We started OpenAI years ago because we felt like something really interesting was happening in AI and wanted to help steer it in a positive direction. It’s honestly just really amazing see how far this whole field has come since then. it’s really gratifying to hear from people like Raymond who using the technology we are building, and others, for many wonderful things. We hear from people who are excited, we from people who are concerned, we hear from people feel both those emotions at once. And honestly, that’s how we feel. Above all, it feels we’re entering an historic period right now where we as a world are going to define a technology will be so important for our society going forward. And I that we can manage this for good.
So today, I want to show the current state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m to show you is what it’s like to build tool for an AI rather than building it for a human. So we have a DALL-E model, which generates images, and we are exposing it an app for ChatGPT to use on your behalf. And you can things like ask, you know, suggest a nice post-TED and draw a picture of it.
(Laughter)
Now you get all the, sort of, ideation and creative back-and-forth and taking care of the details for you that get out of ChatGPT. And here we go, it’s not the idea for the meal, but a very, very detailed spread. So let’s see we’re going to get. But ChatGPT doesn’t just generate images this case — sorry, it doesn’t generate text, it also generates image. And that 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, is all a live demo. This is all generated by the AI as speak. So I actually don’t even know what we’re going see. This looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended with other tools too, for example, memory. You can say “save this for later.” And interesting thing about these tools is they’re very inspectable. So you this little pop up here that says “use the DALL-E app.” by the way, this is coming to you, all users, over upcoming months. And you can look under the hood and see that it actually did was write a prompt just like a human could. And so you sort have this ability to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like to use that information to integrate with other applications too. You can say, “Now make shopping list for the tasty thing I was suggesting earlier.” And make it little tricky for the AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, definitely want to know how it tastes.
But you see that ChatGPT is selecting all these different tools without me having to tell explicitly which ones to use in any situation. And this, I think, shows a new way of thinking the user interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste between them, and usually it’s a great experience an app as long as you kind of know the and know all the options. Yes, I would like you to. Yes, please. Always to be polite.
(Laughter)
And by having this unified language on top of tools, the AI is able to sort of take away all those from you. So you don’t have to be the who spells out every single sort of little piece what’s supposed to happen.
And as I said, this a 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 Instacart. Here’s everything you need. And the thing that’s really interesting is that traditional UI is still very valuable, right? If you at this, you still can click through it and of modify the actual quantities. And that’s something that I shows that they’re not going away, traditional UIs. It’s just we have a new, augmented way build them. And now we have a tweet that’s been drafted for our review, which is a very important 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 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 back to the slides. Now, the important thing how we build this, it’s not just about building these tools. It’s about teaching AI how to use them. Like, what do we want it to do when we ask these very high-level questions? And to this, we use an old idea. If you go to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, can learn it. You could build a machine, like human child, and then teach it through feedback. Have human teacher who provides rewards and punishments as it tries out and does things that are either good or bad.
And is exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine through an unsupervised learning process. We show it the whole world, the whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it with all sorts of skills. For example, if you’re shown a math problem, the only way to actually complete that math problem, say what comes next, that green nine up there, to actually solve the math problem.
But we actually to do a second step, too, which is to teach the AI what do with those skills. And for this, we provide feedback. We the AI try out multiple things, give us multiple suggestions, then a human rates them, says “This one’s better than one.” And this reinforces not just the specific thing that the said, but very importantly, the whole process that the AI used to that answer. And this allows it to generalize. It it to teach, to sort of infer your intent 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 to Khan Academy, they said, “Wow, this so great, We’re going to be able to teach students 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 and 20 hours of his own time to provide feedback to the machine our team. And over the course of a couple of months we able to teach the AI that, “Hey, you really push back on humans in this specific kind of scenario.” we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs down in ChatGPT, that actually kind of like sending up a bat signal to our to say, “Here’s an area of weakness where you gather feedback.” And so when you do that, that’s way that we really listen to our users and sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. you think about asking a kid to clean their room, all you’re doing is inspecting the floor, you don’t know if you’re just teaching to stuff all the toys in the closet. This is a DALL-E-generated image, by the way. And the same sort of applies to AI. As we move to harder tasks, 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 even better feedback to scale our ability to supervise the machine as time goes on. let me show you what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between these two foundational on unsupervised learning and learning from human feedback. And model says two months passed. But is it true? Like, these are not 100-percent reliable, although they’re getting better every time we some feedback. But we can actually use the AI fact-check. And it can actually check its own work. You say, fact-check this for me.
Now, in this case, I’ve actually given the a new tool. This one is a browsing tool where the model can issue search and click into web pages. And it actually writes its whole chain of thought as it does it. says, I’m just going to search for this and it actually the search. It then it finds the publication date and the search results. It is issuing another search query. It’s going to click into the blog post. And all of this you do, but it’s a very tedious task. It’s not a thing that really want to do. It’s much more fun to be in the driver’s seat, to in this manager’s position where you can, if you want, triple-check the work. And out come citations so can actually go and very easily verify any piece of this whole 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 and an AI. Because a human, using this fact-checking tool is it 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, where we have and machines kind of very carefully and delicately designed in how they fit into a problem how we want to solve that problem. We make sure that the humans are providing the management, oversight, the feedback, and the machines are operating in a way that’s inspectable and trustworthy. together we’re able to actually create even more trustworthy machines. I think that over time, if we get this process right, we will be able solve impossible problems.
And to give you a sense of how impossible I’m talking, I think we’re going to be able to rethink almost aspect of how we interact with computers. For example, think about spreadsheets. They’ve been in some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really changed much in that time. And here is a specific of all the AI papers on the arXiv for the past 30 years. There’s about 167,000 of them. you can see there the data right here. But let me show you ChatGPT take on how to analyze a data set like this.
So can give ChatGPT access to yet another tool, this a Python interpreter, so it’s able to run code, just like a data scientist would. so you can just literally upload a file and ask questions about it. very helpfully, you know, it knows the name of the file it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The only here is the name of the file, the column names like you saw and the actual data. And from that it’s able to infer these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its knowledge of knowing that, “Oh yeah, arXiv is a site that people papers and therefore that’s what these things are and that 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 don’t even know what I want to ask. So fortunately, can ask the machine, “Can you make some exploratory graphs?” And once again, is a super high-level instruction with lots of intent behind it. But I don’t even know what want. And the AI kind of has to infer what I might be interested in. And so it up with some good ideas, I think. So a histogram of number of authors per paper, time series of papers per year, word cloud of the paper titles. All that, I think, will be pretty interesting to see. 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 the papers year. Something crazy is happening in 2023, though. Looks like we were on an exponential and it off the cliff. What could be going on there? By the way, all this 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. course, the problem is that the year is not over. So I’m to push back on the machine. [Waitttt that’s not 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 you use that to a fair 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 of the here. I really wanted it to notice this thing, it’s a little bit of an overreach for it to sort of, inferred magically that this is what I wanted. But I inject intent, I provide this additional piece of, you know, guidance. And under the hood, AI is just writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If you noticed, it updates the title. I didn’t ask for that, but know what I want.
Now we’ll cut back 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 very sick dog to 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 a vet, you need to talk to a professional, here are some hypotheses.” He that information to a second vet who used it save the dog’s life. Now, these systems, they’re not perfect. cannot overly rely on them. But this story, I think, shows that human with a medical professional and with ChatGPT as a partner was able to achieve an outcome that would not happened otherwise. I think this is something we should all reflect on, think as we consider how to integrate these systems into world.
And one thing I believe really deeply, is that getting right is going to require participation from everyone. And that’s for deciding how we want it to 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 away from this talk, it’s that this technology just looks different. different from anything people had anticipated. And so we have to become literate. And that’s, honestly, one of the we released ChatGPT.
Together, I believe that we can achieve the mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I … I suspect that within every mind out here there’s a feeling of reeling. Like, suspect that a very large number of people viewing this, you look at that you think, “Oh my goodness, pretty much every single thing about the way I work, need to rethink.” Like, there’s just new possibilities there. Am right? Who thinks that they’re having to rethink the way that we do things? Yeah, mean, it’s amazing, but it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question is just how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google thousands of employees working on artificial intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, the is, we’re all building on shoulders of giants, right, there’s no question. If you look the compute progress, the algorithmic progress, the data progress, of those are really industry-wide. But I think within OpenAI, made a lot of very deliberate choices from the early days. the 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 lot of things that didn’t work, so you only the things that did. And I think that the most important thing has to get teams of people who are very different from each other to work together harmoniously.
CA: Can have the water, by the way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something 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 something at point might emerge?
GB: Yes. And I think that, mean, honestly, I think the story there is pretty illustrative, right? I that high level, deep learning, like we 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. We a lot of things, and one person was working on training a model to predict the next in Amazon reviews, and 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 state-of-the-art sentiment analysis classifier out of it. This model could tell you if review was positive or negative. I mean, today we are just like, come on, can do that. But this was the first time that you saw emergence, this sort of semantics that emerged from this underlying syntactic process. And we knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps the riddle that baffles everyone looking at this, because these things 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 get more of a thing, suddenly different things emerge. happens all the time, ant colonies, single ants run around, when 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 just together. But as you grow the number of houses, things emerge, like suburbs and centers and traffic jams. Give me one moment for you when you just something pop that just blew your mind that you just not see coming.
GB: Yeah, well, so you can try this in ChatGPT, if you add 40-digit —
CA: 40-digit?
GB: 40-digit numbers, the model will it, which means it’s really learned an internal circuit how to do it. And the really interesting thing actually, if you have it add like a 40-digit number plus a 35-digit number, it’ll often it wrong. And so you can see that it’s learning the process, but it hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are in universe. So it had to have learned something general, that it hasn’t really fully yet learned that, Oh, I can of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: what’s happened here is that you’ve allowed it to scale up and at an incredible number of pieces of text. And it is things that you didn’t know that it was going to capable of learning.
GB Well, yeah, and it’s more nuanced, too. So science that we’re starting to really get good at is some of these emergent capabilities. And to do that actually, one of the I think is very undersung in this field is sort of engineering quality. Like, we to rebuild our entire stack. When you think about building a rocket, every tolerance has to be tiny. Same is true in machine learning. You have to get every single piece of the engineered properly, and then you can start doing these predictions. There are all these incredibly smooth scaling curves. tell you something deeply fundamental about intelligence. If you at our GPT-4 blog post, you can see all these curves in there. And now we’re starting to able to predict. So we were able to predict, example, the performance on coding problems. We basically look at models that are 10,000 times or 1,000 times smaller. And so there’s something about that is actually smooth scaling, even though it’s still early days.
CA: So here is, of the big fears then, that arises from this. If it’s fundamental to what’s happening here, that you scale up, things emerge that you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just a risk of something truly terrible emerging?
GB: Well, I think all of these are questions degree and scale and timing. And I think one thing people miss, too, is sort the integration with the world is also this incredibly emergent, sort of, very powerful thing too. And so that’s of the reasons that we think it’s so important to deploy incrementally. so I think that what we kind of see now, if you look at this talk, a lot of I focus on is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to at that math problem and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if this book summary is good? You have to read the whole book. No one wants to that.
(Laughter) And so I think that the important thing be that we take this step by step. And we say, OK, as we move on to book summaries, we have to supervise task properly. We have to build up a track record with machines that they’re able to actually carry out our intent. And I think we’re to have to produce even better, more efficient, more reliable ways of scaling this, sort 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 understanding inside, the system is going to always — we’re going to know that it’s not generating errors, that doesn’t have common sense and so forth. Is it your belief, Greg, that is true at any one 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 and so forth, with high degree of confidence. Can you be sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I that is where we’re headed. And I think that the OpenAI approach here always been just like, let reality hit you in the face, right? It’s this field is the field of broken promises, of all these experts saying X is going happen, Y is how it works. People have been saying neural nets aren’t going to for 70 years. They haven’t been right yet. They 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 got to to the limits of this technology to really see it action, because that tells you then, oh, here’s how can move 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 put it there in public and then harness all this, you know, instead of your team giving feedback, the world is now giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, original story that I heard on OpenAI when you founded as a nonprofit, well you were there as the great sort of check on the companies doing their unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that now Google and Meta and forth 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 have done is responsible here and reckless.
GB: Yeah, we think about these questions all the time. Like, seriously all time. And I don’t think we’re always going to it right. But one thing I think has been important, from the very beginning, when we were thinking how to build artificial general intelligence, actually have it all 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 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 other that I see, which is that you do let hit you in the face. And I think you give people time to give input. You do have, before these machines are perfect, before they super powerful, that you actually have the ability to see them in action. we’ve seen it from GPT-3, right? GPT-3, we really were that the number one thing people were going to do with it was misinformation, try to tip elections. Instead, the number one thing generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are things are much worse. Here’s a thought experiment for you. Suppose you’re sitting in a room, there’s 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 give beautiful to your family and to everyone. But there’s actually also a one thing in the small print there that says: “Pandora.” there’s a chance that this actually could unleash unimaginable evils on world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. 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 Rico an AI conference. I’m sitting in the hotel room just out over this wonderful water, all these people having a time. And you think about it for a moment, if could choose for basically that Pandora’s box to be years away or 500 years away, which would you pick, right? On the hand you’re like, well, maybe for you personally, it’s to have it be five years away. But if gets to be 500 years away and people get more time to get right, which do you pick? And you know, I just really felt it in moment. I was like, of course you do the 500 years. My brother was in the military at the time like, he puts his life on the line in a much more real than any of us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on the you’ve got to approach right. But I don’t think that’s quite playing the as it truly lies. Like, if you look at the whole history of computing, I really mean it I say that this is an industry-wide or even just almost like 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, are happening. And if you don’t put them together, you an overhang, which means that if someone does, or moment that someone does manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time to adjust, knows what kind of safety precautions you get. And I think that one thing I take away is like, even you think development of other sort of technologies, think about nuclear weapons, people talk about being like a to one, sort of, change in what humans could do. But I actually that if you look at capability, it’s been quite smooth over time. And so 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 hearing that you … the model you want us to have is that have birthed this extraordinary child that may have superpowers that humanity to a whole new place. It is our collective responsibility provide the guardrails for this child to collectively teach to be wise and not to tear us all down. Is basically the model?
GB: I think it’s true. And I think it’s also to say this may shift, right? We’ve got to each step as we 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 want from it. And hope is that that will continue to be the best path, but it’s good 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 TED and blowing our minds.
(Applause)