We started OpenAI seven ago because we felt like something really interesting was in AI and we wanted to help steer it in positive direction. It’s honestly just really amazing to see far this whole field has come since then. And it’s really gratifying hear from people like Raymond who are using the we 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 both those emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period right where we as a world are going to define a technology that be so important for our society going forward. And I believe we can manage this for good.
So today, I want to show you the 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 what it’s to build a tool for an AI rather than building it for a human. So we have new DALL-E model, which generates images, and we are exposing as an app for ChatGPT to use on your behalf. And you do things like ask, you know, suggest a nice post-TED and draw a picture of it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details for you that get out of ChatGPT. And here we go, it’s just the idea for the meal, but a very, very spread. So let’s see what we’re going to get. But ChatGPT doesn’t generate images in this case — sorry, it doesn’t generate text, also generates an image. And that is something that really expands the power of what can do on your behalf in terms of carrying out your intent. And I’ll point out, this is a live demo. This is all generated by the AI we speak. So I actually don’t even know what we’re going see. This looks wonderful.
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I’m getting hungry just looking at it.
Now we’ve ChatGPT with other tools too, for example, memory. You can “save this for later.” And the interesting thing about these tools they’re very inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, this is coming to you, ChatGPT users, over 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 this ability to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s saved later, and let 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 the tasty thing I was suggesting earlier.” And make it a tricky for the AI. “And tweet it out for all TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is all these different tools without me having to tell it explicitly which ones to use in situation. And this, I think, shows a new way of thinking about the user interface. Like, we so used to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s great experience within an app as long as you kind of know the and know all the options. Yes, I would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on of tools, the AI is able to sort of take all those details from you. So you don’t have be the one who spells out every single sort little piece of what’s supposed to happen.
And as I said, this is a live demo, so sometimes unexpected will happen to us. But let’s take a look at the Instacart shopping list we’re at 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 is 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 shows that they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we have a that’s been drafted for our review, which is also very important thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the work of the AI we want to. And so after this talk, you be able to access this yourself. And there we go. Cool. you, everyone.
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So we’ll cut back to the slides. Now, the important thing about how we this, it’s not just about building these tools. It’s about the AI how to use them. Like, what do we even want it to do when ask these very high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 paper the Turing test, he says, you’ll never program an to this. Instead, you can learn it. You could a machine, like a human child, and then teach it through feedback. Have a human teacher who rewards and punishments as it tries things out and does things that either good or bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing 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 before.” And this process imbues it with all sorts wonderful skills. For example, if you’re shown a math problem, the 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 do a second step, too, which is to teach the AI what to do with those skills. And this, we provide feedback. We have the AI try out multiple things, us multiple suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the AI used to that answer. And this allows it to generalize. It allows it to teach, to of infer your intent and apply it in scenarios it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the we have to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, they said, “Wow, is so great, We’re going to be able to teach students wonderful things. one problem, it doesn’t double-check students’ math. If there’s some bad in there, it will happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Sal Khan was very kind and offered 20 hours of his own time to provide feedback to the alongside our team. And over the course of a of months we were able to teach the AI that, “Hey, you really push back on humans in this specific kind of scenario.” we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up a bat to our team to say, “Here’s an area of weakness where you should gather feedback.” And when you do that, that’s one way that we really 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 you think about 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 of reasoning applies to AI. As we move to tasks, we will have to scale our ability to provide high-quality feedback. But for this, AI itself is happy to help. It’s happy to help us provide better feedback and to scale our ability to supervise the as time goes on. And let me show you I mean.
For example, you can ask GPT-4 a like this, of how much time passed between these two foundational blogs on unsupervised learning learning from human feedback. And the 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. we can actually use the AI to fact-check. And it can actually check its own work. can say, fact-check this for me.
Now, in this case, I’ve actually the AI a new tool. This one is a browsing where the model can issue search queries and click into web pages. And it actually writes its whole chain of thought as it does it. says, I’m just going to search for this and actually does the search. It then it finds the date and the search results. It then is issuing another query. It’s going to click into the blog post. And all of you could do, but it’s a very tedious task. It’s not a thing humans really want to do. It’s much more fun be in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. And come citations so you can actually go and very verify any piece of this whole chain of reasoning. And it actually turns two months was wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut back the side. And so thing that’s so interesting to about this whole process is that it’s this many-step collaboration between a human an AI. Because a human, using this fact-checking tool doing it in order to produce data for another AI to become more to a human. And I think this really shows the shape of something we should expect to be much more common in the future, where we have humans and machines kind very 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 the management, the oversight, the feedback, and machines are operating in a way that’s inspectable and trustworthy. together we’re able to actually create even more trustworthy machines. And I think that over time, if we get process right, we will be able to solve impossible problems.
And to you a sense of just how impossible I’m talking, I think we’re going to be able to rethink every aspect of how we interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years with VisiCalc. I don’t think they’ve really changed that much that time. And here is a specific spreadsheet of all the papers on the arXiv for the past 30 years. There’s about 167,000 of them. And you can see there data right here. But let me show you the ChatGPT take on how to a data set like this.
So we can give ChatGPT to yet another tool, this 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 ask questions about it. very helpfully, you know, it knows the name of file and 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 from that it’s to infer what these columns actually mean. Like, that semantic 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 that these are integer values and so therefore it’s a number of in the paper,” like all of that, that’s work a human to do, and the AI is happy to help with it.
Now I don’t know what I want to ask. So fortunately, you can ask the machine, “Can you make exploratory graphs?” And once again, this is a super high-level instruction with lots intent behind it. But I don’t even know what I want. the AI kind of has to infer what I might be interested in. so it comes up with some good ideas, I think. So a histogram of the of authors per paper, time series of papers per year, cloud of the paper titles. All of that, I think, will pretty interesting to see. And the great thing is, it can actually do it. Here we go, nice bell curve. You see that three is kind of the common. It’s going to then make this nice plot the papers per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped the cliff. What could be going on there? By the way, all is Python code, you can inspect. And then we’ll word cloud. So you can see all these wonderful things that appear these titles.
But I’m pretty unhappy about this 2023 thing. It this year look really bad. Of course, the problem is that the is not over. So I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted by 13?] So April 13 was the cut-off date I believe. Can you use to make a fair projection? So we’ll see, this is the kind ambitious one.
(Laughter)
So you know, again, I feel like there more I wanted out of the machine here. I wanted it to notice this thing, maybe it’s a little bit of an overreach it to have sort of, inferred magically that this is what I wanted. But inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing code again, if you want to inspect what it’s doing, it’s very possible. now, it does the correct projection.
(Applause)
If you noticed, it even the title. I didn’t ask for that, but it what I want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … A vision of how we end up using this technology in the future. A person his very sick dog to the vet, and the veterinarian made a call to say, “Let’s just wait and see.” And the dog would not be here today had listened. In the meanwhile, he provided the blood test, like, full medical records, to GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” He brought that information a second vet who used it to save the dog’s life. Now, these systems, they’re not perfect. cannot overly rely on them. But this story, I think, shows that human with a medical professional and with ChatGPT as a brainstorming partner was to achieve an outcome that would not have happened otherwise. I think this is we should all reflect on, think about as we how to integrate these systems into our world.
And thing 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 setting rules of the road, for what an AI will won’t do. And if there’s one thing to take away from this talk, it’s this technology just looks different. Just different from anything had anticipated. And so we all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we achieve the OpenAI mission of ensuring that artificial general benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect within every mind out here there’s a feeling of reeling. Like, I suspect a very large 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. Am I right? Who that they’re having to rethink the way that we things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, I my first question actually is just how the hell have done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of employees on artificial intelligence. Why is it you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the algorithmic progress, data progress, all of those are really industry-wide. But I within OpenAI, we made a lot of very deliberate choices from the early days. And the one was just to confront reality as it lays. And that we just really hard about like: What is it going to to make progress here? We tried a lot of things that didn’t work, so you see the things that did. And I think that the important thing has been to get teams of people who are very from each other to work together harmoniously.
CA: Can have the water, by the way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something also just about the fact you saw something in these language models that meant that you continue to invest in them and grow them, that something 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 always knew that was what we wanted to be, was a deep learning lab, exactly how to do it? I think that in the early days, we didn’t know. We a lot of things, and one person was working on training model to predict the next character in Amazon reviews, and got a 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 state-of-the-art sentiment analysis classifier out of it. This model could tell you a review was positive or negative. I mean, today we just like, come on, anyone can do that. But was the first time that you saw this emergence, this of semantics that emerged from this underlying syntactic process. And there we knew, you’ve got to this thing, you’ve got to see where it goes.
CA: So I think this helps explain riddle that baffles everyone looking at this, because these things are described as prediction machines. yet, what we’re seeing out of them feels … just feels impossible that that could come from a 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 you enough of them together, you get these ant colonies that show emergent, different behavior. Or a city where a few 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 saw just something pop that just blew your mind that you just did not coming.
GB: Yeah, well, so you can try this in ChatGPT, 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 thing is actually, if you have it add like 40-digit number plus a 35-digit number, it’ll often get it wrong. And so you can 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 there are in the universe. So it had to have something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to adding arbitrary of arbitrary lengths.
CA: So what’s happened here is that you’ve it to scale up and look at an incredible of pieces of text. And it is learning things that you didn’t know that it going to be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is predicting some of emergent capabilities. And to do that actually, one of the things I is very undersung in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think about building rocket, every tolerance has to be incredibly tiny. Same is true in machine learning. You have to every single piece of the stack engineered properly, and then you can start doing predictions. There are all these incredibly smooth scaling curves. They tell you something deeply fundamental intelligence. If you look at our GPT-4 blog post, can see all of these curves in there. And now we’re starting to be able predict. So we were able to 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 this that is actually smooth scaling, even though it’s still early days.
CA: So is, one of the big fears then, that arises from this. If it’s fundamental to what’s happening here, as you scale up, things emerge that you can maybe in some level of confidence, but it’s capable of surprising you. isn’t there just a huge risk of something truly emerging?
GB: Well, I think all of these are questions of degree and scale timing. And I think one thing people miss, too, is of the integration with the world is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that we think it’s so to deploy incrementally. And so I think that what kind of see right now, if you look at talk, a lot of what I focus on is really high-quality feedback. Today, the tasks that we do, you can 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 is any good? You to read the whole book. No one wants to do that.
(Laughter) And so I think the important thing will be that we take this by step. And that we say, OK, as we move to book summaries, we 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. And think we’re going to have to produce even better, more efficient, more reliable ways of scaling this, sort like making the machine be aligned with you.
CA: So we’re going hear later in this session, there are critics who that, you know, there’s no real understanding inside, the system is going to — we’re never going to know that it’s not generating errors, that it doesn’t have common sense so forth. Is it your belief, Greg, that it is true at one moment, but that the expansion of the scale and the human feedback that talked about is basically going to take it on that journey 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, think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And I that the OpenAI approach here has always been just like, let reality hit in the face, right? It’s like this field is the field of broken promises, of all experts saying X is going to happen, Y is how it works. People been saying 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 that is what you need. I think that our approach has always been, you’ve to push to the limits of this technology to really it in action, because that tells you then, oh, here’s how we can move on to a paradigm. And we just haven’t exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that right way to do this is to put it there in public and then harness all this, you know, of 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 story that I heard on OpenAI when you were as a nonprofit, well you were there as the great of check on the big companies doing their unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held them accountable and was capable slowing the field down, if need be. Or at least that’s kind what I heard. And yet, what’s happened, arguably, is the opposite. That release of GPT, especially ChatGPT, sent such shockwaves through the tech world now Google and Meta and so 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 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. But 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 being, well, you build in secret, you get this super powerful thing, and then you figure the safety of it and then you push “go,” and you hope you it right. I don’t know how to execute that plan. Maybe someone else does. But me, that was always terrifying, it didn’t feel right. And so think that this alternative approach is the only other path that see, which is that you do let reality hit you in the face. I think you do give people time to give input. You do have, before machines are perfect, before they are super powerful, that you actually have the ability 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 do with it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: So spam is bad, but there are things that are much worse. Here’s a thought experiment you. Suppose you’re sitting in a room, there’s a box on the table. You believe that in that is something that, there’s a very strong chance it’s 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 small print there says: “Pandora.” And there’s a chance that this actually unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t do it way. And honestly, like, I’ll tell you a story that haven’t actually told before, which is that shortly after we OpenAI, I remember I was in Puerto Rico for AI conference. I’m sitting in the hotel room just looking over this wonderful water, all these people having a good time. And you about it for a moment, if you could choose for basically that Pandora’s box to be five away or 500 years away, which would you pick, right? On the hand you’re like, well, maybe for you personally, it’s to 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 just really felt it in moment. I was like, of course you do the 500 years. My brother was in the at the time and like, he puts his life on the line in a much more real way any of us typing things in computers and developing this technology at time. And so, yeah, I’m really sold on the you’ve got to approach 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, I 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 there, right, we’re still making faster computers, we’re still improving algorithms, all of these things, they are happening. And if you don’t them together, you get an overhang, which means that if someone does, or the that someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had any to adjust, who knows what kind of safety precautions get. And so I think that one thing I take is like, even you think about development of other of technologies, think about nuclear weapons, people talk about being like a zero to one, of, change in what humans could do. But I actually think that if look at capability, it’s been quite smooth over time. And so the history, I think, of technology we’ve developed has been, you’ve got to do it incrementally you’ve got to figure out how to manage it for each that you’re increasing it.
CA: So what I’m hearing that you … the model you want us to have is that we have this extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it to be wise and not tear us all down. Is that basically the model?
GB: I it’s true. And I think it’s also important to say 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 to 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, thank you so much for to TED and blowing our minds.
(Applause)