouth topic. But isn’t something also just about the fact that you saw something in language models that meant that if you continue to invest in them and them, that something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story is pretty illustrative, right? I think that high level, deep learning, like always knew that was what we wanted to be, a deep learning lab, and exactly how to do it? think that in the early days, we didn’t know. tried a lot of things, and one person was working training a model to predict the next character in Amazon reviews, he got a result where — this is a process, you 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 analysis classifier out of it. This could tell you if a review was positive or negative. I mean, we are 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 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 that could come from prediction machine. Just the stuff you showed us just now. And the key idea of emergence that when you get more of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you enough of them together, you get these ant colonies show completely emergent, different behavior. Or a city where a houses together, it’s just houses together. But as you grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment for you when saw just something pop that just blew your mind that just did not see coming.
GB: Yeah, well, so you can try in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model do it, which means it’s really learned an internal circuit for to do it. And the really interesting thing is actually, if you it add like a 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 it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms than there in the universe. So it had to have learned something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it scale up and look at an incredible number of pieces of text. And it is things that you didn’t know that it was going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re to really get good at is predicting some of these emergent capabilities. And to that actually, one of the things I think is very undersung in field is sort of engineering quality. Like, we had to rebuild our stack. When you think about building a rocket, every tolerance has to be incredibly tiny. Same is in machine learning. You have to get every single of the stack engineered properly, and then you can doing these predictions. There are all these incredibly smooth scaling curves. tell you something deeply fundamental about intelligence. If you look at our GPT-4 blog post, 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 performance coding problems. We basically look at some models that are 10,000 or 1,000 times smaller. And so there’s something about 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, as you scale up, things emerge that you can maybe predict in some level of confidence, but it’s of surprising you. Why 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, sort of the integration with the world is also this incredibly emergent, of, very powerful thing too. And so that’s one of the reasons that 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 providing really high-quality feedback. Today, the tasks that we do, you can them, right? It’s very easy to look at that math problem 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 read the whole book. No one wants to do that.
(Laughter) And so I think the 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 this task properly. We have to build up a record with these machines that they’re able to actually out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways of this, sort of like making the machine be aligned with you.
CA: So we’re going to later in this session, there are critics who say that, you know, there’s no real understanding inside, system is going to always — 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 the scale and the human feedback that you talked about basically going to take it on that journey of actually to things like truth and wisdom and so forth, a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And I think the 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 saying X is going to happen, Y is how works. People have been saying neural nets aren’t going work for 70 years. They haven’t been right yet. might be right maybe 70 years plus one or like that is what you need. But I think that our approach has always been, you’ve to push to the limits of this technology to see it in action, because that tells you then, oh, here’s how we can on to a new paradigm. And we just haven’t exhausted fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do this is to it out there in public and then harness all this, know, instead of just your team giving feedback, the is now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, know, the original story that I heard on OpenAI when were founded as a nonprofit, well you were there as the great sort of check on the big doing their unknown, possibly evil thing with 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. yet, what’s happened, arguably, is the opposite. That your of GPT, especially ChatGPT, sent such shockwaves through the tech that now Google and Meta and so forth are all scrambling to up. And some of their criticisms have been, you are forcing us to this out here without proper guardrails or we die. You know, how do you, like, make the case what you 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 get it right. But thing I think has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, have it benefit all of humanity, like, how are you supposed to do that, right? And that default of being, well, you build in secret, you get this super thing, and then you figure out the safety of and then you push “go,” and you hope you got right. I don’t know how to execute that plan. Maybe someone else does. But for me, was always terrifying, it didn’t feel right. And so think that this alternative approach is the only other path I 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, they are 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 number one thing people were going to do with it was generate misinformation, try to elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a thought for you. Suppose you’re sitting in a room, there’s a box on the table. believe 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 also a one percent thing in the small print that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils on the world. you open that box?
GB: Well, so, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a story that haven’t actually told before, which is that shortly after started OpenAI, I remember I was in Puerto Rico an AI conference. I’m sitting in the hotel room just looking out this wonderful water, all these people having a good time. you think about it for a moment, if you could choose basically that Pandora’s box to be five years away or 500 away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to have it five years away. But if it gets to be 500 years away and get more time to get it right, which do you pick? And know, I just really felt it in the moment. I like, of course you do the 500 years. My brother was the military at the time and like, he puts his life on line in a much more real way than any of us things in computers and 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 playing the field as it truly lies. Like, if look at the whole history of computing, I really mean when I say that this is an industry-wide or even just almost a human-development- of-technology-wide shift. And the more that you sort of, don’t together the pieces that are there, right, we’re still making computers, we’re still improving the 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 what kind safety precautions you get. And so I think that thing I take away is like, even you think development of other sort of technologies, think about nuclear weapons, people talk about being a zero to one, sort of, change in what humans do. But I actually think that if you look capability, it’s been quite smooth over time. And so the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve to figure out how to manage it for each moment you’re increasing 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 have superpowers that take humanity to a whole new place. is our collective responsibility to provide the guardrails for this 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 this may shift, right? We’ve got to take each step as encounter it. And I think it’s incredibly important today that we all get literate in this technology, figure out how to the feedback, decide what we want from it. And my hope is that that will continue be the best path, but it’s so good we’re honestly having debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)