What's Your Problem?As generative AI tools improve, it is becoming easier to digitally manipulate content and harder to tell when it has been tampered with. Today we are talking to someone on the front lines of this battle. Ali Shahriyari is the co-founder and CTO of Reality Defender. Ali's problem is this: How do you build a set of models to distinguish between reality and AI-generated deepfakes?
Pushkin. Earlier this year, an employee working in Hong Kong for an international company got a weird message from one of his colleagues. He was supposed to make a secret transfer of millions of dollars. It seems sketchy. It obviously seems sketchy, so he got on a video call with a bunch of people, including the company's CFO, the chief financial officer. The CFO said the request was legit, so the employee did what he was told. He transferred roughly twenty five million dollars to several bank accounts. As it turned out, the CFO on the video call was not really the CFO. It was a deep fake, an AI generated twin created from publicly available audio and video of the real CFO. By the time the company figured out what was going on, it was too late, the money was gone. I'm Jacob Goldstein and this is What's Your Problem, the show where I talk to people who are trying to make technological progress. My guest today is Ali Shahieri. He's the co founder and chief technology officer at the audaciously named Reality Defender. Ali's problem is this, how can you use AI to protect the world from AI? More specifically, how do you build a set of models to spot the difference between reality and AI generated deep fakes. How'd you get into the defending reality business?
Yeah, so when I started, it was around actually generating videos and deep fikes.
So you were attacking reality before you were defending it.
I wouldn't said we were attacking anything, but we were definitely into looking into this technology. And it is way back before all this stuff kind of went crazy. This is back in like twenty nineteen around that time, So we were building digital twins and we're looking at how do you make it so that it looks realistic? Is it a cartoon looking thing? Is it like a unity three D thing? And then that's when we started to see like these early research papers where they were taking like someone's face and putting it on a video and blending it in and it looked really good, and we were like, oh, maybe we can do the digital twins that way. And while we were like in that business, we were like, you know, probably in a few years someone can download an app and just make anything very easily. And that's kind of the origins of how how we started. We're very mission driven. What we're trying to do here is really protect the world and people from the dangers of AI, but in a way where you know, we want people not to abuse the technology. We're very we love AI, we just don't want it to be abused.
So let's talk about this sort of deep fake detection kind of you know, jen AI detection market more generally, like who's like, who's selling deep fake detection right now, and who's buying what's the what's the sort of market landscape look like.
The type of clients that we have right now are banks. For example, we are currently live with one of the largest banks in the world. When you call that bank, the audio goes through our defake detection models and we're able to tell the call center this person might be a deep fake. And part of that is that's actually happened. Someone's called the bank and they've transferred money out and actually this this goes back to twenty nineteen, so the first incident of defake fraud actually and back in.
Two in nineteen that we're aware of. Right, you're right exactly, So what happened in twenty nineteen.
Yeah, so this is back where this is early and nobody really knew about this, and there was a CEO that called a smaller company that THEO was a parent company calling the child company. The CEO calling the other CEO and he wanted to transfer some money out and it sounded like him and the guy transferred I think it was in UK, about two hundred and three hundred thousand dollars out And that was like the first one of the first ones that we.
Know of, and they got away with it, I believe.
So. Yeah.
And there was an instance earlier this year right where I think it was in Hong Kong and some employee was on a zoom call with the company's CFO and the CFO was like, you know, why are twenty five million dollars or something to some bank account? And then the employee did it and it turned out the CFO on the call was a deep fake, right.
Yeah, So fast were they your client? They were not in our clients unfortunately. But this shows the how quickly the technology is evolving. You know, twenty nineteen audio fast forward a few years now, You've got a zoom call. There'd a bunch of people on it and they all look like people, you know, I know, they're all de fis.
So you were starting to mention. Banks are some of your main clients. Who are some of your other main.
Clients, media companies, I think think of some of the big ones, there is our product this year, especially with the election. You know, back twenty twenty, we thought it would be a problem. It wasn't. This year we think is a big problem. For sure. I think we were early, but it's already this is happening everywhere even this year. This year is the largest election year in the world. More than fifty percent of the people are voting, and we already have documented cases of election issues with the fix.
Okay, media companies, banks, any other kind of big categories of clients.
Yeah, so other ones are government agencies. But in the end, everyone we think, we believe everyone needs this Product's not It shouldn't be up to the people to decide or figure out if something's a deepic. If you're on the social media platform, you shouldn't have to figure out, hey, is this person real or not. It should just be built in and anyone should be able to use it.
Well. Our social media companies either buying or building deep fake detection tools or do they want to like stay out of that business and be like no, we don't want to be in the business of saying yes, this is real, no, this isn't real.
I can tell you we've been in contact and have talked to some social media platforms. I think one issue is they don't have to flag these things. It's up to them, right, there's not a lot of regulation, so I know they're thinking about it. We've chatted with some, but that's the extent of it.
So okay, So let's talk about how it works. And there's two ways that I want to talk about how it works. So one is from the point of view of the user, whoever that may be, and then the other is sort of what's going on under the hood. Right, So let's start with the point of view of the user. If I'm a whatever, a bank, university, a media company who is paying for your service, how does it work for me?
Depends on exactly the user and the use case. If let's say it's a media company, Uh, they're looking at maybe filtering through a lot of content, so content moderation. Actually that would be like a social media company. They're looking at content moderation. Maybe they want they're looking at millions of assets and they want to quickly flag those things if they were in that business. Uh, the bank there For the example I gave the issue, someone could call and biometrics fail. By the way, if you call a bank, some banks say repeat after me, your my voice is my passport? That actually fails. Now what do you think? So a bank wants to make sure the person calling in is actually that person. This is more relevant to more to private banking, where there's actually a one on one relationship between the client and the bank.
And so in that case, So let's take that case. So in that case, someone calls in and talks to their banker. They're a rich person who has a private banker. Basically it's what you're talking about, right, So this rich person calls in and talks to their private banker, and it is the system just always running in the background in that case, And like, how does it work from the point of view of the of the private banker.
Sure, and I have to be careful what I say here, But the high level is the models are listening and if they detect a potential deep fake, they will the call center. That person will get a notification so is integrated into their existing workflow. They'll get a notification that says, hey, this.
Person get like a text or a slack or something they're using. You're talking to a deep fake.
No, they're using software for the bank they're using they're still using a software and there's a dashboard. In that scenario, they do they ascalate, so they might say, let me ask you some more questions or let me call you back.
Huh. Let me call you back is a super safe one, right because if they have a relationship, probably they know the number. They just call them back. Yeah, absolutely, okay, And then how does it work? How does it work for like when you say, like I presume by the way that you can't name your clients. You said a media company and a bank. It's it's secret that they're.
Yeah, we're not allowed to okay.
So let's say a media company. How's it work for a media company?
Their their use case is slightly different, especially right now, as I mentioned around the election, So there there might be something that that's starting to go viral in the news and they want to check, hey, is this a real or not? I would like to say like something like this is usually when something goes viral, the damage is already ton.
Yes, although if you're if you're whatever, the New York Times of the Wall Street Journal. You don't want to repeat the viral lie. Part of your business model is people are paying to subscribe to you because you are more reliable.
Right exactly. So that's why they come to us. They upload the assets and are our web app returns the results I see.
So it's just like you just go to whatever Real Defender dot whatever and you upload the viral video and your machine says it's a fake.
Yeah, So we give results and probabilities that we don't have the ground truth, so we give a probability. There's several different models running, so we use an ensemble of models. We have different models looking at different things, and we give an overall score averaging those. In the case of a video, we actually highlight the areas of a defake. If the person is speaking and they're a fake, there'll be a red box around them. If there is a real they'll be a green box around it.
And well, that latter part sounds more binary as opposed to probabilistic.
We give both. So yeah, there's there was a probably score and there's just the visual.
And so the probabilistic score is basically according to our model, there's a seventy percent chance that this is fake something of that nature.
According to our ensemble of models.
Yes, yeah, our model of models, our fund of funds of models exactly. So so okay, so you're actually looking us toward what's under the hood, right, I'm interested in discussing this on a few levels. Right, there is the sort of broad beyond reality defender. You know, what are the basic ways that the technology works, Like how does deepfake detection gen AI detection work? In a broad way? Like can you talk me through that?
Absolutely? Yeah. There's currently two ways people are looking at this problem. Number one is prominence. For example, you water mark a media that you create, maybe you water market or you digitally sign it, maybe you put on a blockchain somewhere or something like that. But basically there's a source of true that this video is real. Yeah, and there's a water mark. That's number one.
But we're concerned. We're concerned with instances where that is not the case. Right. Our world is full of videos today that are not clearly watermarked, blockchain whatever for prominence. So we have this problem. What are the ways people are solving it?
Yeah? The second way is how we're solving it, which is basically we use AI to detect AI, so we which we call inference. So we train AI models, as I mentioned, a bunch of them to look at various various aspects of plus say video.
So like, is it a sort of generative adversarial network the right term? I mean, it seems like you It seems like if I were making up how to do this, I'd be like, well, I'm gonna have one model that's like cranking out really good deep fikes, but I'll know which ones are the deep fis, and then I'm gonna feed the deep fis and the real ones to my other model, and I'll score it on how well it does, and it'll get really good at figuring out the difference.
Yeah, that's actually exactly how a lot of these work. For if you go to there's a website you can go where it just generates a person every time you go to it a right, and that's actually using again to generate that person. So the way we detect and I can I can give a little bit more detail here. So for example, one of our models which we actually removed, was looking at blood flow. So yeah, so imagine actually in this video lighting and conditions are right, we can actually detect the heartbeat and the blood flow and the veins the way we're looking at each other.
As I'm looking at my weirdly today, maybe because it's hot or because the light hair, I can actually see a vein bulging on my forehead. So, like you're saying, an AI could like measure my pulse from that or something.
In the right conditions. Now, that model has a lot of limitations, and you need to have the right It's basically it has a lot of bias. Right, So we tossed that.
Wait, you're saying it didn't work. You're saying it didn't work.
It worked in the right conditions and the right skin tone, so yeah, so otherwise it was biased. So we this was experimental and we tossed it.
A lot of things. It didn't work. So you tried it and in a broad way it didn't work. It worked in narrow conditions, but you need things that work more broadly. What's another thing you tried that didn't work?
Well, I can tell you every month we may be throwing away models.
Well, presumably there's things that work for a while and then they don't. Right, It's kind of like antibiotics versus bacteria, right, like your adversaries are getting better every day.
Basically, what we use, what we like to use is we like to say we're like an anti virus company. So every time every month there's a new genera of technique, maybe we should go detective. But maybe it's something we don't anticipate and we don't detect, and so we have to make sure we quickly update our models. So and then a model that worked last year, it's completely irrelevant now.
So what else, like, what else is happening technologically on the reality defense side, on the detection side.
Okay, so the way, we have a few different products. One is, as I mentioned, real time audio like scanning and listening for telephone calls. The other one is a place where a journalist or any user can go and upload not just videos, but we also detect images. We also detect audio, We also detect texts like chat GPT, and these tools also explain to a user why something is a deep fake. We don't just give a score. Or for an image, we might put a heat map and see these are the areas that set the model off. For text, we might highlight areas and see these other areas that appear to be generated.
There's a case study you have about a university that is a client of yours that, among other things, uses uses your service to tell when students are turning in papers written by chat GIPT. Basically as I read it, right, like, I just assume that, like everybody writes papers with chat GPT now and there's nothing anybody can do about it. But is that not true? Like if I like have GPT write my paper and then I like change a few words, does that sort of help get let me sail past your defense?
It depends depends how much you change, Yeah, or if you change like over fifty percent, maybe maybe would So it depends.
Over fifty percent is more than a few words. And so can you talk? I mean, I know you can't name the university, but in practice you know how they use it. So you know, somefess runs the student's papers through your software and it says of when student there's a whatever sixty percent chance that this was created using a large language model. I mean, do you know in practice? Obviously the professor could do whatever they want or the university could have whatever policy, but do you know in practice, what do they do with this information like that's that's in a way a harder one to figure out than the like banker who's like, oh, it might be a deep fake on the phone. I'll call you right back for security. Like if my I don't have a banker, but if I had a banker and they did that, I'd be like, oh, that's cool. I'm glad my bank is doing this thing. Whereas with like the professor and the student, that's a much more sort of fraud situation, right, and harder to think of how to deal with again the probabilistic nature of the output of the model.
Yes, I think a couple more things here. First of all, I think even universities are trying to figure out this problem. How to you solve it? You know. But the second thing to note, most of our users are not interested in a text detector. That seems to be a much smaller market. The biggest one is actually audio. It's becoming imagine you get a call from a loved one and send me money, and you send money if you realize is not who it was a defate, right, That's actually a much widely used system.
That's the big one in terms of the business it's interesting. I mean, I wonder if that's partly like relative we think about the video more, but is it partly because deep fake audio is now quite good and there are lots of instances where people will transfer lots of money based solely on audio.
De fake audio is the best and it's getting better, right interested. I used to go to make your voice, maybe I need a minute. Now I need just a few seconds and I can make your voice. It's getting exponentially better. All of them are, but audio is definitely top of the list right now.
Huh And how are you keeping up?
Yeah? I mean, so when we detect audio, it's tricky. There's a lot of factors to think about a person's accent, right what is model biased? Does it not understand or is there an issue where it detects It detects one person with a certain type accent always as a d thing. There's also issues of like noise when when when there's a lot of background noise, the model could be impacted. When there's cosstop, multiple people speaking at the same time, that could impact the model. So there's a variety of factors. And the other thing you think about is our models are more they support multiple languages, so we don't just do English, and so all of these kind of make it very complicated. So when we detect something it's called pre processing, there's a whole bunch of steps to the audio before it actually goes to our AI models where we have to clean up the audio, do certain types of transformations before we push it to the models.
And is that happening in real time with these companies? Huh huh? And and are you like, what is the frontier of preprocessing? Like is it is it an efficiency and speed problem because you're trying to do it in real time and so you're just trying to kind of make the sort of algorithmic part of it as fast and efficient as possible.
Yeah, I mean this is a challenge. There's a lot to be done. So that's an ongoing research. How do we continue to speed up not just a preprocessing, but the inference. And there's a variety of one thing that's called a foundation model. I'm not sure if you heard what those are, but these are extremely large pre train model GPT is a foundation model is a pre train model. And so these models can be useful in some parts of the preprocessing where they can quickly extract certain features for us, and then we can use those two down the pipeline.
Still to come on the show. The problems that Ali is trying to solve. Now, how good are you at detecting de fikes? Can you quantify how good you are?
So the way they usually do this is they look at benchmarks. Right, there's public data sets which we can take and run and we're in the nineties and then but you know that's not the real world.
When you say you're in the nineties, you mean you in a binary sense, you guess correctly ninety percent of the time.
Yeah, So on a public benchmark, we're in the nineties. There's accuracy, precision and recall. Accuracy is how accurate are we Let's say there is one hundred sample set is one hundred, maybe fifty is fake, fifty is real? Right. The accuracy is you take, okay, how many of those did you get right? How many of the real I'm fake? Divided? Right, that's the that's the accuracy. The problem with that is like unbalanced data set, maybe maybe only two is fake and then the other ninety eight are real. So in that case, the accuracy. See if we had said that Okay, everything is real, we would be ninety eight percent. Right, that's not very useful because you missed the defix. So that's why precision and recall coming. They look specifically at how did you do on that specific like the fakes or the reals, So there's more than just accuracy. There's also other factors to look at.
So there's it's kind of like the sort of false positive false negative challenge with medical tests, right you want to test that both says you have the thing, says you have the disease when you have the disease, and also says you don't have the disease when you don't have the disease, And that actually ends up being a really complicated problem given the nature of baselines, right like in your universe, certainly in the universe of people calling their banker. Almost everybody calling their banker is a real person, right, but there are these very high stakes, presumably very rare cases where it is a defake, and so that's like a complicated problem.
It actually is, It absolutely is, and it's something as we work with each customer, we have to tweak those. Someonet higher false positives, someone higher false negatives. It depends on each use case, in the case of a bank, they want to be a bit more cautious. But that also causes a lot of It could cause a lot of pain depending on the volume.
Right, because if every client it's like, oh sorry, I got to call you back to make sure you're not a deep fake, Like that's not great.
Yeah, And if you have thousands of calls a day and even one percent is a false positive or negative, that that creates a lot of work, Yeah, because it adds up.
How do you solve that? What do you do about that?
So the way it works is all about adjusting. You can think of thresholds, right, We can adjust variety of parameters as the output for a model, not just the model itself, but the for example, in an audio as we speak, you know, we could look at okay, how long do you want to listen before you give an answer? You know, maybe maybe? And the longer you listen, the more the more confident, the more that's smart.
That makes sense, right, because it's essentially more data for the model exactly. Yeah, what are you trying to figure out now? Like what is the frontier?
What's really the latest now? And it's just amazing how quickly. It's going as videos. So the videos that we detect are like a face swap, Like you're sitting there speaking and another person's face is on there. That's a face swap. But now you can generate an entire video completely from scratch, and you just type in the description and the video comes out. You can take some you can I can take your voice, a few seconds of your voice. I can then have you say anything I want, which you can clearly see. The bad, bad person can misuse these tools. So the latest is these things are getting really good and.
Over time, like with those videos, is your how is your reliability and accuracy changing? You're getting better or worse or staying the same as the technology to create the deep fix improves.
So what's interesting is it has slowed down in terms of like the signatures, Like we don't need as much data as we used to. So of course there's still a lot of work and we're never going to stop, but it is stabilizing a little bit.
When you say it, what is stabilizing a little bit, So like the.
Defied signatures are stabilizing the way.
The signatures, meaning the giveaways, the things that I can't see, but that your models can see that AI exactly.
So our models going back and give them more detail. They're looking at different attributes of a piece of media, and they pull out those attributes and then they send those to our and house neural networks that steady those attributes.
Like one that you have mentioned, that the company has mentioned publicly is the the sync of audio and video. Right, Yes, maybe that's one where it's gotten better and it doesn't matter anymore, but like it, from what I understand, from what I've read, there was at least a time when the sink of the audio and video tended to be off in deep fake videos. Right? Is that an example of a signature.
So the way that works is we train the model. We say, hey, here's a bunch of people speaking, here's what they look like. Look at the sink. Here's a bunch of people like that or defikes, and look at the sink, and we tune the model so we can tell the difference. That's also happening to a video. By the way, if you look at Sora and some of these new models where someone's are walking, for example, their legs are not like you know, they're not really smooth, or they don't look right, So you can look at that as well. That's the temporal dynamics we call that.
Uh Like temporal dynamics is basically are things proceeding in time in a natural.
Way exactly how things change over time.
So yeah, all of these seem like things that you can just that are going to be fleeting, right. Like my baseline assumption is it'll all get solved. Do you how long do you think you'll be able to defend reality for?
You know, this question comes up all the time where there is always a giveaway or there is always a new way to look at the problem. We're not just looking always at the raw pixels, right, We could look at different aspects. We could look at the frequency. For example, if you look at an image, you can actually break it down into frequencies.
When you say frequency, what do you mean when you say you can look at the frequency? What does that mean?
So? For example, okay, so let's go with audio. You know you can use some of call four yer transformers to actually break up an audio into individual wavelengths science and co science that does a look. You can do the same with for an image, for example, you can break that.
Up like like the analogy of a wave form of audio.
Yeah, it can. It can be translated into a bunch of waves. So so there's multiples that we look at. There's and the AI there's always a giveaway, uh and and and again we're also thinking outside the box, right, like the blood flow for example. Right, But there's other kind of similar things we could think about.
I mean, presumably you know there, you know, renaissance Renaissance Capital. The James Simons is one of the first quant hedge funds, and they made tons of money for a long time. They wildly outperformed the market. Clearly they had a technological advantage. And the thing Simon said, the founder of this math guy about about that company. One of the things he said was like, we actually don't want to hire like finance people who have some story about why a stock is going to outperform, because if there's a story about it that then then somebody else is going to know it already. Right. Their thing was just like, we just give the model all the data and let the model find these weird ass patterns that no human even understands. But they work more often than they don't work, and we make tons of money. And I would think that would be the case for you to some extent that if you could think of a thing like monitoring blood flow or whatever, then the bad guys or whatever, the people who want to make realistic Jenny I would also think of it. And the real kind of secret sauce would be in weird correlations that the model finds that we wouldn't even understand.
Exactly. I mean, that is oftentimes what the model is training on, and the way it determines of something that you think looking at certain features, it is something that we don't even tell it, right, Yeah, it determines on its own.
Like that's the beauty of this kind of new era of whatever, neural networks, machine learning. Right, it's just you throw everything at it and let the machine figure it out.
We like to say we throw the kitchen sink at sometimes.
Yes, yes, I mean, And so when you were talking before about explainability, right about sort of saying in your output, here's why we think it's fake. I feel like that kind of throw everything at it and let the machine figure it out makes it hard to like sometimes you don't know, right, it's just like, well, the machine is very smart in it says this is probably fake, Like yes, that is that intention that can happen.
So you'll look at it. We'll show you an image and it'll say the model was looking at certain areas. And by the way, this also helps us with debugging him bias. Right, maybe it was like for some reason looking at an area of the face that we wouldn't tell. Why would that set off the model? And so in those scenario as we also investigate like why was this area flag? And it could be one hundred percent correct, it's just we do we do have to examine it further.
Could you create a deep fake that would fool your deep fake detector? Yes, haha, Well if you could do it, somebody else could do it. Don't you think I could do it?
Because I have access to a lot more knowledge, right, Like, you know I could if I was running an anti virus company, I could probably write a virus if I knew exactly what we're constantly actually trying to do that.
By the way, yeah, I mean in a sense, that's the whole adversarial network thing, right, Like I guess you have to do that for your detection models or your suite of models to get better, right.
Yeah, So we have what's called red teeming both black box and understanding of the codes. So we're trying to break the models. That's part of the what we do.
Uh huh. And so are there like evil geniuses at your company who can make killer deep fakes?
We definitely have geniuses one hundred percent, but we're in the business of detection, right, we don't. We don't try to generate too much other than just for training the models.
I mean I have to think like, there are many people in the world world who want to make a deep fakes for many reasons, and they're at different levels of technological sophistication. Naively not knowing much about this, I would think you can catch most of them. But if you have people who can beat your models, I would imagine that, say, state actors, countries throwing billions of dollars at this probably also have people who could defeat your models.
Yeah, I mean that's always a case with any cybersecurity company. We are a cybersecurity company. Every cyber security company does its best to defend right, but we did not promise one hundred percent. Our models are always a probability.
Who's who's the best at making deep fikes that you're aware of.
There's a few, right, there's like Sora from OpenAI. There's Runway, there's Synthesia, there's you.
Better be able to catch right, anything I've heard of. You better be really good at the technic. Presumably it's like some like you know, Russian Genius Squad or I don't know, the North Koreans are some things. I would imagine it is some state funded actor, but.
I would I would actually say, you know, we're in a place where this is a problem is getting bigger. But we're in a place where a lot of the defects coming out are actually fron entertainment and they're not like Youth for Evil. You know, you've seen the famous Tom kruzwe or or other actors running around and do things, and those are defakes, right, those are actually pretty good. We detect them, but they're actually very good.
What are you thinking about in the context of the of the election in the US this year and do you have particular clients who are especially focused on election related deep fakes.
Yeah, the media companies are the main ones, and we're ready. We detect the best, the best defakes, right, everything that's coming out we detect, So we're ready and we want to make sure we're there as one avenue of people verifying content. I believe late last year there was an election in Slovenia where there was an audio of one of the candidates saying he's gonna double the price of beer. Yeah, and that actually was a defake. It was caught, but it kind of costed some damage. So it's starting to happen now.
It's an awesomely stupid deep fake. I mean, to me, the real risk of deep fakes is not people believing something that's false. It's people ceasing to believe anything, right. It's just saying, oh, that's probably just a deep fake, right like that. Actually, to me seems like the bigger risk is nothing is true anymore. Nobody cares about the truth anymore.
That's definitely a problem as well. Now we're seeing people saying, oh, this is a defake. That's actually happened. There's a few. I believe it was a Cape Milton video if I'm correct, that was earlier this year, or everyone thought that was a defake and it wasn't. So this kind of problem is happening.
Like that's because people people want to believe things that are consistent with their prior beliefs, and they don't want to believe things that call their prior beliefs into question, right, and so deep fakes in a way are an easy out where if you see something you like, you assume it's true. If you see something you don't like, you assume it's not true, or you assume everything's just kind of bullshit like that to me seems like a big quind of societal level risk of deep fakes.
We'll never fix that. That's something that will never solve. Yeah, people have their own beliefs. You can show them anything, the facts, math, that's not going to fix it all. Yeah.
No, I guess that's a human nature problem, if not an AI problem. We'll be back in a minute with the lighting round. Okay, let's close with a lightning round.
Okay.
How often do people applying to work at Reality Defender use generative AI to write cover letters?
Oh, that's a good one. Not a lot of, but we've seen it for sure. I would say maybe about three percent.
Okay. If I want to use generative AI to write a cover letter to apply to work at Reality Defender, but I don't want to get caught, what should I do.
Change about seventy five percent.
Of the words Okay, who is Gabe Reagan?
Gabe was? I think it was our VP of public relations or something like that. Here's a dfake. We we created him as a as kind of a as kind of a fun joke. But obviously we tell everyone.
Tell me, tell me a little bit more about that.
If you go on certain websites where you put your photo and maybe your job experience, there's quite a large number of deficke profiles on these websites like LinkedIn.
Yes, huh, why why people be doing that?
Sorry scammers?
I'm trying to think, how do you get money out of people by having a fake LinkedIn account?
Oh? I can tell you. Let's say you start the most popular ones that I'm aware of, is like cryptocurrency. Maybe you create a coin and you're like, here's a CEO and here's this person and they have these great LinkedIn profiles. Here's their photo and they're not real, but it sells a story. Right.
Is it right that you founded a clothing company?
I did?
Yes, what's one thing you learned about fashion from doing that?
It's much different than software development.
Sure, I don't think you needed to start a company to learn that. I mean, the marginal cost is not zero for one thing.
Yeah, the software is easy, you write some It's not easy at all. But what I mean is you're writing some code and you ship it. Versus in fashion, you have to have like you got to source the fabric. You gotta you gotta design it, you gotta make the patterns, you gotta cut it, sew it, make sure it fits. It's a lot more work.
What are the chances that we exist in a simulation?
You know, I used to think this is kind of a joke, but I don't know. I'm seeing every every month it seems to get higher. From my perspective.
Why do you say that.
I'm seeing what's happening with tech and what we're building, and there's you can see there's there was one paper where they took a bunch of agents and they gave them all a job and they start to do it and they just started to like create their own kind of like work cloths. Right, I don't know, it shuld be getting there.
So so it's like, well, if we can create a simulation that seems like reality, maybe someone created a simulation that is our reality exactly. Yeah, what do you wish more people understood about AI.
I mean, it's a tool, and I don't think people should be afraid of it. They should embrace it. And you know there's people are just running away from it. It's fantastic, it's great. Embrace it. Just be careful. One thing I'd like to tell, like my friends and family, especially with the e fake audio, have a safe word. As somebody calls you and you're like that's weird, you know, call him back or ask for a safe word.
What do you wish more people understood about reality reality?
I would say, just be aware that you exist. And every day's a gift, So you should be excited that you hear. Like the chances of you existing it's like you've won the lottery a million times, So every day's a gift.
Ali Shakiyari is the co founder and CTO at Reality Defender. Today's show was produced by Gabriel Hunter Chang. It was edited by Lyddy Jean Kott and engineered by Sarah Bruguer. You can email us at problem at Pushkin dot fm. I'm Jacob Goldstein and we'll be back next week with another episode of What's Your Problem
