We started seven years ago because we felt like something really interesting happening in AI and we wanted to help steer in a positive direction. It’s honestly just really amazing see how far this whole field has come since then. And it’s really to hear from people like Raymond who are using the technology are building, and others, for so many wonderful things. We hear from people who are excited, hear from people who are concerned, we hear from people who feel those emotions at once. And honestly, that’s how we feel. Above all, it feels like we’re entering an historic right now where we as a world are going to define a technology that will be so for our society going forward. And I believe that we can this for good.
So today, I want to show you the current state of technology and some of the underlying design principles that we hold dear.
So the first thing I’m to show you is what it’s like to build a for an AI rather than building it for a human. we have a new DALL-E model, which generates images, 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 draw a picture of it.
(Laughter)
Now you get all the, sort of, ideation and creative back-and-forth and taking care of the for you that you get out of ChatGPT. And here we go, it’s just the idea for the meal, but a very, very detailed spread. let’s see what we’re going to get. But ChatGPT doesn’t generate images in this case — sorry, it doesn’t generate text, it generates an image. And that is something that really expands the power of it can do on your behalf in terms of out your intent. And I’ll point out, this is all a live demo. This is all generated by AI as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
(Applause)
I’m getting just looking at it.
Now we’ve extended ChatGPT with other too, for example, memory. You can say “save this for later.” And interesting thing about these tools is they’re very inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, this coming to you, all ChatGPT users, over upcoming months. And can look under the hood and see that what actually did was write a prompt just like a human could. And so you sort of have ability to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s 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 shopping list for tasty thing I was suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT is selecting these different 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, we have these apps, we between them, we copy/paste between 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 to be polite.
(Laughter)
And by having unified language interface on top of tools, the AI is able sort of take away all those details from you. So you don’t to be the one who spells out every single sort little piece of what’s supposed to happen.
And as I said, this a live demo, so sometimes the unexpected will happen to us. But let’s a look at the Instacart shopping list while we’re at it. And you can see sent a list of ingredients to Instacart. Here’s everything you need. And thing that’s really interesting is that the traditional UI still very valuable, right? If you look at this, still can click through it and sort of modify the quantities. And that’s something that I think shows that they’re not away, traditional UIs. It’s just we have a new, augmented way to build them. And we have a tweet that’s been drafted for our review, which also a very important thing. We can click “run,” and there we are, we’re the manager, we’re able 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 there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important thing how we build 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 do when we ask these 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 answer to this. Instead, you learn it. You could build a machine, like a human child, then teach it through feedback. Have a human teacher who provides rewards and punishments as it tries out and does things that are either good or bad.
And this is exactly how train ChatGPT. It’s a two-step process. First, we 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 seen before.” And this process imbues it with all sorts of skills. For example, if you’re shown a math problem, the only way to actually complete that math problem, say what comes next, that green nine up there, is to actually solve the problem.
But we actually have to do a second step, too, which to teach the AI what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, give us suggestions, and then a human rates them, says “This one’s better than that one.” this reinforces not just the specific thing that the AI said, but very importantly, the whole process the AI used to produce that answer. And this allows it generalize. It allows it to teach, to sort of infer your intent apply it in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things have to teach the AI are not 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 teach students wonderful 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 with it.” So we had collect some feedback data. Sal Khan himself was very kind and offered 20 hours of his own to provide feedback to the machine alongside our team. And over the of a couple of months we were able to the AI that, “Hey, you really should push back on humans in this kind of scenario.” And we’ve actually made lots and of improvements to the models this way. And when you push thumbs down in ChatGPT, that actually is kind of like sending up a bat signal to our team say, “Here’s an area of weakness where you should 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 useful for everyone.
Now, providing high-quality is a hard thing. If you think about asking a to clean their room, if all you’re doing is the floor, you don’t know if you’re just teaching them to stuff the toys in the closet. This is a nice DALL-E-generated image, the way. And the same sort of reasoning applies AI. As we move to harder tasks, we will have scale our ability to provide high-quality feedback. But for this, the AI 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 what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between these two blogs on unsupervised learning and 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 some feedback. But we can actually use the AI fact-check. And it can actually check its own work. You say, fact-check this for me.
Now, in this case, I’ve given the AI a new tool. This one is a tool where the model can issue search queries and click into pages. And it actually writes out its whole chain thought as it does it. It says, I’m just going to search for this and it does the search. It then it finds the publication date and the search results. then is issuing another search query. It’s going to click the blog post. And all of this you could do, it’s a 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 of this whole chain of reasoning. And it actually out two months was wrong. Two months and one week, was correct.
(Applause)
And we’ll cut 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 human and an AI. Because a human, using this fact-checking tool is doing it in order to data for another AI to become more useful to a human. I think this really shows the shape of something that should expect to be much more common in the future, we have humans and machines kind of very carefully and delicately designed how they fit into a problem and how we to solve that problem. We make sure that the humans providing the management, the oversight, the feedback, and the are operating in a way that’s inspectable and trustworthy. together we’re able to actually create even more trustworthy machines. I think that over time, if we get this process right, we be able to solve impossible problems.
And to give you a sense of just how I’m talking, I think we’re going to be able rethink almost every aspect of how we interact with computers. For example, about spreadsheets. They’ve been around in some form since, we’ll say, 40 ago with VisiCalc. I don’t think they’ve really changed that much in that time. here is a specific spreadsheet of all the AI on the arXiv for the past 30 years. There’s about 167,000 them. And you can see there the data right here. let me show you the ChatGPT take on how analyze a data set like this.
So we can ChatGPT access to yet another tool, this one a Python interpreter, so it’s to run code, just like a data scientist would. And so you can just upload a file and ask questions about it. And very helpfully, you know, it knows the name the file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information here is name of the file, the column names like you saw 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 world knowledge knowing that, “Oh yeah, arXiv is a site that submit papers and therefore that’s what these things are that these are 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 AI is to help with it.
Now I don’t even know what I want to ask. So fortunately, you can the machine, “Can you make some exploratory graphs?” And once again, this is super high-level instruction with lots of intent behind it. But I don’t even what I want. And the AI kind of has to infer what I be interested in. And so it comes up with some good ideas, I think. So a histogram the number of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, will be pretty interesting see. And the great thing is, it can actually do it. we go, a nice bell curve. You see that is kind of the most common. It’s going to then make this nice plot of the papers year. Something crazy is happening in 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 Python code, 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 unhappy about this 2023 thing. It this year look really bad. Of course, the problem that the year is not over. So I’m going to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you use to make a fair projection? So we’ll see, this is kind of ambitious one.
(Laughter)
So you know, again, I like there was more I wanted out of the here. I really wanted it to notice this thing, maybe it’s a little bit of overreach for it to have sort of, inferred magically that this is what I wanted. But I my intent, I provide this additional piece of, you know, guidance. And under the hood, AI is just writing code again, so if you to inspect what it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If noticed, it even updates the title. I didn’t ask 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 may end up using this technology in the future. person brought his very sick dog to the vet, the veterinarian made a bad call to say, “Let’s just and see.” And the dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the full medical records, to GPT-4, which said, “I am a vet, you 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 overly rely 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 have happened otherwise. I think this is something we should all reflect on, think about as we consider to integrate these systems into our world.
And one thing I believe really deeply, is that getting AI is going to require participation from everyone. And that’s for deciding we want it to slot in, that’s for setting the rules of road, for what an AI will and won’t do. And if there’s one thing to take from this talk, it’s that this technology just looks different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, of the reasons we released ChatGPT.
Together, I believe that can achieve the OpenAI mission of ensuring that artificial general benefits all of 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 of people viewing this, you look at and you think, “Oh my goodness, pretty much every single thing about the way I work, I to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re having to rethink way that we do things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, I guess first question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google has 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 is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the progress, the data progress, all of those are really industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the first one just to confront reality as it lays. And that we just thought really hard like: What is it going to take to make here? We tried a lot of things that didn’t work, so you see the things that did. And I think that the most important has been to get teams of people who are very from each other to work together harmoniously.
CA: Can we have the water, by the way, just here? I think we’re going to need it, it’s a dry-mouth topic. isn’t there something also just about the fact that saw something in these language models that meant that if continue to invest in them and grow them, that something at some might emerge?
GB: Yes. And I think that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, like we always knew that what we wanted to be, was a deep learning lab, and exactly how to do it? I that in the early days, we didn’t know. We tried a lot of things, and one person was on training a model to predict the next character in Amazon reviews, and he got a result — this is a syntactic process, you expect, you know, model will predict where the commas go, where the nouns and are. But he actually got a 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 are like, come on, anyone can do that. But this was the time that you saw this emergence, this sort of semantics emerged from this underlying syntactic process. And there we knew, you’ve to scale this thing, you’ve got to see where goes.
CA: So I think this helps explain the riddle that baffles everyone looking at this, these things are described as prediction machines. And yet, what we’re out of them feels … it just feels impossible that could come from a prediction machine. Just the stuff you showed us just now. And key idea of emergence is that when you get more of a thing, suddenly different things emerge. happens all the time, ant colonies, single ants run around, when bring enough of them together, you get these ant colonies show completely emergent, different behavior. Or a city where few houses together, it’s just houses together. But as grow the number of houses, things emerge, like suburbs and cultural centers and traffic jams. Give me moment for you when you saw just something pop just blew your mind that you just did not coming.
GB: Yeah, well, so you can try this in ChatGPT, if you add 40-digit —
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. And the interesting thing 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, but hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s more atoms than there in the 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: So what’s here is that you’ve allowed it to scale up and look an incredible number of pieces of text. And it is learning that you didn’t know that it was going to capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re to really get good at is predicting some of emergent capabilities. And to do that actually, one of things I think is very undersung in this field is sort of engineering quality. Like, we to rebuild our entire stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in learning. You have to get every single piece of stack engineered properly, and then you can start doing these predictions. There are all incredibly smooth scaling curves. They tell you something deeply 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 were able to predict, for example, the performance on coding problems. We basically look some models that are 10,000 times or 1,000 times smaller. And so there’s about this that is actually smooth scaling, even though it’s still early days.
CA: So here is, one of 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 predict in some level 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 think one thing people miss, too, is sort of integration with the world is also this incredibly emergent, sort of, very powerful thing too. And that’s one of the reasons that we think it’s so important to deploy incrementally. And so think that what we 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 that we do, you inspect them, right? It’s very easy to look at that math problem be like, no, no, no, machine, seven was the correct answer. But summarizing a book, like, that’s a hard thing to supervise. Like, how do you know if book summary is any good? You have to read the whole book. No one wants do that.
(Laughter) And so I think that the important thing will be that we this step by step. And that we say, OK, as we move on to book summaries, have to supervise this task properly. We have to build a track record with these machines that they’re able to actually carry our intent. And I think we’re going to have to produce even better, more efficient, more ways of scaling this, sort of like making the machine aligned with you.
CA: So we’re going to hear later in this session, are critics who say that, you know, there’s no understanding inside, the system is going to always — we’re never going to that it’s not generating errors, that it doesn’t have sense and so forth. Is it your belief, Greg, that it is true at one moment, but that the expansion of the scale and human feedback that you talked about is basically going to it on that journey of actually getting to things like truth wisdom and so forth, with a high degree of confidence. you be sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, the answer is yes, I believe that is where we’re headed. I think that the OpenAI approach here has always been like, let reality hit you in the face, right? It’s like this is the field of broken promises, of all these experts saying X is going to happen, Y how it works. People have been saying neural nets aren’t going to work 70 years. They haven’t been right yet. They might be right maybe 70 years plus one something like that is what you need. But I think that approach has always been, you’ve got to push to the limits this technology to really see it in action, because that you then, oh, here’s how we can move on to a new paradigm. And we just haven’t exhausted fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do this to put it out there in public and then harness all this, you know, instead just your team giving feedback, the world is now giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, the original story that heard on OpenAI when you were founded as a nonprofit, well were there as the great sort of check on the big companies doing their unknown, possibly thing with AI. And you were going to build models sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. Or at least that’s kind of what I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, sent shockwaves through the tech world that now Google and Meta and forth are all scrambling to catch up. And some of their criticisms have been, you forcing us to put this out here without proper or we die. You know, how do you, like, the case that what you have done is responsible and not reckless.
GB: Yeah, we think about these questions the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing I think has been incredibly important, from very beginning, when we were thinking about how to build artificial general intelligence, actually have it benefit all humanity, like, how are you supposed to do that, right? And default plan of being, well, you build in secret, you get super powerful thing, and then you figure out the of it and then you push “go,” and you you got it right. I don’t know how to execute plan. Maybe someone else does. But for me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the only other path that I see, which is that do let reality hit you in the face. And I think do give people time to give input. You do have, before these are perfect, before they are super powerful, that you have the ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the number one people were going to do with it was generate misinformation, try to tip elections. Instead, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra is bad, but there are things that are much worse. Here’s thought experiment for you. Suppose you’re sitting in a room, there’s box on the table. You believe that in that box is that, there’s a very strong chance it’s something absolutely glorious that’s going to beautiful gifts to your family and to everyone. But there’s actually also one percent thing in the small print there that says: “Pandora.” And there’s a chance this actually could unleash unimaginable evils on the world. Do you open that box?
GB: Well, so, 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, is that shortly 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 you about it for a moment, if you could choose for basically that Pandora’s box to be five years or 500 years away, which would you pick, right? On the hand you’re like, well, maybe for you personally, it’s better have it be five years away. But if it gets to 500 years away and people get more time to get it right, which do pick? And you know, I just really felt it in moment. I was like, of course you do the 500 years. My brother in the military at the time and like, he his life on the line in a much more real way any of us typing things in computers and developing technology at the time. And so, yeah, I’m really on the you’ve got to approach this 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 really it when I say that this is an industry-wide or even just like a human-development- of-technology-wide shift. And the more that you sort of, don’t put the pieces that are there, right, we’re still making computers, we’re still improving the algorithms, all of these things, are happening. And if you don’t put them together, get an overhang, which means that if someone does, the moment that someone does manage to connect to circuit, then you suddenly have this very powerful thing, no one’s 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, talk about being like a zero to one, sort of, change in what humans do. But I actually think that if you look at capability, it’s been smooth over time. And so the history, I think, of every we’ve developed has been, you’ve got to do it and you’ve got to figure out how to manage it for each moment you’re increasing it.
CA: So what I’m hearing is that you … the model you want to have is that we have birthed this extraordinary child may have superpowers that take humanity to a whole new place. is our collective responsibility to provide the guardrails for this child to collectively teach to be wise and not to tear us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important say this may shift, right? We’ve got to take step as we encounter it. And I think it’s important today that we all do get literate in this technology, figure out how to provide feedback, decide 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 TED and blowing our minds.
(Applause)