We started seven years ago because we felt like something really was happening in AI and we wanted to help steer it in 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 people like Raymond who are using the technology we building, and others, for so many wonderful things. We hear people who are excited, we hear from people who are concerned, we hear from people feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering an historic period now where we as a world are going to a technology that will be so important for our society going forward. And believe that we can manage this for good.
So today, I want to show you current state of that technology and some of the underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s like build a tool for an AI rather than building it for human. So we have a new DALL-E model, which generates images, and 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 get all of the, sort of, ideation and creative back-and-forth and taking care of the for you that you get out of ChatGPT. And here 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 that is something really expands the power of what it can do on your behalf in terms of carrying your intent. And I’ll point out, this is all a live demo. This is all 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. can say “save this for later.” And the interesting thing about these tools is they’re very inspectable. So get this little pop up here that says “use DALL-E app.” And by the way, this is coming to you, ChatGPT users, over upcoming months. And you can look under the and see that what it actually did was write a prompt like a human could. And so you sort of have this ability inspect how the machine is using these tools, which allows us 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 applications too. You can say, “Now a shopping list for the tasty thing I was earlier.” And make it a little tricky for the AI. “And tweet it out for the TED viewers out there.”
(Laughter)
So if you do make this wonderful, meal, I definitely want to know how it tastes.
But you can see that ChatGPT selecting all these different tools without me having to tell it explicitly which to use in any situation. And this, I think, shows a new of thinking about the user interface. Like, we are 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 know all the options. Yes, I like you to. Yes, please. Always good to be polite.
(Laughter)
And by 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 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 Instacart shopping list we’re at it. And you can see we sent a list ingredients to Instacart. Here’s everything you need. And the thing that’s really interesting is the traditional 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 just we a new, augmented way to build them. And now have a tweet that’s been drafted for our review, which 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 work of the AI if we want to. And so 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, important thing about how we build this, it’s not about building these tools. It’s about teaching the AI how to use them. Like, what do even want it to do 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 paper on Turing test, he says, you’ll never program an answer to this. Instead, can learn it. You could build a machine, like human child, and then teach it through feedback. Have a human teacher 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 unsupervised learning process. just show it the whole world, the whole internet say, “Predict what comes next in text you’ve never seen before.” this process imbues it with all sorts of wonderful skills. For example, you’re shown a math problem, the only way to complete that math problem, to say what comes next, that 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 skills. And 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 than that one.” And this reinforces not just the specific thing that the AI said, but importantly, the whole process that the AI used to produce answer. And this allows it to generalize. It allows it to teach, sort of infer your intent and apply it in scenarios it hasn’t seen before, that it hasn’t received feedback.
Now, the things we have to teach the AI are not you’d expect. For example, when we first showed GPT-4 Khan Academy, they said, “Wow, this is so great, We’re going to be able to students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math there, it will happily pretend that one plus one equals three run with it.” So we had to collect some data. Sal Khan himself was very kind and offered 20 hours of own time to provide feedback to the machine alongside our team. And over the of a couple of months we were able to teach the AI that, “Hey, you really should back on humans in this specific kind of scenario.” And we’ve actually made lots and lots of improvements the models this way. And when you push that down in ChatGPT, that actually is kind of like up a bat signal to our team to say, “Here’s an area of weakness where you gather 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 everyone.
Now, providing high-quality feedback is a hard thing. If think about asking a kid to clean their room, all you’re doing is inspecting the floor, you don’t if you’re just teaching them to stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same sort of applies to AI. As we move to harder tasks, we will have to scale our ability to high-quality feedback. But for this, the AI itself is happy help. It’s happy to help us provide even better and to scale our ability to supervise the machine as time goes on. And me show you what I mean.
For example, you ask GPT-4 a question like this, of how much time passed between these two foundational blogs on unsupervised and learning from human feedback. And the model says months passed. But is it true? Like, these models are not 100-percent reliable, although they’re getting every time we provide some feedback. But we can actually use the AI to fact-check. And it actually check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the AI a new tool. one is a browsing tool where the model can issue queries and click into web pages. And it actually writes out whole chain of thought as it does it. It says, I’m just going to search for and it 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 could do, but it’s a 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 in 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 turns out months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. so thing that’s so interesting to me about this process is that it’s this many-step collaboration between a human and an AI. a human, using this fact-checking tool is doing it in order produce data for another AI to become more useful to a human. I think this really shows the shape of something that we should to be much more common in the future, where we have humans and machines kind of very and delicately designed 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 in a way that’s inspectable trustworthy. And together we’re able to actually create even trustworthy machines. And I think that over time, if get this process right, we will be able to solve impossible problems.
And to give you a sense just how impossible I’m talking, I think we’re going to 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 they’ve really changed that much in that time. And here is a specific of all the AI papers on the arXiv for past 30 years. There’s about 167,000 of them. And can see there the data right here. But let show you the ChatGPT take on how to analyze a data like this.
So we can give ChatGPT access to yet another tool, this a Python interpreter, so it’s able to run code, just a data scientist would. And so you can just literally upload file and ask questions about it. And very helpfully, you know, it knows 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 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 of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a that people submit papers and therefore that’s what these things are and that these are values and so therefore it’s a number of authors in the paper,” like all of that, that’s work a human to do, and the AI is happy to with it.
Now I don’t even know what I to ask. So fortunately, you can ask the machine, “Can make some exploratory 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 kind of has to infer what I might be interested in. And so it comes with some good ideas, I think. So a histogram of the number of authors per paper, time of papers per year, word cloud of the paper titles. All of that, I think, will be interesting to see. And the great thing is, it actually do it. Here we go, a nice bell curve. You that three is kind of the most common. It’s going to then make this nice plot the papers per year. Something crazy is happening in 2023, though. Looks like we were 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 see all these wonderful things that appear in these titles.
But I’m pretty unhappy about this 2023 thing. It makes this look really bad. Of course, the problem is that the is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by 13?] So April 13 was the cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there 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 sort of, inferred that this is what I wanted. But I inject intent, I provide this additional piece of, you know, guidance. And the hood, the 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 you noticed, even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back the slide again. This slide shows a parable of how think we … A vision of how we may end up using technology in the future. A person brought his very dog to the vet, and the veterinarian made a bad to say, “Let’s just wait and see.” And the would not be here today had he listened. In the meanwhile, provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you need to talk to a professional, here some hypotheses.” He brought that information to a second vet used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot overly on them. But this story, I think, shows that a human a medical professional and with ChatGPT as a brainstorming partner was to achieve an outcome that would not have happened otherwise. I think is something we should all reflect on, think about as consider how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI right going to require participation from everyone. And that’s for how we want it to slot in, that’s for setting the of the road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s that technology just looks different. Just different from anything people had anticipated. And so we have to become literate. And that’s, honestly, one of 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. 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, you at that and you think, “Oh my goodness, pretty much every single about the way I work, I need to rethink.” Like, there’s just new possibilities there. Am I right? thinks that they’re having to rethink the way that we do things? Yeah, mean, it’s amazing, but it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my question actually is just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees on artificial intelligence. Why is it you who’s come with this technology that shocked the world?
Greg Brockman: I mean, the is, we’re all building on shoulders of giants, right, there’s no question. If you look at compute progress, the algorithmic progress, the data progress, all of those are industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And first one was just to confront reality as it lays. And that 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 that did. And think that the most important thing has been to get teams of people who are very different each other to work together harmoniously.
CA: Can we have the water, by way, just brought here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about fact that you saw something in these language models meant that if you continue to invest in them grow them, that something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story there is pretty illustrative, right? I that high level, deep learning, like we always knew that was we wanted to be, was a deep learning lab, exactly how to do it? I think that in early days, we didn’t know. We tried a lot things, and one person was working on training a to predict the next character in Amazon reviews, and he a result where — this is a syntactic process, expect, you know, the model will predict where the commas go, the nouns and verbs are. But he actually got a state-of-the-art sentiment classifier out of it. This model could tell you a review was positive or negative. I mean, today are just like, come on, anyone can do that. this was the first time that you saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve to scale this thing, you’ve got to see where it goes.
CA: So I think this helps the riddle that baffles everyone looking at this, because these things are described as machines. And yet, what we’re seeing out of them feels … it just feels that that could come from a prediction machine. Just stuff you showed us just now. And the key idea 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 enough of them 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 the number of houses, things emerge, like suburbs and cultural centers and 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 this in ChatGPT, if 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 how to do it. And the really interesting thing actually, if you have it add like a 40-digit 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 like 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 yet learned that, Oh, I can sort of generalize to adding arbitrary numbers of arbitrary lengths.
CA: So what’s here is that you’ve allowed it to scale up and look at an incredible of pieces of text. And it is learning things you didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. one science 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 is very undersung in this field is of engineering quality. Like, we had to rebuild our 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 predictions. There are all these incredibly smooth scaling curves. They tell something deeply fundamental about intelligence. If you look at our GPT-4 blog post, you can see all of curves in there. And now we’re starting to be able to predict. So we were able to predict, example, the performance on coding problems. We basically look at some models 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: 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 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 that what we kind of see right now, if you look at this talk, a lot of what focus on is providing really high-quality feedback. Today, the 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 the correct answer. But even summarizing a book, like, that’s a hard to supervise. Like, how do you know if this book summary is 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 step. And that we say, OK, as we move on to summaries, we have to supervise this task properly. We to build up a track record with these machines that they’re able to actually out 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 in this session, there are critics who say that, you know, there’s no real inside, the system is going to always — we’re never going to that it’s not generating errors, that it doesn’t have common sense so forth. Is it your belief, Greg, that it true at any one moment, but that the expansion of the scale and the human that you 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 confidence. Can you be sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, short answer is yes, I believe that is where we’re headed. And think that the OpenAI approach here has always been like, let reality hit you in the face, right? It’s like this field the field of broken promises, of all these experts X is going to 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 maybe 70 years plus one or something like that is what need. But I think that our approach has always been, you’ve got to push to the limits of technology to really see it in action, because that tells then, oh, here’s how we can move on to a new paradigm. And we haven’t exhausted the fruit here.
CA: I 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 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 founded as a nonprofit, well you were there as the great of check on the big companies doing their unknown, evil thing with AI. And you were going to models that sort of, you know, somehow held them accountable and was of slowing the field down, if need be. Or least that’s kind of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, sent such through the tech world that now Google and Meta and so forth are all to catch up. And some of their criticisms have been, you are us to put this out here without proper guardrails or 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 the time. Like, seriously all time. And I don’t think we’re always going to get right. But one thing I think has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, actually have benefit all of humanity, like, how are you supposed to that, right? And that default plan of being, well, build in secret, you get this super powerful thing, and you figure out the safety 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 think that this alternative approach is the only other path that see, which is that you do let reality hit you in face. And I think you do give people time give input. You do have, before these machines are perfect, they are super powerful, that you actually have the ability see them in action. And we’ve seen it from GPT-3, right? GPT-3, we really were afraid that the number thing people were going to do with it was generate misinformation, try to elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra is bad, but there are things that are much worse. Here’s a thought experiment you. Suppose you’re sitting in a room, there’s a box the table. You believe that in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually also a one percent thing in the print there that says: “Pandora.” And there’s a chance that this could unleash unimaginable evils on the world. Do you open that box?
GB: Well, so, absolutely not. think you don’t do it that way. And honestly, like, I’ll tell you story that I haven’t actually told before, which is that shortly after we OpenAI, I remember I was in Puerto Rico for an AI conference. I’m sitting the hotel room just looking out over this wonderful water, all these people a good time. And you think about it for a moment, you could choose for basically that Pandora’s box to five years away or 500 years away, which would pick, right? On the one hand you’re like, well, maybe for personally, it’s better to have it be five years away. if it gets to be 500 years away and get more time to get it right, which do you pick? And you know, I really felt it in the moment. I was like, of course you do 500 years. My brother was in the military at the time and like, he puts his on the line in a much more real way any of us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, if look at the whole history of computing, I really mean it when I say this is an industry-wide or even just almost like a human-development- of-technology-wide shift. And more that you sort of, don’t put together 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, you get overhang, which means that if someone does, or the moment someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety precautions you get. And so I that one thing I take away is like, even you think about development other sort of technologies, think about nuclear weapons, people talk being like a zero to one, sort of, change in what could do. But I actually think that if you look at capability, it’s quite smooth over time. And so the history, I think, of every technology we’ve developed has been, you’ve to do it incrementally and you’ve got to figure how to manage it for each moment that you’re it.
CA: So what I’m hearing is that you … the model want us to have is that we have birthed this extraordinary child that have superpowers that take humanity to a whole new place. It is collective responsibility to provide the guardrails for this child to teach it 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 each step we encounter it. And I think it’s incredibly important today that all do get literate in this technology, figure out how provide the feedback, decide what we want from it. And my hope that that will continue to be the best path, it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, you so much for coming to TED and blowing minds.
(Applause)