- 3 minutes ago
Ben Affleck knows a thing or two about artificial intelligence. After selling his AI company to Netflix for a reported $587 million, the American actor divulges the details of what his company is bringing to the streaming giant—and why we need to stay calm around the prospect of AI wiping out our jobs (and humanity).
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00:00The other thing I alluded to before, you saw an AI company this year, Interpositive, to Netflix.
00:07I'm a moron.
00:09Could you briefly explain to me what this thing does and why Netflix wanted it?
00:15Sure.
00:15I've always been kind of into computers since I was young.
00:20And then when film started to move from film, analog film, to digital,
00:25I became more interested in that aspect of it.
00:27And the visual effects workflow, for many years, has included machine learning.
00:31So I can write pretty shitty Python scripts and stuff like that.
00:35Because with convolutional neural networks, which are the sort of precursors to what the transformer can do,
00:41which is just much more computation simultaneously,
00:43that you would do things like look at what's called a tensor,
00:46which is just the numerical translation of a visual image in numbers,
00:50like the batch number, the frame number, the red, green, and blue values of each pixel in each frame.
00:56It's just that simple, right?
00:58That numeric is called a tensor.
00:59And a tensor, you'd use a convolutional neural network to identify patterns in that
01:03that would reveal what's called edge detection or feature extraction,
01:08which is just identifying patterns enough to know this is where the window ledge is
01:14so we can more easily take the green screen image out and replace it with something.
01:18So that was sort of familiar to me.
01:20Early on, because I, prior to Arsequity, had a small visual effects company,
01:25I've worked with GPUs a lot too.
01:27And the visual effects guys said, hey, you should see, there's a couple of Google
01:30and its other company opening hour doing really interesting stuff with like transformers in video.
01:35So I have learned to be able to sort of like, I'm like, oh, I can actually just call up
01:40and go,
01:41hey, it's Ben Affleck.
01:41Can I come and see what you're doing?
01:42And sometimes people say yes to my astonishment.
01:45And so I did that.
01:46And then I had this moment.
01:47I remember it was I called Matt and I was like, dude, we're fucked.
01:50Like you can just, with a few keystrokes, like generate very realistic looking video.
01:56You know, it was like a day or two where I really was thought like, oh,
01:59should we better do all the movies we can now because it's going to run out.
02:02Then I went back and sat down with a bunch of people, engineering teams,
02:07actually looked at the data, how it was captioned.
02:09But I started going, well, hold on, this doesn't work.
02:11You can't really control this.
02:12It's not really consistent.
02:13And if you say move the camera, well, it just generates a camera in the image.
02:15And let's look at how the data is captioned and how this,
02:19because I understood that, you know, machine learning and generative AI,
02:23it's just functions of back propagation.
02:25Like you have to have paired data.
02:27You have to have the question and the answer, essentially,
02:29in order to teach it to reverse engineer how it arrived at that answer
02:32across every vector and transformer.
02:34And so in looking at that, it became clear to me that this was a brilliant technology
02:41developed by people who were brilliant at, you know, these researchers and scientists
02:44who understood and built something new, but that they had no understanding
02:48of the domain expertise of filmmaking, to the point where they didn't even understand
02:53that, like, native filmmaking, unless you're scraping, like, H.265 clips from the internet,
02:59which is how most of these were trained, actually comes with a very specific
03:03and ordered data set.
03:04Like native, you don't even have to caption it, it exists, right, in the raw format.
03:09So once I saw that and sort of argued with and badgered some of these folks,
03:15and I only know enough to, like, sort of think I know enough to get into these conversations,
03:20which is great.
03:20I appreciate the way you're talking to me, like, I understand any of it,
03:23and I'm really flattered and honored by that, so thank you.
03:26I continue.
03:26Well, I'm doing a very good job.
03:27But the point being, I said, there's a different way to do this than those guys that relies
03:32on the understanding of, like, filmmaking and how an image is actually created in the world.
03:36And they said, no, it'll generalize, it'll just learn that with enough data.
03:39I had the experience, like, I started a company called Live Planet in 1999,
03:43where we did, you know, the internet and movies and television, like Project Greenlight,
03:48and it was an algorithm for your screenplay contest and your director's video that you would upload.
03:53And going into ancient history, I had some experience with that,
03:55and I realized when I got some offers, like, hey, well, why don't you work with us
04:00and show us what you're talking about, and we'll do the AI for you.
04:03I thought, well, then all the value, you'll just take all the value.
04:06And these are companies that aren't in Hollywood, that don't care about rights and likeness
04:12or any of that stuff.
04:13And I thought, like, well, I don't want this technology to develop somewhere else
04:16and not be available to anyone who's a filmmaker who wants to make their movie,
04:20because then I realized it's never going to, you're never just going to type your movie out, Scott.
04:23That's just never going to happen.
04:24This is going to be the last thing that gets disintermediated by AI
04:27for reasons that are a lot of complicated and I'm happy to go into.
04:31But I firmly know that that's not the case.
04:34One thing is enormously expensive, actually, which people don't talk about,
04:37but it is to generate video.
04:40The scale of compute is a lot more than language models on language models.
04:44Now, I've gotten extremely large and very costly to train aimed to run inference on.
04:49So I basically went, used some money, and then I had sort of access to capital.
04:54I had started my own business.
04:55I drew some capital down.
04:56I started a second company, and I went and created a data set, because they were like,
05:02can we buy your data?
05:03And I was like, you know, we build data.
05:05Like, you don't have to just buy it.
05:06You can make it.
05:08And, oh, that's not enough.
05:09And I said, but it will be if it's with, no, it won't.
05:11But I went and did it anyway and made, you know, for months and months and months with
05:15a lot of cameras and some other equipment and some patents, like using LiDAR and location-specific
05:22devices and incorporating Unreal and volume stages, built a very robust data set that was
05:27essentially a way of creating AI images that didn't rely on training on some work that
05:35one of my peers did, that I'd have to tell them, oh, yeah, well, part of the intelligence
05:39native to this is something that was learned, you know, in a tiny fraction, but like, nonetheless,
05:44from something you did.
05:45I was not comfortable with that, and I also realized you don't have to do that.
05:48But there was this mad rush, and people didn't want to pause and stop.
05:51So doing that for one specific use and thinking about what is that use going to be, well, the
05:56applied side, right?
05:57Like, which is, how is it actually going to be used?
06:00Not just, oh, look, I can create a fire, right?
06:03But like, okay, well, what do we want to do?
06:05We want to cook food.
06:05We want to heat our homes.
06:06We need a furnace, you know, that kind of stuff.
06:08Built out this data set, hired some really, really great engineers, inference specialists,
06:14and data architects and visual effects folks, started this company, the entire goal of which
06:19was to say, like, to capture this technology for filmmakers and for artists that they could
06:23control, feel comfortable with, and then socialize that.
06:26And go, look, we're not making up movies and replacing everybody.
06:30That's all bullshit.
06:31There's no, that's just not going to-
06:33I mean, I was going to ask you, you know, I think, you're surely aware, like, that,
06:36I think societally at this point, definitely in the creative community, a lot of ambivalence
06:41about AI as a technology.
06:42Yeah, ambivalence, a lot of, like, terror and rage.
06:45Yeah.
06:45And I understand why.
06:47But on the one hand, the only real voices that you've heard, by and large, are constantly
06:53sort of, whether they're puffing up their chest or advertising their product or whatever they're
06:57doing, they're doing it in a way by saying, and it's so powerful, it's going to be wipe
07:01everything out, which is why we need to control it, or which is why you need to download our
07:04model, right?
07:06And there are dangers in AI, to me, that have to do with, yes, because, you know, you have to
07:14use it
07:15responsibly.
07:16And I wouldn't just give an unpredictable stochastic technology the controls over something that had a
07:22life or death impact on people.
07:24Is it going to turn into Skynet and take over the world and be the paper?
07:28No, that's not the case.
07:29Oh, we're good.
07:30Yes, that's not going to happen.
07:31But what might happen is somebody might say, oh, we want to let AI target all of the targets,
07:38you know, for our war machines or do security, right?
07:43Like, and if you come up, like, it'll lock you in or shoot you or something.
07:47If you're going to do that, you're going to have accidents.
07:48It's very different from deterministic computing.
07:50Like, that's what we're used to.
07:51We're used to computers that are like calculators.
07:53It gives you the right answer every single time.
07:55In order for it to do what it does, it has to work fundamentally different from that,
07:59which is why it's called generative or stochastic, which just means, like, to aim.
08:04It doesn't mean exact, right?
08:06It means good at aiming.
08:08Inference, which is, like, running information through the attention layers and the, you know,
08:15the perceptron layers and getting on and then the outputs and generating an output is called
08:22inference because you're inferring.
08:24Like, so you keep, not only can you not rely on it, only, we all know that.
08:28So you'd be a fool to say, yes, I'm going to hand the keys over to this in this very
08:33important matter.
08:34So those are societal concerns.
08:36Do you think the creative community is right to be as concerned as they are?
08:39Anybody who's told, hey, this is going to replace you in your livelihood is fucking concerned.
08:44Like, the notion that that's somehow a surprise is baffles me.
08:48Like, going out and saying, we're going to wipe out all half of white-collar work does little to stir
08:53enthusiasm to adopt your product.
08:56However, look, I think that you would evaluate a person's ability to predict the future in large part based on
09:03how well their past predictions have turned out.
09:06And in this case, already, you have people with a record of, like, wildly off-base predictions.
09:12And there's a lot of power in being the authority who can tell you whether you're going to live or
09:16die.
09:17And there's a lot of click value in being like, are we doomed, right?
09:21That's the first thing you want to click on because as human beings, evolutionarily, we are tuned to pay attention
09:27the most
09:28and remember the most sharply things that could threaten our lives, which is why you have PTSD.
09:32Because your body is still, you know, having these flashbacks or associating a car backfiring with a weapon going off.
09:39Because it's, we have to know what a lion sounds like.
09:42We pay less attention to the good things or the safe things.
09:45What I would think is that you would want to take, like, a measured, thoughtful, responsible approach.
09:51And one of the best tools we have to ensure that is that, like, if you have a company that
09:54hurts people, you're liable for that.
09:57And it's one of the counterweights to the, I think, the desire to accrue more money that's behind the sort
10:03of frenetic race
10:05to just improve regardless that will be helpful in mitigating some of that.