We started OpenAI seven years ago because we like something really interesting was happening in AI and wanted to help steer it in a positive direction. It’s honestly just really to see how far this whole field has come since then. And it’s really gratifying to hear from like Raymond who are using the technology we are building, others, for so many wonderful things. We hear from people who are excited, we from people who are concerned, we hear from people who feel those emotions at once. And honestly, that’s how we feel. Above all, it feels we’re entering an historic period right now where we as a world are going to define a technology will be so important for our society going forward. And I that we can manage this for good.
So today, I want to show you the state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m going to show you what it’s like to build a tool for an AI rather than it for a human. So we have a new DALL-E model, generates images, and we are exposing it as an for ChatGPT to use on your behalf. And you 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 of the details for you that you get out of ChatGPT. And here go, it’s not just the 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 this case — sorry, it doesn’t generate text, it generates an image. And that is something that really expands the of what it can do on your behalf in of carrying out your intent. And I’ll point out, this all 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.
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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.” And the interesting thing these tools is they’re very inspectable. So you get this little pop up that says “use the DALL-E app.” And by the way, this is coming to you, ChatGPT users, over upcoming months. And you can look under the and see that what it actually did was write a prompt just a human could. And so you sort of have this ability to how the machine is using these tools, which allows us to provide feedback them.
Now it’s saved for later, and let me show you what it’s to use that information and to integrate with other applications too. can 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 TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how it tastes.
But you can that ChatGPT is selecting all these different tools without having to tell it explicitly which ones to use any situation. And this, I think, shows a new of thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s great experience within an app as long as you kind of know the menus and all the options. Yes, I would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface top of tools, the AI is able to sort of take away all those from you. So 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 sometimes the will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And you can we sent a list of ingredients to Instacart. Here’s you need. And the thing that’s really interesting is that the traditional UI is still very valuable, right? you look at this, you still can click through it and sort modify the actual quantities. And that’s something that I think shows that they’re going away, traditional UIs. It’s just we have a new, way to build them. And now we have a that’s been drafted for our review, which is also very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re to change the work of the AI if we want to. And after this talk, you will be able to access yourself. And there we go. Cool. Thank you, everyone.
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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 use them. Like, what do we even want it to do when we ask these high-level questions? And to do this, we use an old idea. If you go to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, you can it. You could 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 are either good bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine an unsupervised learning process. We just show it the whole world, the internet and say, “Predict what comes next in text you’ve never before.” And this process imbues it with all sorts wonderful skills. For example, if you’re shown a math problem, the way to actually complete that math problem, to say comes next, that green nine up there, is to solve the math problem.
But we actually have to do second step, too, which is to teach the AI 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, “This one’s better than that one.” And this reinforces not the specific thing that the AI said, but very importantly, the whole process that the used to produce that answer. And this allows it to generalize. allows it to teach, to sort of infer your intent and apply it in scenarios that it hasn’t before, that it hasn’t received feedback.
Now, sometimes the things we have to the AI are not what you’d expect. For example, when first showed GPT-4 to 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 some math in there, it will happily pretend that one plus one equals three and run with it.” So had to collect some feedback data. Sal Khan himself was kind and offered 20 hours of his own time to provide feedback to the machine alongside team. And over the course of a couple of months we were able teach the AI that, “Hey, you really should push back on humans in specific kind of scenario.” And we’ve actually made lots lots of improvements to the models this way. And you push that thumbs down in ChatGPT, that actually is kind of like sending 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 our users and make sure we’re building something that’s more useful for everyone.
Now, high-quality feedback is a hard thing. If you think about asking a kid to clean room, if all you’re doing is inspecting the floor, 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. the same sort of reasoning applies to AI. As we move to tasks, we will have to scale our ability to high-quality feedback. But for this, the AI itself is to 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 ask GPT-4 a question like this, how much time passed between these two foundational blogs unsupervised learning and learning from human feedback. And the model says two months passed. But is true? Like, these models are not 100-percent reliable, although they’re better every time we provide some feedback. But we can actually 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 actually given the AI a new tool. one is a browsing tool where the model can issue search and click into web pages. And it actually writes out its whole chain of thought it does it. It says, I’m just going to for this and it actually does the search. It then it 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 you could do, but it’s a very tedious task. It’s not thing that humans really want to do. It’s much fun to be in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. out come citations so you can actually go and very easily verify any piece of this whole of reasoning. And 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 thing that’s so interesting to me about this whole process that it’s this many-step collaboration between a human and AI. Because a human, using this fact-checking tool is doing it order to produce data for another AI to become useful to a human. And I think this really shows the shape something that we should expect to be much more in the future, where we have humans and machines kind of very and delicately designed in how they fit into a and how we want to solve that problem. We make sure the humans are providing the management, the oversight, the feedback, the machines are operating in a way that’s inspectable trustworthy. And together we’re able to actually create even more trustworthy machines. And I think over time, if we get this process right, we will be able to solve impossible problems.
And to you a sense of just how impossible I’m talking, I think we’re going be able 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 VisiCalc. I don’t think they’ve really changed that much in that time. And here is a specific of all the AI papers on the arXiv for past 30 years. There’s about 167,000 of them. And can see there the data right here. But let show you the ChatGPT take on how to analyze data set like this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And so you can just literally a file and ask questions about it. And very helpfully, you know, it knows the of the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the name the file, the column names like you saw and then the actual data. And that it’s able to infer what these columns actually mean. Like, semantic information wasn’t in there. It has to sort of, put together its world knowledge knowing that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s what things 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 do, and the AI is happy to help with it.
Now I don’t even know what I to ask. So fortunately, you can ask the machine, “Can you make exploratory graphs?” And once again, this is a super high-level with lots of intent behind it. But I don’t even know what I want. And the AI kind has to infer what I might be interested in. And it comes up with some good ideas, I think. So a of the number of authors per paper, time series of per year, word cloud of the paper titles. All of that, think, will be pretty interesting to see. And the great thing is, it can actually it. Here we go, a nice bell curve. You see that three kind of the most common. It’s going to then make this nice of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and dropped off the cliff. What could be going on there? By the way, this is Python code, you can inspect. And then we’ll see word cloud. So you can see these wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year really bad. Of course, the problem is that the year 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 even posted by April 13?] So April 13 was the cut-off I believe. Can you use that to make a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted it to notice thing, maybe it’s a little bit of an overreach for it to have sort of, magically that this is what I wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing again, so if you want to inspect what it’s doing, it’s very possible. And now, it does the projection.
(Applause)
If you noticed, it even updates the title. I didn’t for that, but it know what I want.
Now we’ll cut back to the again. This slide shows a parable of how I think we … A vision of we may end up using this technology in the future. A person brought his very sick dog the vet, and the veterinarian made a bad call to say, “Let’s just and see.” And the dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you need to talk to a professional, here are hypotheses.” He brought that information to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You cannot rely on them. But this story, I think, shows a human with a medical professional and with ChatGPT a brainstorming partner was able to achieve an outcome that would not happened otherwise. I think this is something we should all reflect on, think about we consider how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI is 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 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 we released ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring that artificial intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I that within every mind out here there’s a feeling of reeling. Like, I suspect that a large number of people viewing this, you look at that you think, “Oh my goodness, pretty much every single thing about the way work, I need to rethink.” Like, there’s just new possibilities there. Am I right? thinks that they’re having to rethink the way that we do things? Yeah, mean, it’s amazing, but it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually is how the hell have you done this?
(Laughter)
OpenAI has a hundred employees. Google has thousands of employees working on artificial intelligence. Why is you who’s come up with this technology that shocked the world?
Greg Brockman: I mean, the is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the progress, the data progress, all of those are really industry-wide. But I think within OpenAI, we a lot of very deliberate choices from the early days. And first one was just to confront reality as it lays. And that we just thought really 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 that did. And I think that the most important thing has to get teams of people who are very different each other to work together harmoniously.
CA: Can we the water, by the way, just brought here? I think we’re to need it, it’s a dry-mouth topic. But isn’t there something also just about the fact that you something in these language models that meant that 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 level, deep learning, like we always knew that was we wanted to be, was a deep learning lab, exactly how to do it? I think that in early days, we didn’t know. We tried a lot things, and one person was working on training a model to predict next character in Amazon reviews, and he got a result where — this is a process, you expect, you know, the model will predict where the commas go, where nouns and verbs are. But he actually got a state-of-the-art analysis classifier out of it. This model could tell if a review was positive or negative. I mean, today we are just like, on, anyone can do that. But this was the first time you saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve got to scale thing, you’ve got to see where it goes.
CA: So I think this explain the riddle that baffles everyone looking at this, because things are described as prediction machines. And yet, what we’re seeing of them feels … it just feels impossible that that could from a prediction machine. Just the stuff you showed just now. And the key idea of emergence is that when get more of a thing, suddenly different things emerge. It all the time, ant colonies, single ants run around, when you bring of them together, you get these ant colonies that completely emergent, different behavior. Or a city where a houses together, it’s just houses together. But as you grow the of houses, things emerge, like suburbs and cultural centers and traffic jams. Give me one for you when you saw just something pop that just blew your mind that you just did not coming.
GB: Yeah, well, so you can try this in ChatGPT, if you add 40-digit —
CA: 40-digit?
GB: 40-digit numbers, the model will it, which means it’s really learned an internal circuit for to do it. And the really interesting thing is actually, if have it 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, it hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s more than there are in the 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: 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 that it was going to be capable of 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 do that actually, one the things I think is very undersung in this field sort of engineering quality. Like, we had to rebuild our entire stack. When you about building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get every single piece the stack engineered properly, and then you can start these predictions. There are all these incredibly smooth scaling curves. They tell you something deeply fundamental intelligence. If you look at our GPT-4 blog post, can see all of these curves in there. And we’re starting to be able to predict. So we were able to predict, for example, the on coding problems. We basically look 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 it’s still early days.
CA: So here is, one the big fears then, that arises from this. If it’s to what’s happening here, that as you scale up, things emerge you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just a risk of something truly terrible emerging?
GB: Well, I think all of these are of degree and scale and timing. And I think one thing people miss, too, is sort of the with the world is also this incredibly emergent, sort of, very thing too. And so that’s one of the reasons that we think it’s so to deploy incrementally. And so I think that what we kind see right now, if you look at this talk, a of what I focus on is providing really high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if this book summary is good? You have to read the whole book. No one wants to that.
(Laughter) And so I think that the important will be that we take this step by step. And we say, OK, as we move on to book summaries, we to supervise this task properly. We have to build a track record with these machines that they’re able to actually carry our intent. And I think we’re going to have to produce better, more efficient, more reliable ways of scaling this, sort of making the machine be aligned with you.
CA: So we’re to hear later in this session, there are critics say that, you know, there’s no real understanding inside, the system is going to — we’re never going to know that it’s not generating errors, it doesn’t have common sense and so forth. Is it your belief, Greg, that it is true any one moment, but that the expansion of the scale and the human feedback that you talked is basically going to take it on that journey of actually to things like truth and wisdom and so forth, with a high degree confidence. Can you be sure of that?
GB: Yeah, well, I that the OpenAI, I mean, the short answer is yes, I believe is where we’re headed. And I think that the OpenAI approach here has always just like, let reality hit you in the face, right? It’s this field is the field of broken promises, of all these experts saying X is to happen, Y is how it works. People have saying neural nets aren’t going to work for 70 years. They haven’t been yet. They might be right maybe 70 years plus one something like that is what you need. But I that our approach has always been, you’ve got to to the limits of this technology to really see in action, because that tells you then, oh, here’s how we can move on to a new paradigm. we just haven’t exhausted the fruit here.
CA: I mean, it’s quite controversial stance you’ve taken, that the right way to this is to put it out there in public then harness all this, you know, instead of just your team giving feedback, the is now giving feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, the original story I heard on OpenAI when you were founded as a nonprofit, well you were as the great sort of check on the big companies doing their unknown, possibly evil thing with AI. you were going to build models that sort of, know, somehow held them accountable and was capable of slowing the down, if need be. Or at least that’s kind of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through the tech world now Google and Meta and so forth are all scrambling to up. And some of their criticisms have been, you are us to put this out here without proper guardrails or die. You know, how do you, like, make the case that what you have done responsible here and not reckless.
GB: Yeah, we think these questions 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 were thinking about how to artificial general 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 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 do give people time to give input. You do have, before these machines are perfect, they are 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 the number one thing people were to do with it was generate misinformation, try to elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: So spam is bad, but there are things that are much worse. Here’s a thought for you. Suppose you’re sitting in a room, there’s a box the table. You believe that in that box is something that, there’s 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 a one thing in the small print there that says: “Pandora.” And there’s a that this actually could unleash unimaginable evils on the world. you open that box?
GB: Well, so, absolutely not. think you don’t do it that way. And honestly, like, I’ll tell a story that I haven’t actually told before, which is that after we started OpenAI, I remember I was in Puerto Rico for an AI conference. I’m in the hotel room just looking out over this water, all these people having a good time. And you about it for a moment, if you could choose for basically that Pandora’s box to be five away or 500 years away, which would you pick, right? the one hand you’re like, well, maybe for you personally, it’s to have it be five years away. But if it gets to be 500 years and people get more time to get it right, do you pick? And you know, I just really it in the moment. I was like, of course you the 500 years. My brother was in the military at time and like, he puts his life on the line in a much more real way any of us typing things in computers and developing technology at the time. And so, yeah, I’m really sold on the you’ve to approach this right. But I don’t think that’s quite playing the as it truly lies. Like, if you look at whole history of computing, I really mean it when say that 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 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 don’t put them together, you get an overhang, which 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 to adjust, who knows what kind of safety precautions you get. so I think that one thing I take away is like, even you think about development other sort of technologies, think about nuclear weapons, people 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 over time. And so the history, I think, of technology we’ve developed has been, you’ve got to do it incrementally and you’ve to figure out how to manage it for each moment that you’re increasing it.
CA: So what I’m is that you … the model you want us to have 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 for this to collectively teach it 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 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 my hope is that will continue to be the best path, but it’s so good we’re honestly having this debate we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much for coming to and blowing our minds.
(Applause)