We OpenAI seven years ago because we felt like something interesting was happening in AI and we wanted to help steer it in a positive direction. It’s honestly really amazing to see how far this whole field has come since then. And it’s really gratifying to from people like Raymond who are using the technology are building, and others, for so many wonderful things. We hear from people who are excited, hear from people who are concerned, we hear from people who feel both those at once. And honestly, that’s how we feel. Above all, it feels like we’re entering an period right now where we as a world are going to define a technology that be so important for our society going forward. And believe that we can manage this for good.
So today, want to show you the current state of that technology some of the underlying design principles that we hold dear.
So the first I’m going to show you is what it’s like build a tool for an AI rather than building it for human. So we have a new DALL-E model, which images, and we are exposing it as an app for ChatGPT to use your behalf. And you can do things like ask, you know, suggest a nice post-TED meal 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 get out of ChatGPT. And here we go, it’s not just idea for the meal, but a very, very detailed spread. So let’s see we’re going to get. But ChatGPT doesn’t just generate images in this case — sorry, it doesn’t text, it also generates an image. And that is something that expands the power of what it can do on your behalf in of carrying out your intent. And I’ll point out, this is all live demo. This is all generated by the AI as we speak. So I actually don’t know what we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT with other tools too, 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 coming to you, all ChatGPT users, over upcoming months. And you can under the hood and see that what it actually did was write a prompt just a human could. And so you sort of have this ability to inspect how machine is using these tools, which allows us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like to that information and to integrate with other applications too. You say, “Now make a shopping list for the tasty thing I was suggesting earlier.” And it a little tricky for the AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, meal, I definitely want to know how it tastes.
But you can see that ChatGPT is selecting all different tools without me having 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 thinking of, well, we have these apps, we click them, we copy/paste between them, and 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 to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, the AI able to sort of take away all those details from you. So you don’t have be the one who spells out every single sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes the will happen to us. But let’s take a look at the Instacart shopping list while we’re at it. you can see we sent a list of ingredients to Instacart. Here’s everything you need. And the that’s really interesting is that the traditional UI is still valuable, right? If you look at this, you still can click through it and of modify the actual quantities. And that’s something that think shows that they’re not going away, traditional UIs. It’s we have a new, augmented way to build them. now we have a tweet that’s been drafted for our review, is also a very important thing. We can click “run,” and there we are, we’re the manager, we’re able inspect, we’re able to change the work of the if we want to. And so after this talk, will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut to the slides. Now, the important thing about how we build this, it’s not just about building tools. It’s about teaching the AI how to use them. Like, what do even want it to do when we ask these high-level questions? And to do this, we use an old idea. If you back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program an to this. Instead, you can learn it. You could a machine, like a human child, and then teach through feedback. Have a human teacher who provides rewards and punishments as it things out and does things that are either good or bad.
And this exactly how we train ChatGPT. It’s a two-step process. First, we produce what Turing would called a child machine through an unsupervised learning process. We just show it whole world, the whole internet and say, “Predict what comes 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 actually complete that math problem, to say what comes next, green nine up there, is to actually solve the problem.
But we actually have to do a second step, too, is to teach the AI what to do with skills. And for this, we provide feedback. We have the try out multiple things, give us multiple suggestions, and a 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 that the AI used produce that answer. And this allows it to generalize. It it to teach, to sort of infer your intent and apply it in scenarios it hasn’t seen before, that it hasn’t received feedback.
Now, the things we have to teach the AI are what you’d expect. For example, when we first showed GPT-4 to Khan Academy, said, “Wow, this is so great, We’re going to be able to teach wonderful 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 run with it.” So we had to collect some feedback data. Khan himself was very kind and offered 20 hours of his time to provide feedback to the machine alongside our team. over the course of a couple of months we were able to teach the AI that, “Hey, you should push back on humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to the models way. And when you push that thumbs down in ChatGPT, that actually is of like sending up a bat signal to our to say, “Here’s an area of weakness where you should gather feedback.” so when you do that, that’s one way that we listen to our users and make sure we’re building something that’s more useful for everyone.
Now, providing high-quality is a hard thing. If you think about asking a kid to clean their room, if you’re doing is inspecting the floor, you don’t know if you’re just teaching them to stuff the toys in the closet. This is a nice DALL-E-generated image, by the way. And same sort of reasoning applies to AI. As we move to harder tasks, we will have to our ability to provide high-quality feedback. But for this, the itself is happy to help. It’s happy to help us provide better feedback and to scale our ability to supervise machine as time goes on. And let me show you what I mean.
For example, can ask GPT-4 a question like this, of how much time passed between these two foundational blogs unsupervised learning and learning from human feedback. And the says two months passed. But is it true? Like, these are not 100-percent reliable, although they’re getting better every time we provide some feedback. But can actually use the AI to fact-check. And it can actually check its own work. You say, fact-check this for me.
Now, in this case, I’ve given the AI a new tool. This one is a browsing tool where the model can issue search and click into web pages. And it actually writes out its whole of thought as it does it. It says, I’m just to search for this and it actually does the search. It it finds the publication date and the search results. then is issuing another search query. It’s going to click into the post. And all of this you could do, but it’s very tedious task. It’s not a thing that humans really want to do. It’s much more to be in the driver’s seat, to be in manager’s position 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 and one week, that was correct.
(Applause)
And we’ll back to the side. And so thing that’s so interesting to me about whole process is that it’s this many-step collaboration between human and an AI. Because a human, using this fact-checking tool is it in order to produce data for another AI to become useful to a human. And I think this really shows the shape of something that should expect to be much more common in the future, we have humans and machines kind of very carefully and designed in how they fit into a problem and we want to solve that problem. We make sure that the are providing the management, the oversight, the feedback, and the machines are operating in way that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that over time, if we get this process right, we be able to solve impossible problems.
And to give you a sense of just impossible I’m talking, I think we’re going to be able to rethink almost aspect of how we interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really that much in that time. And here is a specific spreadsheet of all the AI papers the arXiv for the past 30 years. There’s about 167,000 of them. And you can see there the right here. But let me show you the ChatGPT take on how to a data set like this.
So we can give ChatGPT to yet another tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And so you can literally upload a file and ask questions about it. And very helpfully, know, it knows the name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” The only information here is the of the file, the column names like you saw and then the actual data. And from it’s able to infer what these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its world knowledge knowing that, “Oh yeah, arXiv is a site that people submit papers and that’s what these things are and that these are integer values so therefore it’s a number of authors in the paper,” like all of that, that’s 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, can ask the machine, “Can you make some exploratory graphs?” And once again, this is a super high-level with lots of intent behind it. But I don’t know what I want. And the AI kind of to infer what I might be interested in. And so comes up with some good ideas, I think. So a histogram of number of authors per paper, time series of papers per year, cloud of the paper titles. All of that, I think, will be pretty to see. And the great thing is, it can actually do it. we go, a nice bell curve. You see that is kind of the most common. It’s going to make this nice plot of the papers per year. crazy is happening in 2023, though. Looks like we were on an exponential and it dropped off cliff. What could be going on there? By the way, all this Python code, you can inspect. And then we’ll see word cloud. So you can see all these things that appear in these titles.
But I’m pretty about this 2023 thing. It makes this year look bad. Of course, the problem is that the year is not over. 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 by April 13?] So April 13 was the cut-off date I believe. you use 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 was more 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 magically that this what I wanted. But I inject my intent, I this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so if you want inspect what it’s doing, it’s very possible. And now, it does correct projection.
(Applause)
If you noticed, it 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 shows a parable of how I think we … A vision how we may end up using this technology in the future. person brought his very sick dog to the vet, and the veterinarian made a bad to say, “Let’s just wait and see.” And the would not be here today had he listened. In the meanwhile, he the blood test, like, the full medical records, to GPT-4, which said, “I am a vet, you need to talk to a professional, here are some hypotheses.” brought that information to a second vet who used 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 medical professional and with ChatGPT as a brainstorming partner able to achieve an outcome that would not have otherwise. I think this is something we should all reflect on, think about we consider how to integrate these systems into our world.
And one I believe really deeply, is that getting AI right going to require participation from everyone. And that’s for how we want it to slot in, that’s for the rules of the road, for what an AI will won’t do. And if there’s one thing to take from this talk, it’s that this technology just looks different. different from anything people had anticipated. And so we all have become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, believe that we can achieve the OpenAI mission of ensuring artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within mind out here there’s a feeling of reeling. Like, I suspect that a very large of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having rethink the way that we do things? Yeah, I mean, it’s amazing, it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the hell you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of employees working on artificial intelligence. is it you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re all building 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. But I within OpenAI, we made a lot of very deliberate choices the early days. And the first one was just to confront reality as it lays. And that we thought really hard about like: What is it going to take to make here? We tried a lot of things that didn’t work, you only see the things that did. And I 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, 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 fact that you saw something in these language models that that if you continue to invest in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, I the story there is pretty illustrative, right? I think high level, deep learning, like we always knew that was what 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 of things, one person was working on training a model to predict the next in Amazon reviews, and he got a result where — this is a process, you expect, you know, the model will predict the commas go, where the nouns and verbs are. But he got a state-of-the-art sentiment analysis classifier out of it. This model could tell you if review was positive or negative. I mean, today we just like, come on, anyone can do that. But this was first time that you saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve 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 feels … it just feels impossible that that could come a prediction machine. Just the stuff you showed us now. And the key idea of emergence is that when get more of a thing, suddenly different things emerge. It happens all the time, ant colonies, single run around, when you bring enough of them together, you 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 and traffic jams. Give me one moment for you when you saw just something that just blew your mind that you just did see coming.
GB: Yeah, well, so you can try 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. the really interesting thing is actually, if you have it like a 40-digit number plus a 35-digit number, it’ll often get it wrong. And so you 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 than there are in universe. So it had to have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize this to adding arbitrary numbers of lengths.
CA: So what’s happened here is that you’ve allowed it to scale up and look at an number of pieces of text. And it is learning things you didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. So one science we’re starting to really get good at is predicting some these emergent capabilities. And to do that actually, one the things I think is very undersung in this field is sort of quality. Like, we had to rebuild our entire stack. When you think about building rocket, every tolerance has to be incredibly tiny. Same is true machine learning. You have to get every single piece of the stack engineered properly, then you can start doing these predictions. There are all these smooth scaling curves. They tell you something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these curves in there. And now we’re to be able to predict. So we were able predict, for example, the performance on coding problems. We basically look some models that are 10,000 times or 1,000 times smaller. And so there’s something about this that is smooth scaling, even though it’s still early days.
CA: So here is, one of big fears then, that arises from this. If it’s fundamental what’s happening here, that as you scale up, things that you can maybe predict in some level of confidence, but it’s capable surprising you. Why isn’t there just a huge risk of something truly emerging?
GB: Well, I think all of these are questions of and scale and timing. And I think one thing people miss, too, is sort of the integration the world is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that we it’s so important to deploy incrementally. And so I think that we kind of see right now, if you look this talk, a lot of what I focus on is really high-quality feedback. Today, the tasks that we do, you can inspect them, right? It’s very easy to at that math problem and be like, no, no, no, machine, seven was the answer. But even summarizing a book, like, that’s a hard thing supervise. Like, how do you know if this book is any good? You have to read the whole book. 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 on to book summaries, we have to supervise this properly. We have to build up a track record with these machines that they’re to actually carry out our intent. And I think we’re to have to produce even better, more efficient, more reliable ways of scaling this, sort like making the machine be aligned with you.
CA: So we’re going hear later in this session, there are critics who say that, you know, there’s no understanding inside, the system is going to always — we’re never going to that it’s not generating errors, that it doesn’t have sense and so forth. Is it your belief, Greg, it is true at any one moment, but that the expansion of the and the human feedback that you talked about is basically going to take it on that journey actually getting to things like truth and wisdom and forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, short answer is yes, I believe that is where we’re headed. I think that the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like this field the field of broken promises, of all these experts saying is going to happen, Y is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t been right yet. might be right maybe 70 years plus one or something like that is you need. But I think that our approach has always been, you’ve to 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 exhausted the here.
CA: I mean, it’s quite a controversial stance you’ve taken, that right way to do this is to put it there in public and then harness all this, you know, of just your team giving feedback, the world is now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, you know, the original story that heard on OpenAI when you were founded as a nonprofit, well you were there the great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held accountable 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 opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through the tech world that now Google Meta and so forth are all scrambling to catch up. some of their criticisms have been, you are forcing us to put this out here without proper or we die. You know, how do you, like, make the that what you have done is responsible here and reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing I think has incredibly important, from the very beginning, when we were thinking about to build artificial general intelligence, actually have it benefit all humanity, like, how are you supposed 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 you push “go,” and you hope you got it right. I don’t how to execute that plan. Maybe someone else does. But for me, that was always terrifying, didn’t feel right. And so I think that this approach is the only other path that I see, which is that you let reality hit you in the face. And I think you do give people to give input. You do have, before these machines are perfect, before they super powerful, that you actually have 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 it was misinformation, try to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a thought experiment you. Suppose you’re sitting in a room, there’s a on the table. You believe that in that box something that, there’s a very strong chance it’s something absolutely glorious that’s going give beautiful gifts to your family and to everyone. But there’s actually also one percent thing in the small print there that says: “Pandora.” And there’s a chance that actually could unleash unimaginable evils on the world. Do open that box?
GB: Well, so, absolutely not. I think don’t do it that way. And honestly, like, I’ll tell you story that I haven’t actually told before, which is that shortly after we OpenAI, I remember I was in Puerto Rico for an AI conference. I’m sitting in hotel room just looking out over this wonderful water, all these having a good time. And you think about it for moment, if you could choose for basically that Pandora’s box be five years away or 500 years away, which would you pick, right? the one hand you’re like, well, maybe for you personally, it’s better to have it be five years away. But it gets to be 500 years away and people get more time get it right, which do you pick? And you know, just really felt it in the moment. I was like, course you do the 500 years. My brother was in military at the time and like, he puts his life on line in a much more real way than any us typing things in computers and developing this technology at the time. And so, yeah, I’m sold on the you’ve got to approach this right. But I don’t think that’s quite the field as it truly lies. Like, if you at the whole history of computing, I really mean it I say that this is an industry-wide or even just almost like a human-development- of-technology-wide shift. And more that you sort of, don’t put together the that are there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. And if you don’t put them together, 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 adjust, who knows what kind of safety precautions you get. And so I think that one I take away is like, even you think about development of sort of technologies, think about nuclear weapons, people talk about like a zero to one, sort of, change in what humans could do. But actually think that if you look at capability, it’s been smooth over time. And so the history, I think, of technology we’ve developed has been, you’ve got to do it incrementally 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 us to have is that we have birthed this extraordinary child that may superpowers that take humanity to a whole new place. It is our collective to provide the guardrails for this child to collectively it to be wise and not to tear us all down. Is 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 step as we encounter it. And I think it’s incredibly important today that we all get literate in this technology, figure out how to the feedback, decide what we want from it. And hope is that that will continue to be the best path, but it’s so good we’re honestly this debate 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)