We started OpenAI seven years ago because felt like something really interesting was happening in AI and we wanted to help steer it in positive direction. It’s honestly just really amazing to see how far whole field has come since then. And it’s really gratifying to from people like Raymond who are using the technology are building, and others, for so many wonderful things. We hear people who are excited, we hear from people who are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now where we as a world going to define a technology that will be so for our society going forward. And I believe that we manage this for good.
So today, I want to you the current state of that technology and some of the design principles that we hold dear.
So the first I’m going to show you is what it’s like to build a tool an AI rather than building it for a human. we have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to use on behalf. And you can do things like ask, you know, suggest a nice post-TED meal draw a picture of it.
(Laughter)
Now you get all of the, sort of, ideation creative back-and-forth and taking care of the details for you that get out of ChatGPT. And here we go, it’s not the idea for the meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just generate images in this — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the power of what can do on your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. This all generated by the AI as we speak. So I actually don’t even know what we’re 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. So you get this pop up here that says “use the DALL-E app.” 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 like a human could. And so you sort of have this to inspect how the machine is using these tools, allows us to provide 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 shopping list for the tasty thing I was suggesting earlier.” And make it a little for the AI. “And tweet it out for all the viewers 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 selecting all these different tools without me having to tell it which ones to use in any situation. And this, I think, shows new way of thinking about the user interface. Like, we so used to thinking of, well, we have these apps, click between them, we copy/paste between them, and usually it’s a great experience within app as long as you kind of know the and know all the options. Yes, I would like you to. Yes, please. Always to be polite.
(Laughter)
And by having this unified interface on top of tools, the AI is able sort of take away all those details from you. you don’t have to be the one who spells out single sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes the will happen to us. But let’s take a look at the Instacart list while we’re at it. And you can see we a list of ingredients to Instacart. Here’s everything you need. And the thing that’s really interesting is the traditional UI is still very valuable, right? If look at this, you still can click through it sort of modify the actual quantities. And that’s something that think shows that they’re not going away, traditional UIs. It’s just we have a new, augmented way to them. And now we have a tweet that’s been for our review, which is also a very important thing. We can click “run,” there we are, we’re the manager, we’re able to inspect, we’re able to change the work of AI if we want to. And so after this talk, you be able to access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to slides. Now, the important thing about how we build this, it’s just about building these tools. It’s about teaching the AI how to them. Like, what do we even want it to do when we ask very high-level questions? And to do this, we use an old idea. you go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program answer to this. Instead, you can learn it. You could build a machine, like a human child, then teach it through feedback. Have a human teacher who provides rewards and as it tries things out and does things that are either good or bad.
And this is how we train 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 internet and say, “Predict what comes next 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 problem, to say what comes next, that green nine up there, to actually solve the math problem.
But we actually to do a second step, too, which is to teach the what to do with those skills. And for this, we feedback. We have the AI try out multiple things, us multiple suggestions, and then a human rates them, “This one’s better 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. And this allows to generalize. It allows it to teach, to sort of infer intent and apply it in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, the things we have to teach the AI are 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 able to students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math in there, it will pretend that one plus one equals three and run with it.” So we had to collect some data. Sal Khan himself was very kind and offered 20 hours of own time to provide feedback to the machine alongside our team. And over 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 scenario.” And we’ve actually made lots and lots of to the models this way. And when you push that thumbs in ChatGPT, that actually is kind of like sending a bat signal to our team to say, “Here’s an area of weakness where you gather feedback.” And so when you do that, that’s one way that we listen to our users and make sure we’re building something that’s more for everyone.
Now, providing high-quality feedback is a hard thing. If you think asking a kid 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 sort of reasoning applies to AI. As we move harder tasks, we will have to scale our ability provide high-quality feedback. But for this, the AI itself is happy to help. It’s happy to us provide even better feedback and to scale our ability supervise the machine as time goes on. And let show you what I mean.
For example, you can ask GPT-4 a question like this, of how time passed between these two foundational blogs on unsupervised and learning from human feedback. And the model says months passed. But is it true? Like, these models are not 100-percent reliable, although they’re getting better every we provide some feedback. But we can actually use the AI to fact-check. And can actually check its own work. You can say, fact-check for me.
Now, in this case, I’ve actually given the a new tool. This one is a browsing tool where the model can search queries and click into web pages. And it actually writes out its whole of thought as it does it. It says, I’m going to search for this and it actually does search. It then it finds the publication date and the search results. It is issuing another search query. It’s going to click into the 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 this manager’s position where you can, if you want, triple-check the work. And out citations so you can actually go and very easily verify any piece of this whole of reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll back to the side. And so thing that’s so interesting to me about whole process is that it’s this many-step collaboration between a human and an AI. a human, using this fact-checking tool is doing it order to produce data for another AI to become more useful to human. And I think this really shows the shape of something that we should to be much more common in the future, where we have humans and machines kind of very and delicately designed in how they fit into a problem and how want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and the machines are operating in a that’s inspectable and trustworthy. And together we’re able to actually create more trustworthy machines. And I think that over time, if we this process right, we will be able to solve problems.
And to give you a sense of just how impossible I’m talking, I think we’re going to able to rethink almost every aspect of how we 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 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. And can see there the data right here. But let me show you the take on how to analyze a data set like this.
So we can give ChatGPT to yet another tool, this one a Python interpreter, so it’s able to code, just like a data scientist would. And so can just literally upload a file and ask questions about it. And helpfully, you know, it knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The only here is the name of the file, the column names like you saw then the actual data. And from that it’s able to infer what these actually mean. Like, that semantic information wasn’t in there. 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 a human do, and the AI is happy to help with it.
Now I don’t even know what I want to ask. fortunately, you can ask the machine, “Can you make some exploratory graphs?” And once again, this a super high-level instruction with lots of intent behind it. But I don’t 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 per paper, time series papers per year, word cloud of the paper titles. All of that, I think, will be pretty interesting see. And the great thing is, it can actually 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 of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and it dropped the cliff. What could be going on there? By the way, all this is code, you can 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 this year look really bad. Of course, the problem is that the year is over. So I’m going to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers 2022 were even posted by April 13?] So April 13 was the cut-off date I believe. you use that to make a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I like there was more I wanted out of the machine here. I really wanted it to notice thing, maybe it’s a little bit of an overreach it to have sort of, inferred magically that this is what 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 correct projection.
(Applause)
If you noticed, it updates the title. I didn’t ask for that, but it know what want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … A vision how we may end up using this technology in the future. A brought his very sick dog to the vet, and veterinarian made a bad call to say, “Let’s just and see.” And the dog would not be here today had he listened. In the meanwhile, he the 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.” brought that information to a second vet who used it save the dog’s life. Now, these systems, they’re not perfect. You cannot rely on them. But this story, I think, shows that a human with medical professional and with ChatGPT as a brainstorming partner was able to an outcome that would not have happened otherwise. I this is something we should all reflect on, think as we consider how to integrate these systems into our world.
And one I believe really deeply, is that getting AI right going to require participation from everyone. And that’s for how we want it to slot in, that’s for the rules of the road, for what an AI will and won’t do. And if there’s one thing take away from this talk, it’s that this technology just different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we can achieve OpenAI mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s feeling of reeling. Like, I suspect that a very large number people viewing this, you look at that and you think, “Oh my goodness, pretty much every single thing about way I work, I need to rethink.” Like, there’s just possibilities there. Am I right? Who thinks that they’re having to rethink the way we do things? Yeah, I mean, it’s amazing, but it’s also really scary. let’s talk, Greg, let’s talk.
I mean, I guess my question actually is just how the hell have you done this?
(Laughter)
OpenAI has a few employees. Google has thousands of employees working on artificial intelligence. Why is it you who’s up with this technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re 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, we made lot of very deliberate choices from the early days. And the first one was just to confront reality it lays. And that we just thought really hard about like: What is it to take to make progress here? We tried a lot of things that didn’t work, so only see the things that did. And I think that the most important thing has been to teams of people who are very different from each other to together harmoniously.
CA: Can we have the water, by way, just brought here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about the fact that you something in these language models that meant that if you continue to in them and grow them, that something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story is pretty illustrative, right? I think that high level, deep learning, like we knew that was what we wanted to be, was a learning lab, and exactly how to do it? I think in the early days, we didn’t know. We tried lot of things, and one person was working on a model to predict the next character in Amazon reviews, and he got a result where — this a syntactic process, you expect, you know, the model predict where the commas go, where the nouns and verbs are. But he got a state-of-the-art sentiment analysis classifier out of it. This model could you if a review was positive or negative. I mean, today we are like, come on, anyone can do that. But this was first time that you saw this emergence, this sort of semantics that from this underlying syntactic process. And there we knew, you’ve to scale this thing, you’ve got to see where it goes.
CA: So think this helps explain the riddle that baffles everyone looking at this, because these things are described as machines. And yet, what we’re seeing out of them … it just feels impossible that that could come from prediction machine. Just the stuff you showed us just now. And the idea of emergence is that when you get more of a thing, suddenly different emerge. It happens all the time, ant colonies, single run around, when you bring enough of them together, you get these colonies that show completely emergent, different behavior. Or a where a few houses together, it’s just houses together. But as you grow the number houses, things emerge, like suburbs and cultural centers and traffic jams. Give me one moment for you when saw just something pop that just blew your mind that you just not see coming.
GB: Yeah, well, so you can this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which it’s really learned an internal circuit for how to do it. And the really interesting thing is actually, you have it add like a 40-digit number plus a 35-digit number, it’ll get it wrong. And so you can see that it’s learning the process, but it hasn’t fully generalized, right? It’s like you can’t the 40-digit addition table, that’s more atoms than there are in universe. So it had to have learned something general, but it hasn’t really fully yet learned that, Oh, I can sort of this to adding arbitrary numbers of arbitrary lengths.
CA: what’s happened here is that you’ve allowed it to up and look at an incredible number of pieces of text. And it is learning that you didn’t know that it was 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 is predicting some of these emergent capabilities. And to do that actually, one of the things think is very undersung in this field is sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, every tolerance has be incredibly tiny. Same is true in machine learning. You have to get every piece of the stack engineered properly, and then you can start 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 of these in there. And now we’re starting to be able to predict. So we were able predict, for example, the performance on coding problems. We basically at some models that are 10,000 times or 1,000 times smaller. And so there’s something about that is actually smooth scaling, even though it’s still early days.
CA: So here is, one of the fears then, that arises from 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, but it’s of surprising you. Why isn’t there just a huge risk of something terrible emerging?
GB: Well, I think all of these are questions degree and scale and timing. And I think one thing people miss, too, is sort the integration with the world is also this incredibly emergent, sort of, very thing too. And so that’s one of the reasons we think it’s so important to deploy incrementally. And so I think what we kind of see right now, if you at this talk, a lot of what I focus is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven was the correct answer. But even a book, like, that’s a hard thing to supervise. Like, how do you if this book summary is any good? You have to read whole book. No one wants to do that.
(Laughter) so I think that the important thing will be that we take this step step. And that we say, OK, as we move to book summaries, we have to supervise this task properly. have to build up a track record with these 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 the machine be aligned with you.
CA: So we’re going hear later in this session, there are critics who say that, you know, there’s no understanding inside, the system is going to always — we’re going to know that it’s not generating errors, that it doesn’t have common sense and so forth. it your belief, Greg, that it is true at any one moment, that the expansion of the scale and the human that you talked about is basically going to take on that journey of actually getting to things like truth and and so forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe that is 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 this is the field of broken promises, of all these saying X is going to happen, Y is how works. People have been saying neural nets aren’t going 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 that approach has always been, you’ve got to push to the limits of this technology to see it in action, because that tells you then, oh, here’s how we can move on to new paradigm. And we just haven’t exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that the way to do this is to put it out there in public and 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, original story that I heard on OpenAI when you were founded as nonprofit, well you were there as the great sort check on the big companies doing their unknown, possibly thing with AI. And you were going to build that sort of, you know, somehow held them accountable and was capable slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves through tech world that now Google and Meta and so forth all scrambling to catch up. And some of their have been, you are forcing us to put this here without proper guardrails or we die. You know, how do you, like, make the case that you have done is responsible here and not reckless.
GB: Yeah, think about these questions all the time. Like, seriously 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 you supposed to that, right? And that default plan of being, well, you in secret, you get this super powerful thing, and then you figure the safety of it and then you push “go,” you hope you got it right. I don’t know to execute that plan. Maybe someone else does. But for me, that always terrifying, it didn’t feel right. And so I think this alternative approach is the only other path that see, which is that you do let reality hit you in the face. And I think you do people time to give input. You do have, before 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, really were afraid that the number one thing people were going to do with was generate misinformation, try to tip elections. Instead, the number one thing generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are that are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on table. You believe that in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give beautiful gifts your family and to everyone. But there’s actually also a one percent thing in small print there that says: “Pandora.” And there’s a chance that this actually could unimaginable evils on the world. Do you open that box?
GB: Well, so, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a that I haven’t actually told before, which is that after we started OpenAI, I remember I was in Puerto for an AI conference. I’m sitting in the hotel room looking out over this wonderful water, all these people having a good time. And think about it for a moment, if you could choose for basically that Pandora’s to be five years away 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 to be 500 years away and people get more time to get right, which do you pick? And you know, I just really felt it the moment. I was like, of course you do the 500 years. My was in the military at the time and like, he puts his 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 sold on the you’ve to approach this right. But I don’t think that’s playing the field as it truly lies. Like, if you look at the history of computing, I really mean it when I say that this is an industry-wide even just almost like a human-development- of-technology-wide shift. And the more you sort of, don’t put together the pieces that are there, right, we’re still making computers, we’re still 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 does manage to connect to the circuit, then you suddenly have this powerful thing, no one’s had any time to adjust, who knows what kind safety precautions you get. And so I think that one thing I take away is like, you think about development of other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, change what humans could do. But I actually think that if you look capability, it’s been quite smooth over time. And so the history, I think, of every 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 increasing it.
CA: So what I’m is that you … the model you want us have is that we have birthed this extraordinary child that may have that take humanity to a whole new place. It our collective responsibility to provide the guardrails for this child to collectively teach it to wise and not to tear us all down. Is 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 think it’s incredibly important today that we all do get literate in this technology, figure out how provide the feedback, decide what we want from it. And my hope is that will continue to be the best path, but it’s so good we’re honestly this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for coming to TED and our minds.
(Applause)