We OpenAI seven years ago because we felt like something really was happening in AI and we wanted to help steer it in a positive direction. It’s just really amazing to see how far this whole field has come since then. And it’s really to hear from people like Raymond who are using the technology we are building, others, for so many wonderful things. We hear from people who excited, we hear from people who are concerned, we hear people who feel both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an historic period now where we as a world are going to define technology that will be so important for our society forward. And I believe that we can manage this good.
So today, I want to show you the current state of technology and some of the underlying design principles that we dear.
So the first thing I’m going to show you is what it’s like to a tool for an AI rather than building it a human. So we have a new DALL-E model, which generates images, and we are 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 all of the, sort of, ideation and creative back-and-forth and taking care of the details for you that get out of ChatGPT. And here we go, it’s not just the for the meal, but a very, very detailed spread. So let’s see what we’re going to get. ChatGPT doesn’t just generate images in this case — sorry, it doesn’t generate text, also generates an image. And that is something that expands the power of what it can do on your behalf in terms carrying out your intent. And I’ll point out, this is a live demo. This is all generated by the AI we speak. So I actually don’t even know what we’re going see. This looks wonderful.
(Applause)
I’m getting hungry just looking it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You say “save this for later.” And the interesting thing about these is they’re very inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, this coming to you, all ChatGPT users, over upcoming months. And you can under the hood and see that what it actually was write a prompt just like a human could. so you sort of have this ability to inspect how the machine is using these tools, which allows to provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use that and to integrate with other applications too. You can say, “Now make a shopping for the tasty thing I was suggesting earlier.” And make it a little for the AI. “And tweet it out for all TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how it tastes.
But you see that ChatGPT is selecting all these different tools without me having to tell it which ones to use in any situation. And this, think, shows a new way of thinking about the user interface. Like, are so used to thinking of, well, we have apps, we click between them, we copy/paste between them, usually it’s a great experience within an app as long as you kind of know menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, AI is able to 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, so sometimes the will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And you can see sent a list of ingredients to Instacart. Here’s everything you need. And the thing that’s really interesting is the traditional UI is still very valuable, right? If you look at this, still can click through it and sort of modify the quantities. And that’s something that I think shows that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And we have a tweet that’s been 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 change the work of the AI if we want to. And after this talk, you will be able to access this yourself. And we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important thing about how we build this, it’s not just building these tools. It’s about teaching the AI how to them. Like, what do we even want it to when we ask these very high-level questions? And to do this, we use an old idea. If you back to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, you can learn it. You build a machine, like a human child, and then teach it through feedback. Have a human who provides rewards and punishments as it tries things out and does things that either good or bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have a child machine through an unsupervised learning process. We just show it whole world, the whole internet and say, “Predict what comes next text you’ve never seen before.” And this process imbues it all sorts of wonderful skills. For example, if you’re shown a math problem, the way to actually complete that math problem, to say what comes next, green nine up there, is to actually solve the math problem.
But actually have to do a second step, too, which is teach the AI what to do with those skills. for this, we provide feedback. We have the AI try out multiple things, give multiple suggestions, and then a human rates them, says “This one’s better than that one.” And this reinforces not just specific thing that the AI said, but very importantly, the whole process the AI used to produce that answer. And this it to generalize. It allows it to teach, to sort of infer 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. example, when we first showed GPT-4 to Khan Academy, 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 math in there, will happily pretend that one plus one equals three and run with it.” So we had to collect feedback data. Sal Khan himself was very kind and offered 20 hours his own time to provide feedback to the machine alongside our team. And the course of a couple of months we were able to teach the AI that, “Hey, really should push back on humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to the models this way. And you push that thumbs down in ChatGPT, that actually is of like sending up a bat signal to our team to say, “Here’s area of weakness where you should gather feedback.” And so when you do that, that’s one way we really listen to our users and make sure we’re 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 is inspecting the floor, you don’t know if you’re just teaching to stuff all the toys in the closet. This is a 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 feedback and scale our ability to supervise the machine as time goes on. And me show you what I mean.
For example, you can GPT-4 a question like this, of how much time between these two foundational blogs on unsupervised learning and learning from feedback. And the model says two months passed. But is it true? Like, models are not 100-percent reliable, although they’re getting better every we provide 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 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 chain of thought as it does it. says, I’m just going to search for this and it actually the search. It then it finds the publication date and the results. It then is issuing another search query. It’s going click into the blog post. And all of this you do, but it’s a very tedious task. It’s not a thing that humans want 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 so can actually go and very easily verify any piece of whole chain of reasoning. And it actually turns out two months was wrong. Two and one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting to about this whole process is that it’s this many-step collaboration between a human and an AI. Because human, using this fact-checking tool is doing it in order to produce data for another to become more useful to a human. And I this really shows the shape of something that we expect to be much more common in the future, where we have humans machines kind of very carefully and delicately designed in they fit into a problem and how we want solve that problem. We make sure that the humans 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 will able to solve impossible problems.
And to give you a sense of just impossible I’m talking, I think we’re going to be to rethink almost every aspect of how we interact computers. For example, think about spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much in that time. And here is specific spreadsheet of all the AI papers on the arXiv for past 30 years. There’s about 167,000 of them. And you see there the data right here. But let me you the ChatGPT take on how to analyze a set like this.
So we can give ChatGPT access to another tool, this one a Python interpreter, so it’s able to code, just like a data 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 information here is the name of the file, the column names like you saw 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 together its world knowledge knowing that, “Oh yeah, arXiv is a site that people papers and therefore that’s what these things are and these are integer values and so therefore it’s a number of authors the paper,” like all of that, that’s work for a human to do, and the is happy to help with it.
Now I don’t know what I want to ask. So fortunately, you ask the machine, “Can you make some exploratory graphs?” And once again, this is a high-level instruction with lots 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 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 great is, it can actually do it. Here we go, a bell curve. You see 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 we on an exponential and it dropped off the cliff. What could going on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you can all these wonderful things that appear in these titles.
But I’m pretty unhappy about this 2023 thing. It this year look really bad. Of course, the problem is that the is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you that to make a fair projection? So we’ll see, this is the kind ambitious one.
(Laughter)
So you know, again, I feel like there more I wanted out of the machine here. I really wanted to notice this thing, maybe it’s a little bit an overreach for it to have sort of, inferred that this is what I wanted. But I inject my intent, I this additional piece of, you know, guidance. And under the hood, the AI is just code again, so if you want to inspect what it’s doing, it’s very possible. And now, it the correct projection.
(Applause)
If you noticed, it even the title. I didn’t ask for that, but it know what I want.
Now we’ll cut to the slide again. This slide shows a parable of how I we … A vision of how we may end up using this technology the future. A person brought his very sick dog to the vet, and the made a bad call to say, “Let’s just wait and see.” And the dog would not here today had he listened. In the meanwhile, he provided the test, like, the full medical records, to GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” He brought that information a second vet who used it to save the dog’s life. Now, these systems, they’re perfect. You cannot overly rely on them. But this story, I think, that a human with a medical professional and with ChatGPT as a brainstorming partner was able achieve an outcome that would not have happened otherwise. I think this is something we should reflect on, think about as we consider how to integrate these systems our world.
And one thing I believe really deeply, is that getting AI right going to require participation from everyone. And that’s for deciding how want it to slot in, that’s for setting the of the road, for what an AI will and won’t do. And there’s one thing to take away from this talk, it’s this technology just looks different. Just different from anything had anticipated. And so we all have to become literate. And that’s, honestly, one of the reasons we ChatGPT.
Together, I believe that we can achieve the mission of ensuring that artificial general intelligence benefits all 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 look at 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? Who that they’re having to rethink the way that we things? Yeah, I mean, it’s amazing, but it’s also really scary. let’s talk, Greg, let’s talk.
I mean, I guess first question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands employees working on artificial intelligence. Why is it you who’s come up with this technology shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on of giants, right, there’s no question. If you look at the compute progress, the algorithmic progress, data progress, all of those are really industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the first was just to confront reality as it lays. And that we just thought hard about like: What is it going to take to make progress here? We tried a of things that didn’t work, so you only see the things that did. And think that the most important thing has been to teams of people who are very different from each to work together harmoniously.
CA: Can we have the water, 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 about the fact you saw something in these language models that meant if you continue to invest in them and grow them, something at some point might emerge?
GB: Yes. And I think that, I mean, honestly, I think story there is pretty illustrative, right? I think that high level, learning, like we always knew that was what we wanted to be, a deep learning lab, and exactly how to do it? I think that the early days, we didn’t know. We tried a lot of things, and one was working on training a model to predict the next character in Amazon reviews, and he a result where — this is a syntactic process, you expect, know, the model will predict where the commas go, where nouns and verbs are. But he actually got a state-of-the-art sentiment analysis out of it. This model could tell you if 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 emergence, this sort of semantics that emerged from this underlying syntactic process. And 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 are described as prediction machines. And yet, what we’re seeing out of them feels … just feels impossible that that could come from a machine. Just the stuff you showed us just now. And the key idea of is that when you get more of a thing, suddenly different things emerge. It all the time, ant colonies, single ants run around, you bring enough of them together, you get these ant colonies that show completely emergent, different behavior. a city where a few houses together, it’s just houses together. But you grow the number of houses, things emerge, like suburbs and cultural centers and jams. Give me one moment for you when you saw just pop that just blew your mind that you just did not see coming.
GB: Yeah, well, so you 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 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 you can see that 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 there are in the universe. So it had to have learned general, but that it hasn’t really fully yet learned that, Oh, I can sort of this to adding arbitrary numbers of arbitrary lengths.
CA: what’s happened here is that you’ve allowed it to up and look at an incredible number of pieces text. And it is learning things that you didn’t know that it was 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 these emergent capabilities. And to do actually, one of the things I think is very undersung this field is sort of engineering quality. Like, we to rebuild our entire stack. When you think about building a rocket, every tolerance to be incredibly tiny. Same is true in machine learning. You have to get every piece of the stack engineered properly, and then you can start doing predictions. There are all these incredibly smooth scaling curves. They tell you deeply fundamental about intelligence. If you look at our GPT-4 blog post, can see all of these curves in there. And now we’re starting to be to predict. So we were able to predict, for example, performance on coding problems. We basically look at some that are 10,000 times or 1,000 times smaller. And so there’s something about that is actually smooth scaling, even though it’s still early days.
CA: So is, one of the big fears then, that arises from this. it’s fundamental to what’s happening here, that as you scale up, things emerge that you maybe predict in some level of confidence, but it’s of surprising you. Why isn’t there just a huge risk something truly terrible emerging?
GB: Well, I think all of these are 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 see right now, if you look at this talk, a lot what I focus on is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to look at that math problem 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 know if book summary is any good? You have to read the book. No one wants to do that.
(Laughter) And so I think that the important thing will be we take this step by step. And that we say, OK, as we move on to book summaries, we have supervise this task properly. We have to build up a track record with these that they’re able to 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 with you.
CA: So we’re to hear later in this session, there are critics who that, you know, there’s no real understanding inside, the system going to always — we’re never going to know that it’s not generating errors, it doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, but the expansion of the scale and the human feedback that you about is basically going to take it on that journey of getting to things like truth and wisdom and so forth, with a high degree of confidence. you be sure of that?
GB: Yeah, well, I think the OpenAI, I mean, the short answer is yes, I believe that is we’re headed. And I think that the OpenAI approach has always been just like, let reality hit you the face, right? It’s like this field is the of broken promises, of all these experts saying X going to happen, Y is how it works. People have been saying neural nets aren’t going work 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 always been, you’ve got push to the limits of this technology to really see it action, because that tells you then, oh, here’s how we can 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, that the right to do this is to put it out there in and then harness all this, you know, instead of just your team feedback, the world is now giving feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded as nonprofit, well you were there as the great sort of 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 accountable and was capable slowing the field down, if need be. Or at least that’s kind of what I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that now Google and Meta so forth are all scrambling to catch up. And some of their criticisms have been, are forcing us to put this out here without proper guardrails or we die. You know, do you, like, make the case that what you have done is here and not reckless.
GB: Yeah, we think about questions all the time. Like, seriously all the time. And I don’t think we’re always going to it right. But one thing I think has been important, from the very beginning, when we were thinking about how to build artificial general intelligence, have it benefit all of humanity, like, how are you to do that, right? And that default plan of being, well, you build in secret, you get super powerful thing, and then you figure out the of it and then you push “go,” and you you got it right. I don’t know how to execute that plan. Maybe someone 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 I see, which is that you do reality hit you in the 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 to them in action. And we’ve seen it from GPT-3, right? GPT-3, we really afraid that the number one thing people were going to do with was generate misinformation, try to tip elections. Instead, the one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there things that are much worse. Here’s a thought experiment you. Suppose you’re sitting in a room, there’s a box on the table. You believe in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give gifts to your family and to everyone. But there’s actually also one percent thing in the small print there that says: “Pandora.” there’s a chance that this actually could unleash unimaginable evils on world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. honestly, like, I’ll tell you a story that I haven’t told before, which is that shortly after we started OpenAI, I remember I was in Puerto for an AI conference. I’m sitting in the hotel room just out over this wonderful water, all these people having a good time. And you think it for a moment, if you could choose for that Pandora’s box to be five years away or 500 years away, which would you pick, right? On the hand you’re like, well, maybe for you personally, it’s better to have it be years away. But if it gets 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. was like, of course you do the 500 years. My brother was the military at the time and like, he puts his life on the line a much more real way than any of us typing things in computers and this technology at the time. And so, yeah, I’m really sold on the you’ve got 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 of computing, really mean it when I say that this is an industry-wide or even just almost a human-development- of-technology-wide shift. And the more that you sort of, don’t put together pieces that are there, right, we’re still making faster computers, we’re still improving algorithms, all of these things, they are happening. And if you don’t them together, you get an overhang, which means that someone does, or the moment that someone does manage connect to the circuit, then you suddenly have this very powerful thing, one’s had any time to adjust, who knows what kind of safety precautions you get. And so think that one thing I take away is like, even you think development of other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, change in what could do. But I actually think that if you 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 manage it for each moment that you’re increasing it.
CA: So what I’m hearing is that you … the you want us to have is that we have birthed this extraordinary that may have superpowers that take humanity to a whole new place. It is our collective to provide the guardrails for this child to collectively teach to be wise and not to tear us all down. that basically the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve got take each step as we encounter it. And I it’s incredibly important today that we all do get literate this technology, figure out how to provide the feedback, decide what we want from it. And hope is that that will continue to be the best path, it’s so good we’re honestly having this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for coming to TED blowing our minds.
(Applause)