We started seven years ago because we felt like something really interesting happening in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to see far this whole field has come since then. And it’s really gratifying hear from people like Raymond who are using the technology we are building, and others, so many wonderful things. We hear from people 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 an historic period right now where we as a world are going to 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 current state of that technology some of the underlying design principles that we hold dear.
So the first thing I’m going to show you what it’s like to build a tool for an AI than building it for a human. So we have a new DALL-E model, generates images, and we are exposing it as an app for ChatGPT to use on 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 that you get of ChatGPT. And here we go, it’s not just the idea for 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 what it can do on your behalf in terms of carrying out your intent. And I’ll out, this is 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 wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended ChatGPT other tools too, for example, memory. You can say “save this for later.” And the interesting thing these tools is they’re very inspectable. So you get little pop up here that says “use the DALL-E app.” by the way, this is coming to you, all ChatGPT users, over upcoming months. And you can under the hood 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 to provide feedback to them.
Now it’s saved for later, and let me show you what it’s like to that information and to integrate with other applications too. can say, “Now make a shopping list for the tasty I was suggesting earlier.” And make it a little for the AI. “And tweet it out for all the TED out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely to know how it tastes.
But you can see that ChatGPT is all these different tools without me having to tell explicitly which ones to use in any situation. And this, I think, shows a new way thinking about the user interface. Like, we are so used to of, well, we have these apps, we click between them, we copy/paste them, and usually it’s a great experience within an app as long 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 is able sort of take away all those details from you. So don’t have to be the one who spells out every single sort of little of what’s supposed to happen.
And as I said, this is a live demo, sometimes the unexpected will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And you see we sent a list of ingredients to Instacart. Here’s everything you need. And the thing that’s interesting is that the traditional UI is still very valuable, right? If you at this, you still can click through it and of modify the actual quantities. And that’s something that I think shows that they’re going away, traditional UIs. It’s just we have a new, way to build them. And now we have a tweet that’s been drafted our review, which is also a very important thing. 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 to. And 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 slides. Now, the important thing about how we build this, it’s not just about building these tools. It’s about the AI how to use them. Like, what do 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 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 it through feedback. Have a human teacher who provides rewards and punishments as it tries things out and things that are either good or bad.
And this is how we train ChatGPT. It’s a two-step process. First, we produce what Turing have called a child machine through an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes next in text you’ve never seen before.” And process imbues it with all sorts of wonderful skills. For example, if you’re shown a problem, the only way to actually complete that math problem, to say comes next, that green nine up there, is to actually solve math problem.
But we actually have to do a second step, too, which is to teach AI what to do with those skills. And for this, we feedback. We have the AI try out multiple things, give us multiple suggestions, and then human rates them, says “This one’s better than that one.” this reinforces not just the specific thing that the AI said, very importantly, the whole process that the AI used produce that answer. And this allows it to generalize. It allows it to teach, to of infer your intent and apply it in scenarios it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the we have to teach the AI are not what 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 teach students wonderful things. one problem, it doesn’t double-check students’ math. If there’s some bad in there, it will happily pretend that one plus one equals three run with it.” So we had to collect some feedback data. Sal himself was very kind and offered 20 hours of his time to provide feedback to the machine alongside our team. And the course of a couple of months we were 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 improvements to models this way. And when you push that thumbs down in ChatGPT, that actually is kind like sending up a bat signal to our team say, “Here’s an area of weakness where you should gather feedback.” And so when you that, that’s one way that we really listen to our users and make we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. If think about asking a kid to clean their room, if all you’re doing is inspecting floor, you don’t know if you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As we to harder tasks, we will have to scale our ability to provide high-quality feedback. But this, the AI itself is happy to help. It’s happy to us provide even better feedback and to scale our ability to supervise 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 learning from human feedback. And the model says two months passed. But is true? Like, these models are 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 can check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the AI a new tool. This one a browsing tool where the model can issue search queries and into web pages. And it actually writes out its whole chain of thought as does it. It says, I’m just going to search for and it actually does the search. It then it the publication date and the search results. It then is another search query. It’s going to click into the blog post. all of this you could do, but it’s a very tedious task. It’s not thing that humans really want to do. It’s much more fun to be in driver’s seat, to be in this manager’s position where can, if you want, triple-check the work. And out come citations so you can actually go and very verify any piece of this whole chain of reasoning. it actually turns 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 interesting to me about this whole process is that it’s this many-step between a human and an AI. Because a human, using fact-checking tool is doing it in order to produce data for another AI to become more to a human. And I think this really shows the shape of that we should expect to be much more common in future, where we have humans and machines kind of carefully and delicately designed in how they fit into a problem how we want to solve that problem. We make sure the humans are providing the management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re able actually create even more trustworthy machines. And I think that time, if we get this process right, we will be able solve impossible problems.
And to give you a sense of how impossible I’m talking, I think we’re going to be able to rethink almost every aspect of how interact with computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really that much in that time. And here is a spreadsheet 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 me show 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 scientist would. And so you can just literally upload a 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 only information here the name of the file, the column names like saw and then the actual data. And from that it’s able infer 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 people papers and therefore that’s what these things are and that these are integer values so therefore it’s a number of authors in the paper,” like all of that, that’s work for human to do, and the AI is happy to help it.
Now I don’t even know what I want ask. So fortunately, you can ask the machine, “Can make some exploratory graphs?” And once again, this is super high-level instruction with lots of intent behind it. But I don’t even know what I want. And AI kind of has to infer what I might be in. And so it comes up with some good ideas, I think. So a histogram of the number of per paper, time series of papers 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, nice bell curve. You see that three is kind the most common. It’s going to then make this nice plot of papers per year. Something crazy is happening in 2023, though. Looks like we were on an exponential and it off the cliff. What could be going on there? the way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year 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 that to make a fair projection? So we’ll see, is the kind of ambitious one.
(Laughter)
So you know, again, feel like there was more I wanted out of the machine here. 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 hood, the AI is just writing code again, so you want 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 for that, it know what I want.
Now we’ll cut back to the again. This slide shows a parable of how I think we … vision of how we may end up using this technology in the future. A person brought very sick dog to the vet, and the veterinarian made a bad call to say, “Let’s just and see.” And the dog would not be here today 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 to talk to a professional, here are some hypotheses.” brought that information to a second vet who used it to the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely them. But this story, I think, shows that a human a medical professional and with ChatGPT as a brainstorming 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 how to integrate these systems into our world.
And one thing I believe really deeply, is that getting right is going to require participation from everyone. And that’s for 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 thing to away from this talk, it’s that this technology just looks different. Just different anything people had anticipated. And so we all have to literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I that we can achieve the OpenAI mission of ensuring artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that every mind out here there’s a feeling of reeling. Like, I suspect that a very large number of viewing this, you look at that and you think, “Oh goodness, pretty much every single thing about the way work, I need to rethink.” Like, there’s just new 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 also scary. So let’s talk, Greg, let’s talk.
I mean, I guess first question actually is just how the hell have done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why is you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re all building on shoulders giants, right, there’s no question. If you look at compute progress, the algorithmic progress, the data progress, all of those really industry-wide. But I think within OpenAI, we 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 things that didn’t work, so you only see the things did. And I think that the most important thing has to get teams of people who are very different from each other to work together harmoniously.
CA: we have the water, by the way, just brought here? think we’re going to need it, it’s a dry-mouth topic. isn’t there something also just about the fact that you saw something in language models that meant that if you continue to in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, I think the story 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, exactly how to do it? I think that in the early days, didn’t know. We tried a lot of things, and one person was working training a model to predict the next character in Amazon reviews, and got a result where — this is a syntactic process, you expect, know, the model will predict where the commas go, where the nouns and verbs are. But he actually 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 just like, come on, anyone can that. But 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 got to scale this thing, you’ve to see where it goes.
CA: So I think helps explain 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 impossible that that could come from a prediction machine. Just the stuff you showed just now. And the key idea of emergence is that when get more of a thing, suddenly different things emerge. It all the time, ant colonies, single ants run around, when you bring of them together, you get these ant colonies that completely emergent, different behavior. Or a city where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, suburbs and cultural centers and traffic jams. Give me one moment for you when saw just something pop that just blew your mind that just did not see coming.
GB: Yeah, well, so can try this in ChatGPT, if you add 40-digit —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, means it’s really learned an internal circuit for how to do it. And the really interesting thing actually, if you have it add like a 40-digit number a 35-digit number, it’ll often get it wrong. And so 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 than there are in the universe. So it had to have learned something general, that it hasn’t really fully yet learned that, Oh, can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So 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 you didn’t that it was going to be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science we’re starting to really get good at is predicting some of emergent capabilities. And to do that actually, one of the things think is very undersung in this field is sort engineering quality. Like, we had to rebuild our entire stack. When you think building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get single piece of the stack engineered properly, and then you can start these predictions. There are all these incredibly smooth scaling curves. They tell something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these curves in there. And now we’re 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 something about this that actually smooth scaling, even though it’s still early days.
CA: So here is, one of the fears then, that arises from this. If it’s fundamental to what’s happening here, that as you scale up, emerge that you can maybe predict in some level of confidence, but it’s capable surprising you. Why isn’t there just a huge risk of truly terrible emerging?
GB: Well, I think all of are questions of degree and scale and timing. And I one thing people miss, too, is sort of the integration with the world also this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s important to deploy incrementally. And so I think that what we kind of see right now, if you at this talk, a lot of what I focus on is providing high-quality feedback. Today, the 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. But summarizing a book, like, that’s a hard thing to supervise. Like, do you know if this book summary is any good? You to read the whole book. No one wants to that.
(Laughter) And so I think that the important thing will be that we take this step step. And that we say, OK, as we move on to book summaries, we to supervise this task properly. We have to build up a track with these machines that they’re able to actually carry our intent. And I think we’re going to have to produce better, more efficient, more reliable ways of scaling this, sort of making the machine be aligned with you.
CA: So we’re going hear later in this session, there are critics who that, you know, there’s no real understanding inside, the is going to always — we’re never going to that it’s not generating errors, that it doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, but that the of the scale and the human feedback that you about is basically going to take it on that of actually getting to things like truth and wisdom and so forth, a high degree of confidence. Can you be sure of that?
GB: Yeah, well, I think the OpenAI, I mean, the short answer is yes, believe that is where we’re headed. And I think that the approach here has always been just like, let reality hit you in face, right? It’s like this field is the field of broken promises, of all experts saying X is going to happen, Y is how it works. People been saying neural nets aren’t going to work for 70 years. They haven’t been right yet. might be right maybe 70 years plus one or something like that is you need. But I think that our approach has always been, you’ve got to push to the limits this technology to really see it in action, because that tells you then, oh, here’s we can move on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, the right way to do this is to put it out there in public then harness all this, you know, instead of just your team giving feedback, the world is now feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, the original that I heard on OpenAI when you were founded as a nonprofit, well you were there as great sort of check on the big companies doing their unknown, evil thing with AI. And you were going to build that sort of, you know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s kind what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent such shockwaves the tech world that now 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 guardrails or we die. know, how do you, like, make the case that what have done is responsible here 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 very beginning, when we were thinking about how to build artificial general intelligence, actually have it all of humanity, like, how are you supposed to do that, right? And that default of being, well, you build in secret, you get this powerful thing, and then you figure out the safety of it then you push “go,” and you hope you got 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 that I see, which is that you do let reality hit you in the face. And I think do give people time to give input. You do have, before machines are perfect, before they are 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 it was generate misinformation, try tip elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on the table. You that in that box is something that, there’s a very strong chance it’s something absolutely glorious that’s to give beautiful gifts to your family and to everyone. But there’s also a one percent thing in the small print that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils on the world. Do open that box?
GB: Well, so, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a story that I haven’t actually told before, which that shortly after we started OpenAI, I remember I in Puerto Rico for an AI conference. I’m sitting the hotel room just looking out over this wonderful water, all these people having a good time. you think about it for a moment, if you could for basically that Pandora’s box to be five years or 500 years away, which would you pick, right? On one 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 you pick? And know, I just really felt it in the moment. I was like, of course do the 500 years. My brother was in the military at the time and like, he his life on the line in a much more real than any of us typing things in computers and this technology at the time. And so, yeah, I’m sold on the you’ve got to approach this right. I don’t think that’s quite playing the field as it 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 almost 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, you get overhang, which means that if someone does, or the that someone does manage to connect to the circuit, then you have this very powerful thing, no one’s had any to adjust, who knows what kind of safety precautions you get. And so I think that thing I take away is like, even you think about of other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, in what humans could do. But I actually think that you look at capability, it’s been quite smooth over time. And so the history, I think, every technology we’ve developed has been, you’ve got to do it incrementally and you’ve got 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 us to have is that we have birthed this extraordinary that may have superpowers that take humanity to a whole new place. It is collective responsibility to provide the guardrails for this child to collectively teach to be wise and not to tear us all down. Is basically the model?
GB: I think it’s true. And think it’s also important to say this may shift, right? We’ve to take each step as we encounter it. And I think it’s incredibly important today that we do get literate in this technology, figure out how to the feedback, decide what we want from it. And my is that that will continue to be the best path, but it’s so we’re honestly having this debate because we wouldn’t otherwise it weren’t out there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)