We started OpenAI seven years ago because felt like something really interesting was happening in AI and wanted to help steer it in a positive direction. It’s just really amazing to see how far this whole field has come since then. And it’s really gratifying hear from people like Raymond who are using the technology we building, and others, for so many wonderful things. We hear from people are excited, we hear from people who are concerned, we hear from people who feel 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 a world are going to define a technology that be so important for our society going forward. And believe 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 going to show you is what it’s like to a tool for an AI rather than building it for a human. So we a new DALL-E model, which generates images, and we exposing it as an app for ChatGPT to use on behalf. And you can do things like ask, you know, a nice post-TED meal and draw 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 get out 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 to get. ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And that something that 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 all a live demo. This is generated by the AI as we speak. So I actually don’t know 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, example, memory. You can say “save this for later.” And the thing about these tools is they’re very inspectable. So get this little pop 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 under hood and see that what it actually did was write a prompt just like a human could. And you sort of have this ability to inspect how the machine is using these tools, which us to provide feedback to them.
Now it’s saved for later, and me show you what it’s like to use that information and to integrate with other too. You can say, “Now make a shopping list for the tasty thing I was suggesting earlier.” And it a little tricky for the AI. “And tweet out for all the TED viewers out there.”
(Laughter)
So if do make this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is selecting these different tools without me having to tell it which ones to use in any situation. And this, think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, we have these apps, click between them, we copy/paste between them, and usually it’s great experience within an app as long as you kind know the menus and know all the options. Yes, would like you to. Yes, please. Always good 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 one who out every single sort of little piece of what’s supposed happen.
And as I said, this is a live demo, so sometimes the unexpected will happen to us. let’s take a look at the Instacart shopping list while we’re it. And you can see we sent a list ingredients to Instacart. Here’s everything you need. And the that’s really interesting is that the traditional UI is very valuable, right? If you look at this, you still can click through it and sort modify the actual quantities. And that’s something that I think that they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we have tweet that’s been drafted for our review, which is also a very important thing. We can “run,” and there we are, we’re the manager, we’re 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. And there go. Cool. Thank you, everyone.
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So we’ll cut to the slides. Now, the important thing about how we this, it’s not just about building these tools. It’s teaching the AI how to use them. Like, what we even want it to do when we ask these very high-level questions? And do this, we use an old idea. If you go to Alan Turing’s 1950 paper on the Turing test, he says, you’ll program an 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 and punishments as tries things out and does things that are either good or bad.
And this exactly how we train ChatGPT. It’s a two-step process. First, we produce what Turing would have called a child through an unsupervised learning process. We just show it the whole world, the whole internet say, “Predict what comes next in text you’ve never seen before.” And this process it with all sorts of wonderful skills. For example, if you’re a math problem, the only way to actually complete that problem, to say what comes next, that green nine up there, is actually solve the math problem.
But we actually have to do a step, too, which is to teach the AI what to do those skills. And for this, we provide feedback. We have the AI try out things, give us multiple suggestions, and then a human rates them, says “This one’s better than one.” And this reinforces not just the specific thing that the AI said, but very importantly, whole process that the AI used to produce that answer. And allows it to generalize. It allows it 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 the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, said, “Wow, this is so great, We’re going to be able teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math in there, it will pretend that one plus one equals three and run with it.” So we had to collect feedback data. Sal Khan himself was very kind and 20 hours of his own time to provide feedback the machine alongside our team. And over the course a couple of months we were able to teach the that, “Hey, you really should push back on humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to the models this way. And when you push that thumbs in ChatGPT, that actually is kind of like sending up a bat to our team to say, “Here’s an area of weakness where you gather feedback.” And so when you do that, that’s one way we really listen to our users and make sure we’re building 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, if you’re doing is inspecting the floor, you don’t know if you’re just teaching them to all the toys in the closet. This is a nice DALL-E-generated image, by the way. And same sort of reasoning applies to AI. As we move to harder tasks, we will have scale our ability to provide high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even better feedback and to our 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 foundational blogs on unsupervised learning and learning from human feedback. And the model says two months passed. But is true? Like, these models are not 100-percent reliable, although they’re getting every time we provide some feedback. But we can use the AI to fact-check. And it can actually its own work. You can say, fact-check this for me.
Now, in this case, I’ve actually given the AI new tool. This one is a browsing tool where the model can issue search queries 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 it actually does the search. It then it finds the publication date the search results. It then is issuing another search query. It’s going to click into the blog post. And all this you could do, but it’s a very tedious task. It’s not thing that humans really want to do. It’s much fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check work. And out come citations so you can actually and very easily verify any piece of this whole chain of reasoning. And it actually turns out months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And thing that’s so interesting to me about 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 doing it in order to produce for another AI to become more useful to a human. And I think this really the shape of something that we should expect to be much common in the future, where we have humans and machines of very carefully and delicately designed in how they into a problem and how we want to solve that problem. We make that the humans are providing 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 be able solve impossible problems.
And to give you a sense of just how impossible I’m talking, think we’re going to be able to rethink almost every aspect of how interact with computers. For example, think about spreadsheets. They’ve been around in some form since, we’ll say, 40 ago with VisiCalc. I don’t think they’ve really changed that much in that time. And here a specific spreadsheet of all the AI papers on arXiv for the past 30 years. There’s about 167,000 of them. And can see there the data right here. But let me show you the ChatGPT on 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 data scientist would. And so you can just literally upload a file and ask about it. And very 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 for you.” The only here is the name of the file, the column like you saw and then the actual data. And from that it’s to infer what these columns actually mean. Like, that semantic wasn’t in there. It has to sort of, put together world knowledge of knowing that, “Oh yeah, arXiv is a that people submit papers and therefore that’s what these things are and that these are integer values and therefore it’s a number of authors in the paper,” like all that, that’s work for a human to do, and the is happy to help with it.
Now I don’t even know what want to ask. So fortunately, you can ask the machine, “Can you make some exploratory graphs?” once again, this is a super high-level instruction with lots of intent it. But I don’t even know what I want. the AI kind of has to infer what I might be in. And so it comes up with some good ideas, I think. So histogram of the number of authors per paper, time series papers per year, word cloud of the paper titles. of 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 three is kind of the most common. It’s to then make this nice plot of the papers year. Something crazy is happening in 2023, though. Looks we were on an exponential and it dropped off the cliff. What be going on there? By the way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all 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 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 percentage of papers 2022 were even posted by April 13?] So April 13 was cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the of ambitious 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, it’s a little bit of an overreach for it have sort of, inferred magically that this is what wanted. But I inject my intent, I provide this additional of, you know, guidance. And under the hood, the AI is just writing code again, so if want to inspect what it’s doing, it’s very possible. And now, it does correct projection.
(Applause)
If you noticed, it even 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 parable of how I think we … A vision of how may end up using this technology in the future. A person brought very sick dog to the vet, and the veterinarian made a call to say, “Let’s just wait and see.” And the dog not be here today had he listened. In the meanwhile, he provided the test, like, the full medical records, to GPT-4, which said, “I am not a vet, you need to talk to professional, here are some hypotheses.” He brought that information a second vet who used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot rely on them. But this story, I think, shows that a human with a professional and with ChatGPT as a brainstorming partner was able achieve an outcome that would not have happened otherwise. think this is something we should all reflect on, about as we consider how to integrate these systems into our world.
And thing I 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 the rules of the road, for what an AI will and won’t do. if 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 we all have to become literate. And that’s, honestly, one of the reasons released ChatGPT.
Together, I believe that we can achieve the OpenAI mission of that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I 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 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 the way that we do things? Yeah, I mean, it’s amazing, but it’s also really scary. let’s talk, Greg, let’s talk.
I mean, I guess my first 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 it you who’s come up with this technology that the world?
Greg Brockman: I mean, the truth is, we’re all building on of giants, right, there’s no question. If you look at compute progress, the algorithmic progress, the data progress, all of are really industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the first one just to confront reality as it lays. And that we just really hard about like: What is it going to take to progress here? We tried a lot of things that didn’t work, you only see the things that did. And I that the 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 going to need it, it’s dry-mouth topic. But isn’t there something also just about the fact that you saw something these language models that meant that if you continue to invest in them grow them, that something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, like we always knew that was we wanted to be, was a deep learning lab, and exactly how to it? I think that in the early days, we didn’t know. We tried lot of things, and one person was working on training a model predict the next character in Amazon reviews, and he got a result — this is a syntactic process, you expect, you know, the model predict 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 model could tell if a 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 there we knew, you’ve to scale this thing, you’ve got to see where goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things are as prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could come from a machine. Just the stuff you showed us just now. And the key idea of emergence is when you get more of a thing, suddenly different things emerge. It all the time, ant colonies, single ants run around, you 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 houses together. as you grow the number of houses, things emerge, like suburbs and cultural centers and traffic jams. Give one moment for you when you saw just something that just blew your mind that you just did not see coming.
GB: Yeah, well, so 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 an internal circuit for how to do it. And the really interesting is actually, if you have it add like a 40-digit number a 35-digit number, it’ll often get it wrong. And so you can that it’s really learning the process, but it hasn’t generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s atoms than there are in the 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 adding arbitrary numbers of arbitrary lengths.
CA: So what’s 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 be capable learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re starting really get good at is predicting some of these capabilities. And to do that actually, one of the things I think is very undersung in this field sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, tolerance has to be incredibly tiny. Same is true in machine learning. You have to get every piece of the stack engineered properly, and then you can start doing these predictions. There are all these smooth scaling curves. They tell you something deeply fundamental about intelligence. If you look our GPT-4 blog post, you can see all of these curves in there. now we’re starting to be able to predict. So were able to predict, for example, the performance on coding problems. basically 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 of the big fears then, that from this. If it’s fundamental to what’s happening here, as you scale up, things emerge that you can maybe predict in level of confidence, but it’s capable of surprising you. Why isn’t there just a huge risk of truly terrible emerging?
GB: Well, I think all of are questions of degree and scale and timing. And think one thing people miss, too, is sort of the integration with the world is also this emergent, sort of, very powerful thing too. And so that’s one of the reasons that we it’s so important to deploy incrementally. And so I think that what we kind of right now, if you look at this talk, a lot what I focus on is providing really high-quality feedback. Today, the tasks that we do, can inspect them, right? It’s very easy to look at that math problem and like, no, no, no, machine, seven was the correct answer. But summarizing a book, like, that’s a hard thing to supervise. Like, how 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 we this step by step. And that we say, OK, as we move on to summaries, we have to supervise this task properly. We to build up a track record with these machines that they’re able 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 of like making machine be aligned with you.
CA: So we’re going to later in this session, there are critics who say that, know, there’s no real understanding inside, the system is to always — we’re never going to know that it’s generating errors, that it doesn’t have common sense and so forth. Is it your belief, Greg, it is true at any one moment, but that the expansion of the scale and human feedback that you talked about is basically going to take 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 believe that is where we’re headed. I think that the OpenAI approach here has always been like, let reality hit you in the face, right? It’s like this field is the field of broken promises, 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. They haven’t been yet. They might be right maybe 70 years plus one something like that is what you need. But I think that our approach has been, you’ve got to push to the limits of this technology to really see in action, because that tells you then, oh, here’s how we can move on to a paradigm. And we just haven’t exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that the right to do this is to put it out there in public then harness all this, you know, instead of just your team giving feedback, the world is giving feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the 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 to build models sort of, you know, somehow held them accountable and was capable of slowing the field down, if be. Or at least that’s kind of what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent shockwaves through the tech world that now Google and Meta so forth are all scrambling to catch up. And some of their criticisms have been, are forcing us to put this out here without guardrails or we die. You know, how do you, like, make the case that what you have done is here and not reckless.
GB: Yeah, we think about questions all the time. Like, seriously all the time. I don’t think we’re always going 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 intelligence, actually have it benefit all of humanity, like, how are you supposed to do that, right? And default plan of being, well, you build in secret, you get this powerful thing, and then you figure out the safety of and then you push “go,” and you hope you got right. I don’t know how to execute that plan. Maybe someone else does. for me, that was always terrifying, it didn’t feel right. And so I that this alternative approach is the only other path that I see, which is that you do reality hit you in the face. And I think you give people time to give input. You do have, before these are perfect, before they are super powerful, that you actually have the ability to them in action. And we’ve seen it from GPT-3, right? GPT-3, we really were afraid that the one thing people were going to do with it generate misinformation, try to tip elections. Instead, the number one was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are things that are worse. Here’s a thought experiment for you. Suppose you’re sitting in a room, there’s a on the table. You believe that in that box is that, there’s a very strong chance it’s something absolutely glorious that’s going to give gifts to your family and to everyone. But there’s actually also a one percent thing in small print there that says: “Pandora.” And there’s a that this actually could unleash unimaginable evils on the world. Do you that box?
GB: Well, so, absolutely not. I think you don’t it that way. And 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 looking out over this wonderful water, all these people having good time. And you think about it for a moment, if you could choose 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, for you personally, it’s better to have it be years away. But if it gets to be 500 years away people get more time to get it right, which you pick? And you know, I just really felt in the moment. I was like, of course 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 us typing things in computers and developing this technology at the time. so, yeah, I’m really sold on the you’ve got to approach this right. But don’t think that’s quite playing the field as it truly lies. Like, if look at the whole history of computing, I really it when I say that this is an industry-wide or even just like a human-development- of-technology-wide shift. And the more that you sort of, don’t put together pieces that are there, right, we’re still making faster computers, we’re still improving the algorithms, all of these things, they happening. And if you don’t put them together, you an overhang, which means that if someone does, or the moment someone does manage to connect to the circuit, then you suddenly have very powerful thing, no one’s had any time to adjust, who knows kind of safety precautions you get. And so I that one thing I take away is like, even you about development of other sort of technologies, think about nuclear weapons, people talk about being like zero to one, sort of, change in what humans could do. But actually think that if you look at capability, it’s been quite smooth time. And so the history, I think, of every technology we’ve developed has been, you’ve got to it incrementally and you’ve got to figure out how manage it for each moment that you’re increasing it.
CA: So what I’m is that you … the model you want us to have is that we have this extraordinary child that may have superpowers that take humanity to a new place. It is our collective responsibility to provide the guardrails for this child to collectively it to be wise and not to tear us all down. Is that basically model?
GB: I think it’s true. And I think it’s also important to say this shift, right? We’ve got to take each step as we encounter it. And think it’s incredibly important today that we all do get in this technology, figure out how to provide the feedback, decide what we want it. And my hope is that that will continue to be the path, but it’s so good we’re honestly having this because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much for coming to and blowing our minds.
(Applause)