We started OpenAI seven years ago we felt like something really interesting was happening in AI we wanted to help steer it in a positive direction. It’s honestly really amazing to see how far this whole field come since then. And it’s really gratifying to hear people like Raymond who are using the technology we are building, and others, for so wonderful things. We hear from people who are excited, we from people who are concerned, we hear from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels we’re entering an historic period right now where we as world are going to define a technology that will be important for our society going forward. And I believe that we manage 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 the thing I’m going to show you is what it’s like build a tool for an AI rather than building for a human. So we have a new DALL-E model, which images, and we are exposing it as an app for to use on your behalf. And you can do things ask, you know, suggest a nice post-TED meal and draw a of it.
(Laughter)
Now you get all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you get of ChatGPT. And here we go, it’s not just the idea for the meal, but a very, very spread. So let’s see what we’re going to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also generates an image. that is something that really expands the power of what can do on your behalf in terms of carrying out intent. And I’ll point out, this is all a live demo. This is all generated the AI as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry looking at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You can “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this little pop here that says “use the DALL-E app.” And by the way, is coming to you, all ChatGPT users, over upcoming months. And you can look under the and see that what it actually did was write a prompt just like a could. And so you sort of have this ability to how the machine is using these tools, which allows us provide feedback to them.
Now it’s saved for later, and me show you what it’s like to use that 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 the AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So you do make this wonderful, wonderful meal, I definitely to know how it tastes.
But you can see that ChatGPT is selecting all different tools without me having to tell it explicitly ones to use in any situation. And this, I think, shows a new of 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 as you kind of know the menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And by this unified language interface on top of tools, the is able to sort of take away all those details from you. you don’t have to be the one who spells out every single sort of little piece what’s supposed to happen.
And as I said, this is a demo, so sometimes the unexpected will happen to us. let’s take a look at the Instacart shopping list while we’re at it. And you can see we a list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional UI is still very valuable, right? If you at this, you still can click through it and sort of modify actual quantities. And that’s something that I think shows that they’re not going away, UIs. It’s just we have a new, augmented way to build them. now we have a tweet that’s been drafted for review, which is also a very important thing. We can “run,” and there we are, we’re the manager, we’re 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. there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back the slides. Now, the important thing about how we build this, it’s not just about 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 this, we use an old idea. If you go back Alan Turing’s 1950 paper on the Turing test, he says, you’ll never an answer to this. Instead, you can learn it. You could build machine, like a human child, and then teach it through feedback. a human teacher who provides rewards and punishments as tries things out and does things that are either good bad.
And this is exactly how we 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 you’ve never seen before.” And this process imbues it with sorts of wonderful skills. For example, if you’re shown a math problem, the only to actually complete that math problem, to say what comes next, that green nine there, is to actually solve the math problem.
But actually have to do a second step, too, which to teach the AI what to do with those skills. And for this, we provide feedback. have the AI try out multiple things, give us multiple suggestions, then a human rates them, says “This one’s better than that one.” And this reinforces not just the thing that the AI said, but very importantly, the whole process that the AI used to that 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, sometimes the things we have to teach the AI not what you’d expect. For example, when we first showed GPT-4 to Academy, they said, “Wow, this is so great, We’re to be able to teach students wonderful 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 collect some feedback data. Sal Khan himself was very kind and offered 20 of his own time to provide feedback to the machine alongside our team. And over course of a couple of months we were able to teach the that, “Hey, you really should push back on humans in this kind of scenario.” And we’ve actually made lots and lots of to the models this way. And when you push that down in ChatGPT, that actually is kind of like sending a bat signal to our team to say, “Here’s an of weakness where you should gather feedback.” And so 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 asking a kid to clean their room, if all you’re is inspecting the floor, you don’t know if you’re just them to stuff all 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 to scale our to provide high-quality feedback. But for this, the AI itself is happy to help. It’s to help us provide even better feedback and to scale our ability to supervise the machine time goes on. And let me show you what I mean.
For example, you can GPT-4 a question like this, of how much time passed these two foundational blogs on unsupervised learning and learning from human feedback. And model says two months passed. But is it true? Like, these models not 100-percent reliable, although they’re getting better every time we provide some feedback. But we actually use the AI to fact-check. And it can actually check its own work. can say, fact-check this for me.
Now, in this case, I’ve actually given AI a new tool. This one is a browsing where the model can issue search queries and click web pages. And it actually writes out its whole of thought as it does it. It says, I’m just going to search this 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 click into 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 this manager’s where you can, if 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 was wrong. Two 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 between a human and an AI. Because a human, using this fact-checking tool doing it in order to produce data for another AI to become more to a human. And I think this really shows the of something that we should expect to be much more common in the future, where we humans and machines kind of very carefully and delicately designed in they fit into a problem and 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 in a that’s inspectable and trustworthy. And together we’re able to actually create even trustworthy machines. And I think that over time, if we get this process right, we be able to solve impossible problems.
And to give a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every aspect how we 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 they’ve really changed that much in that time. And is a specific spreadsheet of all the AI papers on the for the past 30 years. There’s about 167,000 of them. you can see there the data right here. But let me show you the ChatGPT take on how analyze a data set like this.
So we can give ChatGPT to yet another tool, this one 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 the name of the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the name of the file, column names like you saw and then the actual data. And that it’s able to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, put together world knowledge of knowing that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s these things are and that these are integer values and therefore it’s a number of authors in the paper,” like all that, that’s work for a human to do, and the AI is happy to help with it.
Now don’t even know what I want to ask. So fortunately, you can ask the machine, “Can you make 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 AI kind has to infer what I might be interested in. And so it comes up with some ideas, I think. So a histogram of the number of per paper, time series of papers per year, word cloud of paper titles. All of that, I think, will be interesting to see. And the great thing is, it can actually it. Here we go, a nice bell curve. You that three is kind of the most common. It’s to then make this nice plot of the papers year. Something crazy is happening in 2023, though. Looks like were on an exponential and it dropped off the cliff. What could be going on there? the 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 about this 2023 thing. It makes this year look really bad. Of course, problem 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 of papers in 2022 were even posted by April 13?] So April 13 was cut-off date I believe. Can you use that to make a fair projection? So we’ll see, this the kind of ambitious one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted to notice this thing, maybe it’s a little bit of an overreach 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, the AI is writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does correct projection.
(Applause)
If you noticed, it even updates title. I didn’t ask for that, but it know what I want.
Now we’ll cut back to the again. This slide shows a parable of how I think we … A vision of how we may up using this technology in the future. A person brought his very sick to the vet, and the veterinarian made a bad call say, “Let’s just wait and see.” And the dog would 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 talk to a professional, here are some hypotheses.” He brought that information to a second vet who it to save 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 with a medical professional and with as a brainstorming partner was able to achieve an outcome that would not have 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 getting AI 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, what an AI will and won’t do. And if there’s thing to take away from this talk, it’s that this technology just different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, one the reasons we released ChatGPT.
Together, I believe that can achieve the OpenAI mission of ensuring that artificial intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I that within every mind out here there’s a feeling of reeling. Like, I suspect that a large number of people viewing this, you look at that and you think, “Oh my goodness, much every single thing about the way I work, I need rethink.” Like, there’s just new possibilities there. Am I right? thinks that they’re having to rethink the way that we things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the have you done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why it you who’s come up with this technology that shocked the world?
Greg Brockman: mean, the truth is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the algorithmic progress, the progress, all of those are really industry-wide. But I within OpenAI, we made a lot of very deliberate choices from the days. And the first one was just to confront reality as it lays. And we just thought really hard about like: What is going to take to make progress here? We tried a lot of that didn’t work, so you only see the things that did. And I that the most important thing has been to get teams of people are very different from each other to work together harmoniously.
CA: Can we the water, by the way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something also just the fact that you saw something in these language models that meant that you continue to invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, I mean, honestly, I think the there is pretty illustrative, right? I think that high level, deep learning, like we always that was what we wanted to be, was a deep learning lab, 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 model to the next character in Amazon reviews, and he got a result where — this a 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 positive or negative. I mean, today we are just like, on, anyone can do that. But this was the first time you saw this emergence, this sort of semantics that from this underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve got to see it goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things are as prediction machines. And yet, what we’re seeing out of them feels … it just impossible that that could come from a prediction machine. Just stuff you showed us just now. And the key of emergence is that when you get more of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you bring of them together, you get these ant colonies that show emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow the number 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, you can try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the will do it, which means it’s really learned an internal circuit for how to do it. And the interesting thing is actually, if you have it add like 40-digit number plus a 35-digit number, it’ll often get it wrong. And you can see that it’s really learning the process, but it hasn’t generalized, right? It’s like you can’t memorize the 40-digit table, that’s more atoms than there are in the universe. So it had have learned something general, but that it hasn’t really yet learned that, Oh, I 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 up and look an incredible number of pieces of text. And it learning things that you didn’t know that it was to be capable of learning.
GB Well, yeah, and it’s 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, one of the things think is very undersung in this field is sort engineering quality. Like, we had to rebuild our entire stack. When think about building a rocket, every 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 these predictions. There are these incredibly smooth scaling curves. They tell you something deeply fundamental intelligence. If you look at our GPT-4 blog post, you see all of these curves in there. And now we’re starting to be to predict. So we were able to predict, for example, the performance coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s something about this that is actually smooth scaling, even it’s still early days.
CA: So here is, one of the big fears then, arises from this. If it’s fundamental to what’s happening here, that as you scale up, things emerge you can maybe predict in some level of confidence, but it’s capable of surprising you. Why isn’t just a huge risk of something truly terrible emerging?
GB: Well, think all of these are questions of degree and scale and timing. And I think thing people miss, too, is sort of the integration with the world also this incredibly emergent, sort of, very powerful thing too. And so that’s one the reasons that we think it’s so important to incrementally. And so I think that what we kind of 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 can them, right? It’s very easy to look at that math 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, how do you know if book summary is any 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 by step. And that we say, OK, as 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 actually carry out our intent. And I think we’re going to have produce even better, more efficient, more reliable ways of this, sort of like making the machine be aligned 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, system is going to always — we’re never going know that it’s not generating errors, that it doesn’t have common sense so forth. Is it your belief, Greg, that it is true any one moment, but that the expansion of the scale and the human feedback that you talked about basically going to take it on that journey of actually getting to things like truth and wisdom so forth, with a high degree of confidence. Can you be of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer is yes, believe that is where we’re headed. And I think that the OpenAI approach here has always just like, let reality hit you in the face, right? It’s like this field is field of broken promises, of all these experts saying is going to happen, Y is how it works. People have been saying neural nets aren’t going to for 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 our approach has been, you’ve got to push to the limits of technology to really see it in action, because that you then, oh, here’s how we can move on to a paradigm. And we just haven’t exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that the right to do this is to put it out there in public and then harness all this, know, instead of just your team giving feedback, the world now giving 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 you were founded as a nonprofit, well you were there as the great sort check on the big companies doing their unknown, possibly evil thing with AI. And were going to build models that sort of, you know, somehow held them and was capable of slowing the field down, if need be. Or at least that’s kind of I heard. And yet, what’s happened, arguably, is the opposite. That release of GPT, especially ChatGPT, sent such shockwaves through the world that now Google and Meta and so forth are scrambling to catch up. And some of their criticisms been, you are forcing us to put this out without proper guardrails or we die. You know, how you, like, make the case that what you have done is responsible here and reckless.
GB: Yeah, we think about these questions all the time. Like, seriously all the time. I don’t think we’re always going to get it right. But one thing I think has incredibly important, from the very beginning, when we were thinking how to build artificial general intelligence, actually have it benefit of humanity, like, how are you supposed to do that, right? And that default of being, well, you build in secret, you get this super powerful thing, and then you out the safety of it and then you push “go,” and hope you got it right. I don’t know how to execute that plan. Maybe someone else does. for me, that was always terrifying, it didn’t feel right. And so I think that this alternative is the only other path that I see, which is that you do let reality you in the face. And I think you do give people to give input. You do have, before these machines are perfect, they are super powerful, that you actually have the to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number one thing were going to do with it was generate misinformation, try to elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: So spam is bad, but 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 believe that in box is something that, there’s a very strong chance it’s something absolutely glorious that’s going to give 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 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 a story I haven’t actually told before, which is that shortly after we started OpenAI, remember I was in Puerto Rico for an AI conference. I’m in the hotel room just looking out over this wonderful water, these people having a good time. And you think about it a moment, if you could choose for basically that Pandora’s box be five years away or 500 years away, which would you pick, right? On one hand you’re like, well, maybe for you personally, it’s better have it be five years away. But if it to be 500 years away and people get more time get it right, which do you pick? And you know, I just really felt it in the moment. I like, of course you do the 500 years. My brother was in the military at time and like, he puts his life on the line in a much more real way than of us typing things in computers and developing this technology 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 it truly lies. Like, if you look at the whole history computing, I really mean it when I say that is an industry-wide or even just almost like a human-development- of-technology-wide shift. And the that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. if you don’t put them together, you get an overhang, which means that someone does, or the moment that someone does manage to to the circuit, then you suddenly have this very thing, no one’s had any time to adjust, who knows kind of safety precautions you get. And so I think 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 in what humans do. But I actually think that if you look at capability, it’s been quite smooth over time. And the history, I think, of every technology we’ve developed has been, you’ve to do it incrementally and you’ve got to figure out how to it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you want us to have that we have birthed this extraordinary child that may have superpowers that take humanity to a new place. It is our collective responsibility to provide the for this child to collectively teach it to be wise and not tear us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important to say this shift, right? We’ve got to take each step as encounter it. And I think it’s incredibly important today we all do get literate in this technology, figure out how 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 because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, you so much for coming to TED and blowing minds.
(Applause)