We started OpenAI seven years ago because felt like something really interesting was happening in AI and we wanted to help it in a positive direction. It’s honestly just really 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 hear from who are excited, we hear from people who are concerned, hear from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels like we’re entering historic period right now where we as a world are to define a technology that will be so important for our going forward. And I believe that we can manage this good.
So today, I want to show you the state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m to show you is what it’s like to build a tool for an AI rather than it for a human. So we have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to use your behalf. And you can do things like ask, you know, a nice post-TED meal and draw a picture of it.
(Laughter)
Now you get all of the, sort of, ideation and back-and-forth and taking care of the details for you you 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 see what we’re to get. But ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And that is something that really expands the power of it can do on your behalf in terms of carrying out your intent. And I’ll point out, this all a live demo. This is all generated by the AI as we speak. I actually don’t even know what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry looking at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, over upcoming months. And you can look the hood and see that what it actually did was write prompt just like a human could. And so you sort of have this ability to inspect how the is using these tools, which allows us to provide feedback 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. can say, “Now make a shopping list for the tasty thing I suggesting earlier.” And make it a little tricky for the AI. “And tweet it for all the TED viewers out there.”
(Laughter)
So you do make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT is selecting all these tools without me having to tell it explicitly which ones use in any situation. And this, I think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste them, and usually it’s a great experience within an app long as you kind of know the menus and all the options. Yes, I would like you to. Yes, please. Always good be polite.
(Laughter)
And by having this unified language interface top of tools, the AI is able to sort take away all those details from you. So you don’t have 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 sometimes the unexpected 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 is still very valuable, right? If you look at this, still can click through it and sort of modify actual quantities. And that’s something that I think shows 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 for review, which is also a very important thing. We can “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of the AI if we want to. so after this talk, you will be able to access this yourself. And we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important about how we build this, it’s not just about building these tools. It’s teaching the AI how to use them. Like, what do we even want it to when we ask these very high-level questions? And to do this, use an old idea. If you go back to Alan Turing’s 1950 on the Turing test, he says, you’ll never program answer to this. Instead, you can learn it. You build a machine, like a human child, and then teach through feedback. Have a human teacher who provides rewards and punishments as tries things out and does things that are either good or bad.
And this exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes next in text you’ve never before.” And this process imbues it with all sorts of wonderful skills. For example, if you’re a math problem, the only way to actually complete that problem, to say what comes next, that green nine up there, is to actually solve math problem.
But we actually have to do a second step, too, is to teach the AI what to do with skills. And for this, we provide feedback. We have AI try out multiple things, give us multiple suggestions, then a human rates them, says “This one’s better than that one.” this reinforces not just the specific thing that the said, but very importantly, the whole process that the AI used to produce that answer. this allows it to generalize. It allows it to teach, to sort of your intent and apply it in scenarios that it hasn’t seen before, it hasn’t received feedback.
Now, sometimes the things we have to teach AI are not what you’d expect. For example, when we showed GPT-4 to 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 happily that one plus one equals three and run with it.” So we had to collect some feedback data. Sal himself was very kind and offered 20 hours of his own time to provide feedback to machine alongside our team. And over the course of a couple of months were able to teach the AI that, “Hey, you really should push back on humans in this kind of 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 up a bat signal to our to say, “Here’s an area of weakness where you should feedback.” And so when you do that, that’s one way we really listen to our users and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. If you about asking a kid to clean their room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff all the in the closet. This is a nice DALL-E-generated image, by way. And the same sort of reasoning applies to AI. As we move to harder tasks, will have to scale our ability to provide high-quality feedback. But for this, the itself is happy to help. It’s happy to help us provide even better feedback to scale our ability to supervise the machine as time goes on. And let me show you I mean.
For example, you can ask GPT-4 a question like this, of how time passed between these two foundational blogs on unsupervised learning and learning human feedback. And the model says two months passed. But is it true? Like, these models 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 AI a new tool. This one is a browsing tool the model can issue search queries and click into web pages. it actually writes out its whole chain of thought as it does it. It says, I’m going to search for this and it actually does the search. It then finds the publication date and the search results. It then is another search query. It’s going to click into the blog post. And all this you could do, but it’s a very tedious task. It’s not a that humans really want to do. It’s much more fun to be in the driver’s seat, to in this manager’s position where you can, if you want, triple-check the work. out come citations so you can actually go and easily verify any piece of this whole chain of reasoning. it actually turns out two months was wrong. Two and one week, that was correct.
(Applause)
And we’ll cut back to the side. And so thing that’s interesting to me about this whole process is that it’s this many-step collaboration between a and an AI. Because a human, using this fact-checking tool is doing it order to produce data for another AI to become useful 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 of very carefully and delicately designed in how they fit into a problem how we want to solve that problem. We make sure that the humans are the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that time, if we get this process right, we will be able to solve problems.
And to give you a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every 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 in time. And here is a specific spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. you can see there the data right here. But me show you the ChatGPT take on how to analyze a data set like this.
So can give ChatGPT access to yet another tool, this one a interpreter, so it’s able to run code, just like a data would. And so you can just literally upload a file and ask questions about it. And helpfully, you know, it knows the name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” The information here is the name of the file, the column names like you and then the actual data. And from that it’s able to what these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its knowledge of knowing that, “Oh yeah, arXiv is a site that people submit papers therefore that’s what these things are and that these integer values and so therefore it’s a number of authors in the paper,” like of that, that’s work for a human to do, and the is happy to help with it.
Now I don’t know what I want to ask. So fortunately, you 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 to infer what I might be interested in. And it comes up with some good ideas, I think. So a histogram of the number authors per paper, time series of papers per year, word of the paper titles. All of that, I think, be pretty interesting to see. And the great thing is, it can actually do it. Here go, a nice bell curve. You see that three kind of the most common. It’s going to then make this nice plot of the papers per year. crazy is happening in 2023, though. Looks like we were on an exponential and dropped off the cliff. What could be going on there? By the way, this 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 year is not over. So I’m going to push on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted April 13?] So April 13 was the cut-off date believe. Can you use that to make a fair projection? we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. really wanted it to notice this thing, maybe it’s little bit of an overreach for it to have of, inferred magically that this is what I wanted. But I inject intent, I provide this additional piece of, you know, guidance. under the hood, the AI is just writing code again, if you want to inspect what it’s doing, it’s very possible. And now, it does correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask for that, but it what I want.
Now we’ll cut back to the slide again. This slide shows a parable of how think we … A vision of how we may end up this technology in the future. A person brought his very sick dog to the vet, the veterinarian made a bad call to say, “Let’s just wait see.” And the dog would not be here today had he listened. In the meanwhile, provided 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.” He that information to a second vet who used it save the dog’s life. Now, these systems, they’re not perfect. You cannot overly on them. But this story, I think, shows that human with a medical professional and with ChatGPT as a partner was able to achieve an outcome that would not have happened otherwise. I this is something 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 is to require participation from everyone. And that’s for deciding how we want it to slot in, that’s setting the rules of 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 technology just different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, one the reasons we released ChatGPT.
Together, I believe that we can the OpenAI mission of ensuring that artificial general intelligence all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look that and you think, “Oh my goodness, pretty much single thing about the way I work, I need rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re having to 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, guess my first question actually is just how the hell have you done this?
(Laughter)
OpenAI has a hundred employees. Google has thousands of employees working on artificial intelligence. Why it you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re all 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, made a lot of very deliberate choices from the days. And the first one was just to confront reality it lays. And that we just thought really hard about like: What is it going take to make progress here? We tried a lot of that didn’t work, so you only see the things 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 the way, just brought here? I think we’re to need it, it’s a dry-mouth topic. But isn’t there also just about the fact that you saw something in these models that meant that if you continue to invest in them and grow them, something at some point might emerge?
GB: Yes. And I think that, I mean, honestly, think the story there is pretty illustrative, right? I think that high level, deep learning, like we always that was what we wanted to be, was a deep learning lab, and exactly how to do it? think that in the early days, we didn’t know. We a lot of things, and one person was working training a model to predict the next character in reviews, and he got a result where — this 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 you if a review was positive or negative. I mean, today are just like, come on, anyone can do that. But this the first time that you saw this emergence, this sort semantics that emerged from this underlying syntactic process. And there knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps the riddle that baffles everyone looking at this, because these things are described as prediction machines. yet, what we’re seeing out of them feels … it just feels that that could come from a prediction machine. Just the stuff showed us just now. And the key idea of emergence is that when you get more a thing, suddenly different things emerge. It happens all the time, ant colonies, single ants around, when you bring enough of them together, you get ant colonies that show completely emergent, different behavior. Or a city where a houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and cultural centers and traffic jams. Give me one for you when you saw 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 add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned an circuit for how to do it. And the really interesting is actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. And you can see that it’s really learning the process, it hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are in universe. So it had to have learned something general, but that it hasn’t really fully learned that, Oh, I can sort of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: what’s happened here is that you’ve allowed it to scale and look at an incredible number of pieces of text. And it is learning things that didn’t know that it was going to be capable 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 these emergent capabilities. to do that actually, one of the things I think very undersung in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think about building a rocket, every tolerance has to be tiny. Same is true in machine learning. You have get 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 deeply fundamental about intelligence. If you look at our GPT-4 blog post, you see all of these curves in there. And now we’re starting to be to predict. So we were able to predict, for example, the performance coding problems. We basically look at some models that are 10,000 or 1,000 times smaller. And so there’s something about this that 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 what’s happening here, that as you scale up, things 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 truly terrible emerging?
GB: Well, I think all of these are questions of degree and scale timing. And I think one thing people miss, too, is sort of the integration with the is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that we it’s so important 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 inspect them, right? It’s very to 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 thing to supervise. Like, do you know if this book summary is any good? have to read the whole book. No one wants do that.
(Laughter) And so I think that the thing will be that we take this step by step. that we say, OK, as we move on to summaries, we 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 I think we’re to have to produce even better, more efficient, more reliable ways of scaling this, sort of making the machine be aligned with you.
CA: So we’re going to later in this session, there are critics who say that, know, there’s no real understanding inside, the system is going to — we’re never going to know that it’s not errors, that it doesn’t have common sense and so forth. it your belief, Greg, that it is true at any one moment, but that the expansion of scale and the human feedback that you talked about basically going to take it on that journey of actually getting things like truth and wisdom and so forth, with high degree of confidence. Can you be sure of that?
GB: Yeah, well, I think the OpenAI, I mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI approach here has always just like, let 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 happen, Y is how 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 70 years plus one or something like that is what you need. But I that our approach has always been, you’ve got to push to the of this technology to really see it in action, because 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 the right way 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, bad things are going emerge, it is out there. So, you know, the original story that I heard on when you were founded as a nonprofit, well you there as the great sort of check on the big doing their unknown, possibly evil thing with AI. And you going to build models that sort of, you know, somehow held them and was capable of 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. That release of GPT, especially ChatGPT, sent such shockwaves through the tech world that Google and Meta and so forth are all scrambling to 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 that what you have done is responsible here and reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing I has been incredibly important, from the very beginning, when we were thinking about how to artificial general intelligence, actually have it benefit all of humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, you get this powerful thing, and then you figure out the safety it and then you push “go,” and you hope got 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 I see, which is that you do reality hit you in the face. And I think you do give people to give input. You do have, before these machines perfect, before they are super powerful, that you actually have the ability to see 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 it generate misinformation, try to tip elections. Instead, the number one was generating Viagra spam.
(Laughter)
CA: So Viagra spam 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 the table. You believe that in that box is something that, there’s very strong chance it’s something absolutely glorious that’s going give beautiful gifts to your family and to everyone. there’s actually also a one percent thing in the print there that 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 that way. And honestly, like, I’ll you a story that I haven’t actually told before, which is that shortly we started OpenAI, I remember I was in Puerto Rico for an conference. I’m sitting in the hotel room just looking over this wonderful water, all these people having a time. And you 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? On one hand you’re like, well, maybe for you personally, it’s better to have it be years away. But if it gets to be 500 years away people get more time to get it right, which do you pick? And you know, I just felt it in the moment. I was like, of course you do the 500 years. My brother was the military at the time and like, he puts his life on the line in a much real way than 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 this right. But I don’t think that’s quite playing the field it truly lies. Like, if you look at the history of computing, I really mean it when I say 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 put together pieces that are there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. And if you don’t put them together, get an overhang, which means that if someone does, or the moment someone does manage to connect to the circuit, then you suddenly have this very powerful thing, one’s had any time to adjust, who knows what of safety precautions you get. And so I think that one thing take away is like, even you think about development of sort of technologies, think about nuclear weapons, people talk about being like zero to one, sort of, change in what humans could do. But I actually think that if look at capability, it’s been quite smooth over time. And the history, I think, of every technology we’ve developed has been, you’ve got to do incrementally and you’ve got to figure out how to manage it for each moment that you’re it.
CA: So what I’m hearing is that you … model you want us to have is that we birthed this extraordinary child that may have superpowers that take humanity to whole new place. It is our collective responsibility to provide the guardrails for child to collectively teach it to be wise and not to tear us all down. Is that basically model?
GB: I think it’s true. And I think it’s also important to this may shift, right? We’ve got to take each as we encounter it. And I think it’s incredibly important today that we all do literate in this technology, figure out how to provide the feedback, what we want from it. And my hope is that that will continue to 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 blowing our minds.
(Applause)