We OpenAI seven years ago because we felt like something interesting was happening in AI and we wanted to help it in a positive direction. It’s honestly just really to see how far this whole field has come then. And it’s really gratifying to hear from people Raymond who are using the technology we are building, and others, for many wonderful things. We hear from people who are excited, we hear people who are concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, it feels like we’re entering an historic 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 for good.
So today, I want to you the current state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m going to show you is it’s like to build a tool for an AI rather building it for a human. So we have a new DALL-E model, which images, and we are exposing it as an app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a post-TED meal and draw a picture of it.
(Laughter)
Now you get all of the, sort of, ideation creative back-and-forth and taking care of the details for you that you out of ChatGPT. And here we go, it’s not just the idea for meal, but a very, very detailed spread. So let’s see we’re going to get. But ChatGPT doesn’t just generate images this case — sorry, it doesn’t generate text, it also an image. And that is something that really expands the power of 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 generated by the as we speak. So I actually don’t even know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended ChatGPT with tools too, for example, memory. You can say “save this for later.” the interesting thing about these tools is they’re very inspectable. So you this little pop up here that says “use the DALL-E app.” And by the way, this is to you, all ChatGPT users, over upcoming months. And you can look under the hood and see what it actually did was write a prompt just like a human could. And so you of have this ability to inspect how the machine is using these tools, which us to provide feedback to them.
Now it’s saved for later, and let me show what 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 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 make this wonderful, meal, I definitely want to know how it tastes.
But you can that ChatGPT is selecting all these different tools without me to tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking the user interface. Like, we are so used to of, well, we have these apps, we click between them, we copy/paste between them, usually it’s a great experience within an app as long as kind of know the menus and know all the options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this language interface on top of tools, the AI is able 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 of what’s supposed to happen.
And as I said, this is live 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 can see we sent a list of ingredients to Instacart. Here’s everything need. And the thing that’s really interesting is that the traditional is still very valuable, right? If you look at this, you still can click it and sort of modify the actual quantities. And that’s something that I shows that they’re not going away, traditional UIs. It’s just we have a new, augmented way to them. And now we have a tweet that’s been drafted for our review, which is also a very thing. We can click “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of AI if we want to. And so after this talk, will be able to access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important thing about we build this, it’s not just about building these tools. It’s about the AI how to use them. Like, what do we even want it to do when 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, he says, you’ll program an answer to this. Instead, you can learn it. You could build machine, like a human child, and then teach it through feedback. Have a teacher who provides rewards and punishments as it tries things out and does things that either good or bad.
And this is exactly how train ChatGPT. It’s a two-step process. First, we produce what Turing would have called child machine through an unsupervised learning process. We just show the whole world, the whole internet and say, “Predict what next in text you’ve never seen before.” And this process imbues it with all sorts wonderful skills. For example, if you’re shown a math problem, the only way to actually that math problem, to say what comes next, that green nine there, is to actually solve the math problem.
But we have to do a second step, too, which is to teach AI what to do with those skills. And for this, we provide feedback. We have the AI try out 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 said, but very importantly, the whole process that the AI used produce that answer. And this allows it to generalize. allows it to teach, to sort of infer your intent and apply in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we 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 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 plus one equals three and run with it.” So we had collect some feedback data. Sal Khan himself was very kind offered 20 hours of his own time to provide feedback the machine alongside our team. And over the course of 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 improvements to the models this way. And when you push that thumbs down in ChatGPT, that is kind of like sending up a bat signal to our team to say, “Here’s area of weakness where you should gather feedback.” And when you do that, that’s one way that 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 doing is the floor, you don’t know if you’re just teaching them to all the toys in the closet. This is a nice DALL-E-generated image, the way. And the same sort of reasoning applies AI. As we move to harder tasks, we will have to our ability to provide high-quality feedback. But for this, AI itself is happy to help. It’s happy to help us provide even better and to scale our ability to supervise the machine time goes on. And let me show you what mean.
For example, you can ask GPT-4 a question this, of how much time passed between these two foundational on unsupervised learning and learning from human feedback. And the model says two passed. But is it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. But we actually use the AI to fact-check. And it can actually its own work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This one is a browsing tool where the model issue search queries and click into web pages. And actually writes out its whole chain of thought as it it. It says, I’m just going to search for this and it actually does search. It then it finds the publication date and the search results. It then is issuing another query. It’s going to click into the blog post. And all of this you could do, but it’s very tedious task. It’s not a thing that humans really to do. It’s much more fun to be in the driver’s seat, be in this manager’s position where you can, if want, triple-check the work. And out come citations so you can go and very easily verify any piece of this whole chain of reasoning. And it turns out two months was wrong. Two months and week, that was correct.
(Applause)
And we’ll cut back to side. And so thing that’s so interesting to me about this whole process is that it’s many-step collaboration between a human and an AI. Because a human, this fact-checking tool is doing it in order to data for another AI to become more useful to human. And I think this really shows the shape of something we should expect to be much more common in the future, we have humans and machines kind of very carefully and delicately designed in how they into a problem and how we want to solve that problem. We sure that the humans are providing the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that time, if we get this process right, we will be to solve impossible problems.
And to give you a sense of just impossible I’m talking, I think we’re going to be to rethink almost every aspect of how we interact with computers. For example, about spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago with VisiCalc. don’t think they’ve really changed that much in that time. here is a specific spreadsheet of all the AI on the arXiv for the past 30 years. There’s about 167,000 of them. And 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 ChatGPT access to yet another tool, this one a interpreter, so it’s able to run code, just like a data scientist would. And so you can literally upload a file and ask questions about it. very helpfully, you know, it knows the name of 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 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 knowledge of knowing that, “Oh yeah, arXiv is a that people submit papers and therefore that’s what these are and that these are integer values and so therefore it’s a of authors in 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 even know what I want to ask. So fortunately, can ask the machine, “Can you make some exploratory graphs?” once again, this is a super high-level instruction with lots of intent behind it. I don’t even know what I want. And the AI kind of has to infer I might be interested in. And so it comes with some good ideas, I think. So a histogram the number of authors per paper, time series of per year, word cloud of the paper titles. All that, I think, will be pretty interesting to see. the great thing is, it can actually do it. Here go, a nice bell curve. You see that three kind of the most common. It’s going to then this nice plot of the papers per year. Something crazy happening in 2023, though. Looks like we were on an exponential and dropped off the cliff. What could be going on there? By the way, all is Python code, you can inspect. And then we’ll see word cloud. So can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem is that year is not over. So I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted April 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 here. I really wanted it to notice this thing, it’s a little bit of an overreach for it to have of, inferred magically that this is what I wanted. But I inject intent, I provide this additional piece of, you know, guidance. under the hood, the AI is just 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 slide again. This slide shows a parable of how think we … A vision of how we may end up using this technology in the future. A 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 blood test, like, full medical records, to GPT-4, which said, “I am a vet, you need to talk to a professional, here are hypotheses.” He brought that information to a second vet who used to save the dog’s life. Now, these systems, they’re perfect. You cannot overly rely on them. But this story, think, shows that a human with a medical professional and ChatGPT as a brainstorming partner was able to achieve an outcome that would not have happened otherwise. I this is something we should all reflect on, think about as consider how to integrate these systems into our world.
And one thing I believe deeply, is that getting AI right is going to require from everyone. And that’s for deciding how we want it to in, that’s for setting the rules of the road, for what AI will and won’t do. And if there’s one 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 of the reasons we ChatGPT.
Together, I believe that we can achieve the OpenAI mission ensuring that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out there’s a feeling of reeling. Like, I suspect that a very large number of people viewing this, look at that and you think, “Oh my goodness, pretty much every single thing the way I work, I need to rethink.” Like, there’s just new possibilities there. I right? Who thinks that they’re having to rethink the way we do things? Yeah, I mean, it’s amazing, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, I my first question actually is just how the hell 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 no question. If 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 early days. And the first one just to confront reality as it lays. And that we just thought really hard about like: is it going to take to make progress here? tried a lot of things that didn’t work, so you only the things that did. And I think that the most important has been to get teams of people who are very different from each to work together harmoniously.
CA: Can we have the water, by way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something also just the 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 I that, I mean, honestly, I think the story there is pretty illustrative, right? think that high level, deep learning, like we always knew that was we wanted to be, was a deep learning lab, and exactly how to do it? I think in the early days, we didn’t know. We tried a lot things, and one person was working on training a model to predict the next in Amazon reviews, and he got 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 analysis out of it. This model could tell you if a was positive or negative. I mean, today we are just like, on, anyone can do that. But this was the first that you saw this emergence, this sort of semantics that emerged from this syntactic process. And there we knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps explain the riddle that everyone looking at this, because these things are described as prediction machines. And yet, we’re seeing out of them feels … it just feels that that could come from a prediction machine. Just the you showed us just now. And the key idea of emergence is that when you get more a thing, suddenly different things emerge. It happens all the time, ant colonies, ants run around, when you bring enough of them together, you get these ant colonies show completely emergent, different behavior. Or a city where few houses together, it’s just houses together. But as grow the number of houses, things emerge, like suburbs and cultural and traffic jams. Give me one 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 —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s really learned an circuit for how to do it. And the really interesting thing is actually, if you have add like a 40-digit number plus a 35-digit number, it’ll often it wrong. And so you can see that it’s really learning the process, but hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are in universe. So it had to have learned something general, that it hasn’t really fully yet learned that, Oh, I can of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve it to scale up and look at an incredible number pieces of text. And it is learning things that 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 is predicting some of these emergent capabilities. And to that actually, one of the things I think is undersung in this field is sort of engineering quality. Like, we had to our entire stack. When you think about building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get every piece of the stack engineered properly, and then you can start these predictions. There are all these incredibly smooth scaling curves. tell you something deeply fundamental about intelligence. If you look at our GPT-4 post, you can see all of these curves in there. And now we’re starting be able to predict. So we were able to predict, for example, the performance on coding problems. We basically at some models that are 10,000 times or 1,000 times smaller. so there’s something about this 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 can maybe predict in some level of confidence, but it’s capable surprising you. Why isn’t there just a huge risk something truly terrible emerging?
GB: Well, I think all of are questions of degree and scale and timing. And think one thing people miss, too, is sort of the integration the world is also this incredibly emergent, sort of, powerful thing too. And so that’s one of the that we think it’s so important to deploy incrementally. And I think that what we kind of see right now, if you at this talk, a lot of what I focus on providing really high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if book summary is any good? You have to read whole book. No one wants to do that.
(Laughter) And so I that the important thing will be that we take this step step. And that we say, OK, as we move to book summaries, we have to supervise this task properly. We have to build up a track record with machines that they’re able to actually carry out our intent. And think we’re going to have to produce even better, more efficient, more reliable of scaling this, sort of like making the machine be aligned you.
CA: So we’re going to hear later in session, there are critics who say that, you know, there’s no real understanding inside, the system going to always — we’re never going to know that it’s not errors, that it doesn’t have common sense and so forth. it your belief, Greg, that it is true at any one moment, but that expansion of the scale and the human feedback that you talked is basically going to take it on that journey of actually getting to things like truth wisdom and so forth, with a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the short is yes, I believe that is where we’re headed. And I think the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like this is the field of broken promises, of all these saying X is going to happen, Y is how works. People have been saying neural nets aren’t going to for 70 years. They haven’t been right yet. They 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 to push to the limits of this technology to 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 exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that right way to do this is to put it out in public and then harness all this, you know, instead of your team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, is out there. So, you know, the original story that I heard OpenAI when you were founded as a nonprofit, well you were there as great sort of check on the big companies doing their unknown, possibly thing with AI. And you were going to build models that sort of, you know, held them accountable and was capable of slowing the down, if need be. Or at least that’s kind of I heard. And yet, what’s happened, arguably, is the opposite. That your of GPT, especially ChatGPT, sent such shockwaves through the tech world that Google and Meta and so forth are all scrambling to catch up. And some of their criticisms been, you are forcing us to put this out here without proper guardrails we die. You know, how do you, like, make the case that what you have done is 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. But thing I think has been incredibly important, from the very beginning, when we thinking about how to build artificial general intelligence, actually it benefit all of humanity, like, how are you to do that, right? And that default plan of being, well, build in secret, you get this super powerful thing, then you figure out the safety of it and then you “go,” and you hope you got it right. I don’t how to execute that plan. Maybe someone else does. But me, that was always terrifying, it didn’t feel right. And so I that this alternative approach is the only other path that I see, which that you do let reality hit you in the face. And I think do give people time to give input. You do have, these machines are perfect, before they are super powerful, that you actually have the to see them in action. And we’ve seen it from GPT-3, right? GPT-3, really were afraid that the number one thing people were going to do with it generate misinformation, try to tip elections. Instead, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra is bad, but there are things that are much worse. Here’s thought experiment for you. Suppose you’re sitting in a room, there’s a box on the table. believe that in that box is something that, there’s a strong chance it’s something absolutely glorious that’s going to 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 evils on the world. Do you open that box?
GB: Well, so, absolutely not. I think you don’t it that way. And honestly, like, I’ll tell you a story that haven’t actually told before, which is that shortly after we OpenAI, I remember I was in Puerto Rico for AI conference. I’m sitting in the hotel room just looking out over this wonderful water, all people having a good time. And you think about it a moment, if you could choose for basically that Pandora’s to be five years away or 500 years away, which would pick, right? On the one hand you’re like, well, maybe for you personally, it’s better to it be five years away. But if it gets to 500 years away and people get more time to it right, which do you pick? And you know, I really felt it in the moment. I was like, of course you do the 500 years. My was in the military at the time and like, he puts his life on the line in much more real way than any of us typing things computers and developing this technology at the time. And so, yeah, I’m really sold on the you’ve got to this right. But I don’t think that’s quite playing the field as it truly lies. Like, you look at the whole history of computing, I really mean it when I say that this is industry-wide or even just almost like a human-development- of-technology-wide shift. And the more you sort of, don’t put together the pieces that are there, right, we’re still faster computers, we’re still improving the algorithms, all of these things, are happening. And if you don’t put them together, you an overhang, which means that if someone does, or the moment that someone does manage connect to the circuit, then you suddenly have this very powerful thing, no one’s had any time adjust, who knows what kind of safety precautions you get. And so think that one thing I take away is like, even you think about development of 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 at capability, it’s been quite smooth over time. And so the history, I think, of every we’ve developed has been, you’ve got to do it incrementally you’ve got to figure out how to manage it for moment that you’re increasing it.
CA: So what I’m is that you … the model you want us have is that we have birthed this extraordinary child that may have superpowers that take to a whole new place. It is our collective to provide the guardrails for this child to collectively teach it to be wise and not to tear all down. Is that basically the model?
GB: I it’s true. And I think it’s also important to say this shift, right? We’ve got to take each step as we encounter it. I think it’s incredibly important today that we all do get literate in technology, figure out how to provide the feedback, decide what we from it. And my hope is that that will to be the best 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, thank you so for coming to TED and blowing our minds.
(Applause)