We started seven years ago because we felt like something really interesting happening in AI and we wanted to help steer it in positive direction. It’s honestly just really amazing to see how far this whole field has since then. And it’s really gratifying to hear from people like Raymond are using the technology we are building, and others, for so many wonderful things. hear from people who are excited, we hear from people who are concerned, we hear from people feel both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an period right now where we as a world are going to define a technology that will so important for our society going forward. And I believe that we can this for good.
So today, I want to show you the state of that technology and some of the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s to build a tool for an AI rather than building it a human. So we have a new DALL-E model, which generates images, we are exposing it as an app for ChatGPT to on your behalf. And you can do things like ask, you know, suggest a post-TED meal and draw a picture of it.
(Laughter)
Now you all of the, sort of, ideation and creative back-and-forth and taking care of details for you that you get out of ChatGPT. here we go, it’s not just the idea for the meal, but very, very detailed spread. So let’s see what we’re going get. But ChatGPT doesn’t just generate images in this — sorry, it doesn’t generate text, it also generates an image. And is something that really expands the power of what it can do on your behalf terms of carrying out your intent. And I’ll point out, this is all a live demo. This all generated by the AI as we speak. So I actually don’t even know what we’re going see. This looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve ChatGPT with other tools too, for example, memory. You can say “save this for later.” And the interesting about these tools is they’re very inspectable. So you get this pop up here that says “use the DALL-E app.” And by the way, this is coming you, all ChatGPT users, over upcoming months. And you can look under the hood and see what it actually did was write a prompt just a human could. And so you sort of have this ability to inspect how the machine is these tools, which allows us to provide feedback to them.
Now it’s saved for later, and let me show you it’s like to use that information and to integrate with other too. You can say, “Now make a shopping list for the tasty thing I was suggesting earlier.” make it a little tricky for the AI. “And tweet it out all the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how it tastes.
But you can see that is selecting all these different tools without me having to tell it which ones to use in any situation. And this, I think, shows a new way 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 great experience within an app as long as you of know the 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 of tools, the AI able to sort of take away all those details you. So you don’t have to be the one who spells out every single sort little piece of what’s supposed to happen.
And as I said, this is live demo, so sometimes the unexpected will happen to us. But let’s take a look at Instacart shopping list while we’re at it. And you can see we sent a list ingredients to Instacart. Here’s everything you need. And the that’s really interesting is that the traditional UI is still very valuable, right? If you look this, you 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 to build them. And now we have a tweet that’s drafted for our review, which is also a very important thing. can click “run,” and there 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 will be able to 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 the AI how to use them. Like, what do we want it to do when we ask these very high-level questions? And do this, we use an old idea. If you go to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program an 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 tries things out and things that are either good or bad.
And this is exactly how train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an learning process. We just show it the whole world, whole internet and 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, the only to actually complete that math problem, to say what comes next, green nine up there, is to actually solve the math problem.
But we actually have to do second step, too, which is to teach the AI what to do with those skills. for this, we provide feedback. We have the AI try out things, give us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not just the thing that the AI said, but very importantly, the process that the AI used to produce that answer. this allows it to generalize. It allows it to teach, sort of infer your intent and apply it in scenarios that it hasn’t seen before, it hasn’t received feedback.
Now, sometimes the things we to teach the AI are not what you’d expect. For example, we first showed GPT-4 to Khan Academy, they said, “Wow, this 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 bad math in there, it will happily pretend that one plus equals three and run with it.” So we had to collect some feedback data. Sal Khan himself was kind and offered 20 hours of his own time to feedback to the machine alongside our team. And over the course a couple of months we were able to teach the that, “Hey, you really should 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 signal our team to say, “Here’s an area of weakness you should gather feedback.” And so when you do that, that’s one way that we really listen to our users make sure we’re building something that’s more useful for everyone.
Now, high-quality feedback is a hard thing. If you think about a kid to clean their room, if all you’re doing is the floor, you don’t know if you’re just teaching them to all the toys in the closet. This is a nice DALL-E-generated image, by way. And the same sort of reasoning applies to AI. we move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the AI is happy to help. It’s happy to help us provide even feedback and to scale our ability to supervise the as time goes on. And let me show you what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between these two foundational blogs on 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 provide some feedback. 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 the AI a new tool. This is a browsing tool where the model can issue search queries and click into pages. And it actually writes out its whole chain of thought as 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 click into blog post. And all of this you could do, it’s a very tedious task. It’s not a thing that humans really to do. It’s much more fun to be in driver’s seat, to be in this manager’s position where you can, you want, triple-check the work. And out come citations you can actually go and very easily verify any piece this whole chain of reasoning. And it actually turns out two months was wrong. months 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 more to a human. And I think this really shows the shape of something that should expect to be much more common in the future, where we humans and machines kind of very carefully and delicately in how they fit into a problem and how we to solve that problem. We make sure that the are providing the management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re to actually create even more trustworthy machines. And I think that time, if we get this process right, we will be to solve impossible problems.
And to give you a sense just how impossible I’m talking, I think we’re going to be able rethink almost every aspect of how we interact 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 changed that much in that time. And here is specific spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. And you can see the data right here. But let me show you the take on how to analyze a data set like this.
So we can ChatGPT access to yet another tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And so can just literally upload a file and ask questions it. And very helpfully, you know, it knows the name the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it you.” The only information here is the name of file, the column names like you saw and then the actual data. And from that it’s able 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 site that people submit papers and 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 all of that, that’s for a human to do, and the AI is to help with it.
Now I don’t even know I want to ask. So fortunately, you can ask the machine, “Can you make some exploratory graphs?” 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 up with some good ideas, I think. So a histogram the number of authors per paper, time series of per year, word cloud of the paper titles. All of that, think, will be pretty interesting to see. And the thing is, it can actually do it. Here we go, a nice bell curve. see that three is kind of the most common. It’s going then make this nice plot of the papers per year. Something is happening in 2023, though. Looks like we were an exponential and it dropped off the cliff. What could be going on there? By way, all this is Python code, you can inspect. 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. makes this year look really bad. Of course, the is that the 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 2022 were even posted by April 13?] So April 13 the cut-off date I believe. Can you use that make a fair projection? So we’ll see, this is 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 a little bit of an overreach for to have sort of, inferred magically that this is what I wanted. But I inject intent, I provide this additional piece of, you know, guidance. And under the hood, the is just writing code again, so if you want to what it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, but know what I want.
Now we’ll cut back to slide again. This slide shows a parable of how I think … A vision of how we may end up using this in the future. A person brought his very sick dog to the vet, and the veterinarian made bad call to say, “Let’s just wait and see.” And the dog would not be here had he listened. In the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not a vet, you need to to a professional, here are some hypotheses.” He brought that information to second vet who used it to save 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 with a professional and with ChatGPT as a brainstorming partner was able to achieve an outcome that would not 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 getting AI right is going to require participation from everyone. that’s for deciding how we want it to slot in, that’s for setting the rules of the road, for an AI will and won’t do. And if there’s one to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. And so we all have become literate. And 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 benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within mind out here there’s a feeling of reeling. Like, I suspect a very large number of people viewing this, you look that and you think, “Oh my goodness, pretty much every single thing about way I work, I need to rethink.” Like, there’s just possibilities there. Am I right? Who thinks that they’re having to rethink the way that 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 my question actually is just how the hell have you 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 building on shoulders of giants, right, there’s no question. you look at the compute progress, the algorithmic progress, the data progress, all of those are really industry-wide. I think within OpenAI, we made a lot of deliberate choices from the early days. And the first was just to confront reality as it lays. And that we just thought really hard about like: is it going to take to make progress here? tried a lot of things that didn’t work, so only see the things that did. And I think that most important thing has been to get teams of people who are very different from each other work together harmoniously.
CA: Can we have the water, by 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 that you saw something in these language models that meant that 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 that high level, deep learning, like we always knew that what we wanted to be, was a deep learning lab, and exactly 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 a model predict the next character in Amazon reviews, and he got a result where — this is syntactic process, you expect, you know, the model will where the commas go, where the nouns and verbs are. But he actually got a state-of-the-art sentiment classifier out of it. This model could tell you if a review was positive or negative. mean, today we are just like, come on, anyone can that. But this was the first time that you saw this emergence, this sort of that emerged from this underlying syntactic process. And there 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 baffles everyone at this, because these things are described as prediction machines. And yet, what we’re seeing out them feels … it just feels impossible that that could come from a prediction machine. Just stuff you showed us just now. And the key idea of is that when you get more of a thing, suddenly different things emerge. happens all the time, ant colonies, single ants run around, when you bring enough of them together, you get ant colonies that show completely emergent, different behavior. Or a where a few 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 moment for you when you saw just something pop that just your mind that you just did not see coming.
GB: Yeah, well, so you can try this in ChatGPT, you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will it, which means it’s really learned an internal circuit for to do it. And the really interesting thing is actually, you have it add like a 40-digit number plus 35-digit number, it’ll often get it wrong. And so you see that it’s really learning the process, but it hasn’t fully generalized, right? It’s like you can’t the 40-digit addition table, that’s more atoms than there are in the universe. So it to have learned something general, but that it hasn’t fully yet learned that, Oh, I can sort of generalize this to adding arbitrary numbers of lengths.
CA: So what’s happened here is that you’ve allowed it scale up and look at an incredible number of of text. And it is learning things that you didn’t know that it going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting to really get good is predicting some of these emergent capabilities. And to do that actually, of the things I think is very undersung in this field is of engineering quality. Like, we had to rebuild our entire stack. When you think about building a rocket, tolerance has to be incredibly tiny. Same is true machine learning. You have to get every single piece the stack engineered properly, and then you can start doing predictions. There are all these incredibly smooth scaling curves. tell you something deeply fundamental about intelligence. If you look at our GPT-4 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, 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 is 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 you scale up, things emerge that you can maybe predict some level of confidence, but it’s capable of surprising you. Why isn’t there a huge risk 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, of, very powerful thing 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 this talk, a of what I focus on is providing really high-quality feedback. Today, tasks that we do, you can inspect them, right? It’s easy to look at that math problem and be like, no, no, no, machine, seven was the correct answer. even summarizing a book, like, that’s a hard thing to supervise. Like, how do you if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) And I think that the important thing will be that we this step by step. And that we say, OK, we move on to book summaries, we have to supervise this task properly. We to build up a track record with these machines that they’re able to carry out our intent. And I think we’re going have to produce even better, more efficient, more reliable ways of scaling this, sort of like the machine be aligned with you.
CA: So we’re going to hear later in this session, there critics who say that, you know, there’s no real understanding inside, the system is to always — we’re never going to know that it’s not generating errors, that doesn’t have common sense and so forth. Is it your belief, Greg, that is true at any 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 of actually getting things like truth and wisdom and so forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I that is where we’re headed. And I think that the OpenAI approach here always been just like, let reality hit you in the face, right? It’s like field is the field of broken promises, of all experts saying X is going to happen, Y is how it works. People have been neural nets aren’t going to work for 70 years. haven’t been right yet. They might be right maybe 70 years plus or something like that is what you need. But I that our approach has always been, you’ve got to push to limits of this technology to really see it in action, because that tells 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 out 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 going to emerge, it is there. So, you know, the original story that I heard on OpenAI when were founded as a nonprofit, well you were there the great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were going to models that sort of, you know, somehow held them 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 GPT, especially ChatGPT, sent such shockwaves through the tech that now Google and Meta and so forth are all scrambling to up. And some of their criticisms have been, you are us to put this out here without proper guardrails or we die. know, how do you, like, make the case that what you have done is responsible and not reckless.
GB: Yeah, we think about these all the time. Like, seriously all the time. And don’t think we’re always going to get it right. one thing I think has been incredibly important, from the beginning, when we were thinking about how to build artificial general intelligence, actually have it benefit of humanity, like, how are you supposed to do that, right? And that 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 that plan. someone else does. But for me, that was always terrifying, it didn’t feel right. And so I think this alternative approach is the only other path that I see, which that you do let reality hit you in the face. And I think you do give people to give input. You do have, before these machines are perfect, before they super powerful, that you actually have the ability to them in action. And we’ve seen it from GPT-3, right? GPT-3, we really were that the number one thing people were going to do with was generate misinformation, try to tip elections. Instead, the number one thing generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a experiment for you. Suppose you’re sitting in a room, there’s a on the table. You believe that in that box something that, there’s a very strong chance it’s something absolutely that’s going to give beautiful gifts to your family to everyone. But there’s actually also a one percent in the small print there that 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 I haven’t actually told before, which is that shortly we started OpenAI, I remember I was in Puerto Rico an AI conference. I’m sitting in the hotel room looking out over this wonderful water, all these people having good time. And you think about it for a moment, if you 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 to be 500 away and 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 in the military at the time and like, he his life on 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 to approach this right. But I don’t think that’s quite playing the field as truly lies. Like, if you look at the whole history of computing, I really mean it I say that this is an industry-wide or even just like a human-development- of-technology-wide shift. And the more that sort of, don’t put together the pieces that are there, right, we’re still faster computers, we’re still improving the algorithms, all of these things, they happening. And if you don’t put them together, you get 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 time to adjust, knows what kind of safety precautions you get. And so I that one thing I take away is like, even you think about development of other sort of technologies, about nuclear weapons, people talk about being like a zero to one, sort of, change 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 every we’ve developed has been, you’ve got to do it and you’ve got to figure out how to manage it each moment that you’re increasing it.
CA: So what I’m hearing that you … the model you want us to have is that we have birthed this child that may have superpowers that take humanity to whole new place. It is our collective responsibility to provide the guardrails this child to collectively teach it to be wise 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 technology, figure out how to provide the feedback, decide what we want from it. And hope is that that will continue to be the path, but it’s so good we’re honestly having this debate we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much for coming TED and blowing our minds.
(Applause)