In this season of The TED Interview, conversations with people who make a case for ... optimism. Not some blind, hopeful feeling, but the conviction that somewhere out there are solutions that, given the right attention and resources, can guide us out of the dark place we're in. For the first episode: artificial intelligence. Will innovation in AI drastically improve our lives, or destroy humanity as we know it? Head of TED Chris Anderson sits down with OpenAI CEO Sam Altman, who makes a case for AI's potential to make the future better for all of us -- and explains how his company is leading that charge with an unusual new business model. Listen and subscribe to The TED Interview and more podcasts from the TED Audio Collective wherever you're listening to this.

♪ Hello there, this is Chris Anderson, and I am hugely, hugely, tremendously excited to welcome you to a new series of the TED interview.

Now then, This season, we're trying something new.

We're organizing the whole season around a single theme, albeit a theme that some of you may consider inappropriate.

And yes, I know the world has been hit with some extraordinarily ugly things in the last few years.

Political division, a racial reckoning, technology run amok, not to mention a global pandemic and impending climate catastrophe.

In this context, optimism just can seem so naive and unwanted almost, annoying.

So here's my position, don't think of optimism as a feeling, it's not just this sort of shallow feeling of hope.

Optimism is a search, It's a determination to look for a pathway forward.

Somewhere out there, I believe, I truly believe, there are amazing people whose minds contain the ideas, the visions, the solutions that can actually create that pathway forward.

If given the support and resources they need, they may very well light the path out of this dark place we're in.

So these are the people who can present not optimism, but a case for optimism.

They're the people I'm talking to this season.

Now then, the place I want to start is with AI, artificial intelligence.

This of course is the next innovative technology that is going to change everything as we know it, for better or for worse.

Today we'll see it painted not with the usual dystopian brush, but by someone who truly believes in its potential.

Sam Altman is the former president of Y Combinator, the legendary startup accelerator.

And in 2015, he and a team launched a company called OpenAI, dedicated to 1 noble purpose, to develop AI so that it benefits humanity as a whole.

You may have heard, by the way, recently a lot of buzz around an AI technology called GPT-3 that was developed by OpenAI and proved the quality of the amazing team of researchers and developers they have working there.

We'll be hearing a lot about GPD 3 in the conversation ahead.

But sticking to this lofty mission of developing AI for humanity and finding the resources to realize it haven't been simple.

OpenAI is certainly not without its critics, but their goal couldn't be more important.

And honestly, I found it really quite exciting to hear Sam's vision for where all this could lead.

A lot of people are fearful of the future at this moment in world history.

How would you describe your attitude to the future?

I think that the combination of scientific and technological progress and better societal decision-making, better societal governance is going to solve, in the next couple of decades, all of our current most pressing problems.

But I think we are going to get very safe, very inexpensive, carbon-free nuclear energy to work.

And I think we're going to talk about that time that the climate disaster looked so bad and how lucky we are we got saved by science and technology.

I think we've already now seen this with the rapidity that we were able to get vaccines deployed.

We are going to find that we are able to cure or at least treat a significant percentage of human disease including I think we'll just actually make progress in helping people have much longer, decades longer health spans.

I think in the next couple of decades, that will look pretty clear.

I think we will build systems with AI and otherwise that make access to an incredibly high-quality education more possible than ever before.

I think the lives, you know, when we look forward like 100 years, 50 years even, the quality of life available to anyone then will be much better than the quality of life available in the very best case To anyone today to any single person today.

I think like It's always easy to doomscroll and think about how bad are the bad things are.

But the good things are really good and getting much better.

Is it your sincere belief that artificial intelligence can actually make that future better?

Look, with any technology, I don't think it will all be better.

I think there are always positive and negative use cases of anything new, and it's our job to maximize the positive ones, minimize the negative ones.

But I truly, genuinely believe that the positive impacts will be orders of magnitude bigger than the negative ones.

I think we're seeing a glimpse of that now.

Now that we have the first general purpose AI's built out in the world and available via things like our API, I think we are seeing evidence of just the breadth of services that we will be able to offer as this sort of technological revolution really takes hold.

And we will have people interact with services that are smart, really smart, and it will feel like as strange as the world before, mobile phones feels now to us.

I guess that stands for Application Programming Interface.

It's the technology that allows complex technology to be accessible to others.

So Sam, give me a sense of a couple of things that have got you most excited that are already out there, and then how that gives you visibility to a pathway forward that is even more exciting.

So I think that the things that we're seeing now are very much glimpses of the future.

We released GPT-3, which is a general purpose natural language text model in the summer of

You know, there's hundreds of applications that are now using it in production.

But there are things where people use GPT-3 to really understand the intent behind a search query and deliver results and sort of understand not only the intent but all of the data and deliver the thing of what you want.

So you can sort of describe a fuzzy thing and it'll understand documents, it can understand short documents, not full books yet, but bring you back to the context of what you want.

There's been a lot of excitement about using the generative capabilities to create sort of games or sort of interactive stories or letting people develop characters or chat with a sort of virtual friend.

There are applications that, for example, help a job seeker polish a tailored application for each individual company.

There's the beginning of AI tutors that can sort of teach people about different concepts and take on different personas.

You know, we could go on for a long time, but I think anything that you can imagine that you do today via computer that you would like to really understand and get to know you and not only that, but understand all of the data and knowledge in the world and help you have the best experience that is possible, that will all happen.

What new adjacent possible state is there as

Frame this question from the point of view of someone who's starting out on a career, for example, they're trying to figure out what would be a really interesting thing to do in the future that has only recently become possible.

What are some new things that this opens up?

In a world where you can talk to a computer and get the output that would normally require you hiring the world experts back immediately for almost no money, I would say think about what's possible there.

So that could be like, as you said, what can normally only the best programmer in the world or a really great programmer do for me?

And can I now instead just ask in English and have that program written?

So all these people that you know want to develop an app, they have an idea but they don't know how to program, now they can have it.

You know what does a service look like when Anyone on earth who wants really great medical advice can get better medical advice than any single doctor could ever get because this has the total medical knowledge and reasoning ability that some humanity has ever produced.

You know, when you want to learn something, you have a sort of AI tutor that understands your exact style, how you best learn everything you know, and custom teaches you whatever concept you want to learn.

Someday you can imagine that like, you have an AI that reads your email and your task list and your calendar and the documents you've been sent and any meeting maximally perfectly prepares you and has all of the information that you need in all the context of your entire career right there for you.

We could go on for a long time, but I think these will just be powerful systems.

So it's really fun playing around with GPT-3.

1 compelling example for someone who's more tech space is try Googling the Guardian essay that was written entirely by different GPT-3 queries and stitched together.

It's an essay on why artificial intelligence isn't a threat to humanity, and it's impressive.

I actually tried inputting on 1 of the online UIs.

I asked the question, what is interesting about Sam Altman?

It came back with, I don't understand what you mean by interesting.

It seems to me that this word has no unambiguous meaning in the context of human society beyond its literal definition as somewhat pleasing or entertaining.

I do not believe there to be any such thing as interestingness except in the mind of a human or other sentient being.

But to my knowledge this is an entirely subjective state that varies from person to person.

However, I will grant that this is an interesting question to ask.

Well, so you can grade that as somewhere between profound and gibberish.

Is that almost where the state of play is?

I think somewhere between profound and gibberish is the right way to think about the current capabilities of say GPT-3.

Yeah, I think like we're definitely had a bubble of hype about GPT-3 last summer.

But the thing about bubbles is the reason that smart people fall for them is there's a kernel of something really real and really interesting that people get overexcited about.

And I think people definitely got and still are over excited about GPT-3 today, but still probably underestimated the potential of where these models will go in the future.

And so maybe there's this like short term overhype and long term underhype for the entire field, for text models, for whatever you'd like that's going on.

And as you said, there's like clearly some gibberish in there.

But on the other hand, those were like well-formed sentences, and there were a couple of ideas in there that I was like, oh, like, actually, maybe that's right.

And I think if artificial intelligence, even in its current very larval state, can make us confront new things and sort of inspire new ideas, that's already pretty impressive.

IAN Give us a sense of what's actually happening in

I think it's hard to understand because you read these words seem like someone is trying to mean something.

Obviously I don't think you believe that there's whatever you've built there, that there's a sort of thinking sentient thing that's going, oh, I must answer this question.

So how would you describe what's going on?

You've got something that has read the entire internet, essentially, all of Wikipedia, et cetera.

We've read something that's read like a small fraction, a random sampling of the internet.

We will eventually train something that has read as much of the internet, or more of the internet than we've done right now.

I mean, we're still, I think, relative to what we will have operating at quite small scale with quite small AIs.

But what is happening is there is a model that is ingesting lots of text, and it is trying to predict the next word.

So we use transformers, they take in a context which is a particular architecture of an AI model, they take in a context of a lot of words, let's say like a thousand or something like that, and they try to predict the word that comes next in the sequence.

And there's like a lot of other things that happen, but fundamentally, that's it.

And I think this is interesting, because in the process of playing that little game of trying to predict the next word.

These models have to develop a representation and understanding of what is likely to come next.

And I think it is maybe not perfectly accurate but certainly worth considering to say that intelligence is very near the ability to make accurate predictions.

What's confusing about this is that there are so many words on the internet which are foolish as well as the words that are wise.

And how do you build a model that can distinguish between those 2?

And this was prompted actually by another example that I typed in, like I asked, you know, what is a powerful idea?

I'm very interested in ideas, that was my question.

And it came back with several things, some of which seemed moderately profound, some of which seemed moderately gibberish, but then here was 1 that it came back with.

The idea that the human race has, quote, evolved, unquote, is false.

Evolution or adaptation within a species was abandoned by biology and genetics long ago.

So I'm going, whoa, wait a sec, that's news to me.

And I presume this has been pulled out of some recess of the internet.

Is it possible even in theory to imagine how a model can gravitate towards truth, wisdom, as opposed to just like majority views?

How do you avoid something taking us further into the sort of, you know, the maze of errors and bad thinking and so forth that has already been a worrying feature the last few years?

It's a fantastic question and I think it is the most interesting area of research that we need to pursue now.

I think at this point, the questions of whether we can build really powerful general purpose AI system, I won't say they're in the rear view mirror, we still have a lot of hard engineering work to do, but I'm pretty confident we're gonna be able to.

And now the questions are, like, what should we build?

And how, and why, and what data should we train on?

And how do we build systems not just that can do these, like, phenomenally impressive things, but that we can ensure do the things that we want, and that understand the concepts of truth and falsehood and, you know, alignment with human values and misalignment with human values.

1 of the pieces of research that we put out last year that I was most proud of and most excited about is what we call reinforcement learning from human feedback.

And we showed that we can take these giant models that are trained on a bunch of stuff, some of it good, some of it bad, and then with a really quite small amount of feedback from human judgment about, hey, this is good, this is bad, this is wrong, this is the behavior I want, I don't want this behavior, we can feed that information from the human judges back into the model and we can teach the model, behave more like this and less like that.

And it works better than I ever imagined it would.

And that gives me a lot of hope that we can build an aligned system.

Like I think curating data sets where there's just less sort of bad data to train on will go a very long way.

And as these models get smarter, I think they inherently develop the ability to sort out bad data from good data.

And as they get really smart, they'll even start to do something we call active learning, which is where they ask us for exactly the data they need when they're missing something, or when they're unsure, or when they don't understand.

But I think as a result of simply scaling these models up, building better, I hate to use the word cognition because it sounds so anthropomorphic, but let's say building a better ability to reason into the models, to think, to challenge, to try to understand and combining that with this idea of aligning to human values via this technique we developed, that's going to go a very long way.

Now there's another question which you sort of just kicked the ball down the field to, which is how do we as a society decide to which set of human values do we align these powerful systems?

So if I understand rightly what you're saying there, you're saying that it's possible to look at the output at any 1 time of GPT-3, and if we don't like what it's coming up with, some wise human can say, no, that was off, don't do that.

Whatever algorithm or process led you to that, undo it.

And the system is then incredibly powerful at avoiding that same kind of mistake in future because it sort of back replicates the instruction.

And eventually, and not much longer, I believe that we'll be able to not only say that was good, that was bad, but say that was bad for this reason.

And also tell me how you got to that answer so I can make sure I understand.

But at the end of the day, someone needs to decide who is the wise human or humans who are looking at the results.

So it's a big difference is, you know, someone who grew up with intelligent design worldview could look at that and go, that's a brilliant outcome.

And someone else would say, something's gone awfully wrong here.

So how do you avoid, and this is a version of the problem that a lot of the, I guess, Silicon Valley companies are facing right now in terms of the pushback they're getting on the output of social media and so forth.

How do you assemble that pool of experts who stand for human values that we actually want?

I don't think this is like solely or even not even close to majorly up to open AI to decide.

I think we need to begin a societal conversation now about how we're going to make those decisions, how we're going to make sure we have representational input in that, and how we sort of make these very difficult global governance systems.

My personal belief is that we should have pretty broad rules about what these systems will never do and will always do, but then the individual user should get a system that kind of behaves like they want.

And there will be, you know, people do have very different value systems.

Some of them are just fundamentally incompatible.

No 1 gets to use AI to like exploit other people, for example, and hopefully we can all agree on.

But do you want the AI to like, you know, support you in your belief of intelligent design?

Like, do I think open AI should say it can't?

Even though I vehemently disagree with that as like a scientific conclusion?

I think the thing to remember about all of this is that GPT-3 is still quite extraordinarily weak.

It still has such big problems and is still so unreliable that for most use cases, it's still unsuitable.

But when we think about a system that is like a thousand times more powerful, and let's say a million times more reliable.

You know it just doesn't it doesn't say gibberish very often.

It doesn't totally lose the plot and get distracted.

A system like that is going to be 1 that a lot of the economic activity in the world comes to rely on.

And I think it's very important that we don't have a small group of people sort of saying that you can never use it for this thing that like most of the world wants to use it for, because it doesn't match our personal beliefs.

Talk a bit more about some of the other uses of it, because 1 of the things that's most surprising is it's not just about sort of text responses, it can take generalized human instructions and build things.

So for example, you can say to it, write a Python program that is designed to put a flashing cursor in 1 corner of the screen and the Google logo in the other corner and it can go and do something like that shockingly effectively.

That opens the door to An entirely way to think about programmers for the future that you could you could have people who can program just in human natural language potentially and gain rapid efficiency and let the AI do the engineering?

We're not that far away from the world where you will write a spec in English and for a simple enough program, the AI will just write the code for you.

As you said, you can see glimpses of that even in this very weak GPT-3, which was not trained to code.

Like I think this is important to remember, we trained it on the language on the internet.

Very rarely, you know, internet, language on the internet also includes some code snippets.

So if we really try to go train a model on code itself, and that's where we decide to put the horsepower of the model into, just imagine what would be possible.

But I think what you're pointing to there is that because models like GPT-3, to some degree or other, and it's like very hard to know exactly how much, understand the underlying concepts of what's going on.

And they're not just regurgitating things they found on a website, but they can really apply them and say, oh yeah, I kind of like know about this word and this idea in code, and this is probably what you're trying to do and I won't get it right always, but sometimes I will just generate this brand new program for nothing that anyone has ever asked before and it will work.

And data is data, so it can do that from English to code, it can do that from English to French.

Again, we never told it to learn about translation.

We never told it about the concepts of English and French, but it learned them.

Even though we never said, this is what English is, and this is what French is, and this is what it means to translate,

I mean, for creative people, is there a world coming where the sort of the palette of possibility that they can be exposed to is just explodes?

I mean, if you're a musician, is there a near future where you can say to your AI, okay, I'm going to bed now, but in the morning, I'd love you to present me with a thousand 2 bar jingles with words attached that you think have a sort of meme factor to them.

And you come down in the morning and the computer shows you this stuff and 1 of them you go, wow, that is it.

Is that going to actually be a value add?

We released something last year called Jukebox, which is very near what you described, where you can say, I want music generated for me in this style or this kind of stuff, and it can come up with the words as well, and it's pretty cool.

And I really enjoy listening to music that it creates, and you can do full songs, 2 bars of a jingle, whatever you'd like.

And 1 of my very favorite artists reached out cold to OpenAI after we released this and said that he wanted to talk.

And I was like, whoa, like total fanboy here.

And I was so nervous that he was going to say, this is terrible.

This is like a really sad thing for human creativity.

And he was so excited, and he's like, this has been so inspiring, I want to do a new album with this, you know, it's like giving me all these new ideas, it's making me much better at my job, I'm going to make better music because of this tool.

And that was awesome, And I hope that's how it all continues to go.

We see a similar thing now with DALI, where graphic designers sometimes tell us that they just, they see this new set of possibilities because there's new creative inspiration and their cycle time, Like the amount of time it takes to just come up with an idea and be able to look at it and then decide whether to go down that path or head in a different direction goes down so much.

And so I think it's going to just be this like incredible creative explosion for humans.

And how far away are we Sam before an AI comes up with a genuinely powerful new idea?

An idea that solves a problem that humans have been wrestling with.

It doesn't have to be quite on the scale of, okay, we've got a virus coming, please describe to us what a national rational response should look like.

But some kind of genuinely innovative idea or solution.

Like 1 internal question we've asked ourselves is when will the first genuinely interesting purely AI written TED talk show up?

I will say it's always hard to guess timelines.

I'm sure I'll be wrong on this, but I would guess the first genuinely interesting TED talk thought of written, delivered by an AI is within the kind of the seven-ish year time frame.

And it feels like, I mean, just reading that Guardian essay that was kind of, it was a composite of several different GPT-3 responses to questions about, you know, the threats of robotics or whatever.

If you throw in a human editor into the mix, you could probably imagine something much sooner indeed like tomorrow.

Yeah, so the hybrid version where it's basically a tool-assisted TED talk, but that it is better than any TED talk a human could generate in 100 hours or whatever.

If you can sort of combine human discretion with AI horsepower, I suspect that's like a next year, 2 years from now kind of thing where it's just really quite good.

How do you view the impact of AI on jobs?

There's obviously been the familiar story is that every white-collar job is now up for destruction.

You know, I think it's always hard to make these predictions.

That is definitely the familiar story now.

5 Years ago, it was every blue-collar job is up for destruction.

Maybe like last year, it was every creative job is up for destruction because of things like jukebox.

I think there will be an enormous impact on the job market, and I really hate it.

I think it's kind of gross when people like working on AI pretend like there's not going to be or sort of say, oh, don't worry about it.

I think what is true is every technological revolution produces a change in jobs.

We always find new ones, at least so far.

It's difficult to predict from where we're sitting now what the new ones will be.

And this technological revolution is likely to be, again, it's always tempting to say this time it's different, maybe I'll be totally wrong, but from what I see now, this technological revolution is likely to be more dramatic, more of a staccato note than most.

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The race to build AI that benefits humanity with Sam Altman (from April 2021)