We OpenAI seven years ago because we felt like something really interesting was happening in AI and we wanted help steer it in a positive direction. It’s honestly just really amazing see how far this whole field has come since then. it’s really gratifying to hear from people like Raymond who using the 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 from people who both those emotions at once. And honestly, that’s how we feel. Above all, it feels like we’re entering 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 that we can this for good.
So today, I want to show the current state of that technology and some of the underlying design that we hold dear.
So the first thing I’m going to show you is it’s like to build a tool for an AI rather than building it for human. So we have a new DALL-E model, which generates images, and we are exposing it as an for ChatGPT to use on your behalf. And you can do things like ask, you know, suggest nice post-TED meal and draw a picture of it.
(Laughter)
Now you get all of the, sort of, ideation and back-and-forth and taking care of the details for you that you out of ChatGPT. And here we go, it’s not the idea for the meal, but a very, very detailed spread. So let’s see what we’re to get. But ChatGPT doesn’t just generate images in this case — sorry, it doesn’t text, it also generates an image. And that is something that really expands the power of what it do on your behalf in terms of carrying out your intent. And I’ll out, this is all a live demo. This is generated by the AI as we speak. So I don’t even 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, for example, memory. can say “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this little up here that says “use the DALL-E app.” And by the way, this is to you, all ChatGPT users, over upcoming months. And can look under the hood and see that what it actually did was write a prompt just a human could. And so you sort of have this to inspect how the machine is using these tools, which allows us to feedback to them.
Now it’s saved for later, and let me you what it’s like to use that information and to integrate with applications too. You can say, “Now make a shopping list the tasty thing I was suggesting earlier.” And make it little tricky for the AI. “And tweet it out for all the TED out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want know how it tastes.
But you can see that ChatGPT is all these different tools without me having to tell explicitly which ones to use in any situation. And this, I think, a new way of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great experience within an app as long as kind of know the menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And by this unified language interface on top of tools, the is able to sort of take away all those from you. So you don’t have to be the who spells out every single sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes the unexpected will happen to us. But let’s a look at the Instacart shopping list while we’re at it. And you see we sent a list of ingredients to Instacart. Here’s everything need. And the thing that’s really interesting is that the traditional UI is very valuable, right? If you look at this, you still can click it and sort of modify the actual quantities. And that’s something that I think shows they’re not going away, traditional UIs. It’s just we a new, augmented way to build them. And now we have tweet that’s been drafted for our review, which is also a very thing. We can click “run,” and there we are, we’re the manager, we’re able inspect, we’re able to change the work of the if we want to. And so after this talk, will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to slides. Now, the important thing about how we build this, it’s not just about these tools. It’s about teaching the AI how to use them. Like, what do we even want to do when we ask these very high-level questions? And do this, we use an old idea. If you back to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, you can learn it. You could build a machine, a human child, and then teach it through feedback. Have a teacher who provides rewards and punishments as it tries things out and does things that are either or bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine through an unsupervised learning process. We just show it whole world, the whole internet and say, “Predict what comes in text you’ve never seen before.” And this process imbues with all sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that math problem, to say what comes next, that green up there, is to actually solve the math problem.
But we actually have to a second step, too, which is to teach the what to do with those skills. And for this, we provide feedback. have the AI try out multiple things, give us multiple suggestions, and a human rates them, says “This one’s better than one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that AI used to produce that answer. And this allows it generalize. It allows it to teach, to sort of infer your intent apply it in scenarios that it hasn’t seen before, that hasn’t received feedback.
Now, sometimes the things we have to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, said, “Wow, this is so great, We’re going to be able to students wonderful things. Only one 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 to collect some feedback data. Sal Khan himself was very kind and offered 20 hours of own time to provide feedback to the machine alongside team. And over the course of a couple of we were able to teach the AI that, “Hey, you really push back on humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to the models way. And when you push that thumbs down in ChatGPT, that actually is kind of like sending up bat signal to our team to say, “Here’s an area weakness where 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 something that’s useful for everyone.
Now, providing high-quality feedback is a hard thing. If think about asking a kid to clean their room, if all you’re doing 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, the way. And the same sort of reasoning applies AI. As we move to 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 happy help us provide even better feedback and to scale our ability to the machine as time goes on. And let me show you what mean.
For example, you can ask GPT-4 a question like this, of how much time passed these two foundational blogs on unsupervised learning and learning from human feedback. And model says two months passed. But is it true? Like, models are 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 check its own work. You say, fact-check this for me.
Now, in this case, I’ve actually the AI a new tool. This one is a browsing tool where the model can issue queries and click into web pages. And it actually writes its whole chain of thought 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 and the search results. It then is issuing another search query. It’s going click into the blog post. And all of this you do, but it’s a very tedious task. It’s not a thing that really want to do. It’s much more fun to be in driver’s seat, to be in this manager’s position where you can, you want, triple-check the work. And out come citations so you can actually go very easily verify any piece of this whole chain of reasoning. And it actually turns two 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 that it’s this many-step collaboration between a human and an AI. a human, using this fact-checking tool is doing it in order produce data for another AI to become more useful to human. And I think this really shows the shape something that we should expect to be much more common in future, where we have humans and machines kind of carefully and delicately designed in how they fit into a problem and how we want to solve problem. We make sure that the humans are providing management, the oversight, the feedback, and the machines are in a way that’s inspectable and trustworthy. And together we’re able actually create even more trustworthy machines. And I think that over time, if get this process right, we will be able to solve impossible problems.
And give you a sense of just how impossible I’m talking, I think we’re going to be able rethink almost every aspect of how we interact with computers. For example, think about spreadsheets. They’ve been around in 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 on the 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 the ChatGPT take on how analyze a data set like this.
So we can give ChatGPT access to yet another tool, this a Python interpreter, so it’s able to run code, just like data scientist would. And so you can just literally upload file and ask questions about it. And very helpfully, you know, it knows the name of the file it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse for you.” The only information here is the name of file, the column names like you saw and then the data. And from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It has to sort of, put together its world knowledge 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 and so therefore it’s a 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 don’t even know what I want to ask. So fortunately, you can the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots of behind it. But I don’t even know what I want. the AI kind of has to infer what I be interested in. And so it comes up with some good ideas, think. So a histogram of the number of authors paper, time series of papers per year, word cloud of paper titles. All of that, I think, will be pretty interesting to see. And the great is, it can actually do it. Here we go, a nice curve. You see that three is kind of the common. It’s going to then make this nice plot of the per year. Something crazy is happening in 2023, though. Looks like were on an exponential and it dropped off the cliff. could be going on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you see all these wonderful things that appear in these titles.
But I’m pretty unhappy about this 2023 thing. It makes this look really bad. Of course, the problem is that year is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by 13?] So April 13 was the cut-off date I believe. you use that to make a fair projection? So we’ll see, this the kind of ambitious one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted it to notice this thing, maybe it’s a little of an overreach for it to have sort of, inferred that this is what I wanted. But I inject my intent, I provide this additional of, you know, guidance. And under the hood, the AI just writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does correct projection.
(Applause)
If you noticed, it even updates title. I didn’t ask for that, but it know what want.
Now we’ll cut back to the slide again. This shows a parable of how I think we … vision of how we may end up using this in the future. A person brought his very sick dog to vet, and the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be here today had he listened. In meanwhile, he 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 second vet who used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot overly on them. But this story, I think, shows that a human a medical professional and with ChatGPT as a brainstorming partner was able to achieve an outcome that not have happened otherwise. I think this is something we should reflect on, think about as we consider how to integrate these systems our world.
And one thing I believe really deeply, is that getting right is going to require participation from everyone. And that’s for how we want 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 take away from this talk, it’s that this technology just looks different. different from anything people had anticipated. And so we 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 ensuring that artificial intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within mind out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, look at that and you think, “Oh my goodness, pretty every single thing about the way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to rethink the way that 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 you this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of employees working on artificial intelligence. Why it you who’s come up with this technology that shocked the world?
Greg Brockman: mean, the truth is, we’re all building on shoulders of giants, right, there’s no question. If look at the compute progress, the algorithmic progress, the progress, all of those are really industry-wide. But I think within OpenAI, made a lot of very deliberate choices from the days. And the first one was just to confront reality as it lays. And that just thought 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 think the most important thing has been to get teams of people who are very different from each to work together harmoniously.
CA: Can we have the water, by the way, brought here? I think we’re going to need it, it’s a dry-mouth topic. isn’t there something also just about the fact that you saw something in these language models meant that if you continue to invest in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, think the story there is pretty illustrative, right? I that high level, deep learning, like we always 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. We tried a lot things, and one person was working on training a model to predict the character in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model will predict where the commas go, where the and verbs are. But he actually got a state-of-the-art sentiment classifier out of it. This model could tell you if a review positive or negative. I mean, today we are just like, come on, anyone can do that. But was the first time that you saw this emergence, this sort of semantics that emerged from underlying syntactic process. And there we knew, you’ve got scale this thing, you’ve got to see where it goes.
CA: I think this helps explain the riddle that baffles everyone looking at this, because these are described as prediction machines. And yet, what we’re seeing out of them feels … it just impossible that that could come from a prediction machine. Just the you showed us just now. And the key idea of emergence is that when you more of a thing, suddenly different things emerge. It happens all the time, colonies, single ants run around, when you bring enough of them together, you get these ant colonies 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 and cultural and traffic jams. Give me one moment for you when saw just something pop that just blew your mind that just did not see coming.
GB: Yeah, well, so you can try this in ChatGPT, you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model 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, 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, I can sort of generalize this adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened is that you’ve allowed it to scale up and at an incredible number of pieces of text. And is learning things that you didn’t know that it going to be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really get good at is predicting some of emergent capabilities. And to do that actually, one of the things think is very undersung in this field is sort engineering quality. Like, we had to rebuild our entire stack. When think about building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get every piece of the stack engineered properly, and then you can start doing these predictions. are all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If 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 we able to predict, for example, the performance on coding 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 actually smooth scaling, even though it’s 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 risk of something 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 with the world is also this incredibly emergent, sort of, very powerful thing too. And that’s one of the reasons that we think it’s so important to incrementally. And so I think that what we kind see right now, if you look at this talk, a of what I focus on is providing really high-quality feedback. Today, the tasks we do, you can inspect them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s a thing to supervise. Like, how do you know if this book summary any good? You have to read the whole book. No one to do that.
(Laughter) And so I think that the important thing will be that take this step by step. And that we say, OK, as move on to book summaries, we have to supervise this task properly. We have to build up a record with these machines that they’re able to actually out our intent. And I think we’re going to have produce even better, more efficient, more reliable ways of this, sort of like making the machine be aligned with you.
CA: So we’re going to hear in this session, there are critics who say that, you know, there’s no real inside, the system is going to always — we’re never going to that it’s not generating errors, that it doesn’t have sense and so forth. Is it your belief, Greg, it is true at any one moment, but that the of the scale and the human feedback that you talked is basically going to take it on that journey of actually getting to things like truth and and so forth, with a high degree of confidence. Can you be of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And I think that the OpenAI here has always been just like, let reality hit you the face, right? It’s like this field is the of broken promises, of all these experts saying X is going to happen, is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t been yet. They might be right maybe 70 years plus one or something like that what you need. But I think that our approach always been, you’ve got to push to the limits of this technology to really see it action, because that tells you then, oh, here’s how we can on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a stance you’ve taken, that the right way to do this is to put out there in public and then harness all this, you know, of just your team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, is out there. So, you know, the original story that I heard on OpenAI when were founded as a nonprofit, well you were there as 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, if need be. at least that’s kind of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through the tech that now Google and Meta and so forth are all scrambling to up. And some of their criticisms have been, you forcing us to put this out here without proper guardrails or die. You know, how do you, like, make the case that what you have done responsible here and not reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t think we’re always going to get it right. one thing I think has been incredibly important, from the very beginning, when we were thinking about how build artificial general intelligence, actually have it benefit all of humanity, like, how are supposed to do that, right? And that default plan of being, well, you build in secret, you get super powerful thing, and then you figure out the safety it and then you push “go,” and you hope you got right. I don’t know how to execute that plan. someone else does. But for me, that was always terrifying, didn’t feel right. And so I think that this approach is the only other path that I see, is that you do let reality hit you in face. And I think you do give people time to input. You do have, before these machines are perfect, before are super powerful, that you actually have the ability to see them action. And we’ve seen it from GPT-3, right? GPT-3, we really were that the number one thing people were going to do with it was generate misinformation, try to elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: Viagra spam is bad, but there are things that are much worse. Here’s a thought for you. Suppose you’re sitting in a room, there’s a box on the table. believe that in that box is something that, there’s a 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 percent thing in the 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 do it way. And honestly, like, I’ll tell you a story that I haven’t actually told before, is that shortly after we started OpenAI, I remember was in Puerto Rico for an AI conference. I’m in the hotel room just looking out over this water, all these people having a good time. And think about it for a moment, if you could for basically that Pandora’s box to be five years or 500 years away, which would you pick, right? On the one hand you’re like, well, maybe for personally, it’s better to have it be five years away. if it gets to be 500 years away and people get more time get it right, which do you pick? And you know, just really felt it in the moment. I was like, course you do the 500 years. My brother was the military at the time and like, he puts life on the line in a much more real way than any of us typing in computers and developing this technology at the time. And so, yeah, I’m really on the you’ve got to approach this right. But I don’t think that’s quite 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 you sort of, don’t put together the pieces 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, which means that 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 time to adjust, who knows what kind of safety precautions get. And so I think that one thing I take away like, even you think about development of other sort technologies, think about nuclear weapons, people talk about being like a zero to one, sort of, change what humans could do. But I actually think that if you look at capability, it’s been quite over time. And so the history, I think, of every technology we’ve developed has been, you’ve to do it incrementally and you’ve got to figure how to manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you us to have is that we have birthed this child that may have superpowers that take humanity to a new place. It is our collective responsibility to provide the guardrails for 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 think it’s also important to say this may shift, right? We’ve to take each step as we encounter it. And I think it’s incredibly today that we all do get literate in this technology, figure out how provide the feedback, decide what we want from it. my hope is that that will continue to be best path, but it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, you so much for coming to TED and blowing minds.
(Applause)