We OpenAI seven years ago because we felt like something interesting was happening in AI and we wanted to steer it in a positive direction. It’s honestly just really amazing to see how this whole field has come since then. And it’s really gratifying to hear from like Raymond who are using the technology we are building, and others, so many wonderful things. We hear from people who are excited, we from people who are concerned, we hear from people who feel both those at once. And honestly, that’s how we feel. Above all, it feels like we’re entering an period right now where we as a world are going define a technology that will be so important for our going forward. And I believe that we can manage 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 to show you is what it’s like to build a tool for AI rather than building it for a human. So we have a new DALL-E model, which images, and we are exposing it as an app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a nice post-TED meal and draw a picture it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details you that you get out of ChatGPT. And here we go, it’s just the idea for the meal, but a very, detailed spread. So let’s see what we’re going to get. But ChatGPT doesn’t just generate images in this — sorry, it doesn’t generate text, it also generates an image. And that is something that really the power of what it can do on your behalf in terms carrying out your intent. And I’ll point out, this is all a live demo. This is generated by the AI as we speak. So I actually don’t know what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You say “save this for later.” And the interesting thing these tools is they’re very inspectable. So you get this little up here that says “use the DALL-E app.” And by the way, this is coming to you, all users, over upcoming months. And you can look under the and see that what it actually did was write prompt just like a human could. And so you sort of this ability to inspect how the machine is using these tools, which allows us to feedback to them.
Now it’s saved for later, and let me show you what it’s like use that information and to integrate with other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” And make it a tricky for the AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, definitely want to know how it tastes.
But you see that ChatGPT is selecting all these different tools me having to tell it explicitly which ones to in any situation. And this, I think, shows a way of thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s a experience within an app as long as you kind of know menus and know all the options. Yes, I would like you to. Yes, please. Always good be polite.
(Laughter)
And by having this unified language interface on top tools, the AI is able to sort of take away all details from you. So you don’t have to be the who spells out every single sort of little piece of what’s supposed to happen.
And as said, this is a live demo, so sometimes the unexpected will to us. But let’s take a look at the shopping list while we’re at it. And you can we sent a list of ingredients to Instacart. Here’s you need. And the thing that’s really interesting is that the UI is still very valuable, right? If you look this, you still can click through it and sort modify the actual quantities. And that’s something that I shows that they’re not going away, traditional UIs. It’s we have a new, augmented way to build them. now we have a tweet that’s been drafted for our review, is also a very important thing. We can click “run,” and we are, we’re the manager, we’re able to inspect, we’re able to change the work the 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 to the slides. Now, the important thing about how we this, it’s not just about building these tools. It’s about teaching the AI how to them. Like, what do we even want it to when we ask these very high-level questions? And to do this, we use old idea. If you go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never an answer to this. Instead, you can learn it. You could build a machine, like human child, and then teach it through feedback. Have human teacher who provides rewards and punishments as it things out and does things that are either good bad.
And this is exactly 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 whole internet say, “Predict what comes next in text you’ve never before.” And this process imbues it with all sorts of skills. For example, if you’re shown a math problem, only way to actually complete that math problem, to what comes next, that green nine up there, is to actually the math problem.
But we actually have to do a second step, too, which to teach the AI what to do with those skills. for this, we provide feedback. We have the AI try multiple things, give us multiple suggestions, and then a human rates them, says “This one’s better than one.” And this reinforces not just the specific thing that the AI said, but very importantly, whole process that the AI used to produce that answer. this allows it to generalize. It allows it to teach, to sort of infer your intent apply it in scenarios that it hasn’t seen before, it hasn’t received feedback.
Now, sometimes the things we have teach the AI are not what you’d expect. For example, when first showed GPT-4 to Khan Academy, they said, “Wow, is so great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math in there, will happily 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 own time provide feedback to the machine alongside our team. And over the course of a couple of months we able to teach the AI that, “Hey, you really should push back on humans this specific kind of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you push thumbs down in ChatGPT, that actually is kind of like up a bat signal to our team to say, “Here’s an area of weakness you should gather feedback.” And so when you do that, that’s one 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. you think about 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 to AI. As move to harder tasks, we will have to scale our ability to provide high-quality feedback. But this, the AI itself is happy to help. It’s happy to help us even better feedback and to scale our ability to the machine as time goes on. And let me show you what mean.
For example, you can ask GPT-4 a question like this, of how much passed between these two foundational blogs on unsupervised learning learning from human feedback. And the model says two passed. But is it true? Like, these models are 100-percent reliable, although they’re getting better every time we provide some feedback. But we can actually the AI to fact-check. And it can actually check its own work. You say, fact-check this for me.
Now, in this case, I’ve actually given the AI a tool. This one is a browsing tool where the model can issue search queries and click 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 actually does the search. It then it finds the publication date and search results. It then is issuing another search query. It’s going to into the blog post. And all of this you do, but it’s a very tedious task. It’s not a thing that humans want to 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. out come citations so you can actually go and very easily verify piece of this whole chain of reasoning. And it actually out 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 me about this process is that it’s this many-step collaboration between a human an AI. Because a human, using this fact-checking tool is doing it in to produce data for another AI to become more useful to a human. And I think really shows the shape of something that we should expect to be much more common the future, where we have humans and machines kind very carefully and delicately designed in how they fit into a problem and how we to solve that problem. We make sure that the humans are providing the management, oversight, the feedback, and the machines are operating in a way that’s and trustworthy. And together we’re able to actually create even more machines. And I think that over time, if we get this process right, we will be able solve impossible problems.
And to give you a sense just how impossible I’m talking, I think we’re going be 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 much in that time. And here is a specific of all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you see there the data right here. But let me show you the ChatGPT take how to analyze a data set like this.
So we can give ChatGPT access to another tool, this one a Python interpreter, so it’s to run code, just like a data scientist would. And so can just literally upload a file and ask questions about it. 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 it for you.” The only information here the name of the file, the column names like you saw and then the actual data. from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It has to sort of, put together world knowledge of knowing that, “Oh yeah, arXiv is a site that people submit papers 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 all of that, that’s 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 of intent 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 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, can actually do it. Here we go, a nice curve. You see that three is kind of the most common. It’s going to then this nice plot of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and it dropped off the cliff. What could going on there? By the way, all this is code, you can inspect. And then we’ll see word cloud. So you can see these wonderful things that appear in these titles.
But I’m pretty about this 2023 thing. It makes this year look really bad. Of course, the problem is the year 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 even posted by April 13?] So April 13 was cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel like there was more I out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of an for it to have sort of, inferred magically that is what I wanted. But I inject my intent, provide this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so you want to inspect what it’s doing, it’s very possible. now, it does the correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t for that, but it know what I want.
Now we’ll cut back to the slide again. This slide shows parable of how I think we … A vision of how we end up using this technology in the future. A person brought his very sick to the vet, and the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be here had he listened. In the meanwhile, he provided the test, like, the full medical records, to GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” He that information to a second vet who used it to the dog’s life. Now, these systems, they’re not perfect. You overly rely on them. But this story, I think, shows that a human a medical professional and with ChatGPT as a brainstorming partner was able to achieve an outcome would not have happened otherwise. I think this is something should all reflect on, think about as we consider how integrate these systems into our world.
And one thing believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding we want it to slot in, that’s for setting the rules of the road, for what an will and won’t do. And if there’s one thing to away from this talk, it’s that this technology just looks different. different from anything people had anticipated. And so we all have to become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, I that we can achieve the OpenAI mission of ensuring that artificial general intelligence all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … suspect that within every mind out here there’s a feeling of reeling. Like, suspect that a very large number of people viewing this, look at that and you think, “Oh my goodness, pretty much every thing about the way I work, I need to rethink.” Like, there’s new possibilities there. Am I right? Who thinks that they’re having rethink the way that we do things? Yeah, I mean, it’s amazing, but it’s 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. Google thousands of employees working on artificial intelligence. Why is it you who’s come up with this technology that the world?
Greg Brockman: I mean, the truth is, we’re all building on of giants, right, there’s no question. If you look the compute progress, the algorithmic progress, the data progress, all of are really industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And the first one was to confront reality as it lays. And that we just really hard about like: What is it going to take to make progress here? We tried a of things that didn’t work, so you only see things that did. And I think that the most important thing been to get teams of people who are very different from each other to work together harmoniously.
CA: we have the water, by the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there something also just about the fact that you saw in these language models that meant that if you 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 that high level, deep learning, like we always knew that was what we wanted to be, a deep learning lab, and exactly how to do it? I that in the early days, we didn’t know. We tried 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 a state-of-the-art sentiment analysis classifier out of it. This model tell you if a review was positive or negative. I mean, we are just like, come on, anyone can do that. But this was the first time that saw this emergence, this sort of semantics that emerged from this underlying syntactic process. 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 everyone looking at this, because these things are described as prediction machines. And yet, what we’re seeing of them feels … 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 things emerge. It happens all the time, ant colonies, single ants run around, when you bring enough of together, you get these ant colonies that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But as you grow the number of houses, emerge, like suburbs and cultural centers and traffic jams. Give me one moment for you when you just something pop that just blew your mind that just did not see coming.
GB: Yeah, well, so you can try this in ChatGPT, if you 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s learned an internal circuit for how to do it. the really interesting thing is actually, if you have it like a 40-digit number plus a 35-digit number, it’ll often get it wrong. And so you can see it’s really learning the process, but it hasn’t fully generalized, right? It’s you 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 really fully learned that, Oh, I can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve it to scale up and look at an incredible number of pieces of text. And is learning things that you didn’t know that it was going to be of learning.
GB Well, yeah, and it’s more nuanced, too. So science that we’re starting to really get good at predicting some of these emergent capabilities. And to do that actually, one of things I think is very undersung in this field is sort of quality. Like, we had to rebuild our entire stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in machine learning. You have get every single piece of the stack engineered properly, and you can start doing these predictions. There are all these smooth scaling curves. They tell you something deeply fundamental about intelligence. If you look our GPT-4 blog post, you can see all of these curves in there. And we’re starting to be able to predict. So we were able to predict, for example, performance on coding problems. We basically look at some models are 10,000 times or 1,000 times smaller. And so there’s something about this that actually smooth scaling, even though it’s still early days.
CA: here is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that as you scale up, things emerge that you can predict in some level of confidence, but 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 I think thing people miss, too, is sort of the integration with world is also this incredibly emergent, sort of, very thing too. And so that’s one of the reasons that think it’s so important to deploy incrementally. And so I think that what kind of see right now, if you look at this talk, a lot what I focus on is providing really high-quality feedback. Today, the tasks 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 answer. But even summarizing a book, like, that’s a hard to supervise. Like, how do you know if this book is any good? You have to read the whole book. No wants to do that.
(Laughter) And so I think that important thing will be that we take this step by step. that we say, OK, as we move on to book summaries, have to supervise this task properly. We 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, more efficient, more ways of scaling this, sort of like making the machine be aligned you.
CA: So we’re going to hear later in this session, there critics who say that, you know, there’s no real inside, the system is going to always — we’re never going know that it’s not generating errors, that it doesn’t have common sense and so forth. Is it 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 going to take it on that journey of actually to things like truth and wisdom and so forth, with a high 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, reality hit you in the face, right? It’s like field is the field of broken promises, of all these experts saying X is to happen, Y is how it works. People have been saying neural nets aren’t going to 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. But think that our approach has always been, you’ve got push to the limits of this technology to really see in action, because that tells you then, oh, here’s how we can on to a new paradigm. And we just haven’t exhausted fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the right to do this is to put it out there in public and then harness this, you know, instead of just your team giving feedback, 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 as a nonprofit, well you were there as the sort of check on the big companies doing their unknown, possibly evil thing with AI. And were going to build models that sort of, you know, held them accountable and was capable of slowing the field down, if need be. Or least that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that now Google and Meta and so forth are scrambling to catch up. And some of their criticisms been, you are forcing us to put this out without proper guardrails or we die. You know, how you, like, make the case that what you have done is responsible here and reckless.
GB: Yeah, we think about these questions all the time. Like, seriously all the time. I don’t think we’re always going to get it right. But one thing I think been incredibly important, from the very beginning, when we were thinking about how to build artificial intelligence, actually have it benefit all of humanity, like, how are supposed to do that, right? And that default plan of being, well, you build in secret, you this super powerful thing, and then you figure out the safety of it and you push “go,” and you hope you got it right. I don’t how to execute that plan. Maybe someone else does. for me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the only path that I see, which is that you do let reality you in the face. And I think you do give people time give input. You do have, before these machines are perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve it from GPT-3, right? GPT-3, we really were afraid that the number one thing people were going to with it 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 things that much worse. Here’s a thought experiment for you. Suppose you’re sitting in a room, there’s a on the table. You believe that in that box is something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful gifts to your family and 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 could unleash evils on the world. Do you open that box?
GB: Well, so, absolutely not. I think you don’t do it way. And honestly, like, I’ll tell you a story that I haven’t actually told before, is that shortly after we started OpenAI, I remember I was Puerto Rico for an AI conference. I’m sitting in hotel room just looking out over this wonderful water, all these having a good time. And you think about it for a moment, if you could for basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to it be five years away. But if it gets to be 500 years away and people get more to get it right, which do you pick? And you know, I just really felt in the moment. I was like, of course you do the 500 years. My brother was in military at the time and like, he puts his life the line in a much more real way than any of us typing things in and developing this technology at the time. And so, yeah, I’m really sold on the you’ve got to this right. But I don’t think that’s quite playing field as it truly lies. Like, if you look at whole history of computing, I 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 that you of, don’t put together the pieces that are there, right, we’re making faster computers, we’re still improving the algorithms, all these things, they are happening. And if you don’t put together, you get an overhang, which means that if does, or the moment that someone does manage to to the circuit, then you suddenly have this very powerful thing, 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 development of other sort of technologies, think about nuclear weapons, people talk about being like a to one, sort of, change in 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 technology we’ve has been, you’ve got to do it incrementally and you’ve got to figure out how to manage it for moment that you’re increasing it.
CA: So what I’m hearing is that you … model you want us to have is that we have birthed extraordinary child that may have superpowers that take humanity to a whole place. It is our collective responsibility to provide the guardrails for this child to collectively teach it be wise and not to tear us all down. Is basically the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve to take each step as we encounter it. And I it’s incredibly important today that we all do get literate in this technology, out how to provide the feedback, decide what we want from it. And hope is that that will continue to be the best path, but it’s good we’re honestly having this debate because we wouldn’t otherwise if weren’t out there.
CA: Greg Brockman, thank you so much for coming to TED and our minds.
(Applause)