We started OpenAI seven years ago because we felt something really interesting was happening in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to see how far this whole field come since then. And it’s really gratifying to hear from people Raymond who are using the technology we are building, others, for so many wonderful things. We hear from people who are excited, we from people who are concerned, we hear from people who feel both those at once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now where we a world are going to define 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 build a tool for an AI rather than building it for human. So we have a new DALL-E model, which images, and we are exposing it as an app for to use on your behalf. And you can do things ask, you know, suggest a nice post-TED meal and a picture of it.
(Laughter)
Now you get all of the, sort of, ideation creative back-and-forth and taking care of the details for you that you out of ChatGPT. And here we go, it’s not just the idea the meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just images in this case — sorry, it doesn’t generate text, also generates an image. And that is something that really expands the power of what it do on your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. This is all generated by the AI we 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 ChatGPT with tools too, for example, memory. You can say “save for later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, this 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 just like a human could. And so you sort have this ability to inspect how the machine is using these tools, which allows us to provide feedback them.
Now it’s saved for 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 a list for the tasty thing I was suggesting earlier.” And make it a little tricky the AI. “And tweet it out for all the TED out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I want to know how it tastes.
But you can that ChatGPT is selecting all these different tools without me having to tell it explicitly which ones use in any situation. And this, I think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great experience an app as long as you kind of know menus and know all the options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this unified interface on top of tools, the AI is able to of take away all those details from you. So you don’t to be the one who spells out every single sort of little piece of what’s to happen.
And as I said, this is a live demo, so sometimes the will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And 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 very valuable, right? you look at this, you still can click through and sort of modify the actual quantities. And that’s that I think shows that they’re not going away, traditional UIs. It’s we have a new, augmented way to build them. now we have a tweet that’s been drafted for our review, is also a very important thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the work of the AI if we want to. so after this talk, you will be able to access this yourself. there we 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 the AI how 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 idea. If you go back to Alan Turing’s 1950 on the Turing test, he says, you’ll never program answer to this. Instead, you can learn it. You could a machine, like a human child, and then teach through feedback. Have a human teacher who provides rewards punishments as it tries things out and does things that either good or bad.
And this is exactly how train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an unsupervised process. We just show it the whole world, the whole internet and say, “Predict what comes next text you’ve never seen before.” And this process imbues it with all of wonderful skills. For example, if you’re shown a math problem, the only to actually complete that math problem, to say what comes next, that green nine there, is to actually solve the math problem.
But we actually have to do a step, too, which is to teach the AI what do with those skills. And 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 than that one.” And this reinforces not just the thing that the AI said, but very importantly, the whole process that the AI used to that answer. And this allows it to generalize. It allows it to teach, to sort of infer intent and apply it in scenarios that it hasn’t seen before, that hasn’t received feedback.
Now, sometimes the things we have to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re to be able to teach students wonderful things. Only problem, it doesn’t double-check students’ math. If there’s some bad in there, it will happily pretend that one plus one three and run with it.” So we had to some feedback data. Sal Khan himself was very kind offered 20 hours of his own time to provide to the machine alongside our team. And over the course a couple of months we were able to teach AI that, “Hey, you really should push back on in this specific kind of scenario.” And we’ve actually made and lots of improvements to the models this way. when you push that thumbs down in ChatGPT, that is 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 one way that we listen to our users and make sure we’re building something that’s more useful for everyone.
Now, providing high-quality is a hard thing. If you think about asking a kid to clean their room, all you’re doing is inspecting the floor, you don’t know you’re just teaching them to stuff all the toys in the closet. This is a nice DALL-E-generated image, the way. And the same sort of reasoning applies to AI. As we move harder tasks, we will have to scale our ability to high-quality feedback. But for this, the AI itself is happy to help. It’s to help us provide even better feedback and to scale ability to supervise the machine as time goes on. And me show you what I mean.
For example, you ask GPT-4 a question like this, of how much time passed between these two foundational blogs on learning and learning from human feedback. And the model says two passed. But is it true? Like, these models are 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 can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This one is a browsing tool where the model issue search queries and click into web pages. And it actually writes out its whole of thought as it does it. It says, I’m just going to search this and it actually does the search. It then 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 you could do, but it’s a very tedious task. It’s not thing that humans really want to do. It’s much more fun 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 so you can actually go and very easily verify any piece of this chain of reasoning. And it actually turns out two months was wrong. Two and one week, that was correct.
(Applause)
And we’ll cut back the side. And so thing that’s so interesting to me about this process is that it’s this many-step collaboration between a and an AI. Because a human, using this fact-checking tool doing it in order to produce data for another AI become more useful to a human. And I think this shows the shape of something that we should expect to be much more common the future, where we have humans and machines kind of very carefully delicately designed in how they fit into a problem and how want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and the machines are operating in way that’s inspectable and trustworthy. And together we’re able to actually create even more trustworthy machines. And I 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 to be able rethink almost every aspect of how we interact with computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve changed that much in that time. And here is a specific spreadsheet 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 here. But let me show you the ChatGPT take how to analyze a data set like this.
So we can ChatGPT access to yet another tool, this one a interpreter, so it’s able to run code, just like a data scientist would. And so can just literally upload a file and ask questions about it. And helpfully, you know, it knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it you.” The only information here is the name of the file, the column names you saw and then 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 world knowledge knowing that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s these things are and that these are integer values and so therefore it’s a number of in the paper,” like all of that, that’s work for human to do, and the AI is happy to help with it.
Now I don’t even what I want to ask. So fortunately, you can the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots of intent behind it. But I don’t even what I want. And the AI kind of has to infer what I be interested in. And so it comes up with some ideas, I think. So a histogram of the number of per paper, time series of papers per year, word of the 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 bell curve. You that three 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 off the cliff. What could be on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you see all these wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year look really bad. Of course, the problem is the year is not over. So I’m going to push on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted April 13?] So April 13 was the cut-off date I believe. Can you use that to make a projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like there more I wanted out of the machine here. I really wanted it to notice this thing, 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 provide additional piece of, you know, guidance. And under the hood, the AI is just writing again, so if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, but know what I want.
Now we’ll cut back to 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 to the vet, and the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be here today 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 talk to a professional, here are some hypotheses.” He brought that information to a second vet who used to save the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely on them. But story, I think, shows that a human with a medical professional and with as a brainstorming partner was able to achieve an outcome that would not have happened otherwise. I think is something we should all reflect on, think about as we consider how integrate these systems into our world.
And one thing believe really deeply, is that getting AI right is going to require participation everyone. And that’s for deciding how we want it 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 thing to take away this talk, it’s that this technology just looks different. Just different from anything people 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 general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect within every mind out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, you look at that and you think, “Oh goodness, pretty much every single thing about the way work, I need to rethink.” Like, there’s just new 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, I guess first question actually is just how the hell have done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why is it you who’s come up with this technology shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on shoulders giants, right, there’s no question. If you look at compute progress, the algorithmic progress, the data progress, all of those are really industry-wide. But think within OpenAI, we made a lot of very deliberate choices from early days. And the first one was just to confront as it lays. And that we just thought really hard about like: What is it to take to make progress here? We tried a lot things that didn’t work, so you only see the things that did. And I think that most important thing has been to get teams of people who are different from each other to work together harmoniously.
CA: 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 in these language models that meant that if you continue to invest in them and them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, like we knew that was what we wanted to be, was a deep lab, and exactly how to do it? I think that in the early days, didn’t know. We tried a lot of things, and one person was on training a model to predict the next character in reviews, and he got a result where — this a syntactic process, you expect, you know, the model will where the commas go, where the nouns and verbs are. But he actually a state-of-the-art sentiment analysis classifier out of it. This could tell you if a review was positive or negative. mean, today we are just like, come on, anyone do that. But this was the first time that you saw this emergence, sort of semantics that emerged from this underlying syntactic process. And there knew, you’ve got to scale this thing, you’ve got to see where goes.
CA: So I think this helps explain the riddle that baffles looking at this, because these things are described as prediction machines. And yet, what we’re seeing out them feels … it just feels impossible that that could come from a prediction machine. Just 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 show completely emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow the of houses, things emerge, like suburbs and cultural centers traffic jams. Give me one moment for you when you saw just something that just blew your mind that you just did 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 do it, which means it’s really learned an internal for how to do it. And the really interesting thing actually, if you have it add like a 40-digit number plus 35-digit number, it’ll often get it wrong. And so you can see that it’s really learning 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, but it hasn’t really fully yet learned that, Oh, I can sort of generalize this to adding arbitrary of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to scale and look at an incredible number of pieces of text. And is learning things that you didn’t know that it going to be capable 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 emergent capabilities. And to do that actually, one of the I think is very undersung in this field is sort of quality. Like, we had to rebuild our entire stack. When think about building a rocket, every tolerance has to be incredibly tiny. Same is in machine learning. You have to get every single piece the stack engineered properly, and then you can start doing these predictions. There are all these incredibly scaling curves. They tell you something deeply fundamental about intelligence. If you look our GPT-4 blog post, you can see all of curves in there. And now we’re starting to be to 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 early days.
CA: here is, one of the big fears then, that from this. If it’s fundamental to what’s happening here, that as scale up, things emerge that you can maybe predict in level of confidence, but it’s capable of surprising you. Why isn’t there just a huge risk something truly terrible emerging?
GB: Well, I think all of these are questions of and scale and timing. And I think one thing miss, too, is sort of the integration with the world is 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 right now, if you at this talk, a lot of what I focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s easy to look at that math problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, do you know if this book summary is any good? You have to read whole book. No one wants to do that.
(Laughter) so I think that the important thing will be that take this step by step. And that we say, OK, 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. I think we’re going to have 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 hear in this session, there are critics who say that, you know, there’s no real understanding inside, the system going to always — we’re never going to know it’s not generating errors, that it doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, but that the expansion of scale and the human feedback that you talked about is basically to take it on that journey of actually getting to things like truth and wisdom so forth, with a high degree of confidence. Can you be of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer is yes, I believe is where we’re headed. And I think that the OpenAI approach has always been just like, let reality hit you in the face, right? It’s this field is the field of broken promises, of all experts saying X is going to happen, Y is it works. People have been saying neural nets aren’t going work for 70 years. They haven’t been right yet. They might be right maybe 70 years plus or something like that is what you need. But think that our approach has always been, you’ve got to to the limits of this technology to really see in action, because 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 is to put it out in public and then harness all this, you know, instead of just your giving feedback, the world is now giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, the original story that I heard on OpenAI you were founded as a nonprofit, well you were there as great sort of check on the big companies doing their unknown, evil thing with AI. And you were going to build models that sort of, know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. That your release GPT, especially ChatGPT, sent such shockwaves through the tech world now Google and Meta and so forth are all to catch up. And some of their criticisms have been, you are forcing us to put this here without proper guardrails or we die. You know, how do you, like, make case that what you have done is responsible here and reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t think we’re always to get it right. But one thing I think has been incredibly important, the very beginning, when we were thinking about how to build artificial general intelligence, have it benefit all of humanity, like, how are supposed to do that, right? And that default plan of being, well, you build in secret, get this super powerful thing, and then you figure out the of it and then you push “go,” and you hope you got right. I don’t know how to execute that plan. Maybe else does. But for me, that was always terrifying, it didn’t feel right. And so I think this alternative approach is the only other path that I see, which is that you do let hit you in the face. And I think you do give people time give input. You do 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 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, but 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. You believe in that box is 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 thing the small print there that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable on the world. Do 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 that I haven’t actually told before, which is that after we started OpenAI, I remember I was in Puerto Rico for an AI conference. I’m in the hotel room just looking out over this water, all these people having a good time. And you think about it for moment, if you could choose for basically that Pandora’s box be five years away or 500 years away, which you pick, right? On the one hand you’re like, well, maybe for you personally, it’s better to have be five years away. But if it gets to be 500 away and people get more time to get it right, which do you pick? And know, I just really felt it in the moment. was like, of course you do the 500 years. My brother was in the at the time and like, he puts his life on the line in much more real way than any of us typing things in computers developing this technology at the time. And so, yeah, I’m really sold the 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 it when I say that this is industry-wide or even just almost like a human-development- of-technology-wide shift. the more that you sort of, don’t put together the pieces that there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. if you don’t put them together, you get an overhang, means that if someone does, or the moment that does manage to connect to the circuit, then you suddenly have this very thing, no one’s had any time to adjust, who what kind of safety precautions you get. And so think that one thing I take away is like, you think about development of other sort of technologies, think about nuclear weapons, people talk being like a zero 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 the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve got to figure out how manage it for each moment that you’re increasing it.
CA: what I’m hearing is 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 to provide the guardrails for this child to collectively teach it to be wise not to tear us all down. Is that basically the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve to take each step as we encounter it. And think it’s incredibly important today that we all do literate in this technology, figure out how to provide the feedback, decide what want from it. And my hope is that that will continue to be best path, but it’s so good we’re honestly having this debate because we wouldn’t otherwise it weren’t out there.
CA: Greg Brockman, thank you so much coming to TED and blowing our minds.
(Applause)