We started OpenAI seven years ago because we felt something really interesting was happening in AI and we to help steer it in a positive direction. It’s honestly really amazing to see how far this whole field has come since then. And it’s gratifying to hear from people like Raymond who are using the technology are building, and others, for so many wonderful things. We from people who are excited, we hear from people are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. Above all, it feels like we’re entering an historic right now where we as a world are going to define a technology that 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 show you is what it’s like 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 can do things like ask, know, suggest a nice post-TED meal and draw a picture of it.
(Laughter)
Now get all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you out of ChatGPT. And here we go, it’s not just idea for the meal, but a very, very detailed spread. So let’s see we’re going to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also an image. And that is something that really expands the power of what can do on your behalf in terms of carrying your intent. And I’ll point out, this is all a demo. This is all generated by the AI as speak. So I actually don’t even know what we’re going see. This looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended ChatGPT with other too, for example, memory. You can say “save this for later.” the interesting thing about these tools is they’re very inspectable. So you get this little pop up that says “use the DALL-E app.” And by the way, this is coming to you, all ChatGPT users, upcoming months. And you can look under the hood and that what it actually did was write a prompt just a human could. And so you sort of have this ability to inspect the machine is using these tools, which allows us to feedback to them.
Now it’s saved for later, and me show you what it’s like to use that information to integrate with other applications too. You can say, “Now make a shopping list for tasty thing I was suggesting earlier.” And make it a little tricky for the AI. “And it out for all the TED viewers out there.”
(Laughter)
So if you make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT selecting all these different tools without me having to tell it which ones to use in any situation. And this, I think, shows a new of thinking about the user interface. Like, we are 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 of 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 details from you. you don’t have to be the one who spells out every single sort of little piece of what’s to happen.
And as I said, this is a live demo, so the unexpected will happen to us. But let’s take a look at the Instacart shopping list while we’re it. And you can see we sent a list of ingredients to Instacart. Here’s you need. And the thing that’s really interesting is that the traditional UI still very valuable, right? If you look at this, you still can click through it and of modify the actual quantities. And that’s something that I think that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And now we have a tweet that’s been drafted our review, which is also a very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able to change the 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.
(Applause)
So we’ll cut to the slides. Now, the important thing about how we build this, it’s not about building these tools. It’s about teaching the AI how to use them. Like, do we even want it to do when we ask these high-level questions? And to 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 build a machine, like a child, and then teach it through feedback. Have a human teacher provides rewards and punishments as it tries things out and does things that are good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised process. We just show it the whole world, the whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it with sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that math problem, to say what next, that green nine up there, is to actually solve the problem.
But we actually have to do a second step, too, which to teach the AI what to do with those skills. And for this, we provide feedback. We have AI try out multiple things, give us multiple suggestions, and a human rates them, says “This one’s better than that one.” And this not just the specific thing that the AI said, but importantly, the whole process that the AI used to produce that answer. And allows it to generalize. It allows it to teach, to of infer your intent and apply it in scenarios that it hasn’t before, that it hasn’t received feedback.
Now, sometimes the we have to teach the AI are not what you’d expect. For example, when we first showed GPT-4 Khan Academy, they said, “Wow, this is so great, We’re going be able to teach students wonderful things. Only one problem, 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 it.” So we had to collect some feedback data. Sal himself was very kind and offered 20 hours of his time to provide feedback to the machine alongside our team. over the course of a couple of months we were to teach the AI that, “Hey, you really should back on humans in this specific kind of scenario.” we’ve actually made lots and lots of improvements to the models this way. And you push that thumbs down in ChatGPT, that actually is of like sending up a bat signal to our to say, “Here’s an area of weakness where you gather feedback.” And so when you do that, that’s one way that we really to our users and make sure we’re building something that’s more useful for everyone.
Now, high-quality feedback is a hard thing. If you think asking a kid to clean their room, if all you’re is inspecting the floor, you don’t know if you’re just teaching to stuff all the toys in the closet. This a nice DALL-E-generated image, by the way. And the same sort of reasoning applies AI. As we move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the itself is happy to help. It’s happy to help provide even better feedback and to scale our ability to supervise the machine as time goes on. And me show you what I mean.
For example, you can GPT-4 a question like this, of how much time 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 we some feedback. But we can actually use the AI to fact-check. And it actually check its own work. You can say, fact-check for me.
Now, in this case, I’ve actually given the AI a tool. This one is a browsing tool where the model can search queries and click into web pages. And it actually writes out its chain of thought as it does it. It says, I’m going to search for this and it actually does the search. It it finds the publication date and the search results. It then is issuing another query. It’s going to click into the blog post. And of this you could do, but it’s a very 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 verify any piece of this whole chain reasoning. And it 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 me about this whole process is that it’s many-step collaboration between a human and an AI. Because human, using this fact-checking tool is doing it in to produce data for another AI to become more to a 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 very carefully and delicately designed how they fit into a problem and how we want to that problem. We make sure that the humans are the management, the oversight, the feedback, and the machines are in a way that’s inspectable and trustworthy. And together we’re able to actually create even trustworthy machines. And I think that over time, if we get this process right, we will be to solve impossible problems.
And to give you a sense of just how I’m talking, I think we’re going to be able to rethink every aspect of how we interact with computers. For example, think spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve changed that much in that time. And here is a specific spreadsheet all the AI papers on the 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 take on how analyze a data set like this.
So we can give ChatGPT access to yet another tool, 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 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 the file, the column names like you saw and then the actual data. And that it’s able to infer what these columns actually mean. Like, semantic information wasn’t in there. It has to sort of, put its world knowledge of knowing that, “Oh yeah, arXiv is a site people submit papers and therefore that’s what these things are and that these are integer and so therefore it’s a number of authors in the paper,” like of that, that’s work for a human to do, and the AI is 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 kind of has infer what I might be interested in. And so it comes up with some ideas, I think. So a histogram of the number of authors paper, time series of papers per year, word cloud of the titles. All of that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here go, a nice bell curve. You see that three is of the most common. It’s going to then make this plot of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and it off the cliff. What could be going on there? By way, all this is Python code, you can inspect. And then we’ll see word cloud. you can see all these wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year look bad. Of course, the problem is that the year is not over. So I’m going to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were even by April 13?] So April 13 was the cut-off date I believe. Can you use that make a fair projection? So we’ll see, this is the kind of 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, it’s a little bit of an overreach for it to sort of, inferred magically that this is what I wanted. But I inject my intent, I provide additional piece 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, does the correct projection.
(Applause)
If you noticed, it updates the title. I didn’t ask for that, but it know what I want.
Now we’ll cut to the slide again. This slide shows a parable how I think we … A vision of how we may end up using this in the future. A person brought his very sick dog the vet, and the veterinarian made a bad call say, “Let’s just wait and see.” And the dog would not here today had he listened. In the meanwhile, he the blood test, like, the full medical records, to GPT-4, said, “I am not a vet, you need to 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 perfect. You cannot overly rely on them. But this story, I think, shows that a with a medical professional and with ChatGPT as a brainstorming partner was able to achieve an outcome would not have happened otherwise. I think this is something we all reflect on, think 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 from everyone. that’s for deciding how we want it to slot in, that’s for setting the rules the road, for what an AI will and won’t do. And if there’s one to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. so we all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we can achieve the OpenAI of ensuring that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … suspect that within every mind out here there’s a of reeling. Like, I suspect that a very large number of people viewing this, you look at and you think, “Oh my goodness, pretty much every single thing about the way work, I need to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks they’re having to rethink the way that we do things? Yeah, I mean, it’s amazing, it’s also really scary. So 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 a few hundred employees. has thousands of employees working on artificial intelligence. Why is it you who’s come up this technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s no question. If you look at compute progress, the algorithmic progress, the data progress, all of those are industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the first one was to confront reality as it lays. And that we just thought really hard about like: is 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. I think that the most important thing has been to get teams of who are very different from each other to work harmoniously.
CA: Can we have the water, by the way, brought here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about the that you saw something in these language models that that if you continue to invest in them and 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 think that high level, deep learning, like we knew that was what we wanted to be, was a deep learning lab, and 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 the next character in Amazon reviews, and he got result where — this is a syntactic process, you expect, you know, the model will predict where commas go, where the nouns and verbs are. But he actually got a state-of-the-art sentiment analysis classifier of it. This model could tell you if a was positive or negative. I mean, today we are just like, come on, can do that. But this was the first time that saw this emergence, this sort of semantics that emerged from underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve got to see where goes.
CA: So I think this helps explain the riddle that baffles everyone looking at this, because things are described as prediction machines. And yet, what we’re seeing of them feels … it just feels impossible that that could come a prediction machine. Just the stuff you showed us just now. And key idea of emergence is that when you get 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 colonies that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But as you grow number of houses, things emerge, like suburbs and cultural and traffic jams. Give me one moment for you when you saw something pop that 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 internal circuit for how to do it. And the really interesting thing is actually, if have it add like a 40-digit number plus a 35-digit number, it’ll often get it wrong. And so can see that it’s really learning the process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms than there are in the universe. So had to have learned something general, but that it hasn’t fully yet learned that, Oh, I can sort of generalize this adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it scale up and look at an incredible number of of text. And it is learning things that you didn’t know that was going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting really get good at 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 entire stack. When think about building a rocket, every tolerance has to be tiny. Same is true in machine learning. You have to get every single of the stack engineered properly, and then you can doing these predictions. There are all these incredibly smooth scaling curves. They tell you something fundamental about intelligence. If you look at our GPT-4 blog post, you can see all these curves in there. And now we’re starting to be able predict. So we were able to predict, for example, the performance coding problems. We basically look at some models that 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 then, that arises from this. If it’s fundamental to what’s happening here, that as scale up, things emerge that you can maybe predict some 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 I one thing people miss, too, is sort of the integration the 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 that we kind of see right now, if you look at this talk, a lot of what 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 math problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s a hard thing supervise. Like, how do you know if this book summary is good? You have to read the whole book. No wants to do that.
(Laughter) And so I think that the important will be that we take this step by step. And that say, OK, as we move on to book summaries, have to supervise this task properly. We have to up a track record with these machines that they’re able to actually carry out our intent. I think we’re going to have to produce even better, more efficient, more ways of scaling this, sort of like making the machine aligned with you.
CA: So we’re going to hear later this session, there are critics who say that, you know, there’s real understanding inside, the system is going to always — we’re never to know that it’s not generating errors, that it doesn’t have common and so forth. Is it your belief, Greg, that it is true at any moment, but that the expansion of the scale and the human feedback that you talked is basically going to take it on that journey actually getting to things like truth and wisdom and forth, with a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the short is yes, I believe that is where we’re headed. And I think the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like this field the field of broken promises, of all these experts saying X is going to happen, Y is it works. People have been saying neural nets aren’t to work for 70 years. They haven’t been right yet. They might be right maybe 70 years plus or something like that is what you need. But I think our approach has always been, you’ve got to push to the of this technology to really see it in action, 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 it out there in and then harness all this, you know, instead of just your giving feedback, the world is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, you know, the original story that I heard OpenAI when you were founded as a nonprofit, well were there as the great sort of check on big companies doing their unknown, possibly evil thing with AI. you were going to build models that sort of, know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s kind of what I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, such shockwaves through the tech world that now Google and Meta and so forth are scrambling to catch up. And some of their criticisms have been, you forcing us to put this out here without proper or we 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 the time. Like, all the time. And I don’t think we’re always 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 general intelligence, have it benefit all of humanity, like, how are you supposed to do that, right? And that default of being, well, you build in secret, you get this super powerful thing, then you figure out the safety of it and then push “go,” and you hope you got it right. I don’t know how to execute that plan. Maybe else does. But for me, that was always terrifying, it didn’t right. And so I think that this alternative approach is the only other path that see, which is that you do let reality hit in the face. And I think you do give people time to give input. You have, before these machines are perfect, before they are super powerful, that you have the ability to see them in 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 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 that are much worse. Here’s a thought experiment for you. Suppose you’re sitting a room, there’s a box on the table. You believe that in that box something that, there’s a very strong chance it’s something absolutely glorious that’s going to give beautiful gifts to family and to everyone. But there’s actually also a one percent thing in the print there that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils on world. Do you open that box?
GB: Well, so, absolutely not. I think don’t do it that way. And honestly, like, I’ll tell you a that I haven’t actually told before, which is that shortly after we started OpenAI, remember I was in Puerto Rico for an AI conference. I’m sitting in the room just looking out over this wonderful water, all these people having good time. And you think about it for a moment, if you choose for basically that Pandora’s box to be five years away or 500 years away, would you pick, right? On the one hand you’re like, well, for you personally, it’s better to have it be five years away. But if it to be 500 years away and people get more to get it right, which do you pick? And you know, I really felt it in the moment. I was like, of course do the 500 years. My brother was in the military at the time and like, he his life on the line in a much more real way any of us typing things in computers and developing technology at the time. And so, yeah, I’m really sold the you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, if you at the whole history of computing, I really mean it when say that this is an industry-wide or even just almost like human-development- of-technology-wide shift. And the more that you sort of, don’t together the pieces that are there, right, we’re still faster computers, we’re still improving the algorithms, all of these things, they are happening. if you don’t put them together, you get an overhang, which that if someone does, or the moment that someone manage to connect 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 you think about development 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 if you look at capability, it’s been quite smooth over time. And the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve to figure out how to manage it for each moment that you’re increasing it.
CA: So what I’m is 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. 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. I think it’s also important to say this may shift, right? We’ve got to take each step as encounter it. And I think it’s incredibly important today we all do get literate in this technology, figure out how to the feedback, decide what we want from it. And hope is that that will continue to be the path, but it’s so good we’re honestly having this debate we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)