Jeff Nielsen (4:48)

And what was that impact? How did they react to it? And how did you feel hearing their reactions?

Powell Garcia (4:57)

So for me, it was very weird. The project itself, when we thought about this, it felt a bit like Minority Report, this kind of almost dystopian, futuristic idea. But because, I don't know, we had, like, this instinct that this could be helpful. We tried, nevertheless. And then the first participants were people that we know or people that were friends of friends, or the grandparents of people that we knew. Right. So kind of close environment. And so we were comfortable there. And we with these participants, for each one, it was a bit different. But I remember the second participant that explained his memory, he was talking about his brother who died almost 10 years ago. He was talking about when they were kids and they used to do some stuff in the field with their mother and so on. And then we generated an image of that specific situation. And when Juan. Juan was the participant, when he saw the image, he started to cry. Like, it was like, oh, I never had an image of my brother like this. And this is a very beloved memory of me. So just being able to actually tangibilize, like, physicalize this into an image was impactful. I Think for other people, it was a bit different. Some people actually wanted to create images of sometimes traumatic things, things that happened to them that were important but were not happy and happy memories. And for them, in this case, it was more about dignifying the past, about saying that happened. And this is another proof that this happened. It's not only in my head. This is another media that I can use now to explain what really happened to me and how I got here. Right. So there are several stories, and we have been doing that for, like, these two years with a lot of different people that really wanted to share traumatic events and situations that were, like, really, really difficult for them, but that somehow externalizing them and transforming them into an image that is a universal language to say so was also kind of liberating. I have seen a lot of people feeling kind of a release when they see the image.

Jeff Nielsen (7:34)

Yeah, well, I mean, it's so interesting. And as you said, it's such an emotional journey for these people, whether it's positive emotion or trauma or both. Did you ever feel, pow, doing this? That, oh, maybe we've gone too far as a studio, or maybe there's something happening here that's now kind of beyond our level of expertise? And do we work directly with people? Do we work with other organizations? Or did you feel kind of firmly in control of where this is going?

Powell Garcia (8:07)

I think at the beginning, we didn't. And that's why we only worked with family members and people really close to us. And then when we started to see that we could create a methodology that was more controlled and that there were certain factors that we could actually isolate and guide very well, we felt very well. But then when we started to work with, for example, migrant communities, we started to join forces with different organizations. So something that we always do is whenever we generate synthetic memories, and there are always three people involved, there is the interviewee, the interviewer, and the prompter. The interviewer also has to be from the same community as the interviewee. That means that they share this common background, they understand each other in their mother tongue, and so on. And that's a way that you can really reduce the potential risks. Right. Of misunderstanding, disalignment, and so on. And also, when I say community, I say someone that have experienced something similar. Right. Even if sometimes it's a bit difficult because the time gap and the different political moments that we're living in different parts of the world. But we always try to work with local organizations that somehow already have, like, this local community.

Jeff Nielsen (9:38)

Right. So what sorts of you Know, you mentioned, you know, Greece and the Syrian refugee camps. What sort of places are you actually deploying this right now? And you know, do you have a broader vision for where it could be? And, you know, where does this go from here?

Powell Garcia (9:54)

Yeah, I think for us, probably one of the biggest moments was when we started to work with care care homes and nursing homes. So I had this friend who was actually like working in a nursing home. He was taking care of 12 patients with dementia. And he was telling me about this methodology that he was following called reminiscent therapy. And this is a kind of therapy maybe you have heard about that is very common specifically in the States, but since the 60s has kind of grown. And it's the use of music, for example, music from the times of that person or food or smell or the voice of someone that that person recognizes to actually trigger this kind of cognitive enhancement for a while. It's temporary. And then we said, what if we kind of cross these methodologies and do some pilot experiments? So we did a couple of pilot experimen here in Barcelona and the results were really, really good. The engagement of the participants were really high. And then right now we are actually in deployment of this more clinical perspective on this. And we have partnerships with the University of Toronto, the University of the British Columbia, and we are exploring exactly which is the best way to integrate that in a safe, also ethical, protected and controlled way into a clinical environment. But the first results have been really, really good. And I think in that sense, for me, the future goes very much in that direction. Understanding that this could be actually used for therapy and it could be like I have seen it, but it's subjective and it's not academic in that sense. But I think with three, four years of research, with the right partners, we will get into a really good position. And this could be actually deployed and used in a lot of different centers for very almost no cost. So that's what is interesting, that it's something that it's very accessible. You don't need really hardcore computing power. You don't need more than an Internet connection and just $20 per month thing. So it's something that could be very accessible for a lot of people and could actually improve the life not only of the patients with the mention Alzheimer, but also family members, caregivers and as well as all the communities that have lost all these images because of the reasons other than health.

Jeff Nielsen (12:40)

Pal. One of the things that I found so amazing about this and what caught me by surprise is when you actually look at the, you know, the footage of this, it's not. It's not picture perfect, right? It's not like this high quality. Everything is perfect fidelity. You know, it looks like Hollywood. Which is one of the concerns that people have about AI video is, you know, it's got these shortcomings. But, you know, having heard you talk about it, that's not really a shortcoming, right? Like, it almost creates an advantage that it has, you know, a dreamlike quality. How did that. How does that work? And how are you using that to your advantage?

Powell Garcia (13:18)

It was purely accidental. As we were really testing the very early models of generative AI with images. This is how these algorithms were at that point. But then when we started to use more realistic models, we started to see that the engagement started to be poorer. Like, lower people started to feel less connected to the memories we were generating. And we stayed there for a while. We were like, what's going on? Maybe we are doing something wrong. And then one day we did the experiment of going back to one of the early models. And then participants started to engage again. And then we said, okay, that was the problem. And then we kind of this. And this is, of course, an hypothesis, but our hypothesis is that it's not the factual accuracy of an image that actually brings this connection, but the emotional embedding. Right? Whenever something is super realistic, you try, without knowing, subconsciously, you try to find the things that were not exactly like you remember, while when you do something that is more blurry and defined, a bit more abstract, and it's more about the symbolic representation, and therefore you're not focused on the detail because you understand that this is not a photo of the moment. And then what we are creating here is instead of an image or a photography, we are creating an image that works as a vector of memory. If I say that this ring that I have here was a ring that was given to me by my grandmother, this ring will not only be a ring, will be my grandmother ring, and this will be a vector to my memory, to my grandmother, and so on. So I think these images are like artificial generations of vectors of memory, right?

Jeff Nielsen (15:14)

So they're more like they can be a vessel that people then project their own memories onto versus being perfectly accurate.

Powell Garcia (15:22)

Exactly. And I think blurriness and undefinition also has a lot to do and match very well with the way our memory works. Because our memory, other if you have photographic memory, these are very low. A low percent of society, like most of us, like, the way we remember things is we remember a couple of things of that situation. And then every time we remember, we reconstruct the rest. And I think it's very similar to how these models work. They do probabilistic imagination at some point, and when they are more blurry, they focused more on the things that you specifically that say that were like this. So I think that that's one common theme between early models and memories. Human memories, biologic memories.

Jeff Nielsen (16:12)

Yeah, it's so interesting and it is not something I would have expected, but it's like, it's just really nice that you can do it. I'm sure it makes it easier to do it with less power or less, you know, advanced models, which is so cool. Is there, you know, as you talk about this methodology and how you get this right and walk through the sensitivity of this, Were there other lessons or other things that you found? You know, oh, this is something you have to get right. That makes a big deal, you know, for the interviewee.

Powell Garcia (16:46)

So I think the most important thing is that everyone knows, what are you gonna do? Because it's a weird interaction to say, hey, we are gonna reconstruct visual memories from your past. For most of the people, it's kind of a science fiction. So something that we always do is we do a bit of kind of play. We play with the models we try to generate. For example, we start with dreams, and then we show how the models work and when they know what they can do and when they feel that they are under control of the situation, then we can go and start actually asking things. And we always start by what is your earliest memory? And from there we start to kind of find which is the best way to create a symbolic memory of a whole story. Because normally images try to encapsulate a story that is bigger than the image itself. Right. It's not because you remember a specific place or a specific person. It's more about what that person meant to you or what that place meant to you. Right. And depending on that, you will build the image in one way or the other. So it's always about the story and having kind of the instinct to find the right image to represent the story.

Jeff Nielsen (18:13)

Yeah, yeah, it makes sense. You don't want to start with something traumatic. You want to ease them into what the technology can do and build up toward it. It sounds like.

Powell Garcia (18:24)

Yeah. And then what is very important, of course, is to explain that you are not creating an image of that moment, a specific photographic image, because otherwise people can feel disappointed at some point. Like setting the expectations. Yes. Where is my mother? I was expecting to see. And then yes. So I think setting up the expectations, showing examples of other things that have been done before are important. And then you have to, if you are part an interviewee or an interviewer or a prompter, you have to just be ready for whatever. Because sometimes people connect like right away. And sometimes it takes time for people to just go through these and then say, ah, no, this was not exactly like this. It was during the night. This was. The table was green, not brown. And then you need to fine tune and find the right way to get to the image. And sometimes you don't get to the image. You need to find another memory. I remember this kind of interview that we had with a 94 year old man, an engineer, and he remembered very well when he was 4 years old and the first time that he was in a car and he remembered the model of the car, he remembered the color of the seat, he remembered everything in so much detail that for us was really difficult to reconstruct that image. And we spent almost an hour and a half building up that memory.

Jeff Nielsen (19:58)

The right prompt. Yeah.

Powell Garcia (20:00)

When he saw it at the end, he said, yes, that was it. And it was such a joy for everyone. Like we had been one hour and a half just figuring out, but it was worth it. So sometimes you need patience on that.

Jeff Nielsen (20:17)

It's such an amazing technology and pao, hearing how you're using it, it's very clearly centered on using it as a force for good, how to help people in need, whether it's refugees or who are trying to reconstruct their memories because of Alzheimer's or advanced stage. Have you been approached or are you working with any more commercial organizations that are interested in this for corporate use or marketing or anything like that? And if so, what does that look like and does that application concern you at all or how do you feel about it being used more broadly?

Powell Garcia (20:58)

I mean, we have collaborated with Google, but it has been with Google Arts, which at the end is a foundation and it's trying to push for this project to expand through other spaces. So I think the commercial use of this, I think I saw something, I saw like this company that were actually doing something similar for people who were almost dying and they wanted to reconstruct like the book of their life. And this was kind of a commercial project, not very successful. I think the marketing was quite creepy. But yeah, but I think, I guess at some point there will be commercial interest. In our case, this is part of a bigger research that we are doing with artificial intelligence, specifically Generative, that is trying to understand that. Of course there is this dichotomy. There is these people saying this is the end of the world Harare. This will destroy everything that we know, and so on. And we have these other people with this very naive perspective that artificial intelligence will solve all our problems, we will not need to work anymore, or just it will find all the cures to our diseases. And I think we are a bit in between in this critical perspective on how we can use that, having into account the responsibility that it has to hold something like this. And I think really for our generation, we are, it's too late to be pessimistic, so we really need to build up stuff with this amazing. But we have to say it's really, really amazing technology that can actually change a lot of things. But it's not neutral like this kind of technologies. Artificial intelligence is never neutral. And I think it was Nicolas Car who said that the value of a well made tool lies not only in what it produces for us, but what it produces in us, within us. Right. So I think when we are creating systems, technologies, methodologies, tools, I always try to think more in that dimension and maybe that's why we are not so interested in like this more commercial aspect of, of the technology. Although of course it drives the market, it drives a lot of things, but, but sometimes it doesn't drive a lot of impact in people. Right. So yes, I think Synthetic Memories is a good example of this, of an experiment that started as a kind of an artistic experiment and experience and then started to grow from there. And now it's funded mainly with research funds. So it's people that is really hopeful that this could be actually used for dementia patients, which is a gigantic problem in the world right now with, with a society that is getting older and older. So yeah, I think that's one way. But we have so many other projects like this one, Skeptic Reader, which is a plugin that we released a month ago. It's a plugin that we published online for free. And it's kind of a bullshit detector for the Internet. So you can use it in YouTube, in any new media channel. And it uses standard LLMs to actually find logical fallacies and ways that articles could be manipulating or just biased towards one side and not the other and just not giving all the information or contextual information so you can actually have a critical opinion. And this again is another example of how we can use this technology to actually engage with information in a deeper way. I think Synthetic memory is more about how we can connect with each other and with our past in a deeper way using this tech. And Skeptic Reader, it's a technology to actually be more critical. Instead of just delegating if something is truth or not, just try to be more critical about what you read or.

Jeff Nielsen (25:03)

And sorry, how I missed it. What's that technology called?

Powell Garcia (25:06)

Skeptic Reader.

Jeff Nielsen (25:07)

Okay, Skeptic Reader.

Powell Garcia (25:09)

Yeah, yeah. It's a Chrome and Firefox plugin. And. And it's very.

Jeff Nielsen (25:16)

Yeah, as you said, it's a bullshit detector, Right?

Powell Garcia (25:19)

Exactly. If someone is listening to this podcast, they can actually try it with this podcast itself.

Jeff Nielsen (25:24)

Also, it uses audio too, not just text.

Powell Garcia (25:26)

Yes, yes. Well, it uses the transcript of YouTube.

Jeff Nielsen (25:32)

I'm so curious to hear late to try it later and see if we're full of shit on this podcast. That's hilarious.

Powell Garcia (25:38)

It always finds something and I'm sorry, Always find a way.

Jeff Nielsen (25:44)

Yeah. So do you typically point it at journalism websites or, I don't know, government websites? What is it typically pointed at?

Powell Garcia (25:52)

Yes, mainly like media outlets. So it's more about. From Fox News to the Guardian and everything in between. But of course, now a lot of people is using YouTube as an information channel and any kind of like, channel that is more like publishing news, any kind of updates on wars, on situations, political situations, natural disasters and so on.

Jeff Nielsen (26:20)

And how long does it. You just got me curious about how long does it take to actually analyze an article or a piece of media?

Powell Garcia (26:27)

We're talking about five seconds, something like this.

Jeff Nielsen (26:29)

Wow.

Powell Garcia (26:29)

Yeah.

Jeff Nielsen (26:30)

Yeah, I mean, that's. That's such a powerful tool for consumers to be able to, you know, before they even consume something. Is this worth my time? You know, what kind of slant does it have?

Powell Garcia (26:38)

I'm starting to doubt that there is something that we are skeptic about our own creation, about the skeptic breather. That is because people is lazy in general. I don't want to say that, but a lot of people is very comfortable, likes comfortability, and this plugin actually makes you uncomfortable sometimes because, for example, there are journalists that I love and I love how they write and how they think and so on. And I started to use this plugin with them because I will not use it with journalists that I already can smell the bullshit. I will use it with the journalists that I like. And then I started to see certain biases and certain situations that I said, oh, this is making me think more and more and more. And sometimes you need. Yeah, I mean, it's. I think it's more for the people that is using it right now that I know mainly come from the academy. Right. So it's people that is already in that space of research and really want to go to the. To the end of the stuff.

Jeff Nielsen (27:45)

Well, and there's a separate issue. I mean, maybe there's two separate issues. The first one is, are you actually willing to stop reading journalists that you like if there's bias? And then the second one is, is there any journalism that is truly free of bias or close to free of bias? Because if there isn't anywhere to turn to where there isn't that bias, you're kind of stuck, right?

Powell Garcia (28:08)

Well, I don't think so. I don't think that because something is biased, you should stop reading it.

Jeff Nielsen (28:13)

Okay. It's awareness.

Powell Garcia (28:15)

Yes. It's just understanding that this is not neutral, that this is not the truth. It's just perspective. Right. And I think that is missed most of the time. And right away in this moment of polarization in most of the Western world, you can feel that, like, people say things as if there were the only truth possible. And this is very dangerous, I think. And Skeptic Reader actually was designed to kind of depolarize, to say, even if you read people that you like, you can find problems in the. In these arguments. So, yeah, it's also about showing that there is a lot of gray areas. And I think now simplistic messages are very at fashion. And I think we need more, sometimes more gray. More spaces of. Yeah, yeah. I don't know.

Jeff Nielsen (29:08)

More skepticism and nuance.

Powell Garcia (29:10)

Yes. And not only skepticism is also about creating common spaces that can join forces of people from different perspectives. And I think if you can be critical with the people and journalists that you like, then you are opening a door for also people that think totally different to share this conversation and be more aware that you can be similar to them in so many ways.

Jeff Nielsen (29:37)

Right.

Powell Garcia (29:37)

So it's more about building tools for critical thinking. Yeah.

Jeff Nielsen (29:41)

No, I love that. And to me, it comes back to what you were talking about with this sort of mission of how do you use these tools to unlock something within us or humanize us more in some way? Am I articulating that properly in terms of your mission for the studio, would you frame that a little bit differently?

Powell Garcia (30:03)

Well, so the studio, domestic data streamers, actually, the focus is to fight indifference towards data, which is how we can actually transform statistics, data, and like this very raw kind of information landscape into something that can actually make people move. It was not long ago that there was like this huge new here in Europe, but it was, I think, something even bigger in the States, that it was the cost of corruption in the country, in the whole continent. And it was like really hardcore. It was our 90 billion, something like this, a figure that, when I saw it, I was angry, but I was not angry about the figure. I was angry about the journalists that put the figure there. Because no one can understand the difference between 1 billion and 90 billion, like only billionaires. That is a very small fraction of society. So I said, look, we truly need systems that can actually make us understand these situations, because the bigger realities we're living to, they are big numbers. Like when we talk about massive migration, climate change, economy, like all these things has to do with big numbers and statistics. And we, our brains are not. We don't have the hardware to actually understand what is hidden behind these numbers. And if we don't understand that, how are we going to do anything to change it? If we don't get angry because there is corruption, because we cannot give a dimension to this corruption, how are we going to do anything to change it? So for me, actually transforming this raw data into something that is comprehensible and that people can interact and understand in a deeper level is a way of making a change, of making people want to do things differently. And this is the small part of this whole ecosystem that we try to tackle with the studio that is transforming data and information and information technology into systems that can help us understand better what is hidden behind.

Jeff Nielsen (32:02)

Right? So for people, for people out there who, you know, have data but are struggling to tell stories with it or struggling to, you know, change behaviors or outcomes with it. Do you, do you have any principles or any, you know, beliefs around what are the best tactics for getting people's attention beyond just, you know, throwing a big number at them?

Powell Garcia (32:24)

I think the most important one is to talk about something that is important to the people that you are talking. I think familiarity, that's why we use the name domestic, because we are always trying to figure out the most domestic, daily way to express information. The biggest, I think the most common example is whenever there is a fire that they give the size of the fire in swimming pools, in Olympic swimming pools. And this kind of brings kind of an idea of how big is that area, right? So bring always metaphors. We can only understand something when we can connect it to something that we already understand. We need metaphors. And that's why it's so important to use art, poetry and design to actually bring this data to life. That's one, I think Very important idea behind communicating information. And the second one is to truly talk about something. Why this is important, because sometimes we just grab information and we put it there, but we don't talk about the consequences of this information being there. So I think a lot of times we forget the why, why I should care about this big number. And this is more about contextualization, give people context. So whenever you show the information, they say, wow, okay, we need to change something. Otherwise it just numbers. And numbers live there in this abstract symbolic universe.

Jeff Nielsen (34:11)

So applying that back to the case of the cost of corruption, did you come to a way to represent that in a way to get people's attention? Was there an answer in that case, or is that one you're still figuring out?

Powell Garcia (34:25)

Of course, we do a lot of projects around this, and the first thing that we do is pointing out whenever there is data that is not being explained correctly. So, for example, banks are very good at explaining data, but they explain it in a way that is very comfortable for them. So, for example, a bank will always say that they have recovered 1,400 properties that were in default. They will never say that they have left 1,400 homes uninhabited and 1,400 families out of their homes.

Jeff Nielsen (34:56)

Right? Yeah.

Powell Garcia (34:58)

So it's also about understanding the vocabulary that you use around information and the alphemisms that are used sometimes to talk about things. So sometimes it's just about giving clarity. What are you actually saying when you are saying this? What are you hiding? Because data, of course, can hide a lot of information. Language, I think, not only describes the world, but also constructs it. Right? And if we create a language for information to be there, correct, a very clear language, people will understand the world and will construct a world that is closer to the reality. And I think right now we live in a situation or in a moment that a big part of the society is kind of disentangled with the other. We are so separated because we don't use even the same language of data, the same language of words, to describe the specific same world.

Jeff Nielsen (36:02)

So I'm just thinking about that, Pao, because you mentioned earlier that AI is not a neutral tool. Right? It's a tool. It can be used for anything. And it sounds like you believe the same thing about data, right? That despite our best intentions, data is not inherently neutral. It can be manipulated. So how do you. How do you use that to depolarize? Like, is there a way to use this to bring people together? Or what do we have to do to make sure that we're more informed about that. And can, you know, I think you mentioned earlier, like find common spaces.

Powell Garcia (36:40)

Yeah. Going to this idea of not neutrality. I think that there is a story that I love that was about the. During, like one of the first rebellions of the Jews in Rome was over. The data was over the census because they did not want to be counted. Counting something in the first is the first form of power because it is understanding everything in its parts. So it's like giving kind of the size of something so you can control it. And this problem is still happening in some parts of South America because being in the census means that you have to be part of the military. Right. And a lot of people is trying to hide their kids so they are not on the census. So the military cannot come to this house and pick up the kids when they are in need to go and join the military. So data has always had something to do with power and control. And that's why, I mean, data, and specifically artificial intelligence is never neutral because it's something disconnected to a worldview and someone holds it like it's at the end, a piece of information that you can use in your advance. And at the same time, data is kind of reductionist because it's beautiful and it's very tempting to distribute the entire world into a single code. Right. And universal law that can govern any kind of phenomena, like two hemispheres, five continents, male and female, animal and vegetables, singular and plural. Right. Left, four seasons. We like to classify things into categories. Right. It's very comfortable because it gives you this false feeling that you control it, that you understand it. And we try to do that to of course have a feeling that everything is a bit under control and we can comprehend it. And actually it has helped us a lot. But unfortunately the does not work like this. It has never worked like this. And, and I think a big part of the polarization we are living right now is because this classification bias that we have that we say, oh, this person is a liberal or this person is. I don't. You already use all the prejudice around that group to classify that person. And, and this is becoming more and more because I think algorithms and artificial intelligence kind of give more power to this idea, to the idea that everything can be classified and predicted. So how LLMs work are based on this, on prejudice, on biases, very strong biases that are very functional, of course, but that kind of power or empower the intrinsic bias that we have as human beings. And I think the way to de. Escalate that is to kind of break these borders. So for example, Skeptic Reader is a way of doing that as we are showing the gray areas, even if they're in the people that you like. And synthetic memories for me is a way of doing that because we are doing the same with different generations of people. Now you can see what other people went through. Right. And you can see your eyes to see that you don't need language. Like it's just a visual reconstruction on that. But I think it has also to do a lot with the fields and the professions that have always been leading certain tools. So for example, artificial intelligence is a field that is preeminently developed by white men like you and me from western countries. And most of the times they come from engineering like informatic engineering schools. So that's a very small portion of the wall and that's a very like it's biased.

Jeff Nielsen (40:38)

Right. They, they brings its own bias to the. Yeah.

Powell Garcia (40:41)

So I think the future for artificial intelligence, or a brighter future, it will be of course part of a more diverse set of people thinking about it. Like from lawyers to journalists to poets to all kinds of people from different worlds, different countries, trying to figure out which is the right data set, which is the right b and building not one general AI, but several very diverse, very biased towards different things, models that can be used for some things, one for some others. And I think that will be very helpful for the polarization, the fact that we don't have one way, but several of them.

Jeff Nielsen (41:25)

So this to me is one of the big trends and risks I think of our time, right, Is can we democratize AI? Can it go down a path of being open sourced and for everybody versus how much of it is controlled by the same four mega corporations and they have all the data about everybody. Are you optimistic about that? Pow. Are you worried about it? How do you see that unfolding and what role, if any, do you see your organization as playing there?

Powell Garcia (41:56)

You know, I was very pessimistic when Deep SEQ was released. And I think the open source kind of had a kick. I don't know anymore because the stage is changing so fast that it's really difficult to see if private corporations or open source will kind of go wild and win this kind of battle. I certainly see artificial intelligence as a kind of a centralization of power because at the end for big corporation, it's easier to integrate in the already existing tools that we are already using so it will concentrate more power. And of course, the other way, the other day I was talking with someone at Token AI and they were telling me, well, you know, the thing is that the data that you could get from Google is very. You can get a lot of information of someone, of course, but the data that you can get from the conversations of ChatGPT from one individual, we are going to another level of intimacy. Right. And I think it was Harari who said we are going from the battle of attention from all the social media assets and so on. Like I want to stay you the maximum amount of time in my platform to a battle of intimacy that is more, not so much about the amount of time, but about the amount of trust that you give to a certain private company. And for me this is like probably the most dangerous part that we are going beyond a certain amount of, I know, control and trust to certain corporate institutions that of course, even though they try their best sometimes to do like the right thing, maybe they are corporate institutions and they have stakeholders and they have to make a revenue out of it. So they will prioritize the revenue over certain social well being as we have seen with Meta and other organizations. So I think that's very problematic. But of course, as well, as long as there are other options, it's okay.

Jeff Nielsen (44:13)

Yeah, I think, yeah. So I want to come back to. That was, that was super, super enlightening and it gave me a lot to think about and I could, I could follow that train of thought for a very, very long time. But, but just, you know, in the interest of time as well, I do want to come back to domestic data streamers and some of the projects, you know, you're working on. I mean where, where do you see this technology going in the next few years and what upcoming projects that you're willing to talk about do you have in the pipeline?

Powell Garcia (44:46)

So right now what we are exploring is how we can help certain organizations with information entanglement that we say. So for example, here in Spain, one of the, well we were talking the other day with is kind of the public service institution for children that have no parents, right. So they are hosted in a public institution and they have a lot of kids there and sadly they don't have enough time to publish all the, to report all the things that are happening there because the ministry asked them to report on every little thing that happened. So a big part of the time of the caregivers is focused on reporting on bureaucracy.

Jeff Nielsen (45:37)

Not care.

Powell Garcia (45:38)

Not care. So actually what was scary to me is that one of the social workers told me that from the five days of work there are three that are spent in bureaucracy.

Jeff Nielsen (45:48)

Oh, my God.

Powell Garcia (45:50)

In taking care of the kids. And then so what we are trying to do is like workflows, for example, they can use voice, note from like telegram or signal, and they will be transformed directly into a report with the right language, everything correct. And then they will need to draft it in just like five minutes instead of two hours. And only with that, we were like kind of getting that we were actually improving two days less of working bureaucracy. So that's, of course, two days more of caregiving and taking care of the kids. And this, for me, really, lighting. And other than this, we are doing projects mainly with cultural institutions and museums. We will be releasing a new project at the Barbican in London, I think in April, March, I'm not sure about. Other than this, we are doing several projects more connected to the way museums can use artificial intelligence in. In their environment. So, for example, how you can explain history from a more contemporary perspective, how you can personalize the narratives to the different audiences that you have. And this kind of examples and exercises that we really enjoy.

Jeff Nielsen (47:10)

Oh, that's so. That's so interesting. And comes back, I guess, to the viewpoint of how do you make it four individual people versus just one, you know, kind of blanket engagement?

Powell Garcia (47:22)

Well, you know, one of the last projects we did, it was really fun. It was around facial that we were using these facial recognition models to detect whenever a politician was falling asleep into the Congress or the Parliament. So we created these bots that will connect to streaming cameras from Congress of over 36 different countries and create headmaps of the sleepiest areas of the Congress and Parliament. Because, of course, no one is watching this.

Jeff Nielsen (47:53)

No, no.

Powell Garcia (47:55)

But we can now with these bots. So I think that that's an interesting project because it turns a technology of control in a technology of accountability. Right. So the idea is to release this algorithm and make it open source for anyone who want to use it in their own parliament and probably in a couple of months.

Jeff Nielsen (48:15)

I love that. You know, Pao, just taking a moment to reflect on, you know, the amazing and interesting stuff you're doing around, as you just said, accountability, but also, you know, recovering memories, you know, democratizing AI, humanizing data. You know, you mentioned that, you know, domestic, you know, the domestic data streamers project started more than 10 years ago. Did you have any idea that this is where this was going? And, you know, how does it compare in your mind to that, you know, original vision of what you guys would be up to?

Powell Garcia (48:49)

No, no, there was no vision at all at the beginning of the studio, it was kind of, yes, I think a group of friends with Alexandra, Dani, Joan and Paul and Axel trying to figure out like, which was the, the right way to explore data and art, which was the thing that we were curious about. I think there was a mythical quote from Bruce Mo, the designer, and he said something like, if you're a design studio, the main job you have, as the name suggests, is to study. And I think this is something that we have always tried to do to understand that our work is not only about creating things, but to understand them. And I think that kind of have brought us here in not just solving problems, on trying to find the right solution for a specific need of a client or a partner, but more trying to understand what is the problem that is behind and the technologies that exist in the environment. And it's like having the right environment to later on find solutions or find ideas that can help with that and sometimes are very lateral, not what you expected at all. So in that sense, it was never expected to get here and do these kind of projects.

Jeff Nielsen (50:13)

I love that. That's, you know, it's so exciting and the stuff you're doing is so cool. So I can't wait to see, you know, what's coming out of the studio next. Want to say a big thank you, Paute, for joining us today. You've given me lots and lots to think about, so really appreciate it. Thanks for being here.

Powell Garcia (50:30)

Thanks for having me.