We started OpenAI seven ago because we felt like something really interesting was happening in AI and we to help steer it in a positive direction. It’s just really amazing to see how far this whole field has come then. And it’s really gratifying to hear from people like Raymond who are using technology we are building, and others, for so many wonderful things. We hear from people are excited, we hear from people who are concerned, we hear people who feel both those emotions at once. And honestly, that’s how we feel. Above all, it like we’re entering an historic period right now where 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 you current state 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 to build a tool for an AI rather than building it for a human. we have a new DALL-E model, which generates images, and we are exposing it as an app ChatGPT 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 the, sort of, ideation and creative back-and-forth and taking care of the details you that you get out of ChatGPT. And here we go, it’s not 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 this case — sorry, doesn’t generate text, it also generates an image. And that is something really expands the power of what it can do on your behalf in of carrying out your intent. And I’ll point out, this is a live demo. This is all generated by the AI as we speak. So I actually don’t even what 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 they’re very 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. you can 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 to provide feedback to them.
Now it’s saved for later, and let me show you what it’s like to that information and to integrate with other applications too. can say, “Now make a shopping list for the tasty thing I was earlier.” And make it a little tricky for 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 to how it tastes.
But you can see that ChatGPT is selecting all different tools without me having to tell it explicitly which ones use in any situation. And this, I think, shows new way of thinking about the user interface. Like, we so used to thinking of, well, we have these apps, we click between them, we copy/paste them, and usually it’s a great experience within an app long as you kind of know the menus and know all options. Yes, I would like you to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, the AI able to sort of take away all those details from you. So don’t have 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 at the Instacart shopping while we’re at it. And you can see we sent a list ingredients to Instacart. Here’s everything you need. And the thing that’s really interesting is the traditional UI is still very valuable, right? If you look 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 have a new, augmented way to build them. And now we a tweet that’s been drafted for our review, which is a very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re to change the work of the AI if we want to. so after this talk, you will be able to this yourself. And 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 teaching the 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 back to Alan Turing’s 1950 on the Turing test, he says, you’ll never program an to this. Instead, you can learn it. You could build a machine, like a child, and then teach it through feedback. Have a human teacher who rewards and punishments as it tries things out and things that are either good or bad.
And this is exactly we train ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes next in text you’ve seen before.” And this process imbues it with all sorts of wonderful skills. example, if you’re shown a math problem, the only way to actually complete that problem, to say what comes next, that green nine there, is to actually solve the math problem.
But we have to do a second step, too, which is to teach the AI to 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 that one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process the AI used to produce that answer. And this allows it to generalize. It allows to teach, to sort of infer your intent and apply it scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach AI are not what you’d expect. For example, when first showed GPT-4 to Khan Academy, they said, “Wow, this so great, We’re going to be able to teach wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some math in there, it will happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Sal Khan himself was kind and offered 20 hours of his own time provide feedback to the machine alongside our team. And over course of a couple of months we were able teach the AI that, “Hey, you really should push back on humans in specific kind of scenario.” And we’ve actually made lots and of improvements to the models this way. And when you push thumbs down in ChatGPT, that actually is kind of like up a bat signal to our team to say, “Here’s an area of weakness you should gather feedback.” And so when you do that, that’s one way that we listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback is hard thing. If you think about asking a kid clean their room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff all the toys in closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the AI is happy to help. It’s happy to help us even better feedback and to scale our ability to the 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 foundational blogs on unsupervised learning and learning from human feedback. And the says two months passed. But is it true? Like, these models not 100-percent reliable, although they’re getting better every time we provide some feedback. But we can use the AI to fact-check. And it can actually check own work. You can say, fact-check this for me.
Now, in case, I’ve actually given the AI a new tool. one is a browsing tool where the model can issue search queries and click web pages. And it actually writes out its whole chain of as it does it. It says, I’m just going search for this and it actually does the search. It then it finds the publication date the search results. It then is issuing another search query. It’s 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 to be in the driver’s seat, to be in this manager’s position you can, if you want, triple-check the work. And out come citations 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 back the side. And so thing that’s so interesting to about this whole process is that it’s this many-step between a human and an AI. Because a human, this fact-checking tool is doing it in order to produce data another AI to become more useful to a human. I think this really shows the shape of something that we should to be 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 make sure that the humans are the management, the oversight, the feedback, and the machines are in a way that’s inspectable and trustworthy. And together we’re to actually create even more trustworthy machines. And I think that time, if we get this process right, we will able to solve impossible problems.
And to give you a sense of how impossible I’m talking, I think we’re going to able to rethink almost every aspect of how we interact with computers. example, think about spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago with VisiCalc. don’t think they’ve really changed that much in that time. here is a specific spreadsheet of all the AI papers the arXiv for the past 30 years. There’s about 167,000 of them. And you can see the data right here. But let me show you ChatGPT take 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 able to run code, like a data scientist would. And so you can just literally a file and ask questions about it. And very helpfully, you know, it knows name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” only information here is the name of the file, column names like you saw and then the actual data. from that it’s able to infer what these columns actually mean. Like, that semantic wasn’t in there. It has to sort of, put together its knowledge of knowing that, “Oh yeah, arXiv is a 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 the paper,” like all of that, that’s work for human to do, and the AI is happy to with it.
Now I don’t even know what I to ask. So fortunately, you can ask the machine, “Can you make some exploratory graphs?” And once again, this a super high-level instruction with lots of intent behind it. But don’t even know what I want. And the AI of has to infer what I might be interested in. so it comes up with some good ideas, I think. So a histogram the number of authors per paper, time series of per year, word cloud of the paper titles. All of that, I think, will pretty interesting to see. And the great thing is, it can actually it. Here we go, a nice bell curve. You see that three is kind the most common. It’s going to then make this nice plot the papers per year. Something crazy is happening in 2023, though. Looks like we were on exponential and it dropped off the cliff. What could be on there? By the way, all this is Python code, you can inspect. And then we’ll word cloud. So you can see all these wonderful that appear in these titles.
But I’m pretty unhappy this 2023 thing. It makes this year look really bad. Of course, problem is that the year is not over. So I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 even posted by April 13?] So April 13 was 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 was more I wanted of the machine here. I really wanted it to notice this thing, maybe it’s a little bit an overreach for it to have sort of, inferred magically that this is what I wanted. But inject my intent, I provide this additional piece of, you know, guidance. And under the hood, the AI just writing code again, so if you want to what it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If you noticed, it updates the title. I didn’t ask for that, but it what I want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … A vision how we may end up using this technology in the future. A person his very sick dog to the vet, and the veterinarian a bad call to say, “Let’s just wait and see.” And the dog would be here today had he listened. In the meanwhile, provided the blood test, like, the full medical records, to GPT-4, said, “I am not a vet, you need to talk to a professional, here are 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 rely on them. But this story, I think, shows a human with a medical professional and with ChatGPT a brainstorming partner was able to achieve an outcome that would not happened otherwise. I think this is something we should reflect on, think about as we consider how to integrate these into our world.
And one thing I believe really deeply, is that getting right is going to require participation from everyone. And that’s deciding how we want it to slot in, that’s for the rules of the road, for what an AI will and won’t do. And there’s one thing to take away from this talk, it’s this technology just looks different. Just different from anything people had anticipated. And so we all have to literate. And that’s, honestly, one of the reasons we 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 mean … I suspect that 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 the way I work, I need to rethink.” Like, there’s just new possibilities there. Am right? Who thinks that they’re having to rethink the way 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 my first question actually is just 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 this technology that shocked the world?
Greg Brockman: I mean, truth 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, all of those are really industry-wide. But I think OpenAI, we made a lot of very deliberate choices from the early days. And first one was just to confront reality as it lays. And that we just thought hard about like: What is it going to take to make progress here? We tried a of things that didn’t work, so you only see the that did. And I think that the most important thing has been to get teams of who are very different from each other to work together harmoniously.
CA: we have the water, by the way, just brought here? think we’re going to need it, it’s a dry-mouth topic. But isn’t there something also just the fact that you saw something in these language models meant that if you continue to invest in them grow them, that something at some point might emerge?
GB: Yes. And I 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 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 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 predict where the commas go, where the nouns and are. But he actually got a state-of-the-art sentiment analysis out of it. This model could tell you if a review was positive or negative. I mean, we are just like, come on, anyone can do that. But this was first time that you saw this emergence, this sort semantics that emerged from this underlying syntactic process. And there we knew, you’ve got to scale thing, you’ve got to see 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 out them feels … it just feels impossible that that come from a prediction machine. Just the stuff you showed us just now. the key idea of emergence is that when you get more of a thing, suddenly different things emerge. happens all the time, ant colonies, single ants run around, when you bring enough of together, you get these ant colonies that show completely emergent, behavior. Or a city where a few houses together, it’s houses together. But as you grow the number of houses, emerge, like suburbs and cultural centers and traffic jams. Give me moment for you when you saw just something pop that just blew your mind you just did not see coming.
GB: Yeah, well, so can try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned an internal circuit how to do it. And the really interesting thing is actually, if you have add like a 40-digit number plus a 35-digit number, it’ll often get it wrong. 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 table, that’s more atoms than there are in the universe. So had to have learned something general, but that it hasn’t really fully yet learned that, Oh, I sort 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 look at an incredible of pieces of text. And it is learning things that you didn’t know that it going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. And to do that actually, one of the I think is very undersung in this field is of engineering quality. Like, we had to rebuild our entire stack. When think about building a rocket, every tolerance has to be tiny. Same is true in machine learning. You have get every single piece of the stack engineered properly, and you can start doing these predictions. There are all incredibly smooth scaling curves. They tell you something deeply about intelligence. If you look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting to able to predict. So we were able to predict, for example, the performance on coding problems. We look at some models that are 10,000 times or 1,000 smaller. And so there’s something about this that is actually smooth scaling, even it’s still early days.
CA: So here is, one the big fears then, that arises from this. If it’s fundamental to what’s happening here, as you scale up, things emerge that you can maybe predict in level of confidence, but it’s capable of surprising you. isn’t there just a huge risk of something truly terrible emerging?
GB: Well, think all of these are questions of degree and and timing. And I think one thing people miss, too, is of the integration with the world is also this emergent, sort of, very powerful thing too. And so that’s of the reasons that we think it’s so important deploy incrementally. And so I think that what we of see right now, if you look at this talk, a lot of what I 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, was the correct answer. But even summarizing a book, like, that’s a hard thing supervise. Like, how do you know if this book summary is any good? You to read the whole book. No one wants to do that.
(Laughter) And so I think that important thing will be that we take this step by step. And we say, OK, as we move on to book summaries, have to supervise this task properly. We have to up a track record with these machines that they’re able to actually carry our intent. And I think we’re going to have to produce better, more efficient, more reliable ways of scaling this, sort of like making machine be aligned with you.
CA: So we’re going hear later in this session, there are critics who say that, you know, there’s real understanding inside, the system is going to always — we’re never going to know that it’s not generating errors, it doesn’t have common sense and so forth. Is your belief, Greg, that it is true at any one moment, that the expansion of the scale and the human that you talked about is basically going to take it on that journey of getting to things like truth and wisdom and so forth, with a high of confidence. Can you be sure of that?
GB: Yeah, well, I 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 like this field the field of broken promises, of all these experts saying X is going to happen, Y is how works. People have been saying neural nets aren’t going to work for 70 years. haven’t been right yet. They might be right maybe 70 years one or something like that is what you need. But I think that approach has always been, you’ve got to push to the limits of technology to really see it in action, because that tells then, oh, here’s how we can move on to new paradigm. And we just haven’t exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that the right way do this is to put it out there in public and then harness all this, you know, instead just your team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, it is there. So, you know, the original story that I heard on OpenAI when were founded as a nonprofit, well you were there as the great of check on the big companies doing their unknown, evil thing with AI. And you were going to 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 kind of I heard. And yet, what’s happened, arguably, is the opposite. That your release GPT, especially ChatGPT, sent such shockwaves through the tech world that now and Meta and so forth are all scrambling to catch up. And some of their criticisms been, you are forcing us to put this out here without proper guardrails or die. You know, how do you, like, make the that 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 we were thinking how to build artificial general intelligence, actually have it benefit all humanity, like, how are you supposed to do that, right? And that default plan of being, well, you in secret, you get this super powerful thing, and then you out the safety of it and then you push “go,” and you you got it 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 the only path that I see, which is that you do let hit you in the face. And I think you do people time to give input. You do have, before machines are perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve it from GPT-3, right? GPT-3, we really were afraid the number one thing people were going to do it was generate misinformation, try to tip elections. Instead, the number one thing was generating 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 box on the table. You believe that in that is something that, there’s a very strong chance it’s something absolutely glorious that’s going to give beautiful gifts your family and to everyone. But there’s actually also a one percent thing in the print there that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils the world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. And honestly, like, I’ll tell a story that I haven’t actually told before, which is that after we started OpenAI, I remember I was in Rico for an AI conference. I’m sitting in the room just looking out over this wonderful water, all these having a good time. And you think about it for a moment, if could choose for basically that Pandora’s box to be five years away or 500 years away, which would pick, right? On the one hand you’re like, well, maybe you personally, it’s better to have it be five years away. But if it to be 500 years away and people get more to get it right, which do you pick? And you know, I just felt it in the moment. I was like, of you do the 500 years. My brother was in the at the time and like, he puts his life the line in a much more real way than any of us typing things in and developing this technology at the time. And so, yeah, I’m sold on the you’ve got to approach this right. But don’t think that’s quite playing the field as it lies. Like, if you look at the whole history 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. And the more that you of, don’t put together the pieces that are there, right, we’re still faster computers, we’re still improving the algorithms, all of these things, are happening. And if you don’t put them together, you get an overhang, which that if someone does, or the moment that someone manage to connect to the circuit, then you suddenly have this very powerful thing, one’s had any time to adjust, who knows what kind safety precautions you get. And so I think that one thing I take away is like, even think about development of other sort of technologies, think about nuclear weapons, talk about being like a zero to one, sort of, change in what humans do. But I actually think 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 to it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model want us to have is that we have birthed this extraordinary child that may have superpowers that take to a whole new place. It is our collective responsibility provide the guardrails for this child to collectively teach it be wise and not to tear us all down. that basically the model?
GB: I think it’s true. And I think it’s also important say this may shift, right? We’ve got to take each step as we it. And I think it’s incredibly important today 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 to the best path, but it’s so good we’re honestly having this debate because we wouldn’t otherwise if weren’t out there.
CA: Greg Brockman, thank you so much for coming to TED blowing our minds.
(Applause)