The technology is a tool. So it's almost the same thing as saying, like, how good is a hammer? Like, hammers are great at certain things, and they're terrible at other things.

To me, that is a very basic level of creativity. I'm not saying AI is creative like Picasso or Mozart. But I did not tell it to put that card there.

Yeah, but the programmer told it to do something spontaneous. It's designed to make you trust it. It's not designed to be right. Um, it's also not conscious. My point is, GenAI isn't learning truth from humans. It's just learning what humans want to hear.

Elan Musk said that, um, ChatGPT or GenAI in general, any LLMs are not designed to tell you the truth. They're designed to tell you something that pleases you.

You can tell it one plus one is three, and it'll say, "Oh, I'm sorry, of course, one plus one is three." Then you realize that this is just this is a design, and that's the trick that the hype machine has has done, has created for us. So, GenAI is kind of like even more broad because it's, um, one of the most heightened hype machines.

Kathy, what's your take on GenAI? Has it changed a lot from what we had, like, a couple of years ago, and where is this taking us?

I mean, it's it's different in the sense that, um, it's a general-purpose AI, right? Most of the machine learning and AI that we are used to, with exceptions, um, are scoring systems that score people for risk, typically, you know, like which tell decide on things like the APR on your credit card, or the amount you pay for insurance, or whether you should be accepted to college. The exceptions are important, though. The exceptions are like Google Search, or the, you know, recommendation engines, like whether you get recommended a movie on Netflix, or possibly what you should be recommended on Instagram or TikTok, right? So those are more broad, but they're not as broad as GenAI. So GenAI is kind of like even more broad. Um, and it's it's interesting for me because it's, um, one of the most heightened hype machines. Like, it's it it is not itself a hype machine, but it is accompanied by the most intense marketing hype machine that I've seen since sort of big data was reinvented more than 10 years ago. We haven't seen as much marketing about any products like with some of the big ones like, uh, as you say, big data or the cloud. It was like a lot of marketing.

Or the internet itself, like the. But at the end of the day, I I remember hearing Max Tegmark from MIT, probably, I don't know if you were there at the same time, but, um, I remember him saying that we don't have to confuse the hype of the product and companies being overvalued with the core of the technology, because the internet, it changed our lives. And we still, I mean, that that it's hyped, it doesn't mean that it's not useful, or that it's going to be like socially changing.

Yeah, I mean, I I liken GenAI as a sort of revolutionary tool to Google Search. Google Search changed our lives because until then, all we had was AltaVista, and we couldn't really use it. It wasn't that useful. Like, but once Google Search wasn't introduced, we could actually, like, you know, we could find the information on the internet, and it started all sorts of possibilities, like all the all the bulletin boards were accessible, we had Wikipedia like springing up. So we really had information at our fingertips in a new way. And I think when we think about adjusting to GenAI, we should think about how we adjusted to Google Search. It was revolutionary, but it didn't change who we were so much.

But at the end, it is a cultural change because we think things differently than we do them now because of these kind of technologies.

Well, I I would like to take a pause there. Like, I feel like we could we could decide how much of a revolution it is if, you know, um, especially if we are aware of the hype, um, around GenAI. Like, one of the things I I like to emphasize when I talk to people about GenAI is the extent to which it has been designed to make us trust it, when in fact, it's not really. Well, first of all, it's not trustworthy.

Um, it's also not conscious. Um, it's also just, in some sense, repackaging what we already get from the internet and Google Search and Reddit. Um, it's repackaging it in a way that is meant to be, um, triggering our trust.

Okay.

But if we think about it just that way, like, think of it as like a conversational version of Reddit,

then it it's not that big a deal.

Yeah. It actually, the thing is, it it really sounds what it says. It sounds right. It sounds like, uh, like it speaks to you with lots of confidence, and then it makes you trust it or believe what it says. But of course, hallucinations are a real problem, and they still there. It's not that much anymore, or it feels like it's not that much, but I guess because especially most of us, we use it at our work, and then we use it with things that we know. So when it hallucinates, we kind of feel safe that we can catch it. But I think one of the biggest differences with, like, maybe, uh, Google Search or other technologies like big data is that GenAI is in the hands of everybody, not just in the hands of people that really know what to do with it. So, is it a good thing or is it a bad thing that is like so democratic?

Okay. Well, I'm gonna back you up for that. Okay.

I I like to, um,

I'm gonna push back on the even the notion of hallucination.

Okay. I would prefer to call it being wrong,

like being incorrect, being false, just being lying,

unreliable.

Okay.

Because hallucination, if you think about it, is endowing it with consciousness.

Yeah.

And that's the trick that the hype machine has

has done, has created for us. Um, you know, I I read this paper, um, it was about GenAI, but I think it gave away more than it was trying to, because it was like pro a GenAI, um, in a big way. But it was about like the ancient Greek notion of rhetoric, and how like in order to make a persuasive argument, which was what the study of rhetoric was all about, you have all these different con, you know, you have these characteristics of an argument, like you have to display expertise. You have to, this is the one that killed me, you have to display the ability to admit you're wrong. You know, there's all sorts of things that sort of when you do those things, um, in an argument, in a rhetorical argument, you gain the trust of the people around you.

Um, and so I'm sure you know that if you tell Gen, like ChatGPT, like, "No, you're wrong about this," it will apologize.

Totally.

But do you know that if you tell them, if you tell GenAI or ChatGPT that you're wrong about something where it's actually right, it will also apologize? Of course, it will always, I guess, in the system prompt, it's always predefined that we are right

over whatever it believes.

Exactly. It's like the customer is always right type of programming.

But my point being, like, that if you step back for a moment and you realize that it will just apologize for whatever, you can tell it, "One plus one is three,"

and it'll say, "Oh, I'm sorry, of course, one plus one is three." Yeah.

Then you realize that this is just this is a design. It's designed to make you trust it. It's not designed to be right, right?

It's not correcting itself. And to your to answer your question, like, I don't think it's that democratic. It's it's not as democratic.

It's not more democratic than Google Search was.

By the way, Google Search was democratic in the sense

that it eventually learned from clicks, like what is quote unquote more valuable as a website, you know? So, in some sense, it was gathering information by its usage, um, just as I'm sure GenAI is doing. But like, GenAI doesn't really learn. I guess my point is, GenAI isn't learning truth from humans. It's just learning what humans want to hear.

Yeah, totally. I think, um, it was Elon Musk that said that, um, Chat or GenAI in general, any LLMs are not designed to tell you the truth. They're designed to tell you something that pleases you. And I think that comes with a reinforced, uh, human learning, you know, that this technique where LLMs are trained on a final phase on, if you're like, thumbs up or thumbs down. So, basically, it's like the cookie on a dog. If you sit when I say sit, I give you a cookie. And if not, I don't. So, basically, you learn to sit, but you're not really trying for truth or anything. You're just trying to say an answer that pleases me. And then obviously, I think that doesn't makes it undemocratic or not. It just, I think my view of democratic is that almost everyone nowadays can have access to an LLM that has a level of, I don't know, intelligence. We can talk about your definitions, but at least a level of knowledge that I don't have. So I can ask it about the Roman Empire, or I can ask it about anything, and I could get something that it's much closer to have a proper answer to that than myself, that I don't have the knowledge. And I think that makes it really democratic, that almost everybody, at least in the first world, we can afford the $20 a month for ChatGPT and get access to this technology.

How is that different from Google Search then?

Well, it's conversational, and I think that makes a difference on the interface. Uh, I think that especially

I agree that it feels different, but in terms of actual access to facts, do you really think it's different?

Not in fact. I think when you ask it things, it's not much different than Google. It's faster to me. Like, uh, it's easier to ask than actually browse through the 10 blue links and find which one is the one that has the information, etc. But the point is that it can do things, not only answer questions. So when I make a custom GPT that for me, it's like I do when I when I go to a company to integrate AI into their workforce and stuff like that. I think the custom GPTs is the best thing that OpenAI made for companies. They are really powerful with a very, very low curve of learning. Uh, with a very simple, like, natural language prompting, you can get the thing that can make you, like, I don't know, a company report, and every month you can do it once and over again in the way that you train or gave documentation. So I think this is really powerful for people to optimize their productivity, and I think the simplicity of talking to it.

Yeah.

It's incredible. Of course, you could create other technologies before that did similar things.

Uh, like there was automation before before GenAI, but I think that's where it was key that it got something super simple that helps me do my job faster.

Okay. But if you don't mind, I'm going to just like, really be careful about differentiating between a couple of things. One is access to facts.

Mhm.

And then one is, you know, access to like conversationality, right? So sort of the ease of interaction. And then finally, the third thing, which you just mentioned, is how how helpful is it to do my job?

And those things are really three different things. Um,

but we cannot have one without the other, or or can we? Um, I think we could do. I think we can separate those things. I think we can isolate those things. Like, I I think like access to Wikipedia by itself is addressing the facts stuff.

Um, you know, I think like a data scientist, like I'm thinking like, how is this trained? If we think of it just as trained on all the information on the internet, which is an approximation to the truth, we can think of the conversation that we will have with, uh, ChatGPT or other types of LLMs as like, basically talking to somebody who's posting on Reddit,

right? Like that's where it's getting its conversation

tone and stuff, which is actually a tiny little sliver of like human intercourse, right? Like human conversation. Um, so it's ex from my perspective, like extremely biased towards like loud, loud mouths,

you know, totally. Yeah, because

who have like way too much time on their hands?

Because that's the content he was trained on. No, if I'm not wrong, nowadays, um, data scientists and mathematicians that are training these models are aiming more for quality data than actually too much data. No, I think

No, they're not. No.

I wish that were true.

Okay. Um, I've I've talked to people who are like training the next generation of LLMs, and they're like, "Oh, we don't have enough data, so we're going to synthesize data."

Yeah, exactly. That that's

I mean, that's not quality.

But the data you synthesize, it's synthesized in a direction that you want, I guess. Like, so basically, it's avoiding the trolls from Reddit and all this kind of

It might be avoiding a cham, but I don't think it's avoiding Reddit. Like, there's just not enough data out there for them to actually ignore data.

And how hard it is to just go through all the data and clean it up from the data you don't want. I guess

I'm sure there's weighting and there's weighting towards Wikipedia, but Wikipedia is tiny compared to the amount of data they're sucking up.

Yeah.

And if they're going to the length of like synthesizing fake data, then you know

that there is not enough data. Yeah. Exactly. But but is it is it a bad thing that they synthesizing data? Because I thought it was a good thing because probably we can give a sample of what is good data. Of course, it can be biased by whoever makes it, but there is like a sample of what you consider a good data, and you ask GPT to make more of this. So at the end, it may not be as good as all the books if we had Shakespeare working full-time on this, but at the end of the day, it can do like better than probably what you the average thing you find on the internet. Listen, I mean, I think we can, again, I'm going to separate things, right? Um, like I think we can distinguish between like something that is great and something that is useful, you know, and and and the great, the the notion of is it good? Is it good at something? It it's a very narrow question, like, I you'd have to tell me exactly how you're using it. Mhm.

You know, I'm an auditor of algorithms, and as such, like, I only audit. I don't I would never audit ChatGPT as a whole. That would just not be in my in my vocabulary. Right.

Okay.

So, when I get hired to audit an algorithm, I'm like, "How are you using it? Exactly."

Are you hiring people based on this?

Yeah. Because I guess the use of it is can make it good or bad. Exactly. The technology by itself.

The technology is a tool.

Yeah. So, it's almost the same thing as saying, like, "How good is a hammer?"

Well, hammers are great at certain things, and they're terrible at other things. So, you just can't answer that question. Don't don't be fooled into answering a question that vague.

You just have to say, "Exactly how are you using the hammer? Who's who's wielding the hammer? Are they good at their job? Are they drunk?" You know, there the context is everything.

And and what is your feeling about the context of society? Like, given what you know about society and the years you've been in earth with us, um, what do you think is going to happen with the GenAI? Is it going to be good or is it going towards ending as bad as social media?

Um, well, I'm stating there that social media

Yeah, that's a lot. Yeah, you added a lot. Um, I would say that like, it's going to be good for certain people and bad for other people, right? So, one thing I'll tell you, and I always do that, I apologize, but that's my job. My job is as an auditor to think, "Who are the stakeholders? Who's winning? Who's losing?" Exactly. Right. So, there's

for every winner, there's going to be a couple of losers. And like, it's very important to think two-dimensionally like that on the two-dimensional like matrix of

stakeholders and their concerns or benefits, right?

But I would say, like, is it good at things? Yes, it's unbelievably good at things. Yeah. Here's a couple of things that GenAI is good at.

One of them is, um, it's probably going to replace call centers. Mhm. Totally.

Um, especially narrowly defined call centers that are like, "Trouble with your, like, Verizon smartphone or whatever."

And we already know this because when we try to get help when we call people, we will never talk to another human again. It's just not happening, right? We just get to talk to a chatbot.

So, it's good at that. It's already replaced thousands, if not hundreds of thousands of jobs that have been offshored to the Philippines and other places. Um, and so the winner is the company, the losers are all those people who lost their jobs.

But at the same time, like, there is like a very clear example of that, which is Clara. It's a fintech from Europe, and they partnered with OpenAI and they developed like a chatbot, and they were very famous because at the beginning of the year, they published the results of the first month of the chatbot, which was basically that, uh, the chatbot did the same scoring as like human, uh, customer service. But then if you look into the data, it passed from 11 minutes of resolution time to 2 minutes. So, I always put the example that when I buy shoes and I don't like them and I want to give them back, that chatbot gave me 9 minutes back of my life. Then if you calculate this nine minutes multiplied by the amount of things I give back in the year, and then by the amount of people in Spain, uh, the the what the chatbot has given to society, even if it has taken, actually, in the case of Clara, 700 jobs, um, the amount of things it gave to society, it's really positive in the end, because it's a lot of minutes that you are recovering.

But of course, these poor 700 souls that lost their job are not in in the best place. So I guess we are in that scenario, you mean that some winners, some losers. So, at the end of the day, do you think in a in a global, is it going to be like the rich win and the rest of society loses, or is it going to be more balanced? Are you like able to give it like a, "This is going to be okay for us?"

Just point out one other thing that might be happening with that 9-minute

Yeah.

window is that like they just might be hanging up on people that they're like, "This is complicated, and I want to keep my numbers down," so I'm hanging

Because the AI is looking for the results. No.

They're not human.

So, they're going to just sort of

optimize

to whatever works for them. And if they're, what if their optimization goal is to minimize the amount of time

on calls, they're going to hang up on you. Like, you just don't know. You know what I'm saying?

Yeah. It depends on what they rate them on. No. Yeah. What you mean is like that, as these systems, they look for the best evaluation from whatever metrics they set them up, they will try to reach that, and whatever shortcuts they can take to reach that is like the cookie from the dog. No. So, so if it's like shorter call time, it could end up being useless.

I I say this having experienced this exact thing last week when my Wi-Fi wasn't working. I just got hung up by the chatbot like three times.

So,

you already have customer service AI in in the US. Here, I don't. I mean, I I seen this advert from Bland AI. It says like, the the AI you already talk to, but I still believe that

in Spain, we don't have yet customer service with AI. I don't know. Maybe.

Well, get ready. Buckle up, baby. And I'll tell you what else, like, you might not even know.

Yeah, exactly. That's the point. Because have we passed the Turing test?

Oh, who cares? I just [ __ ] the Turing test.

Okay.

Um, the Turing test was not even like the Turing test we t we talk about is not even what Turing came up with. Like, he came up with a much more complicated thing.

But if you're asking me whether AI is conscious, the answer is no.

No, not conscious. But can I tell the difference with nowadays?

Can we tell the difference? I think it really depends on how deep the conversation goes.

Okay.

You know, conversation you and I are having right now is more profound than a conversation I've ever had with a chatbot.

Yeah, probably.

Yeah. I guess

but even a short conversation with a chatbot, I would say you can tell they're they're not a human.

Okay. Even even if it's like this new ChatGPT advanced mode and stuff like the last technology that sounds human.

You know, I actually, I don't want to get bogged down into conversations like that, because the truth is, like, for me, it's either never going to happen or it's already happened.

You know, like, it's let's not count the years until we have AGI, right? Because

the truth is, as soon as we had, I'm going to go back to Google Search, then we had kind of superhuman abilities that we just got used to.

And when we have really good, um, LLMs or GenAI happening, we're going to get used to that too.

Um, and and I do think the important question, which we I've kind of avoided, but like, or you you asked, but we haven't gone back to, is like, what about those people that lose their jobs?

Like, how fast will people lose their jobs? People lost their jobs when tractors were invented.

Yeah.

But then they went to the city and got other jobs in manufacturing. So the question is whether that, what what are the next category of jobs, and are there going to be enough of them quickly enough, or are we going to have a revolution on our hands? I think that's really the question.

And do you have an answer for that, or an opinion at least?

I don't know yet. I don't know.

Because it's looking fast. I mean, I'm a I come from photography, and I'm a photographer, and like summer last year, we realized that we could not tell the difference anymore between an AI image and an image created by someone like me. That's my point. It's like, it either is never happening or already happened.

Mhm. The same happened with like translators. They mostly not getting any job. I think graphic designers are on the verge of it, and like many more. I I mean, even like, um, like, yeah, plenty of people, like actors, probably going to be replaced by avatars, and then if you're not already famous, it's going to be very complicated. We will start seeing, and we're already seeing, like digital celebrities that are coming up, and they don't exist, and they are getting like hundreds of thousands of followers on social media, etc. So how far do you think, or how quick, better, because I think everyone agrees that if we think about like a thousand years from now, we all think about Star Trek or Star Wars, and we think that jobs are a whole different concept. But of course, when you start dropping down the timeline, it's where the discussion is. So I don't think anyone thinks that we will work forever, but probably many people did not think that their generation may not have to work.

Yeah. So, yeah, Elon is saying now that five years from now, it's optional to work. Man, do you think this is like

I just want to say as an aside, I [ __ ] hate that guy. So every time you say his name, I just get

Okay, I try to mention

But that's okay. You could just go ahead and say it. It's a provoking.

No, it's it's the good thing about him is that he's like very, um, controversial, but he brings the topic on the table because he has reached, he does. He does. And then the point is like, if in five years, we have to think about UBI, um, then this is going too quick. But if it's in 50 years, maybe we can adapt.

Okay. But Elon, if we're going to go there, like, he is the richest man in the world.

Is he willing to share any of the money he's making?

I don't know.

That's what it comes down to, you know. And I'm glad you brought up Star Trek because I'm a huge

Oh, me too.

Huge Trek fan. And do you know that they they refer obliquely, um, and then there's like one episode, I don't know which which series, maybe Voyager, I don't remember which series, but like one episode about the actual moment of revolution, which was in San Francisco. Do you remember this?

No, because I watched the the Italian series, not the last one.

Oh, okay.

Spoiler alert.

Spoiler alert. There's a moment when they're like, "Oh, no. We can't keep going in this capitalistic sense because there's not enough jobs." And we and and there was an actual revolution.

Okay.

And people revolted, and they were like, "No, we need like universal." It was beyond universal, like

healthcare, right? It was like universal everything.

Yeah.

Um, but it was a bloody revolution, you know? So, like, that's that's the question we have to ask.

Yeah.

Is, um

But I just wanted to to finish, like,

just to be clear, I mentioned the farmers being replaced by tractors. We've also seen

the entire industry of music being replaced by Spotify plus Taylor Swift, right? It's like completely flattened out.

Like, that's happened already to so many industries. I think people are up in arms now, especially like freelancers, you know, artists. It's like because it's happening to them too.

Yeah.

And like copy editors, and anybody who like used to write copy for a living. Totally. Any kind of writing. I'm a writer myself. I actually kind of wake up in the morning wanting to write a new book and then go to bed at night thinking no one reads anymore.

Because why would anybody read? Yeah.

Because all of it is schlock, because it's all ChatGPT generated, or like you suspect it is. Right. So, it's just I'm just saying, like, the thing that's been happening to a lot of people is now happening to even more people. So, in some sense, like, we shouldn't be surprised at all. That's what technology does. In another sense, we should be like, "Well, maybe we can figure out how to have solidarity with each other,"

and like, move towards the next step without a bloody revolution.

Yeah. Because I think the revolution doesn't comes from the fact that technology gets better. It comes from the fact that it comes too quick for society to adapt. And I think that's our problem right now. We never had a technology that is like evolving as quick as AI. It's just like we had like, yeah, we we had like an industrial revolution, but even that took like several years, and AI in the last two years has changed. I mean, I discovered AI at the beginning of 2023, and since then, it's been a non-stop changing, and like things that I thought will happen in the next 10 years happening in one year.

Yeah.

Is it I'll back you up though. Yeah. Like my contention when I wrote "Weapons of Mass Destruction," which I wrote 10 years ago, was that every bureaucracy was being replaced by by AI. It wasn't called AI then. It was called big data.

But think about that. That means that every HR, every human resources, like department in every large company was being replaced by an algorithm that was it was already like vacating huge swaths of bureaucratic jobs, and bureaucratic jobs are a lot of jobs, right? And then and that's true in government as well as in corporate settings. I agree with you that GenAI is is doubling down on that phenomenon, but it's not a new thing. Like, it's really the technology of predictive algorithms. That's, you know, so many, if you think about our jobs, so many of our jobs is giving people what they expect or what they want.

Yeah.

Whether it's art, or music, or, you know, deciding who gets into this job, or who gets that loan, or who gets this mortgage.

There is so many jobs at stake here. So I agree with you, but I just don't want to say it's the last two years.

It's the last 20 years. No, totally speeding up.

Totally. I think it just it just speed up to a point where ChatGPT came into the market, and then people like me, that we don't have any knowledge about programming or anything, it just fell in our hands. And I think that's what made it viral, because basically people like me, we can use it now. Like before, it was like relegated to those of you that had the knowledge and means to be able to use the models. Yeah. Yeah.

But when it became tools, and I have it in my PowerPoint, then basically it becomes like every single human on earth can take advantage of this technology.

I like that. And you know, like my, I guess my, the sort of premier example would be like stock trading.

Yeah.

Stock trading used to be like a burly industry of like these sweaty men who used to be football players, like fighting to get their like trades over the desk on time. And now those stock market floors are empty because they've been replaced by computers. I used to be one of those quantitative traders. I was one of those nerds. And you're right that it was like only some people can even think about doing that. And now, what the difference is that it's been handed over.

Yeah. Now you don't know Robinhood or it or any of these apps, and you're like trading stock like knowing nothing about it.

So messed up.

Yeah. Yeah. And which is messed up because you don't have any like any knowledge of it, and you're just gambling with it. And that's that's probably

Oh my god. Can we complain about cryptocurrency too?

Oh, yeah, absolutely. I'm not a big fan of cryptos.

I do think, I, I mean, I take your point, like, that like this technology has made it too easy to do certain things. And one of those things is like trading in stupid ideas,

like cryptocurrency,

like meme coins, and

not even that ones, even the good ones.

Yeah. All that stuff. It's just like an incredible Ponzi scheme, and it just, it makes me really sad to see how many people are swept up in it. So, at the end, like, if we have to simplify, do you think that AI is going to take us to a better place than we were, or is it going to be like, for me, social media had a good purpose, but it went really wrong, and from being something that had to give everyone a voice, it became something that it just became like an a place to bully people and to just make them feel bad with themselves, and everyone living off an expectation of what people thinks. And I think that was our first lost battle against AI, and I'm not very convinced if the next one is going to go better. Like, what do you think?

Well, first, I think I want you to read my book, "Shame Machine," because that's really what I'm talking about there. Um, I completely agree with you.

That social media has like,

if you don't mind, I'm going to just say a little bit, which is that like,

for me, that the sh the sort of old-school shame industry was were these like direct-to-consumer industries, like the beauty industry or the weight loss industry,

um, that would sort of shame people directly and say, "If you want to solve your bad feelings, then buy our products." Yeah.

And then they would sell you products that do not solve the problem, right? Um, and

but they were pushing that on that

but they kept on pushing on that bruise,

um, and building more bruises, right? Some some of them literally set out to make people feel ashamed of themselves. There was this in article I found that was like, in Japan, women didn't feel, um, self-conscious about body hair. So the razor companies needed to first shame them about body hair, and then create a thus creating a market, and then selling them the razors, you know.

So that was like the d the old-school shame industry. It's been around for thousands of years. Um, the new version of this is social media, which does this ingenious thing of getting us to shame each other for free.

But we still can't get enough of it because it feels so good to get retweeted or liked or whatever,

because the algorithms are really good as well at showing you what to, to reward you for, you know,

shaming other people, um, and being righteous about about like what the rules are around here, and then of course, splitting us into smaller and smaller norm groups so that we like hate each other, and like also surfacing the most shameful thing about that other norm group that we can now pounce on. So it's like, it's quite an amazing trick, if you can do it right.

I mean, they did like a really good job at what they were trying to do. But but the thing is, is GenAI going the same direction? Because on there is a big difference. Give me, give me tell me a little bit more about what you're thinking.

Okay. So, so on, I think like one of the biggest differences for me that gives me a little bit of hope that this may end up in a better place than social media is that social media, we assume it had to be free and for charge. We are paying.

So they can make money without using us, but I don't think the money we pay is enough for that. So I would be kind of happy that they raised the fees if that meant that they will not exploit us. But I'm sure that like a year from now, we already seen some experiments where they are starting to put advertising. So when ChatGPT recommends your TV, it's sponsored. And that is kind of, as you said, like this thing sounds like it knows it's [ __ ] It knows what he's saying. So I'm believing it. So when someone pays ChatGPT for saying certain things, and that could go really wrong, like, because one thing is that recommends me a Sony TV instead of an LG, but then at some point it will go political, and then what is it doing to democracy when you have like your personal assistant that is with you 24/7, and all of a sudden it starts giving you or preparing the land for you to go one way or another politically, and just because they got paid. So I think that's really dangerous there. M

So I don't know how we going to make sure that that doesn't happens, but I think that would be one of the worst outcomes of AI, because I really, I'm really sure that AI is going really personal, and we will have like a small AI with us full-time, all the time.

Okay, a few things. First of all, we can choose not to do that.

We still have, yes, we have the power to choose that as a group, society, as a collective.

Um, second of all, I think it's already happening, you know. Um, then that's why I started by saying like, we should think of ChatGPT as trained by Reddit. That means we are picking up the politics of Reddit.

In in ChatGPT, and that's definitely what I

find when I work with ChatGPT, like when I program with it, it's like it has opinions. It already has. There's no

there's no non-political opinions, right? Like it's all politics, you know? And like, there was an interesting Bloomberg, um, journalistic, um, analysis of like ranking, um, resumes for a for some kind of job description that you know, standard, like racist, sexist, ageist, like

um, results, and it was just ChatGPT, like, who's better at, who's going to be a better fit for this job, and it was randomized except for the names. It was just like classic sociological racism and sexism, you know? So, it's all embedded in there. Like, the politics are embedded in there.

But that's because the way we trained it, or because it's been

Because we're training it on human data, and human data is biased. Exactly. And and human data is

not represented it, like human human behavior and opinion is not represented by the data.

Because we have these loud mouths, right? That's where we started.

Okay. So those are two of the reactions. But I think the the most important reaction to what you just said is, I completely agree with you. Like, we haven't seen the final step of of large language models, which is how do you tie this to a revenue model?

Exactly.

The business model isn't there yet. And they are losing a lot of money on letting the high school seniors like get their admissions essays like written for free by ChatGPT. Like, they're losing a [ __ ] ton of money. They're not going to continue to do that forever. And so the question becomes, what is the business model going to look like? And I agree with you that it's going to be advertising, because that's that's what it is.

That's where it's been in Google. That's what it's been in social media.

The entire internet is built on advertising. Um, and people just aren't used to paying

for these things. They and they've they've made that trade. They've been socialized to make that trade. I'm not saying it's a good thing. I'm not saying it's inevitable. But our habits are that we make the trade of giving out our blood in the form of data, private data, personal data, in in return for like, in return for this kind of service. And the service here is like, how do I cheat on my homework?

Yeah.

And by the way, I'm in terms of like democratizing cheating, I'm totally for this. I just want to say that.

Okay. Okay. So I guess you're like thinking in the way I thinking that this may end up badly. Actually, OpenAI, they got some documents filtered to the information, uh, from the last, um, raise of capital, where they were saying that they planning to lose money until 2029, and they plan to lose $44 billion until then, and then they will make $100 billion in 2029. So I I think Sam Altman has a pretty clear idea of what the business model is going to be, which obviously we don't know, but I think he's very clear on the idea of how is it going to be. And there is a turning point for some reason in 2029, where maybe technology is good enough for what he's planning for, or whatever. But yeah, I think, yeah, I think they're losing lots of money nowadays, so that's what doesn't gives me hope. And you know, I don't know how you feel about Meta open-sourcing everything, which in one way you can look like a greenwash. It looks like they're trying to be here the good guys, trying to make everything open source, but at the same time, we know that Meta open-sourced social media to just make money on your time, so on your attention. So I'm really skeptical about the open-source version. Are you pro open source, or or more about controlling it with small, like some big companies that have the tech?

Um, you you always do this. You like ask me, like, you say something, and then you ask me a question, but I want to respond. Sam's just like,

Sam Altman has a god complex, and we shouldn't trust a single thing he [ __ ] says. Okay. He's almost as bad as Elon. All right. So that that was

I need you to make me a ranking of what's the

the worst guys.

Like the worst guys. Yeah. Um, but what was the question?

About Meta? What, how do you feel about open source?

How do you feel about is completely [ __ ] useless to almost everyone?

Okay.

Right. And then for the people who can code and can set up enormous server server farms to build things, which is a very small elite group of people, they still don't have the traction and the like the network power to make anything work. Um, having said that, like, okay, it's going to help the competition in the business competition in terms of of like who's going to win the large language models war, but I don't feel like it's really helping anybody except Nvidia.

Okay. Yeah. That's why Nvidia is like promoting it so much.

Enormous bubble. I just want to say short Nvidia. That's my

You think so? I invested in Nvidia on the summer of last year, and so far it's been really

invested good at a good time. Don't invest now.

No, no, definitely. I think now it's really high. Yeah.

That's all I mean.

I I mean, I'm sure they will get. Do you think there will be competition, or it will be just that they will just blow?

I just think it's like we're at the point now of this enormous, like competition for who's going to control the market in large language models, and everyone's throwing their hat into the ring. So, everyone has to have these chips that are in short supply. And then the next phase is going to be, how do we do this on much less data, much less, um, energy wasted, hopefully.

Hopefully.

Um, how do we make lightweight versions of this that are still useful to to replace people with from their jobs?

Yeah, but that's what's happening. You know, because like from what used to be like nowadays, there's models that you can almost run on your phone that are as good as ChatGPT. So obviously, I guess we are on the path for, if ChatGPT 4 is actually doing part of my job, I guess in a year or two, I will have a like an open-source model on my phone that will be as good as now is ChatGPT 4, and we will have five or six as the top thing, but there will be a point where the tide is high enough for covering my needs with like a model that is like the standard for everyone. I don't even need to pay for it, because the other ones are like really much higher up. So these ones are going to be like not really paid attention to. But I guess that that's the normal path. Again, we go back to the point where technology has to improve. It's part of it. The problem is how fast it is improving. No, because then then that's the difference it makes.

I have a question for you.

Okay.

When I, the first time that the guest asked me questions, and I love it. When I went to give a TED talk, which was a million years ago, like 2018 or 2017.

Million years ago.

Yeah, kind of felt that way.

Okay.

Um, I had been interviewing all these truck drivers whose livelihood was being threatened by surveillance, right? So they are being surveilled. The cameras in their trucks are inward and outward. The outward ones were to figure out

how to drive a truck automatically.

Yeah. The inward ones were to make sure that they didn't ever go to the bathroom for more than five minutes. Like their quality of life was crap, right?

Okay.

That's just an example.

of the kind of person I interviewed for my job. I also interviewed teachers that were being fired by algorithms that nobody could explain to them. You know, just all these people who really saw this revolutionary of algorithms as like an oppressive force.

But when I went to the TED talk, the audience was made up of people who were so excited, like in this kind of boyish way. And when I say boy, I mean they were all men, okay, and very rich men. And they were all so excited about this technology because they were like, "Oh, this is going to make me smarter. It's going to be like me plus this chip in my brain that lets me access all the information."

>> Okay. >> And of course, my question to them was like, "Don't you have Google search already?" like we already have that chip in our brain. It's just >> just there is a different >> a little bit further away from our brain.

>> But anyway, my point is that it was the attitude the divide the cultural divide of attitude and what I'm hearing from you and here's my question is like you're go both ways.

>> Yeah, I do. And that's my dichotomy because I I think the person I most recognize myself with is with Demi as habits. He says a thing that if you're an accelerationist like if you want AI to come as soon as possible that you're not really aware of how much it's going to impact society because if you knew you would want it to be like calm but slow. I I would not have a problem the AI gets better. I just think society cannot handle it. So my problem here is that I think the technology is going to be amazing. It's going to cure cancer. It's going to make so many positive things for us but it's going to overrun us on the on the meantime. At least our generation. I have two kids. I have a kid. She's seven and the boy is 10. And I think by the time they are like adults, this is going to be probably better organized or I hope so because right now if I had a kid that was 16, I would be really not knowing.

>> I have a kid that's 16. >> Okay. Sorry. But um so what is your advice like what should she study? What should she do? Because maybe whatever she's studying or what actually my feeling is what we've been telling these young people to study over the last 10 years which is programming. We've been telling them you be a programmer, you're set for life, you're going to be okay. This is going to be useless in like maybe two years or I don't know if you share that, but I think like chatd is programming really good. And obviously not as good as the best of ours, but definitely better than most of people. Uh right now, for example, if I have a problem on my website, I don't call my programmer anymore. I can just send a picture of it and the link to CHP and it tells me what I have to change on the CSS. And that's probably very basic for a programmer. It's not like deep Python. But at the end of the day, if today is doing that, what is it going to do tomorrow? So obviously we can touch a ceiling. But it doesn't it looks like so far on the last two years of my experience.

>> Well, okay, I'll first I'll answer the question which is what do I tell my kids? I have three kids.

>> Okay. >> Um I tell them to be flexible. I tell them to be willing to learn anything because what they have to be good at is being good at things, you know, like they have to be nimble and like you're right. Uh it doesn't make sense to be good at programming basic languages anymore. It does I mean you can do that. That's fine. But if if you're only doing that, you're going to be left behind.

>> replaced. Yeah. >> Um and that's a really important thing. I would also say though that I'm I'm more worried about your kids than my kids.

>> Okay? >> Because my kids are, you know, there's still I I don't think it's going to happen in the next three years. I think it's going to happen in the next 30 years.

>> 30 >> maybe the next 15 years. Like I >> I >> and I I you know, not to be like super pessimistic, but I really don't know what's going to happen >> in you know, to your kids or to kids that are not yet born or about to >> No, totally. Totally. I don't know it either. I I just to some degree I almost prefer that it happens in 30 than in three years whatever it has to happen and whatever is this revolution or change or whatever social thing is because I think there will be less people damaged by it if it's in 30 than if it's like that soon because it will not come it will not come like all of a sudden out of the blue like I think like it was Connor Healey I think it's called he was saying like last year that every time you train a big model it's like throwing dice I don't think we are at that stage yet where like out of chat GBD6 could come out AGI but I think >> I mean I I wish it was coming on that on that amount of time because I think that gives society time to prepare because on the way we will see kind of symptoms no we will see like small sparks of AGI that will start getting us to realize that we have to work on a universal basic income or it will give us like some sparks that make us realize that we have to change the way that people makes money because workbased money will not work anymore or I don't know different things that may give us time to adapt.

>> Let me ask you another question. >> Okay. >> What if AGI happened tomorrow because between you and me I don't think AGI is ever going to happen slash it's already happened.

>> Okay. So how how do we define >> assume it's already happened? >> How do we define AGI? >> Just to define AGI that's that's of course the problem. But like when I say I don't think it'll ever happen. I don't think computers will ever be conscious.

>> Okay? You know, but I do think that computers and algorithms are already better than we are at a lot of things.

>> I agree with that. I think >> it so why don't we just assume it happens whatever it is and then ask ourselves what would actually be different about today's world.

>> Well, first of all, me as a company owner, it would not make sense to hire people because I could hire AIS or AI services or whatever because I assume a >> that's already true. Let me let me just say okay that's what I was saying at the very beginning like we didn't >> my company didn't hire like well I have a tiny company but new new startups they don't hire HR >> they have high hiring algorithms >> or they they hire less people in HR >> way fewer one person who basically >> supervises the AI that's already true >> yeah I I think that's the realistic but the truth is that not all startups do this like if we check now like I don't know any small company that's set up in Barcelona on the last year I'm sure they still hiring people but there will be a point where it be so so so popular so common so democratic that you will not even consider hiring someone and then here in Spain we have an employment rate maybe I don't know maybe around 10% or something like it's quite high >> what happens when the unemployment rate is like 40%.

>> Yeah. >> So that's where it gets pretty ugly. So to me the difference is if this AGI means 16 20% or it means 40%. I think that's the edge of the sword where it becomes like bloody and becomes messy or where it becomes sustainable with a crisis you know, like so I think for me what happens if tomorrow AG happens it depends on how >> big it is of a deal if it's as big as a deal like for me AGI the definition that I recognize the most is an AI that can do most of productive work of humanity that humanity is doing at the moment >> okay >> so if >> that's a good definition that's actually my favorite I've ever heard Great. So if it gets to that point for me it's a complicated situation if we don't have the social structure to hold it.

>> I do think but I do you agree with me that like we should already be asking that question like absolutely for that. >> Absolutely. I've been super harsh on politicians here in Spain that like you know we had elections like not long ago there is nothing about AI in their programs and I think it should be the main thing I should I mean there is some stuff going on in Europe the act etc.

>> Yes. Oh. Oh god. You want to talk about pathetic? there's there's like nothing happening in the in at the federal level but in the states in the states you had the the 1047 which was like bailed out at the end and in California but at the end of the day like the AI act which looks around the world like we are like leading like the regulation of AI it's talking about the use of AI which is obviously something necessary like you cannot make boweapons with this but it's not talking about how we're going to handle the work problem and I think >> there is many problems with AI that may happen but I think the first ones and the biggest ones is deep fakes and jobs and I think no one is talking.

>> Tell me more. Why do you think that's such a big deal? >> You don't think it is >> the loss of truth from like >> truth is a big deal. I don't think deep fakes represents that. Well, I think when you can I mean I think people maybe it's because I come from the from the photography and video market, but I think people takes um videos as a proof of truth and then when you can fake videos, you can make like talking heads that sound like exactly like Elon Mus whatever or any other. Um, of course, if we take it to the political level, there was this this thing in America where someone cloned Joe Biden in a very poorly way and then they called saying not to go to the primary elections or whatever. Anyway, um I think this is a problem for society and it's a big one and I think we already there like we've been doing deep fakes for a long time, but before to have inference on a election in America, it had to be Russia or it had to be like a big state. Now my kid 10 years old can make like a deep fake of Trump, send it on Twitter, make it viral and boom, you know.

>> Okay, but let me give you an alternative way of thinking about that which makes me sleep at night or helps me sleep at night. >> Thank you. >> Which is that like that's what's actually brilliant about my kids. My kids don't believe anything. >> Yeah. you know, and like I agree that like that can be a problem if they like lean into nihilism, but they know better than my generation that like yeah, you can't believe what you see on Facebook. In fact, don't go to Facebook. It's cra it's crappy. >> Yeah. >> You know, they are not on social media. I'm not saying that nobody has bought into that anymore, but I'm just saying maybe we can think of it as u we had a like a different kind of pandemic where we believed everything we saw on social media and we're inoculating ourselves becoming immune. >> Exactly. >> Our ne at least our children are I don't think your your children are going to be like bought in. >> No, they're very critical about things. Actually, one of the amazing things happened like few days ago we were talking about something about football. Football is really big here like soccer. saw some football practice >> and then my kid and me were talking about someone the goalkeeper of a team and stuff and then I asked Chad GPT and Chad GPT got it wrong and my kid I was like he's dad and Chad GPT are telling you you're wrong and my kid is like no no dad I know for sure this guy is the goalkeeper and I was so proud when I googled it and then I found the right answer and I was so proud that he was like critical enough to believe on his own opinion over mine and Chad GPT which is like right >> a really big heavy load And I think that was really amazing. But but at the end of the day I think like what if this happens may have like some time before this happens where like society is immune to okay whatever everything is fake so I don't believe anything I see on a screen but I think there will be a while where a lot of people will believe what they see on a screen and there will be tools to make this fake very easily very quickly like there are already um open source models to do like talking heads or cloning audio like we do like probably this podcast as they hearing you speak Spanish and that's been a yeah you don't speak Spanish so what do you speak Spanish >> okay would be much easier for me >> okay so so the point is like we are at this point where maybe until this moment that we just rebuilt people did not know or did not realize that you don't speak Spanish >> right >> so yeah I think >> I don't know I I'm just gonna like I agree with you I'm just taking a contrarian opinion because because it's fun we're on a podcast um but I just I just feel like yes misinformation is a huge problem but on the one hand like it's gotten so bad that it's obvious you know and so we have to grapple with it in real time and we have to talk very directly about what is authority why do we trust authority you know how do we doublech checkck I mean having said that like one of the alarming things that I I've come across I don't know if this happened here but when you're in the states like a month ago and you Google something it doesn't just give you the results even though of course the first two pages is advertising but then below that you can find maybe some answers but it first gives you like Gemini or whatever some kind of crappy large language model answer and it just occurs to me like as soon as this starts happening that like if Gemini gives me some [ __ ] and I won't call it a hallucination because Gemini doesn't have consciousness but like if it just is wrong it tells me wrong information and and I try to search to corroborate it. I'm getting more large language model response like what does it mean to corroborate in the in this landscape where everything everything >> exactly >> is gen AI.

>> Yeah. Exactly. Because like all the news outlets are made by LLMs nowadays or at least they reduce a lot. They're journalists. So basically it's going to get to a point where it's that's my whole point. it's going to get to a point where it's impossible to know if it's true or not because basically everything is going to be LLM generated. So, so yeah, I think that's the problem where we going like perplexity now. It's like getting like lots of traction in market. It's basically like an LLM doing the search and then bringing you whatever the hell he wants, right?

>> And I can be as biased as they want. >> Well, okay. It it wants uh so so at the end is like really >> it's going to be really complicated. That's that's what I put deep fakes on like for me that information that Gemini is giving at the end is like a creation.

>> So deep fake for you is like a bookmark for >> yeah for any any No, it's any synthetic content created. It can be visual, audio or text as well like >> Right. Right. >> Uh now we are finding like books in Amazon that are fully written by LLMs but they are like sold as if there was an author behind. So there's a point where I can tell the difference. I mean, uh, I kind of disagree with you that everything that DLMs produce is crap. I think >> No, no, that's not what I meant. Like >> probably better than average human produces. >> That's why it's really useful to cheat on your homework. >> Exactly. So, >> it's better than most students. >> I'm just saying when it's crap, you can't >> you can tell the difference >> because when you try to do the research, it's more LLMs. >> Exactly. Llms all the way down. >> Yeah. And it's gonna be And it's gonna get even worse, I think. I think it's gonna get worse and worse. >> Yeah. So what would be your advice if you were like okay like you be choosing to advise the president of the US what is the first action needs to be done to get this on the right track? >> Do we have to blow the whole thing out? you know, I mean, my my job is to audit algorithms for all sorts of consistencies or inconsistencies. And so, I think my my suggestion would be to like force transparency on how crappy things are. And I of course wouldn't call it hallucinations, but like yes, I would say like, you know, there should be a disclaimer on any result of if like these facts are not real. Okay, >> this is this is this is not based on truth. >> They are most like Chachib has one that says Chajib can get things wrong or something like that or I don't know if it's CL or Yeah, but they're like minimal or not really. >> They're not the whole point of it is to get you to trust it, right? We went through that. So like >> it doesn't really count it, you know. >> Um yeah. So for me it's like the the fact that that all of these large language models have no notion of truth, hasn't really been public publicly available, right? It is like we're supposed to trust it except for exceptions. No, we should know that that it doesn't have notions of truth. It only fix the next word >> that people say on Reddit. >> Okay, >> that's all it does. um it says said, "Oh, would this be a word that somebody on Reddit would say?" Like that's if you think about it that way, then you immediately stop trusting it. And that's what I would suggest, right?

>> Okay. >> Um and that's why I wrote my book. That's like why I do what I do, which is like why are we trusting these things to be trust? Like why are we trusting them? We shouldn't be. Having said that, there's they're super useful, right? Um and so the other thing I would suggest to a policy maker which I do is to um publicly measure the extent to which we can or cannot trust things.

>> Okay? >> Like how does it fail? How often does it fail? What are we measuring? What do we define by fair? Is it fair to these various protected groups? Like how are we measuring that? Can we appeal that measurement? Because some people will think that's the wrong way to measure fairness. you know, like having that discussion which is to say really having an ethical discussion about its use.

>> Yeah. Yeah. I had I had the other day here like a quantum physician I don't know the word in English um but basically he's the the boss of the quantum physics in Singapore in Abu Dhabi and really amazing guy and he was saying it's the time now to decide what we want AI to do not to actually legislate like we are putting like written down laws instead of like deciding as a society what do we want it to do yeah >> it's like >> we should be able to have a discussion a public discussion with everyone that is involved which is basically everyone on earth about what do we want AI to be able to do in our lives like how far do we want it to go no and that's not being done like things like AI acting you cannot do bioweapons you cannot do that well maybe we don't want it to do anything at all and no one has asked that question so it's basically like we are just getting it like it's it's like uh I got very viral Tik Tok on a podcast I went where I was saying like who the hell gave the right to these people to change the without asking for permission. And I I feel that way. I'm not saying I'm not capitalizing and using AI because everyone says you're giving talks about AI. So it's kind of but I have this kind of dual point of view where I think this is really useful and I'm taking advantage of it for my own but then I think this is going to end up really badly if we don't do something soon and I'm not very sure what has to be done and that's where I try to find on vision of more experts.

>> Right. Well, I mean, that's Well, I'm not an expert more any more than anyone. >> Well, you are definitely. >> Well, I'm an expert on how AI works. >> Exactly. >> But I'm not an expert on how we should protect the public good. >> Okay. >> From this technology and that I agree with you is a public discussion that we need to have.

>> Um like it's really a question of like >> it's a philos philosophical question too, right? What is the role of government? Is the role of government to restrict the use use cases of this technology or is the role of government to make sure that whatever happens with technology people have a basic dignity >> exactly >> in their lives and um I could go either way but neither of those things is happening.

>> Exactly. Yeah. The point is that the conversation is not on the table. So that is the big problem. This conversation isn't on that table because the Sam Alman's of the world keep on going to Congress and convincing somehow convincing Congress with their twinkly eyes that no jobs are going to be lost even while they're selling these products to companies saying you'll be able to replace hundreds if not thousands of workers with this technology. It is like a complete shitow contradiction.

>> That's what's happening. >> It is. It is. But um I seen the people from the big techs like Sundar Pitai, Sam Alman saying they need to be regulated. But I think then when it comes to regulation they are totally against it. So it's basically like >> it's a lobbying effort. >> Yeah. Exactly. So they want to be regulated to the extent that they look like okay this is good enough for us. We can bypass it. It reduces competition.

>> Right. And that that is where I my expertise comes into play which is like I can see through that particular lobbying effort. Right. So they're saying they want to be regulated, but when you ask them what the regulation should look like, it is useless and will not stop them at all. >> It just will stop smaller companies than them to not compete with them. >> Exactly. Yeah. That's the only thing it'll accomplish is it'll make them bigger. >> Yeah. Well, taking advantage of your mathematician and there is some things that uh I don't understand about AI, maybe you can enlighten me on that things. I have a few very quick questions like do you think AI is intelligent? No.

>> Is it the wrong term? We did the wrong naming from the beginning. >> Yes, it is. >> How would you define it then? >> I mean, it's good at things, right? I mean, actually, some AI is quite good at chess, right? That like the whatever it is that is good at chess. It's very finite toy universe with a well- definfined outcome, which is winning the game of chess.

>> It's very good at chess. And and similarly, go. Mhm. >> What it's not good at at all is um like ethical quandies like figuring out what um sort of weighing different outcomes for different stakeholders and trying to figure out like what would be the best overall you know situation here because that's not how AI is trained. AI is trained on quite specific definition of success an objective function >> with penalty for mistakes >> and it is incredibly you know like >> I wouldn't say linear but like it is like one-sided right it's like that's all it cares about. Now, I'm not saying that couldn't be a nuanced definition of of success, and that is sort of my life's goal is to figure out how to make that definition of success um adhere to specific specific ethical constraints. Um but as it's trained now by companies, it is optimized to a sort of usually pretty stupid definition of success like maximize accuracy, maximize efficiency, or maximize profit with no constraints. Okay.

>> So, like yeah, it's it's um it's not going to be intelligent in the way that we think of intelligence as like how do you solve this problem without hurting anyone, without doing something that's, you know, morally wrong. >> Okay? >> Like those are just so obvious to us when we're we're thinking about how to do something. Hopefully, >> for most people, >> they they they're like unspoken constraints. >> Yeah. It's like common sense. common sense constraints, but like that's not how AI thinks. I won't even I accidentally use the word thinks. They don't think, right? They're trained towards one goal. That goal is typically dumb.

>> Okay. So, that behavior does it means that they don't reason either or reasoning is something different. >> Well, okay. So, then you're then you're asking me like it depends on which AI you're talking about. Like you could argue like depending on what you mean by reason that like the chess AI reasons about how good to play how well to play chess like reasons about the next move in the following sense. I mean, that's fine. I I don't want to quibble about the notion of of of reasoning. I've already talked about Gen AI. It doesn't have a notion of truth.

>> Yeah. >> Um so it's not trying to say truthful things. It's just trying to predict the next word in a sentence. But that's where I remember um a statement from Ilia Susker. He said like okay imagine the case where you have like a police novel like book and then it has all these intrinkate things and exposing the case in one way or another the way we like these kind of novels and then you arrive to the last page where the policeman says okay so the assessin is and then AI has to predict that word. So if it predicts that we're right to predict that we're right it had to reason through the book. Does it makes any sense? Maybe we even like I think sometimes we given to some words too much value like it's reason, creativity, intelligence. Are they maybe words that we are giving them too much human sense beyond what they actually mean? Because I think AI to me it looks like it reasons. And then I'm going to say something that Hinton said. He said if it looks like it does it, it's maybe because it's doing it. And maybe it's imitating reasoning or maybe it is reasoning. If we are talking about consciousness, I don't think they are conscious or they will be anytime soon. But the point is that reasoning it just means you look at certain facts and make a conclusion out of them. So I would say it does but you know it better than me. You know much better these machines. Well, I mean, let me say it this way. Like, Google had figured out a long time ago that it's easier to translate between languages based on like this corpus of translations that it already had. And this sentence from French was translated into this sentence in Spanish um multiple times in exactly the same way. So if you give me this sentence in in French, I will give you this sentence in Spanish. It was just like I've seen this happen so many times that this is the answer. Um and it had so much data that it was it was finding commonly done things a lot. So it it was and it was good at it, right? That's not reasoning. That's pattern matching.

>> But it looks like reasoning, >> right? >> So that's what I would argue is happening most of the time. And and and I I just want to say that I don't blame you for projecting or anyone for projecting reasoning onto this. First of all, it's it's designed to make us trust it, >> feel his way. Yeah. >> But also, it does look like reasoning. Um if it if it were coming from a human, we would think, "Oh, you're smart." You know, >> it's not coming from a human, though. It's coming from like a pattern matching engine. >> Yeah. >> Um that's good at its job. >> Yeah. at the end of the day is like a mathematical algorithm making conclusion based on data. No, that's the point. But >> and more important than that, like going back to your example of the um spy novel and like the the mystery novel is like it would be able to predict the killer if mystery novels like this were formulaically written >> and it's like almost exactly the same plot, which we see, right? We see that if you watch Mrs. Marvel and all the all those different >> spy or like Agatha Christie novel like they are formulaic and there are common themes and almost exactly the same plot a bunch of times. So maybe it could come up with the right answer >> and that's interesting. It's a kind of a party trick if you will of AI. But what it cannot do is is reason like in a human and moral way to come up with a new answer to a new question.

>> So that gives me to the next point like I guess you don't think they're creative. >> No. >> Okay. >> I think we can endow creativity to it the products. >> Because with your prompting you make it do something that was not done before, I guess. Or what do you mean? I mean we Yes. For example, okay, I do think that um I think it'd be a great world >> if artists, visual artists could use AI to build new art >> and given and get paid for that. >> Yeah, I mean, it's it's getting there. The problem is that it's a very small window. I think like um I have lots of because I have a photography academy as well and I have a lot of like students that they sell stock and now they selling AI stock, not camera stock anymore. But this is not going to last for long because all the AI stock websites they already have their builders to build images. So the client is going to prompt and not the photographer in the way. But I think that's that's a matter of temporary but it's going to be a window like prompt engineering. It's going to be profession but it's not going to last for long because these things are trained to understand us better. So at some point it will be the same if I talk to it or my mom talks to it. It will not make a difference because the thing will understand better I guess. But >> I have a little bit more hope than you do. Like I agree that the jobs that we have now are going to they're going to shift radically, but I do think that human art and human creativity is something that is just simply not predictable.

>> Okay. So, I do think that like the technology as good as it's going to get at like doing stuff that's been done in the in the, you know, in the style of this so-called this artist, you know, it's going to be really good at that and people are not going to need to pay for that stuff anymore. But, but true creativity, musical or artistic, that's that's still going to be unique. I >> think it would be relegated to the value that we give to have a Picasso just for the sake. I mean there is plenty of people that can paint like Picasso and they just do their litographies or whatever or copies but then you still want to have a real one but that's not because of the art itself. No not you but like people in general but that's not just because of the art itself it's just because of what it means to have a picasso the intrinsic value of it because there is not so many etc. So I think uh there is this company in America started something that is called made by humans. It's kind of a label that they give companies that don't use AI in any process.

>> And I think there's going to be something like handiccraft. >> You know, there will be something that we will give value to it, right? >> But now you can see lots of products that are from factories that they are made to look handiccraft. >> Yeah. >> With imperfections and stuff like that, you know, like the the nuggets theory from McDonald's where they make them different shapes just for for you to think that they are cut by hand. But at the end of the day, they not. So I don't know how long this will last. But down to the creativity path I think and that is something it was really difficult for me to understand and assume but I think AI is creative because when I prompt for an image on a image generator like me journey or whatever I ask for like a German guy with a white shirt sunglasses etc in a city vibrant light but then it puts a car there and that car is gray. I did not prompt for a gray car. It could be in white. It could be in black. It could be in a red. It could be in a sap. It could be in a uh any kind of car, but it chosen and that's really where it crosses the line of free will. It it has chosen to put that one car, that specific car. To me, that is a very basic level of creativity. I'm not saying AI is creative like Picasso or Mosart, but I did not tell it to put that car there.

>> Yeah, but the programmer told it to do something spontaneous. >> Okay. And then but spontaneous, it's creative. >> Okay. I know. Yeah, >> it's creative if you think so, but it doesn't think so because it doesn't have consciousness. I like like larger point is that like creativity is something we endow >> art with. >> Okay. >> And famous artists are ones that like a lot of people agree this is creative, right? >> Okay. >> It's not it's not you know what I mean? I'm just I'm saying that if it works for you. >> Yeah. >> I guess it's semantic arguing. You know like in in many people if you tell them that this original because it never this image doesn't exist before then they are more >> comfortable with it than if you talk about creativity. So I think there is a lot of what how do we define words to then find if actually we can >> I agree >> you just said something very interesting. You said the programmer has told the AI to do something but then there is a concept that I never fully understood and I can't believe actually it's real which is the concept of the black box. Are AI black boxes? Can you explain what is the blackbox concept of an AI? I mean black boxes is just a very general idea which is just that you have input and something happens and then you get something out right it's it it means that it's mysterious what happens in the box right >> but is it true that we don't understand how they do what they do beyond the surface >> um yes not only is that true but it's been true for big data algorithms for decades like we don't understand almost any of the machine learning algorithms that I work with.

>> Yeah, Actually, some of the positive points of using those algorithms is that they do things that our mind could not connect all these all these dots. No, like I remember there was one case here in Barcelona where they used like a big data AI thing to predict where there will be more accidents on the traffic where there will be more crash just because of the rain, because of the state of the road and the temperature etc. But there was to a point where they will deploy police without knowing why. Just saying like this thing said go to that street and they would go there. But >> obviously I mean, it gets to the point where big data and AI can do things that we cannot even get to connect.

>> Interesting example with the traffic. I mean, so yes and no. So let me let me give you a slightly different example where >> the answer is still yes or no but it's it's a little bit more obvious what's going on which is um >> the things called predictive policing algorithms >> um which use which use like prior arrests and locations of prior arrests to send police to those neighborhoods. This is used all over the United States um and it was in particular used in New York City where I was living when I wrote about it. Um and there the you know, we on the one hand we don't understand the algorithm because it's complicated uses like neural nets which no human being can really explain this coefficient being 0.01 instead of 0.02 means such and such they won't be able to explain >> at that granular level what what is going on inside the algorithm. On the other hand, if you look at the data that it's trained on, you realize that it's just going to send police back to the same exact neighborhoods where police are, you know, duh.

>> Yeah. >> Like you're overpolicing black neighborhoods and arresting people for smoking pot >> in Harlem, >> but not in on Wall Street where everyone's walking around with cocaine in their pocket. Like if you if you started sending cops to Wall Street, checking their pockets, you'd have way more arrest records there and you'd have an algorithm sending cops to >> I was going to say because we haven't been arresting people in that white neighborhoods, there is no data about that and the algorithm cannot propose that because it doesn't it's not in the training.

>> So yeah and and that's really important for my from my work is like we don't need to understand the blackbox to audit the algorithm. No, because you audited the output and the input. >> All we need to know is how does it treat different people and is it fair? >> Okay. But then when when we try to put this under control and then like AI act or legislators in America etc. they say like you need to tell me that this is not going to be able to do that before you release the product >> but we cannot technically know what it's going to be able to do >> like I don't know like that they cannot do bioweapons. uh there is prompt injection, there is all these things and then >> like I think the blackbox concept is kind of like a technical limitation that it invalidates most of the regulation we can do against AI because I can tell you okay you can release AI as far as it cannot do this this and this but you cannot grant that until you train it because there is emerging capabilities and like this blows my head like when when they tell me like all of a sudden this thing started to speak uh in a different language because the user but it was not programmed to speak in that language.

>> Yeah. >> Uh that was from Google and this blows my mind. This I always put this example that I buy an oven for my house to make pizzas and I can expect that there is some deviation from the expects like where the oven can have like an amount of degrees maybe hotter or colder than it was supposed to be but I will never expect that when I want to do a pizza the oven calls the pizza house and brings me a pizza home. you know, this is kind of like an emerging capability that I will never expect it to do, but that's the best way I can get someone to explain me um that these things until we train them, we don't know what they're capable of. Okay. Well, this is let me just say that this is different. It's a different fact about Genai than it is about most of the preceding generations of algorithms. So, you've got me there.

>> Okay. >> Like an algorithm that predicts um the default rate of a mortgage borrower is not going to order pizza, >> right? It's just >> much easier to keep track of like the constraints and how are you treating >> black borrowers versus white borrowers. That's kind of question. Having said that, I do think it's overstated a little quite a bit um about like what could go wrong with um Gen AI in terms of powers like calling the the pizza place like don't give it access to the phone like sorry but like what >> the can we because then it's when it gets really messy know when like AI may be being useful to us it's getting like in our daily life and then all of a sudden it will ask me to >> call to reserve my restaurant, but then it has that phone of the pizza house >> or it has the access to the phone because it can manipulate and they are very good actually manipulating. So um well maybe the word is not is not that because it's it implies there is some intention but then obviously these things don't have intention but but they are like pleasing people. Have you seen this last trend where you ask the chat GPT tell me something that uh you know about me that I don't know about and then it just goes through all the memory it has about you and it just makes something clever and it not is flattering you and then he's like oh look Chachi Pitt thinks I'm smart and you catch yourself being like you're an idiot. You're thinking this thing is just trained for that.

>> Oh, it sure is. >> But then it's basically they are kind of like really good at manipulating us and knowing how to make us happy or how to give us what we want.

>> But that's again that's a computer programmer trained that >> it's like it's a flattering sub routine. >> It's not an emerging capability that they had. >> You should not think of it that way. You should think of it as like a very explicit I just want everyone to close their eyes and imagine Sam Alman and his cronies in a boardroom >> deciding how do we flatter the [ __ ] out of our users so they love us and they use us and trust us.

>> That was very obvious when they presented the advanced voice and they presented with a voice that was very similar to Scar and it was so flattering and like so flirting and sexy and >> Yep. very intentional obviously because she told them not to do it and they did it anyway, right? I think I think that was like a point where they made a mistake. Like one of these things that you can see the true colors. You can see true colors there. And >> well, if you squint, you can see their true colors a lot more. >> Yeah. What in what things do you see the >> all of this intention? All of this trust stuff. By the way, I you know, like it gets so hyperbolic that we're talking about like what if like the AI started a nuclear war? What? Like no. Like there are guards. We can do this. This is not an inevitable science fiction like dystopia dystopia like >> there are only certain people that >> can start a nuclear war. >> What about >> let's keep it that way. >> Okay. Yeah. Exactly. Let's let's keep it out of the hands of the AI. What is your feeling with because we hear a lot of like uh Joffrey Hinton, Joshua Benjio, Max Techmar talking about like important scientists like recently they got a Nobel Prize. Um but they talk about the dangers the AI could get out of control. And then you have on the other side which is one of the problems that normal people like me we have. And then you have like Yan Leon that thinks like all of this [ __ ] you guys lost your minds. This is it's impossible that we make a car without inventing first the brakes. And what what is your your take on all

of this? Like, can AI get out of control, or do you think there's no way?

It's funny because, like, I don't agree with Yan Lun about much, but I do agree with him mostly about this stuff. Um, I, I do, I, I really do think that there's, like, a mismatch between people's, like, their awe of this technology, which I think part of that, by the way, is like supported by their desire to be gods. You know, there's just like a little bit of like a god complex in these engineers who are like, "We created intelligence that is superhuman intelligence." Like, they are God creating creating creatures, right, that are superhuman. Like, there's some part of them that just wants to think that, and that's feeding into this, but like, it's not, it's not the case. These are these are good at predicting the next word in sentences. Like, we don't have to, we don't have to let it have that much power over us. It's just, it's a choice we're making.

And can we, can we tweak it to that point? Because I've seen like some papers from Claude, they were trying to like, these alignment papers where they try to make AI have the same objectives, uh, Golden Bridge paper and stuff.

Um, that's, of course, that's what I work on, right? Like, you know, I'm not saying I have nothing in common with these, these folks that are worried. I'm worried too, but I'm worried about what we're already doing, not about some futuristic.

I totally agree on that, you know. And I think there is a very important thing here that the more you think about the doom scenario, the less you think about work and deepfakes, which I think is our biggest problems right now, that the AI as it is, it can already disrupt.

I'll go further. I think it's an intentional lobbying effort to keep us from thinking about what's actually happening right now.

Oh, wow, that's conspiric.

It's, it's a little bit, it's a little bit, but actually, you know, not that much. I think the effect of altruist movement is putting [ __ ] tons of money into getting us to think about doomsday scenarios so we don't deal with what's happening today.

All right. So we are dump, like numbed about what's happening right now and just, like, "Oh, we don't have to worry about people losing their jobs because we're avoiding everyone getting killed."

The other wave is bigger. Yeah. And that's a bigger deal. Humanity evaporating is bigger, even though it would happen in 4,000 years with probability 0.000001%. That's more important than a few people losing their.

Let's just swallow the pill. No.

Yeah. Let's just. Okay, that's an interesting take. Yeah, I, I, I had thought about that. But of course, when you get like big names and obviously like now, uh, I think for many people that didn't know anything about AI, these last weeks where Demis got the Nobel and Hinton as well got the Nobel, I think it's giving them more authority and they are actually the people that is saying like, "Guys, be careful with this stuff." I do think we need to be careful and I, I also think that they're like good people that are that are earnest.

Mhm. But I, I think that they're, you know, I just think that they're like getting over.

Overreacting.

They're overresponding to a a theoretical problem instead or may not happen.

Truly responding to a current problem.

Okay. Yeah, that's a. I like this take. I really like it. What do you think is the feeling on the industry? Like when you're in America, your colleagues, you probably relate to other mathematicians. Make as much money as soon as f, as soon as possible. That's the only, that's the only actual take in the United States.

Okay? And that United States being ahead of the curve is an asymmetrical advantage over every other country. And if we don't keep that advantage, then China will win.

Oh, wow. So it's really polarized on China, America.

It's very about national security. If, if there's ever any kind of like, "Shouldn't we be careful?" It's like, "Chi, do you want China to win?" Is that what you want?

Okay. So, it's basically an arms race.

Oh, yeah. Totally. Which, by the way, like, why are we trying to win against China?

Yeah, exactly. That is like not the right thing to do. And if the ones that say that this can get really out of hand are right, it doesn't matter who wins because AI is going to win if that's the case. No. So, it doesn't really matter. It doesn't.

I'm just saying that their model is a surveillance state in a way that's even beyond our corporate surveillance state.

Yeah. We're just saying something.

All right. Explain me what's the difference between big data and Gen AI. What is the main difference here? Like, why it's.

Gen AI is what we've been talking about. It's like a very general purpose tool that can be used for any conversation or any kind of like art form, right? It's not a scoring system. Almost all actual, um, other things are predicting a particular score of of a particular thing. I mean, actually GenAI under the hood is predicting a score.

I was going to say it's, it's based on the same.

Probability of the next word. That's the score. It's maximizing the probability of the next word, but it's comes out and is interpreted as a conversation.

Right? Whereas like Netflix recommendation engines is literally scoring all the movies that it thinks you haven't seen based on your, you know, what you've liked in the past and ranking in order and showing you the top 20 or whatever it is. So it's like much more directly a score and, you know, then you have the swaths of big data algorithms that are like suggesting what your rank should be in a college application process. I mean, it's it basically almost always scoring you on some one-dimensional range.

But isn't a GenAI doing the same but in a much more broad spectrum of things like basically.

Different words. Yeah. I can give it for words, but well, with multimodals, we can do it in in different, like, I can send a video to AI Studio to Gemini, and it's multimodal, can check the video and give me an opinion if that's a good video or a bad video. So it's basically.

So that's a, but that would be a meta scoring system.

Be a scoring system, and you can ask, and yes, the answer is yes, you can ask GenAI to score something.

Okay. Um, but it's going to give you the answer that it's got highest scored, you see what I mean? It's scoring at a, at a, at a minute level at all times. And then it's basically the same technology repackaged to cover more topics, but of course, not being so specialized is not able to do the same thing as like big data algorithms. It doesn't do the same thing. That's true. I mean, because it's trained to score words, not to, not to be mathematically accurate or consistent, right? And and you see that, you know, you could ask ChatGPT the same question a bunch of times and it always gives you different answers, different answers.

There's no consistency.

You can ask it a basic math question. It doesn't have the right answer.

Okay. And then like, is it a better thing than algorithms or a worse thing because you have less control?

Okay. Yeah. But I mean like than a specialized algorithms. Okay. So it's an algorithm. Let's start there. Like, what is an algorithm?

An algorithm is just something that predicts something based on historical patterns.

Okay. So this is predicting the next word based on all the cor, the corpus of all of its training data, which is on Reddit.

Okay. On Wikipedia.

Yeah. But you've been really like, actually, you wrote one of the bestselling books on the topic where algorithms can be really bad for society. Yes. So how does something that is making a result based on a training data it's supposed to make a decision wrong, worse than my own? How, how.

Just to be clear, like these things are tools, and I think of them neutrally.

Okay. They're not evil. Humans decide to use them in evil ways.

Okay. So the tool itself, it's not bad for society. It's depending on the use you make out of them. So there are good algorithms, or sorry, that's a bad definition. There are algorithms that are used for the good.

Almost never.

Okay. Can you give me some examples of uh really bad algorithms beyond the ones that are racist? Because sometimes I guess the algorithm is doing something that is racist without the person who made the algorithm or trained the algorithm intending to. It's just because of the data set is corrupted or like it's bad. So what are like examples of algorithms that are actually that they've been made with the purpose of of like enriching these people and like being bad for society? What examples? What the most dramatic.

I think, and I, I worked in adtech, right, um, which is, you know, the the world of the internet, like clicking on ads and stuff. There is a whole, you know, so I'm sure everyone who's listening to this podcast knows that like you are evaluated, you're you're given all these services on the internet, and in return, you're you're giving them data about yourself. You're prof, you're getting profiled. Not so much in Europe, cousin in the States. But, um, then you are sussed out for how much you're worth. How much is your click worth? And for people like me, when I have, I have extra money. I, I'm a knitter. I like handmade things.

Yeah. Um, and I like nice yarn. You know, the answer is always going to be like, "Show me yarn because I will click on it and buy expensive yarn, right?" But what about people who don't have a lot of money? Then there's a whole swath of like predatory advertising, um, that is literally meant to pick off people that are vulnerable to like, let's say, a gambling addiction.

Okay. Um, I think that's a pretty good example of an evil use of algorithms.

But any kind of vulnerability, actually going back to my shame machine book, anything that you can be ashamed of and made to to buy a product to try to solve.

Yeah. So basically any algorithm made to exploit your weakness in.

A lot of algorithms.

For enriching is bad.

I mean, I would consider that bad if it's exploitative and and like there's a lot of algorithms that end up being bad not intentionally. You ask me for an intentionally.

Yeah. Exactly. Algorithm.

Yeah. Because then the point is obviously if I intentionally want to make an algorithm good or bad, it all depends on, I guess, what data I give it as a base of what's good or bad. So I guess.

Can I give you an example?

Yeah. Of the same algorithm being used for good or evil.

Okay. Because I, I love this because actually for me, it's like a little puzzle that I like to play with myself. It's like, here's a algorithm that's being used for bad, but how could it be used for good? Okay? You know, and I think most of the time there's an answer. But this is an algorithm that, um, was based on a questionnaire that you would give to incoming freshmen in college to find out how they're doing. And there was a college, um, and I don't, I, I'm not sure of the name, so I don't want to say the wrong name, but there was a college in the South in the United States that gave everybody this questionnaire with the intention of getting rid of the ones that were struggling.

Why? Because the college wanted to improve its ranking in the US News and World Report college ranking system, which is this evil, I'm sure not sure if you have this in Europe, but like.

Something like that. It's pretty uniquely American crap where like everybody cares about this ranking system. So all the colleges want to get a better ranking and um, they'll do anything to get that better ranking. And one of the major ingredients in their ranking is the what's called freshman retention. How many of the freshmen at start, okay, actually finish the year and then, and there's another graduation rate, but like freshman retention is a big one. But it doesn't start till like October 15th. That's when the official like number of freshmen get that started.

Okay. Comes like, that's the official number. So if you can get rid of a struggling freshman before that date, and they don't count. So this awful, awful college gave all these freshmen this, actually forced their teachers, their professors to give this questionnaire, and one of the professors got wind of the plan and and fed it to the student newspaper, and so they ran a story and like everyone got fired. It was a huge mess. Okay. So, that's pretty evil.

Yeah. Right? Like, let's get rid of these kids who are often, by the way, first-generation college students from, um, you know, poor families. You know, they are struggling. You know, the same algorithm was essentially the same algorithm was given to, um, the freshmen at UT Austin in college in in Texas, um, in order to figure out which kids needed extra help.

Okay. Right. It's the same, just is the intention of it and what you do with the results.

Exactly. Because I guess one of the things is that an algorithm, it just gets to an answer, and the answer is kind of natural. It just depends what you do with the answer. No.

Yeah. Sometimes I say like, you know, because the subtitle of my book was "How Big Data Increases Inequality and Threatens Democracy." But the, the, "increases inequality" thing is like, my job as a data scientist was to make lucky people luckier and unlucky people unluckier. Mhm. And that's how you can tell an algorithm is pretty [ __ ] up.

Right. Totally. So it's a pretty rare algorithm that does the opposite, right? But it's not, it's, and and unfortunately, capitalistic incentives is why that's true.

I was going to say, because at the end of the day, it's the theory of the hammer used for like fixing a chair or used for killing someone, but at the end of the day, the tool doesn't has anything to do. So you should be able to regulate to put the law that makes that if you use the algorithm for that, then you go to prison. Is that existing actually in in America? Like, is there any regulation of what you do with the data you pick up or because here we're very protective of data in Europe, which I'm not sure if it's a good thing or not, but, um, because obviously it's giving us some, I would be in favor of almost any regulation related to it if it was global, but then it gives you like the fact that you cannot compete with other markets, so that is like a complicated line. But anyway, the the thing is, if you can define like if you make a bad use of the hammer, you go to prison, but if you make a good use of a hammer, it's totally fine. That's okay. And you don't have to actually regulate the hammer itself. But.

Yeah. Is this existing? Is in America there is like laws against.

I mean, I, yeah, thank you for framing it that way. Like, nobody's going to prison. I mean, there might be fines. They might stop doing it because they don't like getting fined, or maybe put out of business, or the public is bad, but it's not criminal. Um, but to be clear, I completely agree with that approach. Like, you cannot, um, well, you can, but I don't think it's wise to abolish an algorithm. I think it's wise to regulate the use of an algorithm when it is high stakes. And that's what I like about the EU AI Act, right? They're not saying don't use ChatGPT or whatever. They're saying when it's high risk, make sure it's working well. And that's the only thing you could possibly do that's reasonable.

But then there is these things where when we take it to AI, which is, I mean, maybe is it right to say it's more complex than uh traditional algorithms of big data like AI because it's kind of like this black box where.

More things happen or.

Well, yes and no. But when you're narrowly defining a use case, it's probably not more complex. No, absolutely. But the point is, how can we make sure that no one uses it for that purpose if we don't regulate the tool? And that probably takes us to the view of weapons in America, firearms between America and Europe, where here we have much lower rate of people killed by firearms because we don't have access to them. And that's a very clever or a very clear example of something where regulation went over the use because you can use weapons for hunting, I guess. And I guess that's for protection. I don't know, whatever, uh, that's the reasoning behind the the.

Legislation carries that. Well, I don't think ChatGPT is actually a gun.

Okay. So, I don't, I think we're stretching the metaphor too far. I think typically what happens is bureaucracies with big power use algorithms in weaponized ways, and so you have to, you have to be careful about how they are using those, uh, you know, exactly example, hiring or firing people.

Okay. Like one of the examples I have in my book is like teachers getting fired by an a random number generator, almost almost a random number generator. And it wasn't, I would say this is another intentionally bad algorithm if you, if you wanted to know, um, only some teachers were being fired, not all teachers, like basically teachers of poor kids were being fired, and it was awful. Um, but the, but it's a random number generator. You can't like abolish random number generators, right? Do you see what I'm saying? Like, you have to be like thoughtful about how is it being used.

Okay. And a random number generator has no power as such. It is only powerful when people in a bureaucracy wield it as if it is somehow knowledgeable and trustworthy.

Okay. So then where, where I'm trying to go is, okay, we have this tool which is capable of doing like helping researchers to cure cancer and all of that thing, like, let's put in the same box the whole GenAI thing, no, like AlphaFold with ChatGPT, it's not the same, but let's, let's put it together for the example, and we have this technology that can do that, but at the same time, it's the same technology that will take over the market of like customer service and get all these people fired of their jobs. It's not going to be the use of the tool, or shall we forbid companies to use the tool to make money? Because that's the core point of a company, like a CEO, he has like a, like an obligation of making the most money. So actually a CEO that can apply GenAI to get people of the customer service fired and make more money for the company is what he should be doing actually according to what his job is.

And if they can, if they can prove or give strong evidence, because there's no real proof, it's not mathematics, it's data. But if they can build strong evidence that what they're doing is legal and fair and reasonable, then go ahead, use that.

But where, where's the line of reasonable and fair? Like firing 700 people from Clara is actually fair and reasonable.

Firing people from what?

From Clara. These people that they put the chatbot and got 700 people out. Is this.

I don't know. I don't know that example. My point is that probably not, you know, if it, if it came up as an extreme case, but my point is that like the EU AI Act isn't asking, is asking for it to, you know, build a case, build a case that what you're doing is reasonable. I'm just saying that that's.

Yeah. Yeah. No, I totally agree when we put it in things like, yeah, anywhere, any, any things that can do bioweapons or whatever. It, it just has like some very clever, like very clear examples of things that are obviously that way and this is not changeable. But.

I don't know. I don't.

I think there's some side effects.

I don't know if they can actually prevent ChatGPT from showing people how to build a bomb. That's my point. That's my point.

There's a lot of bypassing capacities. I've heard a lot of different people. And if they can't do it, then maybe they should just shut down ChatGPT. That wouldn't be the worst thing in the world.

Okay. Yeah, that's that's where it was leading to. Like, if it gets to the point where we cannot avoid the tool to have like a negative consequence beyond all the positive consequence it may have. Does it get to the point where we have to be like, "Okay guys, this is just not good for us." So.

Right. That's.

Well, again, I, I think we're going to like, I'm going to lean back on the earlier conversation we had that like this has to be a public conversation, and it should not be up to Sam Altman and his cronies in Silicon Valley.

Or Elon Musk.

Or Elon Musk. Okay, let's finish up with, um, where do you think we were going to be in 5, 10, 20 years? Like, what, what is your prediction of where this is going?

Um, I think that a lot of people will have lost their job.

Okay. And, uh, and then the question is like, what, what are the next, what's the next generation of jobs? And I don't know.

I don't know.

Okay. Yeah. I, I see it in the same way. I see that there is going to be a transition, and that transition is going to be painful for most of society. I think after we will be all right, and then probably we get to the Star Trek point where we just our job is exploring the universe. But I think in between, there will be a time where it's going to be tough, like the industrial revolution probably was tough for many people. I want to thank you for being here today. It was really amazing to talk to you. It was like a really deep conversation. I appreciate it. So thank you very much.

Appreciate it. Thanks.

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The Big Tech Companies Are LYING and Elon Musk is TRASH 🧠🤖 — MIT Mathematician Cathy O’Neil

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