We started OpenAI seven ago because we felt like something really interesting was happening in AI we wanted to help steer it in a positive direction. It’s honestly really amazing to see how far this whole field has since 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 who are excited, we hear from people who are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period right where we as a world are going to define a technology that will be so important for our going forward. And I believe that we can manage this for good.
So today, I want show you the current state of that technology and some the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s to build a tool for an AI rather than building it a human. So we have a new DALL-E model, 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 draw a of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of the details for you that you get of ChatGPT. And here we go, it’s not just the idea for the meal, a very, very detailed spread. So let’s see what we’re going get. But ChatGPT doesn’t just generate images in this — sorry, it doesn’t generate text, it also generates an image. that is something that really expands the power of what it can do on your behalf in terms carrying out your intent. And I’ll point out, this is a live demo. This is all generated by the as we speak. So I actually don’t even know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry just looking it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And the thing about these tools is they’re very inspectable. So you get this little up here that says “use the DALL-E app.” And by the way, is coming to you, all ChatGPT users, over upcoming months. And you can look the hood and see that what it actually did was write a just like a human could. And so you sort of have this ability to inspect how machine is using these tools, which allows us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like to that information and to integrate with other applications too. You can say, “Now make a shopping for the tasty thing I was suggesting earlier.” And make it a little for the AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is selecting all different tools without me having to tell it explicitly which ones to use in any situation. And this, think, shows a 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 as as you kind of know the menus and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And by this unified language interface on top of tools, the AI is able to of take away all those details from you. So don’t have to be the one who spells out single sort of little piece of what’s supposed to happen.
And as I said, this a live demo, so sometimes the unexpected will happen to us. But let’s take look at the Instacart shopping list while we’re at it. you can see we sent a list of ingredients Instacart. Here’s everything you need. And the thing that’s really 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 something that I think shows that they’re going away, traditional UIs. It’s just we have a new, augmented way to them. And 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 the manager, we’re to inspect, we’re able to change the work of AI if we want to. And so after this talk, you will be able to this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important thing about how we build this, it’s not about building these tools. It’s about teaching the AI to use them. Like, what do we even want to do when we ask these very high-level questions? to do this, we use an old idea. If you back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never an answer to this. Instead, you can learn it. could build a machine, like a human child, and then teach it through feedback. Have a human teacher provides rewards and punishments as it tries things out and things that are either good or bad.
And this is how we train ChatGPT. It’s a two-step process. First, we produce what Turing would have a child machine through an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it with all of wonderful skills. For example, if you’re shown a problem, the only way to actually complete that math problem, to say comes next, that green nine up there, is to actually the math problem.
But we actually have to do a second step, too, which is teach the AI what to do with those skills. for this, we provide feedback. We have the AI try multiple things, give us multiple suggestions, and then a rates them, says “This one’s better than that one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the used to produce that answer. And this allows it generalize. It allows it to teach, to sort of infer your intent and apply in 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 we first showed GPT-4 to Khan Academy, said, “Wow, this is so great, We’re going to able to teach students 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 one equals three and run with it.” So we had to collect feedback data. Sal Khan himself was very kind and offered 20 hours his own time to provide feedback to the machine alongside our team. And over the course of couple of months we were able to teach the AI that, “Hey, you really should back on humans in this specific kind of scenario.” And we’ve actually made lots and of improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of like sending up a bat signal to team to say, “Here’s an area of weakness where you should gather feedback.” so when you do that, that’s one way that we really listen to our users and 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 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 toys in the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As we move 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. let me show you what I mean.
For example, you can ask GPT-4 a question like this, how much time passed between these two foundational blogs on unsupervised learning and learning human feedback. And the model 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 actually use the AI to fact-check. And it can actually its own work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. one is a browsing tool where the model can issue search and click into web pages. And it actually writes out its whole chain thought as it does it. It says, I’m just going to search for this and actually does the search. It then it finds the publication date and the results. It then is issuing another search query. It’s going to into the blog post. And all of this you do, but it’s a very tedious task. It’s not thing that humans 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 citations so you can actually go and very easily verify any piece of this whole chain of reasoning. it actually turns out two months was wrong. Two months one week, that was correct.
(Applause)
And we’ll cut back to side. And so thing that’s so interesting to me about this whole process is it’s this many-step collaboration between a human and an AI. Because a human, using this fact-checking is doing it in order to produce data for AI to become more useful to a human. And I think this really the shape of something that we should 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 how we want to solve that problem. We make sure that the humans providing the management, the oversight, the feedback, and the machines are operating in a that’s inspectable and trustworthy. And together we’re able to actually create more trustworthy machines. And I think that over time, if we this process right, we will be able to solve problems.
And to give you a sense of just how impossible I’m talking, I think we’re going to able to rethink almost every aspect of how we 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 they’ve really changed that much in that time. And 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 there the data right here. But let me show you the ChatGPT take how to analyze a data set like this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able to run code, just like a scientist would. And so you can just literally upload file 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 value file, “I’ll it for you.” The only information here is the of the file, the column names like you saw then the actual data. And from that it’s able to infer what these actually mean. Like, that semantic information wasn’t in there. It has sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a site that people submit 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 human to do, and the AI is happy to help with it.
Now I don’t even know I want to ask. So fortunately, you can ask machine, “Can you make some exploratory graphs?” And once again, is a super high-level instruction with lots of intent behind it. I don’t even know what I want. And the AI kind of has to infer what might be interested in. And so it comes up with some good ideas, think. So a histogram of the number of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, will pretty interesting to see. And the great thing is, can actually do it. Here we go, a nice curve. You see that three is kind of the common. It’s going to then make this nice plot of the papers per year. 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 is Python code, you can inspect. And then we’ll see word cloud. So you can all 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 is that the year is not over. So I’m going to push back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What of papers in 2022 were even posted by April 13?] So April 13 was the cut-off date believe. Can you use that to make a fair projection? So we’ll see, this is kind of ambitious one.
(Laughter)
So you know, again, I feel there was more I wanted out of the machine here. I really wanted to notice this thing, maybe 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 this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so if you want to what it’s doing, it’s very possible. And now, it the correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask for that, it 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 this technology the future. A person brought his very sick dog to the vet, and the veterinarian a bad call to say, “Let’s just wait and see.” And the would not be here today had he listened. In the meanwhile, provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” He brought that information to a second vet who it to 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 brainstorming was able to achieve an outcome that would not have otherwise. I think this is something we should all reflect on, think about as we consider to integrate these systems into our world.
And one I believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding how we it to slot in, that’s for setting the rules of the road, for what AI will and won’t do. And if there’s one thing to take away from talk, it’s that this technology just looks different. Just different from anything people had anticipated. And so all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we achieve the OpenAI mission of ensuring that artificial general intelligence all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s a feeling reeling. Like, I suspect that a very large number of people viewing this, you look that and you think, “Oh my goodness, pretty much every single about the way I work, I need to rethink.” Like, there’s new possibilities there. Am I right? Who thinks that they’re to rethink the 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 my first question actually is just how the hell have you done this?
(Laughter)
OpenAI has few hundred employees. Google has thousands of employees working on artificial intelligence. Why is it you who’s up with 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 industry-wide. But I think within OpenAI, we made a lot of very deliberate choices from early days. And the first one was just to confront reality as it lays. that we just thought really hard about like: What is going to take to make progress here? We tried a of things that didn’t work, so you only see the things that did. And think that the most important thing has been to 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 also about the fact that you saw something in these language models that that if you continue to invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, I mean, honestly, I think the story there is illustrative, right? I think that high level, deep learning, like we always knew was what we wanted to be, was a deep learning lab, and exactly how do it? I think that in the early days, we didn’t know. We tried a of things, and one person was working on training model to predict the next character in Amazon 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 actually got a state-of-the-art sentiment analysis classifier out of it. This model could tell you if a was positive or negative. I mean, today we are just like, on, anyone can do that. But this was the first time that saw this 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 see where it goes.
CA: So think this helps explain the riddle that baffles everyone looking at this, these things are described as prediction machines. And yet, we’re seeing out of them feels … it just impossible that that could come from a prediction machine. the stuff you showed us just now. And the key of emergence is that when you get more of a thing, suddenly different things emerge. It all 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 the number of houses, things emerge, like suburbs and centers and traffic jams. Give me one moment for when you saw just something pop that just blew mind that you 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 will do it, which it’s really learned an internal circuit for how to 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 it wrong. And so you can see that it’s 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 are in the universe. So it had have learned something general, but that it hasn’t really fully yet learned that, Oh, I can sort generalize this to adding arbitrary numbers of 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 learning that you didn’t know that it was going to be of learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. And to do actually, one of the things I think is very undersung this field is sort of engineering quality. Like, we to rebuild our entire stack. When you think about building a rocket, every tolerance to be incredibly tiny. Same is true in machine learning. You have to every single piece of the stack engineered properly, and then you can start doing predictions. There are all these incredibly smooth scaling curves. They tell you deeply fundamental about intelligence. If you look at our GPT-4 blog post, you can see all of these curves there. And now we’re starting to be able to predict. So were able to predict, for example, the performance on problems. We basically look at some models that are 10,000 or 1,000 times smaller. And so there’s something about this that is smooth scaling, even though it’s still early days.
CA: So here is, one of 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 some level confidence, but it’s capable of surprising you. Why isn’t there just huge risk of something truly terrible emerging?
GB: Well, think all of these are questions of degree and scale and timing. I think one thing people miss, too, is sort of the integration with the world is this incredibly emergent, sort of, very powerful thing too. so that’s one 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 I focus on is providing really high-quality feedback. Today, the tasks that we do, you can them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was the answer. But even summarizing a book, like, that’s a thing to supervise. Like, how do you know if this summary is any good? You have to read the book. No one wants to do that.
(Laughter) And so I think that the important thing be that we take this step by step. And that we say, OK, as we move to book summaries, we have to supervise this task properly. We have to build a track record with these machines that they’re able to carry out our intent. And I think we’re going to have to produce even better, efficient, more reliable ways of scaling this, sort of like making the be aligned with you.
CA: So we’re going to hear later in this session, there are critics say that, you know, there’s no real understanding inside, system is going to always — we’re never going know that it’s not generating errors, that it doesn’t common sense and so forth. Is it your belief, Greg, that it is at any one moment, but that the expansion of the scale and the human that you talked about is basically going to take it on that journey of actually to things like truth and wisdom and so forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer is yes, believe that is where we’re headed. And I think the OpenAI approach here has always been just like, 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 work for 70 years. They haven’t been right yet. might be right maybe 70 years plus one or something that is what you need. But I think that our approach always been, you’ve got to push to the limits of this technology really see it in action, because that tells you then, oh, here’s how we can move on a new paradigm. And we just haven’t exhausted the here.
CA: I mean, it’s quite a controversial stance you’ve taken, the right way to do this is to put out there in public and then harness all this, you know, instead just your team giving feedback, the world is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded a nonprofit, well you were there as the great of check on the big companies doing their unknown, possibly thing with AI. And you were going to build models that sort of, you know, somehow held them and was 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. your release of GPT, especially ChatGPT, sent such shockwaves through the world that now Google and Meta and so forth are all scrambling catch up. And some of their criticisms have been, you are forcing us to this out here without proper guardrails or we die. You know, do you, like, make the case that what you done is responsible here and not reckless.
GB: Yeah, think about these questions all the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing think has been incredibly important, from the very beginning, we were thinking about how to build artificial general intelligence, actually have it benefit all of humanity, like, are you supposed to do that, right? And that default of being, well, you build in secret, you get super powerful thing, and then you figure out the of it and then you push “go,” and you hope got it right. I don’t know how to execute that plan. Maybe someone does. But for me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the other path that I see, which is that you let reality hit you in the face. And I you do give people time to give input. You do have, these machines are perfect, before they are super powerful, that you have 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 with it was generate misinformation, to tip elections. Instead, the number one thing was generating 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 a box the table. You believe that in that box is something that, there’s very strong chance it’s something absolutely glorious that’s going give beautiful gifts to your family and to everyone. there’s actually also a one percent thing in the print there that says: “Pandora.” And there’s a chance that this could unleash unimaginable evils on the world. Do you open that box?
GB: Well, so, absolutely not. I you don’t do it that way. And honestly, like, I’ll tell you story that I haven’t actually told before, which is that shortly we started OpenAI, I remember I was in Puerto for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these people having a time. And you think about it for a moment, you could choose for basically that Pandora’s box to five years away or 500 years away, which would you pick, right? the one hand 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 people get more time to get it right, which do you pick? you know, I just really felt it in the moment. I was like, of course you do 500 years. My brother was in the military at the time like, he puts his life on the line in much more real way than any of us typing things 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 you at the whole history of computing, I really mean when I say that this is an industry-wide or even almost like a human-development- of-technology-wide shift. And the more that sort of, don’t put together the pieces that are there, right, we’re making faster computers, we’re still improving the algorithms, all these things, they are happening. And if you don’t put them together, you get overhang, which means that if someone does, or the moment that does 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 of precautions you get. And so I think that one thing I away is like, even you think about development of other sort of technologies, about nuclear weapons, people 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 quite smooth over time. And so the history, I think, of every technology we’ve developed has been, you’ve got do it incrementally and you’ve got to figure out how manage 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 take humanity to a whole new place. It is our collective responsibility to provide the guardrails for child to collectively teach it to be wise and to tear us all down. Is that basically the model?
GB: I think it’s true. And I think it’s important to say this may shift, right? We’ve got to take each as we encounter it. And I think it’s incredibly important today we all do get literate in this technology, figure how to provide the feedback, decide what we want from it. And my hope is that that continue to 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 coming TED and blowing our minds.
(Applause)