We started seven years ago because we felt like something really interesting 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 come since then. And it’s really gratifying to hear people like Raymond who are using the technology we are building, and others, so many wonderful things. We hear from people who are excited, we hear people who are concerned, we hear from people who feel both those emotions at once. 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 define a technology that will be important for our society going forward. And I believe that 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 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 a human. So have a new DALL-E model, which generates images, and we are exposing as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you get out of ChatGPT. And here 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 case — sorry, it doesn’t text, it also generates an image. And that is something really expands the power of what it can do your behalf in terms of carrying out your intent. And I’ll point out, this is all a demo. This is all 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 looking at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And interesting thing about these tools is they’re very inspectable. you get this little pop up here that says “use the DALL-E app.” by the way, this is coming to you, all ChatGPT users, over months. And you can look under 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 the machine using these tools, which allows us to provide feedback them.
Now it’s saved for later, and let me show 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 I was suggesting earlier.” And make 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 how tastes.
But you can see that ChatGPT is selecting these different tools without me having to tell it explicitly which to use in any situation. And 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 great experience within an app as long as you kind know the menus and know all the options. Yes, I would like you to. Yes, please. Always to be polite.
(Laughter)
And by having this unified language on top of tools, the AI is able to sort of take away all details from you. So you don’t have to be the one who spells out every single of little piece of what’s supposed to happen.
And as I said, this a live demo, so sometimes the unexpected will happen us. But let’s take a look at the Instacart list while we’re at it. And you can see we sent a list ingredients to Instacart. Here’s everything you need. And the thing that’s really interesting is that the traditional is still very valuable, right? If you look at this, you still can through it and sort of modify the actual quantities. that’s something that I think shows that they’re not away, traditional UIs. It’s just we have a new, augmented to build them. And now we have a tweet that’s been 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 AI 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 the slides. Now, the important thing about we build this, it’s not just about building these tools. It’s about teaching the how to use them. Like, what do we even it to do when we ask these very high-level questions? And do this, we use an old idea. If you go back to Alan Turing’s 1950 on the Turing test, he says, you’ll never program answer to this. Instead, you can learn it. You build a machine, like a human child, and then teach it through feedback. Have a human 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 two-step process. First, we produce what Turing would have a child machine through an unsupervised learning process. We just show it the whole world, the internet and say, “Predict what comes next in text you’ve seen before.” And this process imbues it with all sorts of wonderful skills. For example, you’re shown a math problem, the only way to actually complete that math problem, to say comes next, that green nine up there, is to actually solve the math problem.
But we actually have do a second step, too, which is to teach the what to do with those skills. And for this, we provide feedback. We have the AI out multiple things, give us multiple suggestions, and then human rates them, says “This one’s better than that one.” 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 infer your intent and 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 not what you’d expect. For example, when we first showed GPT-4 to Academy, they said, “Wow, this is so great, We’re going to be 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 plus one equals and run with it.” So we had to collect some feedback data. Sal Khan himself very kind and offered 20 hours of his own time to feedback to the machine alongside our team. And over the of a couple of months we were able to the AI that, “Hey, you really should push back on in this specific kind of scenario.” And we’ve actually lots and lots of improvements to the models this way. And you push that thumbs down in ChatGPT, that actually is kind of sending up a bat signal to our team to say, “Here’s area of weakness where you should gather feedback.” And so you do that, that’s one way that we really listen to our users and make we’re building something that’s more 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 to stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same sort reasoning applies to AI. As we move to harder tasks, we will 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 and to scale our ability to supervise the machine as time goes on. let me show you what I mean.
For example, can ask GPT-4 a question like this, of how time passed between these two foundational blogs on unsupervised learning and from human feedback. And the model says two months passed. is it true? Like, these models are not 100-percent reliable, although they’re getting better every time provide some feedback. But we can actually use the AI to fact-check. it can actually check its own work. You can say, fact-check for me.
Now, in this case, I’ve actually given the AI a new tool. This one a browsing tool where the model can issue search queries click into web pages. And it actually writes out its whole chain of thought it does it. It says, I’m just going to search for this and it actually the search. It then it finds the publication date and search results. It then is issuing another search query. It’s going to click the blog post. And all of this you could do, but it’s very 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 position where can, if you want, triple-check the work. And out come citations you can actually go and very easily verify any piece of whole chain of reasoning. And it actually turns out two months wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut back to the side. And so that’s so interesting to me about this whole process is that it’s this many-step collaboration a human and an AI. Because a human, using this fact-checking tool is it in order to produce data for another AI become more useful to a human. And I think this shows the shape of something that we should expect to be much more common in future, where 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 humans are providing the management, the oversight, the feedback, the machines are operating 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 we this process right, we will be able to solve impossible problems.
And to give a sense of just how impossible I’m talking, I think we’re going to be able to almost every aspect of how we interact with computers. For example, about spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much that time. And here is a specific spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. you can see there the data right here. But let show you the ChatGPT take on how to analyze a data set like this.
So we give ChatGPT access to yet another tool, this one a Python interpreter, so it’s able to run code, like a data scientist would. And so you can literally upload a file and ask questions about it. very helpfully, you know, it knows the name of 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 the file, the column names like you saw and then the actual data. from that it’s able to infer what these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its world knowledge knowing that, “Oh yeah, arXiv is a site that people submit papers therefore that’s what these things are and that these integer values and so therefore it’s a number of authors in the paper,” like of that, that’s work for a human to do, and AI is happy to help with it.
Now I don’t even know what I to ask. So fortunately, you can ask the machine, “Can you make some exploratory graphs?” And once again, is a super high-level instruction with lots of intent it. But 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 good ideas, I think. So a histogram of the number of authors per paper, time series of per year, word cloud of the paper titles. All of that, I think, be pretty interesting to see. And the great thing is, it actually do it. Here we go, a nice bell curve. You see that three is kind the most common. It’s going to then make this nice plot of the papers per year. Something is happening in 2023, though. Looks like we were on an exponential it dropped off the cliff. What could be going there? By the way, all this is Python code, can inspect. And then we’ll see word cloud. So can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. It makes this look really bad. Of course, the problem is that the 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 the cut-off date I believe. Can you use that to make fair projection? So we’ll see, this is the kind ambitious one.
(Laughter)
So you know, again, I feel like there was I wanted out of the machine here. I really wanted it to notice this thing, maybe it’s little bit 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 piece of, know, guidance. And under the hood, the AI is writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does the projection.
(Applause)
If you noticed, it even updates the title. didn’t ask for that, but it know what I want.
Now we’ll cut back the slide again. This slide shows a parable of how I think … A vision of how we may end up using this in the future. A person brought his very sick dog to the vet, and the made a bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. In the meanwhile, he provided blood test, like, the full medical records, to GPT-4, which said, “I am not a vet, need to talk to a professional, here are some hypotheses.” He brought that information to a second vet used it to save the dog’s life. Now, these systems, they’re not perfect. You overly rely on them. But this story, I think, shows a human with a medical professional and with ChatGPT as brainstorming partner was able to achieve an outcome that would not have happened otherwise. think this is something we should all reflect on, think about as we consider how to integrate systems into our world.
And one thing I believe really deeply, is that getting AI right is going 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 an AI will and won’t do. And if there’s one to take 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, I believe we can achieve the OpenAI mission of ensuring that artificial general benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out there’s a feeling of reeling. Like, I suspect that a very 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 possibilities there. 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, guess my first question actually is just how the hell you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands employees working on artificial intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re all building shoulders of giants, right, there’s no question. If you look at the progress, the algorithmic progress, the data progress, all of are really industry-wide. But I think within OpenAI, we made lot of very deliberate choices from the early days. And the first was just to confront reality as it lays. And that we just thought really hard about like: What it going to take to make progress here? We tried a lot of things didn’t work, so you only see the things that did. And I that 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, just here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about the fact you saw something in 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 I think that, mean, honestly, I 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 a learning lab, and exactly how to do it? I that in the early days, we didn’t know. We tried a lot of things, and one person working on training a model to predict the next in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model will predict where the go, where the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model tell you if a review was positive or negative. mean, today we are just like, come on, anyone do that. But this was 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 this thing, you’ve got to see where it goes.
CA: I think this helps explain the riddle that baffles everyone looking this, because these things are described as prediction machines. And yet, what we’re seeing out of them … it just feels impossible that that could come a prediction machine. Just the stuff you showed us just now. And the idea of emergence is that when you get more of a thing, different things emerge. It happens all the time, ant colonies, single ants run around, when bring enough of them together, you get these ant colonies that completely emergent, different behavior. Or a city where a few houses together, it’s just together. But as you grow the number of houses, emerge, like suburbs and cultural centers and traffic jams. Give me moment for you when you saw just something pop just blew your mind that you just did not coming.
GB: Yeah, well, so you can try this ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned an internal for how to do it. And the really interesting thing 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 memorize 40-digit addition table, that’s more atoms than there are in universe. So it had to have learned something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to 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 is learning things that you didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. one science that we’re starting to really get good is predicting some of these emergent capabilities. And to that actually, one of the things I think is very undersung in this is sort of engineering quality. Like, we had to rebuild our stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in machine learning. You to get every single piece of the stack engineered properly, and then you can doing these predictions. There are all these incredibly smooth curves. They tell you something deeply fundamental about intelligence. If look at our GPT-4 blog post, you can see all of curves in there. And now we’re starting to be able predict. So we were able to predict, for example, the performance on problems. We basically look at some models 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 early days.
CA: So is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that you scale up, things emerge that you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just a huge 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 integration with the world is also this incredibly emergent, of, very powerful thing too. And so that’s one of reasons that we think it’s so important to deploy incrementally. And I think that what we kind of see right now, if you look at this talk, a of what I focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s very easy look at that math problem and be like, no, no, no, machine, seven was the correct answer. But even summarizing book, like, that’s a hard thing to supervise. Like, how do you if this book summary is any good? You have to read the whole book. No one wants to that.
(Laughter) And so I think that the important thing will be that we take this step step. And that we say, OK, as we move on book summaries, we have to supervise this task properly. We have to build up a track record with machines that they’re able to actually carry out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways of scaling this, of like making the 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 going always — we’re never going to know that it’s not generating errors, it doesn’t have common sense and so forth. Is your belief, Greg, that it is true at any one moment, but that expansion of the scale and the human feedback that you talked about is basically going to take it that journey of actually getting to things like truth and wisdom and so forth, with a degree of confidence. Can you be sure of that?
GB: Yeah, well, I think the OpenAI, I mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI here has always been just like, let reality hit in the face, right? It’s like this field is the field of promises, of all these experts saying X is going happen, Y is how it works. People have been neural nets aren’t going to work for 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like is what you need. But I think that our has always been, you’ve got to push to the limits this technology to really see it in action, because that tells then, oh, here’s how we can move 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, instead just your team giving feedback, the world is now feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, the original story that heard on OpenAI when you were founded as a nonprofit, well you were as the great sort of check on the big doing their unknown, possibly evil thing with AI. And were going to build models that sort of, you know, somehow held them accountable and was of slowing the field down, if need be. Or least that’s kind of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, sent shockwaves through the tech world that now Google and Meta and forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to put this here without proper guardrails or we die. You know, do you, like, make the case that what you have done is responsible and not reckless.
GB: Yeah, we think about these questions the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing I think has been incredibly important, from very beginning, when we were thinking about how to build artificial general intelligence, actually have it benefit all humanity, like, how are you supposed to do that, right? And that default plan of being, well, you build secret, you get this super powerful thing, and then you figure out the safety of it and you push “go,” and you hope you got it right. 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 think that this alternative approach the only other path that I see, which is that you do let reality hit you the face. And I think you do give people time to input. You do have, before these machines are perfect, they are super powerful, that you actually have the ability to see them in action. 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 thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there things that are much worse. Here’s a thought experiment you. Suppose you’re sitting in a room, there’s a box the table. You believe that in that box is that, there’s a very strong chance it’s something absolutely that’s going to give beautiful gifts to your family and to everyone. there’s actually also a one percent thing in the small print there that says: “Pandora.” there’s a chance that this actually could unleash unimaginable evils on the world. Do open that box?
GB: Well, so, absolutely not. I think don’t do it that way. And honestly, like, I’ll tell you story that I haven’t actually told before, which is that after we started OpenAI, I remember I was in Rico for an AI conference. I’m sitting in the hotel room just out over this wonderful water, all these people having a good time. you 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? the one hand you’re like, well, maybe for you personally, it’s better have it be five years away. But if it gets be 500 years away and people get more time get it right, which do you pick? And you know, I really felt it in the moment. I was like, of you do the 500 years. My brother was in the military the time and 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. And so, yeah, I’m really on the you’ve got to approach this right. But I don’t think that’s quite playing the as it truly lies. Like, if you look at the whole history of computing, really mean it when I say that this is industry-wide or even just almost like a human-development- of-technology-wide shift. And the more you sort of, don’t put together the pieces that there, right, we’re still making faster computers, we’re still improving the algorithms, of 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 someone does manage to to the circuit, then you suddenly have this very thing, no one’s had any time 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 talk 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 smooth over time. And so the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve got to figure out to manage it for each moment that you’re increasing it.
CA: So what I’m hearing that you … the model you want us to is that we have birthed this extraordinary child that have superpowers that take humanity to a whole new place. It our collective responsibility to provide the guardrails for this to collectively teach it to be wise and not to tear us all down. Is that 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 that we all do get literate in this technology, figure out how to the feedback, decide what we want from it. And my is that that will continue to be the best path, it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you much for coming to TED and blowing our minds.
(Applause)