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 has since then. And it’s really gratifying to hear from people like Raymond who are the technology we are building, and others, for so wonderful things. We hear from people who are excited, we from people who are concerned, we hear from people who both those emotions at once. And honestly, that’s how we feel. Above all, it feels we’re entering an historic period right now where we as a world are going to a technology that will be so important for our society going forward. And I believe we can manage this for good.
So today, I to show you the current state of that technology 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 for a human. So we have a new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on your behalf. And you do things like ask, you know, suggest a nice post-TED meal and draw a picture it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details for you that you get out of ChatGPT. here we go, it’s not just the idea for the meal, but very, very detailed spread. So let’s see what we’re to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also generates an image. And that is that really expands the power of what it can do your behalf in terms of carrying out your intent. And I’ll point out, is all a live demo. This is all generated the AI as we speak. So I actually don’t know what we’re going to see. This looks wonderful.
(Applause)
I’m 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 that says “use DALL-E app.” And by the way, this is coming to you, all ChatGPT users, over upcoming months. And can look under the hood and see that what it actually was write a prompt just like a human could. And so you of have this ability to inspect how the machine is using these tools, which allows us to provide to them.
Now it’s saved for later, and let me show you what it’s like to use that and to integrate with other applications too. You can say, “Now a shopping list for the tasty thing I was suggesting earlier.” And it a little tricky for the AI. “And tweet it out for the TED viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, definitely want to know how it tastes.
But you can see ChatGPT is selecting all these different tools without me to tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking about user interface. Like, we are so used to thinking of, well, we these apps, we click between them, we copy/paste between them, and usually it’s a great within an app as long as you kind of the menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, the AI is able sort of take away all those details from you. So you don’t have to the one who spells out every single sort of little piece what’s supposed to happen.
And as I said, this is a demo, so sometimes the unexpected will happen to us. But let’s a look at the Instacart shopping list while we’re at it. you 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 very valuable, right? If you look at this, you still can through it and sort of 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 a tweet that’s been for our review, which is also a very important thing. can click “run,” and there we are, we’re the manager, we’re able to inspect, we’re able to the work of the AI if we want to. And so 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 back to the slides. Now, important thing about how we build this, it’s not just about building these tools. It’s about teaching AI how to use them. Like, what do we even want it to when we ask these very high-level questions? And to this, we use an old idea. If you go to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, you can it. You could build a machine, like a human child, and then teach it through feedback. Have human teacher who provides rewards and punishments as it tries things and does things that are either good or bad.
And this is exactly we train ChatGPT. It’s a two-step process. First, we what Turing would have called a child machine through an learning process. We just show it the whole world, the whole internet and say, “Predict what next in text you’ve never seen before.” And this imbues it with all sorts of wonderful skills. For example, if you’re a math 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, is to teach the AI what to do with skills. And for this, we provide feedback. We have AI try out multiple things, give us multiple suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the specific thing that the said, but very importantly, the whole process that the AI used to produce that answer. this allows it to generalize. It allows it to teach, to sort of infer your and apply it in scenarios that it hasn’t seen before, it hasn’t received feedback.
Now, sometimes the things we have teach the AI are not what you’d expect. For example, when we first 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 math in there, it happily pretend that one plus one equals three and run with it.” So we had collect some feedback data. Sal Khan himself was very kind and offered 20 hours of his own to provide feedback to the machine alongside our team. And the course of a couple of months we were able to teach the AI that, “Hey, really should push back on humans in this specific kind of scenario.” And we’ve actually lots and lots of improvements to the models this way. when you push that thumbs down in ChatGPT, that actually is kind of like sending 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 that we really listen to our users and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. If you think about asking a to clean their room, if all you’re doing is inspecting the floor, you don’t if you’re just teaching them to stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same of reasoning applies to AI. As we move to tasks, we will have to scale our ability to provide high-quality feedback. But for this, AI itself is happy to help. It’s happy to help us provide even better feedback and to scale ability to supervise the machine as time goes on. And let show you 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. 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 can actually use the AI to fact-check. And it can check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the AI a new tool. This one is a browsing where the model can issue search queries and click web pages. And it actually writes out its whole chain thought as it does it. It says, I’m just going to for this and it 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 click into the blog post. all of this you could do, but it’s a tedious task. It’s not a thing that humans really want do. It’s much more fun to be in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. out come citations so you can actually go and very easily any piece of this whole chain of reasoning. And actually turns out two months was wrong. Two months and 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 collaboration between a human and AI. Because a human, using this fact-checking tool is doing it in order to produce data another AI to become more useful to a human. And I think this really the shape of something that we should expect to much more common in the future, where we have humans and machines kind of very carefully and delicately in how they fit into a problem and how we want to solve that problem. make sure that the humans are providing the management, oversight, the feedback, and the machines are operating in way that’s inspectable and trustworthy. And together we’re able to actually create more trustworthy machines. And I think that over time, we get this process right, we will be able to solve problems.
And to give you a sense of just how impossible I’m talking, think we’re going to be able to rethink almost aspect of how we interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much in that time. here is a specific spreadsheet of all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you can see there data right here. But let me show you the ChatGPT take on how to analyze a data like this.
So we can give ChatGPT access to another 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 a file 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 file, “I’ll parse it for you.” The only information here is the name of the file, the names like you saw and then the actual data. And that it’s able to infer what these columns actually mean. Like, that semantic information wasn’t in there. has to sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv is site that people submit papers and therefore that’s what these things are and that are integer values and so therefore it’s a number of authors in the paper,” all of that, that’s work for a human to do, and the is happy to help with it.
Now I don’t even know what I want ask. So fortunately, you can ask the machine, “Can you make some graphs?” And once again, this is a super high-level instruction with lots of behind it. But I don’t even know what I want. And the AI of has to infer what I might be interested in. And so it up with some good ideas, I think. So a histogram of the of authors per paper, time series of papers per year, word cloud of the paper titles. All of that, think, will be pretty interesting to see. And the thing is, it can actually do it. Here we go, a nice bell curve. You see that is kind of the most common. It’s going to then make this nice plot of the papers year. Something crazy is happening in 2023, though. Looks like we were on an exponential and dropped off the cliff. What could be going on there? By way, all this is Python code, you can inspect. And then we’ll word cloud. So you can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem that the year is not over. So I’m going to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by April 13?] April 13 was the cut-off date I believe. Can you use that to make fair projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, I like there was more I wanted out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of an overreach for it to have sort of, inferred that this is what I wanted. But I inject intent, I provide this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so you want to inspect 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 for that, but it know what I want.
Now we’ll cut back to the again. This slide shows a parable of how I think we … A of how we may end up using this technology in the future. A brought his 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, provided the blood test, like, the full medical records, to GPT-4, which said, “I am not vet, you need to talk to a professional, here are some hypotheses.” He brought that information to a vet who used 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 brainstorming partner was able to achieve an outcome that would not have happened otherwise. I think this something we should all reflect on, think about as we consider how to these systems into our world.
And one thing I believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding how we want 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 away from this talk, it’s that this technology just different. Just different from anything people had anticipated. And so we have to become literate. And 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)
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Chris Anderson: Greg. Wow. I mean … I that within every mind out here there’s a feeling of reeling. Like, I suspect that a very large of people viewing this, you look at that and you think, “Oh my goodness, pretty every single thing about the way I work, I need rethink.” Like, there’s just new possibilities there. Am I right? Who that they’re having to rethink the way that we do things? Yeah, I mean, it’s amazing, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, I my first question actually is just how the hell have done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of employees working on artificial intelligence. 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 of giants, right, there’s question. If you look at the compute progress, the algorithmic progress, the data progress, all of those really industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the first one was just confront reality as it lays. And that we just thought really hard like: What is it going to take to make progress here? We tried lot of things that didn’t work, so you only see things that did. And I think that the most thing has been to get teams of people who are different from each other to work together harmoniously.
CA: Can we the water, by the way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something just about the fact that you saw something in language models that meant that if you continue to invest in them and them, that something at some point might emerge?
GB: Yes. And I think that, I mean, honestly, I think story there is pretty illustrative, right? I think that high level, deep learning, like always knew that was what we wanted to be, was deep learning lab, and exactly how to do it? I think that in the days, we didn’t know. We tried a lot of things, one person was working on training a model to predict the next in Amazon reviews, and he got a result where — this is syntactic process, you expect, you know, the model will where the commas go, where the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model could tell you if a review was or negative. I mean, today we are just like, come on, anyone can do that. But this the first time that you saw this emergence, this sort of semantics that from this underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve got see where it goes.
CA: So I think this helps the riddle that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing out of them feels … it just feels that that could come from a prediction machine. Just the stuff showed us just now. And the key idea of is that when you get more of a thing, different things emerge. It happens all the time, ant colonies, single run around, when you bring enough of them together, you get ant colonies that show completely emergent, different behavior. Or a city where few houses together, it’s just houses together. But as grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment for when you saw just something pop that just blew your mind that 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 will do it, which means it’s really learned an internal circuit for to do it. And the really interesting thing is actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. so you can see that it’s really learning the process, it hasn’t fully generalized, right? It’s like you can’t the 40-digit addition table, that’s more atoms than there in the universe. So it had to have learned general, but that it hasn’t really fully yet learned that, Oh, can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve allowed to scale up and look at an incredible number pieces of text. And it is learning things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really get good at is predicting of these emergent capabilities. And to do that actually, one of the things think is very undersung in this field is sort engineering quality. Like, we had 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 get every single piece of the engineered properly, and then you can start doing these predictions. There are these incredibly smooth scaling curves. They tell you something deeply fundamental intelligence. If you look at 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. We basically look at some that 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 days.
CA: So here is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that as you scale up, emerge that you can maybe predict in some level confidence, but it’s capable of surprising you. Why isn’t there just a huge of something truly terrible emerging?
GB: Well, I think all of these are questions of degree scale and timing. And I think one thing people miss, too, is sort of the integration with world is also this incredibly emergent, sort of, very powerful thing too. And so that’s of the reasons that we think it’s so important to deploy incrementally. And so I think what we kind of see right now, if you look at this talk, a lot of 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 to read the whole book. No one wants to do that.
(Laughter) And I think that the important thing will be that we take this step step. And that we say, OK, as we move on to book summaries, we to supervise this task properly. We have to build a track record with these machines that they’re able to actually carry out our intent. And think we’re going to have to produce even better, 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, you know, there’s no real inside, the system is going to always — we’re going to know that it’s not generating errors, that doesn’t have common sense and so forth. Is it your belief, Greg, that it is true at any moment, but that the expansion of the scale and human feedback 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 confidence. Can you be sure of that?
GB: Yeah, well, I that the OpenAI, I mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI approach here has always just like, let reality hit you in the face, right? It’s like field is the field of broken promises, of all these experts saying is going to happen, Y is how it works. People have been saying nets aren’t going to work for 70 years. They haven’t been right yet. They might be right maybe 70 plus one or something like that is what you need. I think that our approach has always been, you’ve got push to the limits of this technology to really it in action, because that tells you then, oh, here’s we can move on to a 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 then harness all this, you know, instead of just your team giving feedback, the world now giving feedback. But … If, you know, bad things are going to emerge, is out there. So, you know, the original story that I heard on when you were founded as a nonprofit, well you there as the great sort of check on the big companies doing their unknown, possibly evil thing AI. And you were going to build models that of, you know, somehow held them accountable and was capable of slowing the field down, need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. That your of GPT, especially ChatGPT, sent such shockwaves through the world that now Google and Meta and so forth are all scrambling to catch up. some of their criticisms have been, you are forcing 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 reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t think we’re going to get it right. But one thing I think has been important, from the very beginning, when we were thinking about how to build artificial intelligence, actually have it benefit all of humanity, like, how are you to do that, right? And that default plan of being, well, you build secret, you get this super powerful thing, and then you out the safety 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 I think that this alternative approach is the only other path that I see, which is you do let reality hit you in the face. And I think you give people time to give input. You do have, before these machines are perfect, they are super powerful, that you actually have the to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number one thing people were going to with it was generate misinformation, try to tip elections. Instead, the number thing 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 box on 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 small print that says: “Pandora.” And there’s a chance that this actually could unleash evils on the world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. honestly, like, I’ll tell you a story that I haven’t actually told before, which is shortly after we started OpenAI, I remember I was in Rico for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these people having a good time. And you think it for a moment, if you could choose for basically that Pandora’s box to be years away or 500 years away, which would you pick, right? On one hand you’re like, well, maybe for you personally, it’s to have it be five years away. But if it to be 500 years away and people get more time to get it right, do you pick? And you know, I just really it in the moment. I was like, of course do the 500 years. My brother was in the military at the time like, he puts his life on the line in a much more real way than of 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 right. But I don’t think that’s quite playing the field as it truly lies. Like, if look 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 you sort of, don’t put the pieces that are there, right, we’re still making faster computers, we’re improving the algorithms, all of these things, they are happening. And you don’t put them together, you get an overhang, means that if someone does, or the moment that someone does manage connect to the circuit, then you suddenly have this very powerful thing, no one’s had any to adjust, who knows what kind of safety precautions you get. And I think that one thing I take away is like, even think about development of other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, in what humans could do. But I actually think that if look at capability, it’s been quite smooth over time. And so the history, I think, of technology we’ve developed has been, you’ve got to do it incrementally and you’ve got to out how to manage it for each moment that you’re it.
CA: So 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 the guardrails for this child to collectively teach it to be wise and not tear us all down. Is that basically the model?
GB: I think it’s true. And I think it’s also to say this may shift, right? We’ve got to take each step we encounter it. And I think it’s incredibly important today that we all get literate in this technology, figure out how to provide feedback, decide what we want from it. And my is that that will continue to be the best path, but it’s so we’re honestly having this debate because we wouldn’t otherwise it weren’t out there.
CA: Greg Brockman, thank you so much for coming to and blowing our minds.
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