We started OpenAI seven years ago we felt like something really interesting was happening in AI and wanted to help steer it in a positive direction. It’s honestly really amazing to see how far this whole field has come then. And it’s really gratifying to hear from people like Raymond are using the technology we are building, and others, for so wonderful things. We hear from people who are excited, we hear from who are concerned, we 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 are going to define a technology that will be so important for our society going forward. I believe that we can manage this for good.
So today, I want to show you the current state that technology and some of the underlying design principles that we hold dear.
So the first I’m going to show you is what it’s like to a tool for an AI rather than building it for a human. we have a new DALL-E model, which generates images, we are exposing it as an app for ChatGPT to use on behalf. And you can do things like ask, you know, suggest a nice post-TED and draw a picture of it.
(Laughter)
Now you get of the, sort of, ideation and creative back-and-forth and taking care the details for you that you get out of ChatGPT. And here go, it’s not just the idea for the meal, but very, very detailed spread. So let’s see what we’re going get. But ChatGPT doesn’t just generate images in this — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the power of what 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 by the AI as we speak. So I actually don’t even know what we’re going to see. This wonderful.
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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.” the interesting thing about these tools is they’re very inspectable. So you get this little pop 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 the hood and see that what it actually did was write a prompt just like human could. And so you sort of have this ability to inspect how the machine is these tools, which allows us 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 list for the tasty I was suggesting earlier.” And make it a little tricky for the AI. “And tweet out for all the TED viewers out there.”
(Laughter)
So if you do make this wonderful, meal, I definitely want to know how it tastes.
But can see that ChatGPT is selecting all these different tools without me having to tell it which ones to use in any situation. And this, I think, a new way of thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a experience within an app as long as you kind know the menus and know all the options. Yes, I like you to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, the AI is to sort of take away all those details from you. you don’t have to be the one who spells every single sort of little piece of what’s supposed happen.
And as I said, this is a live demo, so the unexpected will happen to us. But let’s take look at the Instacart shopping list while we’re at it. And you see we sent a list of ingredients to Instacart. Here’s you need. And the thing that’s really interesting is that the UI is still very valuable, right? If you look at this, you still can click through it and of modify the actual quantities. And that’s something that I think shows they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we have a tweet that’s been for our review, which is also a very important thing. We click “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 after this talk, you will able to access this yourself. And there we go. Cool. you, everyone.
(Applause)
So we’ll cut back to the slides. Now, important thing about how we build this, it’s not just about these tools. It’s about teaching the AI how to them. Like, what do we even want it to do when we ask very high-level questions? And to do this, we use an old idea. you go back to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, you learn it. You could build a machine, like a human child, then teach it through feedback. Have a human teacher who provides rewards and punishments as it tries out and does things that are either good or bad.
And is exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine through an learning process. We just show it the 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 problem, the only way to actually complete that math problem, to say comes next, that 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 out multiple things, give us multiple suggestions, and then human rates them, says “This one’s better than that one.” And this reinforces just the 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 infer your intent and apply it in scenarios that hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have 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 things. Only 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 and run with it.” So we to collect some feedback data. Sal Khan himself was kind and offered 20 hours of his own time to provide feedback to machine alongside our team. And over the course of a couple of months we able to teach the AI that, “Hey, you really should push back on humans in specific kind of scenario.” And we’ve actually made lots and lots of improvements to the models this way. when you push that thumbs down in ChatGPT, that is kind of like sending up a bat signal our team to say, “Here’s an area of weakness where should gather feedback.” And so when you do that, that’s one way that we really listen to our and make sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is hard thing. If you think about asking a kid to their room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff all the toys in the closet. This is a DALL-E-generated image, by the way. And the same sort of reasoning applies to AI. we move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the itself is happy to help. It’s happy to help 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 ask GPT-4 a question like this, of how much passed between these two foundational blogs on unsupervised learning and learning from human feedback. And the model says months passed. But is it true? Like, these models not 100-percent reliable, although they’re getting better every time provide some feedback. But we can actually use the AI to fact-check. And can actually check its own work. You can say, fact-check this for me.
Now, in this case, I’ve actually the AI a new tool. This one is a browsing where the model can issue search queries and click into web pages. And it actually writes out its chain of thought as it does it. It says, I’m just to search for this and it actually does the search. It then it finds the publication date the search results. It then is issuing another search query. It’s going to click into the blog post. And all this you could do, but it’s a very tedious task. It’s not a thing humans really want to do. It’s much more fun be in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. And 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, that correct.
(Applause)
And we’ll cut back to the side. And so that’s so interesting to me about this whole process is that it’s this many-step collaboration between a and an AI. Because a human, using this fact-checking is doing it in order to produce data for another AI to become more useful to a human. I think this really shows the shape of something that should expect to be much more common in the future, where we have and machines kind of very carefully and delicately designed how they fit into a problem and how we want to solve that problem. We sure that the humans are providing the management, the oversight, feedback, and the machines are operating in a way that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that over time, we get this process right, we will be able solve impossible problems.
And to give you a sense just how impossible I’m talking, I think we’re going to be able to rethink almost every aspect how we interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years with VisiCalc. I don’t think they’ve really changed that in that time. And here is a specific spreadsheet of the AI papers 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 ChatGPT take on how to analyze a data set like this.
So we can give ChatGPT access to 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 knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” only information here is the name of the file, the names like you saw and then the actual data. from that it’s able to infer what these columns actually mean. Like, that semantic information wasn’t in there. has to sort of, put together its world knowledge knowing that, “Oh yeah, arXiv is a site that submit papers and therefore that’s what these things are and that these integer values and so therefore it’s a number of authors in the paper,” like of that, that’s work for a human to do, and the AI is happy to help it.
Now I don’t even know what I want to ask. So fortunately, you can ask 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 know what I want. And the AI kind of has to infer what I be interested in. And so it comes up with good ideas, I think. So a histogram of the number of per paper, time series of papers per year, word cloud the paper titles. All of 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 is kind of most common. It’s going to then make this nice plot of the papers per year. crazy is happening in 2023, though. Looks like we were on an and it dropped off the cliff. What could be on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you can see all these wonderful things appear in these titles.
But I’m pretty unhappy about 2023 thing. It makes this year look really bad. Of course, the 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 percentage of papers 2022 were even posted by April 13?] So April 13 the cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. really wanted it 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 inject intent, I provide this additional piece of, you know, guidance. And the hood, the AI is just writing code again, so you want to inspect what it’s doing, it’s very possible. now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back to the slide again. This slide shows a parable how I think we … A vision of how we may end using this technology in the future. A person brought his very sick dog 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, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you need to to a professional, here are some hypotheses.” He brought that information to a vet who used it to save the dog’s life. Now, systems, they’re not perfect. You cannot overly rely on them. this story, I think, shows that a human with medical professional and with ChatGPT as a brainstorming partner was able to an outcome that would not have happened otherwise. I think this is something should all reflect on, think about as we consider how integrate these systems into our world.
And one thing believe really deeply, is that getting AI right is going to require participation from everyone. that’s for deciding how we want it to slot in, that’s for the rules 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 that this technology just looks different. different from anything people had anticipated. And so we all 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 … I suspect that within mind out here there’s a feeling of reeling. Like, I suspect that a very number of people viewing this, you look at that you think, “Oh my goodness, pretty much every single thing the way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to rethink way that we do things? Yeah, I mean, it’s amazing, but it’s really 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. Google thousands of employees working on artificial intelligence. Why is it you who’s up with this technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. If you look the compute progress, the algorithmic progress, the data progress, all of those are really industry-wide. I think within OpenAI, we made a lot of very deliberate from the early days. And the first one was just to confront reality it lays. And that we just thought really hard about like: is it going to take to make progress here? We tried lot of things that didn’t work, so you only see things that did. And I think that the most important thing has to get teams of people who are very different from each to work together harmoniously.
CA: Can we have the water, by the way, brought here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about the that you saw something in these language models that meant that if you continue invest in them and grow them, that something at some point might emerge?
GB: Yes. 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 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 person was working training a model to predict the next character in Amazon reviews, and he got a where — this is a syntactic process, you expect, 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 like, come on, anyone can do that. But this the first time that you saw this emergence, this sort of semantics emerged from this underlying syntactic process. And there we knew, you’ve got scale this thing, you’ve got to see 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, what we’re out of them feels … it just feels impossible that could come from a prediction machine. Just the stuff you us just now. And the key idea of emergence is that you get more of a thing, suddenly different things emerge. happens all the time, ant colonies, single ants run around, when bring enough of them together, you get these ant colonies show completely 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 blew your mind 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 model do it, which means it’s really learned an internal circuit for to do it. And the really 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 so you can 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 than there are in the universe. it had to have learned something general, but that hasn’t really fully yet learned that, Oh, I can sort generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s here is that you’ve allowed it to scale up and at an incredible number of pieces of text. And is learning things that you didn’t know that it was going to capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re to really get good at 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 rebuild our stack. When you think about building a rocket, every tolerance has 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 something 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 to be able to predict. So we were able predict, for 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 is actually smooth scaling, though it’s still early days.
CA: So here is, one of the big then, that arises from this. If it’s fundamental to what’s here, that as you scale up, things emerge that you maybe predict in some level of confidence, but it’s capable of you. Why isn’t there just a huge risk of something terrible emerging?
GB: Well, I think all of these questions of degree and scale and timing. And I think thing people miss, too, is sort of the integration with the is also this incredibly emergent, sort of, very powerful thing too. And so that’s of the reasons that we think it’s so important to deploy incrementally. And I think that what we kind of see right now, you look at this talk, a lot of what I focus on is providing really high-quality feedback. Today, tasks that we do, you can inspect them, right? It’s very to look at that math problem and be like, no, no, no, machine, seven was the answer. But even summarizing a book, like, that’s a thing to supervise. Like, how do you know if this summary is any good? You have to read the book. No one wants to do that.
(Laughter) And so I that the important thing will be that we take this by step. And that we say, OK, as we move on book summaries, we have to supervise this task properly. We to build up a track record with these machines they’re able to actually carry out our intent. And I we’re going to have to produce even better, more efficient, more reliable ways of scaling this, sort of like 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 is to always — we’re never going to know that it’s not generating errors, that it doesn’t have common and so forth. Is it your belief, Greg, that it true at any one moment, but that the expansion of the scale and the human that you talked about is basically going to take it on that of actually getting to things like truth and wisdom so forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe that is where we’re headed. And I that the OpenAI approach here has always been just like, let reality hit in the face, right? It’s like this field is the of broken promises, of all these experts saying X is going to happen, Y is how it works. have been saying neural nets aren’t going to work for 70 years. 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 has always been, you’ve got to push to the limits of this technology to really it in action, because that tells you then, oh, here’s how we 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 the right way to do this is to it out there in public and then harness all this, you know, of just your team giving feedback, the world is giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, original story that I heard on OpenAI when you founded as a nonprofit, well you were there as the sort of check on the big companies doing their unknown, possibly evil thing with AI. you were going to build models that sort of, you know, somehow held them and was capable of slowing the field down, if be. Or at least that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves the tech world that now Google and Meta and forth are all 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 here and not reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t think we’re going to get it right. But one thing I think has been incredibly important, the very beginning, when we were thinking about how to build artificial intelligence, actually have it benefit all of humanity, like, how you supposed to do that, right? And that default plan of being, well, build in secret, you get this 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 else does. for me, that was always terrifying, it didn’t feel right. And so I think that alternative approach is the only other path that I see, which is that do let reality hit you in the face. And I think you do people time to give input. You do have, before machines are perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that 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 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 that in that box is something that, there’s a very strong chance it’s something glorious that’s going to give beautiful gifts to your family and everyone. But there’s actually also a one percent thing in small print there 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, 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 Rico for an AI conference. I’m sitting in the room just looking out over this wonderful water, all these people a good time. And you think about it for a moment, if you could choose for basically Pandora’s box to be five years away or 500 away, which would you pick, right? On the one hand you’re like, well, maybe you personally, it’s better to have it be five years away. But if it to be 500 years away and people get more time to get it right, which 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 the and like, he puts his life on the line in a much more way than any of us typing things in computers 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 as it truly lies. Like, if you look at the history of computing, I really mean it when I say this is an industry-wide or even just almost like human-development- of-technology-wide shift. And the more that you sort of, don’t put together the that are there, right, we’re still making faster computers, we’re still improving the algorithms, of these things, they are happening. And if you don’t them together, you get an overhang, which means that if someone does, or the moment someone does manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time to adjust, knows what kind of safety precautions you get. And so I think that thing I take away is like, even you think about development of other sort of technologies, think about weapons, people talk about being like a zero to one, sort of, change in what humans could do. 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 got to it incrementally and you’ve got to 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 want us have is that we have birthed this extraordinary child that may have superpowers that humanity to a whole new place. It is our collective responsibility to provide the guardrails this child to collectively teach it to be wise and not tear us all down. Is that basically the model?
GB: I think it’s true. And I think it’s important to say this may shift, right? We’ve got to take each as we encounter it. And I think it’s incredibly important today that we all do get in this technology, figure out how to provide the feedback, decide what want from it. And my hope is that that will continue to be best path, but it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, you so much for coming to TED and blowing minds.
(Applause)