Analyzing AI's impact on society through art and film

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Well, hello Chris. How's it going today?

Doing really well. How are you today, Daniel?

I'm doing good. I know in one of our previous news and updates show, we started kind of going down the rabbit hole of AI and art and I'm really excited today because we have Brett Gaylor here with us from Mozilla and he's really working at the intersection of AI and media and so really excited to have you here. Brett, welcome.

Thanks. Nice to be here. Hi Chris. Hi Daniel.

Hi. Yeah, so Brett, why don't you give us a little bit of a background of how you got involved in AI and media and Mozilla. Just give us a little bit of background about yourself.

Sure. I'm a documentary filmmaker is usually how I describe the work that I do. But actually scratch that, I would call myself a documentarian. I'm kind of, you know, platform agnostic to be. To be nerdy. So I make documentarian.

I like it.

Yeah, I make non creative nonfiction work on the Internet about the Internet, using the Internet. So kind of a three part trifecta there. So I made some feature documentaries. One of them was about remix cultures called a Remix Manifesto. And that was, you know, kind of a. In the early aughts, you know, around 060708, we were sort of seeing the Internet become a place where kind of an emerging folk culture of people taking media and remixing it, downloading it to their Computer. And that whole process was really creating a lot of anxiety in traditional legacy media, but also pointing kind of a way towards a more participatory culture of the Internet. So I made a documentary about that sort of period of time, and it was a really hopeful time for those of us who were involved in the Internet's early days. It kind of felt like this really democratic moment where anybody who wanted to participate in the culture around them was suddenly able to do that much more easily and to be able to find a good, kind of a global audience all around the world.

The Internet was going to democratize everything and solve all our problems, wasn't it?

It was like, it was just, whoa, yeah, didn't you do that? And Nazis. You know, that's. So, yes, the Internet did do this. But then we sort of saw a few cracks in the utopian kind of landscape. And around 2013, I started thinking about, you know, the ways in which the business model of the Internet was leading to an increasing amount of collection of personal data about people that use the Internet and sort of this creeping surveillance capitalism sort of model that was beginning to emerge as the sort of de facto, especially since in that earlier moment that I described in the Internet's evolution, it was like, oh, wow. And here's another great platform, Facebook. It's going to encourage us to share everything and connect all the people. Boy, did it. It really did. They were really, really good at encouraging us to share things. And so at that time, I had started to work at Mozilla and had created with a lot of amazing community members, a system called Popcorn js, which was essentially a way to synchronize a piece of media, like a video or an audio file, with web events. So you could say at this time, make the web page do this, or pull in this piece of data, or fire this JavaScript command. And I thought, what would it be like to make a documentary about online privacy that would play like a movie, but it included your data? And so that was the inspiration behind Do Not Track, which turned into quite a large documentary series that was produced with the National Film Board of Canada, several partners in France. And it did really well. That kind of played around the world. It received a Peabody Award, which is sort of a recognition of media.

Congrats.

Yeah, thank you. It was fun. That year was David Letterman and the folks behind Mr. Robot and just some amazing, amazing media. And it was kind of cool to see documentary work about the Internet, sort of in the mix of important social issues.

It probably also illustrates people's Genuine interest and concern over the topic as well, I would imagine.

And I also just wanted to note that I find it ironic that as we record this today, it happens to be election day in America and a lot of these issues are very top of mind as we're going through that.

Yeah, that was definitely not the case when we started to make Do Not Track. But over the course of making the series, it became much more relevant. For instance, we had one of the episodes, the third episode was about. It was sort of like, if you look at it now, it's kind of naive. It's like, what could you know about somebody from their Facebook profile? And we're like, we found this sort of niche study by researchers at Cambridge University who feel that they're able to predict your personality based on your likes in Facebook. And that was the AI researcher Mikhail Kosinski, who did. He basically created this model where you could correlate person's likes on Facebook to a really high degree with their psychological profile. So it was a vetted and peer reviewed study where I think what they did was took interviews with people's family members and their friends and psychologists and sort of plotted you on what's called the Ocean model, which stands for openness, conscientiousness, extroversion, I want to say agreeableness and neuroticism. So that's your, that's your ocean model. So you exist somewhere in an axis of all of those points. And so what Kaczynski was able to do was correlate like, say the one that we always used was apparently like, if you like the Dark Knight that correlated really highly with a low score in Extroversion, for example. And so basically they took large group of likes on Facebook and then did these interviews with folks and sort of said, here it is. I could log in with my Facebook ID and it would look at all my likes and then say like, here it is. You're this open or you're this neurotic. The nuts thing is it's pretty accurate. And so anyway, we used that, that became an API called Apply Magic Sauce and we used that in the Do Not Track documentary to sort use people's personal data to sort of show how these emerging. This is AI really was starting to work. Now then what happened after the film came out, or probably while we were making it, was this startup called Cambridge Analytica requested the licensing of that API. They were denied by the University of Cambridge. And then they were like, well, we're just going to basically copy their approach and out of Spite. We'll call our company Cambridge Analytica and the rest is history.

Seems like some of us are familiar with that. The rest of the story. I've heard of that story.

Yeah. The rest of the story is. It's interesting. He actually has another very controversial paper, I don't know if you've seen it, but attempts to guess a sexual orientation based on a Facebook profile photo. So he.

I've seen this.

Yeah.

He tends to create these like highly controversial sort of mind bomb sort of studies that kind of illustrate some of the bleeding edge dragons of these emergent like machine learning or AI systems.

So is that kind of where. I mean, I know that kind of up? You know, recently in your profile you're kind of heading up these creative media Awards for Mozilla. Was kind of all of that that you've mentioned that was kind of in the mix leading up to Cambridge Analytica and all of those things? Is that part of the driver for network and how you got involved in that? Or.

And also, could you take a second and just kind of describe Mozilla for anyone in our audience who isn't familiar with it? They may know Firefox, but just kind of give a quick intro as you answer.

Yeah, you guys got to keep me on track too, because I could. It's all good stuff.

Yeah, this is good, man.

This could be a long answer, but I'll keep it short. Yeah. So most people are familiar with Firefox, but Firefox is one aspect of the mission of the, of the Mozilla foundation, which is a project basically to keep the Internet open and accessible public resource for all of humanity. So we're guided by a manifesto which I would encourage anybody to check out online. Yeah. And it really comes out of the recognition that the Firefox project and the open source code is a useful instrument in the market to make sure that there is some, you know, web browser and sort of user agent that exists in the world that is independent and is not one of corporate driven. Yeah, exactly. And however we don't, we don't, we don't disparage the commercial interest on the Internet and we value independence at the same time. So Firefox is one thing that Mozilla does, but the Mozilla foundation that I work with also does various interventions to ensure what we call Internet health. So we see the Internet as an ecosystem, and for that ecosystem to be healthy, we need to kind of tend it. It's like, imagine that the Internet was like the ocean. Well, if you want the ocean to Continue to be healthy. You gotta make sure people aren't polluting it or somebody hasn't overfished it, or, you know, and so sometimes that requires work. And so for us, that work is giving out grants and awards to sort of promising approaches to Internet health. We also have a really robust fellows program. So we support sort of leaders in this effort to keep the Internet healthy. So that could be like a policy person, like a lawyer, or it could be a technologist, or it could be an activist, or in my case, it's media makers who are trying to explain this stuff to the public.

So that sounds incredibly dynamic as a mission for Mozilla, because, I mean, as this is evolving so quickly, then I guess it has to really keep track of new developments that are coming out. Obviously, AI, as we're talking about that, how is Mozilla involved in AI? How does it use it internally, and where is it taking that? How is it choosing to participate?

At Mozilla, we kind of recognize that these emergent AI systems are just becoming part of our computing environment. What's the quote? It's like when you're fundraising, it's AI. When you're hiring, it's machine learning, and when you're implementing, it's linear regression. I think this is what we have some of the best minds in engineering that think about the technical infrastructure of the Internet. And just a lot of the elements of this computing environment now feature some of these computing principles. So trying to look at large patterns, trying to build systems that evolve over time, that's just kind of part of the way that you make software these days. And so we want those systems to have the same kind of values that we expect and push for. In other words, aspects of the Internet. We want it to be transparent so you understand how it works. We don't want more data collected about you than is necessary. We want other engineers to be able to see the code so that they can confirm that there isn't bias in those systems. We want the Internet to be built by as wide a cross section of society as possible. So by that, I mean it isn't just engineers in Silicon Valley who are creating these systems. They are consulting with civil society. They are consulting with potentially the groups that are evaluated by these systems. So that's a complicated answer to kind of a big question is like, what does Mozilla think about AI? We think that it needs to serve humanity, and we think that it needs to be open and free and healthy.

Okay, sign me up. I'm ready.

Yeah, that's A good answer. I'm wondering if, like, and maybe this is a more personal question as well, but, you know, in terms of, like, the current trajectory of AI, you know, as a community, as, you know, practitioners, as researchers, do you see the community, you know, embracing those sorts of values or do you see it, you know, kind of steering, steering in another direction that's maybe concerning to you?

I think there's some really positive signs that, you know, that let's call it the computer science community is realizing that, you know, you need to think about the social implications of what you build. And that's why Mozilla is making efforts to support that. So we just launched a program to support promising approaches to ethics in computer science education, for instance. You know, if you look at what's come out of Google a lot lately, you know, there's some promising signs from both, like employees and management that they recognize that there needs to be some really, like, bright lines that separate where artificial intelligence technology should not be monetized. The examples of not wanting to have their technology used in military contracts, for example, or you're seeing a lot of employees of Amazon, for instance, not want those facial recognition technologies used in immigration or other really sensitive areas where you need a lot of public oversight and transparency and how those systems are built. I think that's happening. What maybe concerns me is the sort of speed at which all this is changing and kind of a feeling of that there's sort of a manifest destiny in the way that these technologies are built. So it's like, oh yeah, any place where we could collect data, let's just collect it, and then we'll assume that there will be a use for that, that machine learning algorithm yet to be invented will solve. I think you sort of see that a little bit in maybe what's promised to governments or cities about how they can save money or make difficult decisions at scale using machine learning or artificial intelligence. I don't know if you guys are familiar with this report that ProPublica did on the US justice system where some states were using machine learning systems to predict people's probability of re offense.

I'm familiar with it. I've read up on it.

Yeah. And so that's like an area where it's like, you know, maybe we should just let the humans continue to make these decisions because it's very difficult to sort of see where exactly bias can occur because these algorithms are so complex and it's so difficult to give them data that isn't collected in a manner that doesn't reinforce an existing bias of the past. So that's a real concern about these automated decision making systems is oftentimes they just reinforce previous inequalities or frankly, racist systems that have evolved. Yeah.

In that report you mentioned, if I recall, there was an inappropriate bias against African Americans as a result of that. Correct me if I'm wrong, but I think that's what the result was.

If anybody's interested, you can just look up propublic. The report is called machine bias. And it basically took two people that were eligible for bail. And the system basically predicts whether one person is going to reoffend or not. And two people with a very, with similar crimes, an African American woman and a Caucasian man. And while the man actually had much more serious previous offenses, the African American woman was denied her bail because she was assessed to be a higher risk than the man. So now why is that? And is that because African Americans in the United States are incarcerated at a higher level than Caucasian people? That's true. Does that mean that that woman, that specific woman, is more likely to reoffend then that man? No, it does not. But the system assessed them this way because it's looking at this history in the United States that unfortunately incarcerates black people at a much higher level than it does white people. And so then what happened in this case is the man got bail and he reoffended and she didn't get bail, was, you know, left in jail for a longer period of time. And so that's just sort of like doubling down on some of the problems that we, that we see in these systems. So in this case it's kind of like reinforcing problems of the past.

Yeah. And we recently had a talk with Lindsey Zoologa, who works in hiring, and we were talking about some of the biases there as well. And some of these things come up very subtly. She was talking about just the fact that you have fewer women applicants, for example, in software engineering positions. And so thus you have less data. If only one woman applies for the position in software engineering and the AI, for whatever reason determines that that wasn't a good candidate, then it can generalize to all women applying in software engineering. Right. And so these things come up and they have, they have a huge impact on people's real lives. And I know that we're going to talk here in a second about some of the awards that Mozilla has given out recently, but I know that those are focused around AI's impact on society. Are these the types of impacts on society that You're. That you're imagining in terms of maybe a biased AI, you know, giving certain people privilege or whatever it is, or in general, how do you kind of see the biggest impacts of AI that AI is having now and maybe in the near future?

Yeah, that's exactly right. This is basically the way that we framed it, is projects that use this kind of media advocacy to highlight some of these unintended consequences of artificial intelligence and places where we want to be thoughtful about how we apply it. So we're awarding media makers who are kind of exploring these topics in a way that lay people can understand that, unlike the three of us, don't think about this stuff obsessively every day. It's like, for the first time, when you talk to people about AI, I'm sure you both get this all the time. If you were to talk to somebody at a Christmas party, they'd be like, oh, you mean Terminator. And, you know, like, you're like, you know, like, problems in the future with AI. You mean, like, that the robots are coming for all of us, and you mean they're not. Well, maybe. And there is some real stuff happening right now in the world that we want to be thoughtful of, and that actually can be either course corrected or, you know, with intention and thoughtful design and, like, the proper application of ethics can maybe turn out all right. We don't have to worry about a future, you know, robot invasion. So it's these questions of bias, but it's also, you know, I mean, I could talk about some of them. There's one that I'm really excited about. It's called Stealing youg Feelings by a really fun and funny engineer slash comedian, which is a fun mix that you don't really.

It's a perfect combo.

I know, right? So Noah has created this project that looks at facial recognition systems and specifically patents that Snapchat has recently filed to be able to do facial recognition on, like, groups of people. So what he's gonna do is use the webcam of your computer to, in real time, analyze what your face is doing while you're watching the film so that he can, you know, either shock you or make you laugh or make you surprised or make you angry, and the film can react to your emotions while you're watching it. And that sort of is the perfect example of what we want to do, because an audience that watches it, it's not like you're just telling them that cameras can, you know, detect their feelings. You're showing them, like, their feelings will be detected in real time. And that will change the movie. So that's one you know. And it's like, it's not. That's not necessarily biased, but it's a. It's an issue and would be remiss.

A little bit creepy.

Little bit creepy.

Good use of masking tape on your laptop.

Exactly. Might lead to, you know, duct tape flying off the shelves. There's a fun one called A Week With Wanda that basically simulates an AI that tries to be helpful in your life, but goes off the rails and starts to suggest things to you that you may or may not want. This one, it's going to be kind of all done with. It's like an episodic serialized email exchange with you. So the AI might one week be like, oh, I noticed that you wanted to. To spend more time with quality friends. So I went ahead and deleted like, half of your Facebook friends because you don't talk to them anymore. So the idea like that this AI is being, like, a little bit too keen and too helpful. There's one called Survival of the Best Fit which does address a lot of these. The issues that you were mentioning, Daniel, about biases in workplace hiring. So they want to, like, show that by simulating a job application process. Another one that I'm like super excited about is called the Training Commission, which is basically like a work of creative fiction. Stay with me here. It's a speculative fiction from the future that is looking. That takes place when a truth and reconciliation committee is struck to see what happened with an artificial intelligence. That basically something cataclysmic happened in society. So this AI, we don't exactly know what happened. The AI either, I don't know. Did it assume that it needed to fix some part of the way that humanity was structured? Did it. What exactly happened? So we have to piece together what happened in this story by the events that are told to us about this truth and reconciliation condition. I'm murdering this one. Ingrid is going to be really sad to hear my explanation. And then there's another great one that looks called what do youo See? That examines the difference between what an image recognition system sees and what a human sees. So it uses a lot of these, like, edge cases of where AI systems can't understand. Like when people. When it shows a picture of somebody wearing a mask, for example, this one, Mate Me or Eat Me, is going to be all about bias in dating apps. And it's basically like a game where you swipe right or left to choose which monster you want to date and then It'll sort of show in that process how quickly bias and reinforcement can lead to discrimination within those sort of dating systems.

So, Brett, I'm thoroughly interested and intrigued by all these projects that. That you just mentioned. I kind of have, like, a general question about all of these. Given that, you know, Chris and I are kind of like AI practitioners, I guess you would call us. I'm really interested to hear your perspective on. Think that in this effort to explain kind of like how AI works and expose some of these things, like bias and other things, why it's so important to involve creative people, maybe artists and, like you were mentioning, comedians, filmmakers, writers, why it's essential that we kind of involve those people in helping us tell that story.

I think it's really important to involve creative people because often their job is to give a language to things that we don't know how to talk about yet. You know, like, if you think about. Again, if you think about your friends that aren't obsessing over these topics, when they sort of encounter something that we might call machine learning or artificial intelligence, where it's sort of a decision is taken that they didn't understand, they might just call it creepy. They don't really know. They don't have, like, the contours of where. Of how they should feel about these systems and where they might affect them. So it's kind of like one of the roles of creative people is to sort of map out that landscape and also to actually map it out emotionally.

Would this be kind of in the idea of design thinking, in terms of applying that methodology to this creatively?

That's a good way of thinking about it. You mean, like, kind of going and.

Analyzing what you're trying to get to almost from scratch, and then figuring out how this fits in? Is that where you're going with it?

Yeah. So it's interesting. We try to think about the impact that these projects will have with their intended audience, and we work with the people that we award in this way to really get them thinking about the who is this for? What change do you want to see in that person? So you might just say it's like, oh, it's for, like, we talked to Noah about this, and he was like, oh, it's for millennials. And I want them to feel angry. And it's like, okay, so that's gonna shape how you create that work. You know, the platforms that it goes out onto, the sort of references that would be included in it, the tone, the length, all of that. And so it's Important for creative people to be leading the charge in that, because that's their job, and their work is to create a reaction in people. And so we kind of need that in these sort of early days of kind of creating a multidisciplinary approach to responsibly building these AI systems. Because we need to know what people think about AI in their lives and what people think about machine learning. And especially if we know some of the right policy interventions and some of the right design questions that needed to be asked, we need to quickly help the public catch up to where people at the leading edge of this stuff have been thinking. So if you think about all the, you know, the questions of bias, there are, like, really concrete proposals of how you can design these systems, but they're not gonna. They're not gonna get traction if the public doesn't understand them or understand the urgency to them.

So where does that take you? So if you. If you start getting a handle on public perception of these technologies and how they're affecting their life, what do you, as an artist, go do with that? How do you take that new information and do something productive with it?

I think it's, you know, it's not necessarily about. Let me see how I want to dig into that one. I think this work is most effective when the makers really understand the change they want to see in a member of the audience. So if you're going back to that example of Noah, if you feel like you want to anger this person, then what can you expect that that person would be willing to do? Once you've achieved that emotion, is it like, oh, you want them to delete Snapchat, or you want them to write to their member of Congress, or you want them to share it with a friend, or you want them to complete a quick survey? If you're able to incite the curiosity or the emotion that you intend, you sort of have a little bit of a window where you can get people to do things.

So if they have, at this point, a sense of awareness and some perception of that, that AI is involved. Going back to Noah's project, where the camera is used to do emotion detection based on. On facial reactions, is that. Does the awareness itself. In other words, if you are the viewer of that show and you're. You're looking at your laptop or TV with a camera on it, does that awareness change the reality that you're in? In other words, if I'm a viewer and I don't know that AI is being used in this and the. The movie Is reacting. How is that different from. If I'm a savvy person regarding AI and I know that's happening with the camera that's looking at me right now, and I'm still experiencing. How does that change the reality that the. That the person is engaged in?

You mean, like, how is it different if they just are told that versus if they, like, feel it in their bones that. That.

Because we've kind of debated the. The idea of the public being aware of, you know, in quote, kind of how AI works and why is that important and what. What is the meaning of our life when you have an educated person in the audience and they're aware of their experience being shaped by AI versus someone, that's not where or does it not matter at all?

So I think it matters. It's interesting. I was just in London at the Mozilla Festival, and inside the tube, there was all these ads for programs that would teach young people how to code. And the way that they were trying to get kids to do this was, like, presenting it as magic. It was like you have this magic wand, and if you know the spells, you can cast them and you can make the world whatever you want. And I think that's a terrible approach because it's not magic. It's actually humans that make really specific and concrete decisions that lead to really specific outcomes. So to answer your question, I think that what the opportunity we have to show people how these systems are built is to realize that, like, none of this stuff is a foregone conclusion. If we don't like the way that these systems make us feel, or we don't like the effects that they have on some of the more vulnerable members of society, there's an opportunity to change that. And when you see how it is working with your data or with an algorithm that's kind of presented to you and you see like, oh, I get it. It takes these three things and compares them and then says, okay, these two are alike and this one isn't alike. And you can sort of see how that can lead to things like confirmation bias, or you can see the system, then you're much more likely to sort of say, like, oh, this can be changed. This. These people are just doing it wrong. Or, you know, like, in the same way that, like, if you're building a bridge, this is how you build it, and so that it won't fall down. And so we need to, you know, add those principles to the ways in which you design these AI systems that like, oh, yeah, you can't use data like that because it's, because it's clearly biased or you just give people the clear understanding that none of this is to be taken for granted. It all up for design.

Yeah, I'm really glad you brought that up because frequently I have this moment when I am teaching, like corporate workshops to people that haven't done machine learning or AI before. There's this moment, and I literally, I see it in their eyes, where it's almost like a disappointment because they think that they were going to like, learn something magical and level up and be wizards, but they really just find out that, you know, machine learning and AI, it's, it's actually kind of a set of well defined functions that you execute in code. And it's really just kind of a way of combining those in, in a certain process. And it's not like, you know, you sprinkle fairy dust over your computer and then the magical AI comes, comes about. So I'm, I'm really glad that you brought that up. I think it's an important thing even for technical people to realize that this isn't, this isn't kind of a, a magical thing that is outside of our control, but it really is. It does have design behind it.

You know, Daniel, I think you're totally misleading your workshops though, because you walk into the classroom with the wizard's hat and the robes on and everything. I'm just saying I think you're setting it up. I think you're setting them up.

Yeah, well, Brett is wanting creative people to be involved, so I do my part. So I was wondering, you know, from that perspective, maybe Brett, you know, there's this group of like, practitioners like Chris and I, there's kind of researchers, and then there's maybe artists or designers or filmmakers or whatever it is that can help tell this story. How best do you think, or what opportunities are there for like, practitioners like us or other people to maybe lend a hand in telling this story along with creators or designers? Because I'm imagining like these projects that you're funding. Maybe the lead person is like a filmmaker or whoever it is, but like you were talking about with the feeling recognition thing, there is a technical element to that that has to be built and figured out. So how can we as practitioners kind of engage with, with creative people to tell these stories?

So actually, all of these projects do incorporate some type of AI within the actual creative approach. So they all actually have to have a real algorithm that does the sort of creative piece within it. So almost all of these folks that we're supporting are kind of these hybrid folks that are creative people and have a, an ability to tinker in engineering. Having said that, it's like there's one that's really fun project, it's called do not draw a Penis. And this is basically a comment on the sort of algorithmic censorship process that you see in systems like YouTube or other kind of user generated content systems. Because all those systems are now applying machine learning to ensure that nothing bad happens on these platforms.

They also like nudity detection and certain things.

They also, it has to be noted, employ thousands of people, unfortunately to have to look at a lot of those images so that we don't have to. And they also employ some of these systems. Now this particular project basically presents you with a blank canvas, invites you to draw whatever you want. And knowing the Internet, some people are gonna draw guess what? Penises. And so the program will basically say like, detect that and tell them like, hey, you know, you shouldn't be drawing that. This is a safe for work project and kind of like turn their penis into a flower or, you know, a tree. That project uses sort of object recognition libraries, projects like Google, Quickdraw, but of course there is no penis detection within that system. So they've had to create their own huge library of, you guessed it, thousands and thousands of crudely drawn penises collected from the wide reaches of the Internet. So my point here is, and I have one, is that a lot of these projects use existing libraries and existing approaches. So the more of that stuff that can be open source, the more creative people can be. They can sort of innovate on the content layer, if you will, of the project rather than having to like create entire, the entire stack of artificial intelligence systems that are needed.

That is a tool that would make a big impact right now in my family's life. I have a first grader and they're at that stage and we're very active about going in the classroom and participating. And they're all at that stage where penises or anything else you can think of farts, you know, are funny and cool. And they're trying to figure that out in the world. And tool like that would actually be a delightful thing at this moment in my life.

Oh yeah. I have a kid in grade two and it's like you just give him a blank sheet of paper and he draws the poop emoji on everything. It's like, dude, enough with the poop emoji in there. But imagine an AI system that. And each time he tried to draw the poop emoji is like, you're not allowed to do that. I don't think we want to live in that future either where his 10 is embedded with like a naughtiness filter that when it like goes onto any blank sheet of paper, it sort of censors what he's saying. So but that's kind of the reality.

That's a good point.

Some of these systems are building, you know, if we, if we assume and agree that the future creative palette of our children is going to be the Internet, we really want to make sure that it's, that, you know, we're not preventing them from expressing themselves. That's a great point. I don't want to see penises over everything and I don't want to live in a world where certain combinations of lines cannot exist on a blank canvas. Tough stuff. We're going to solve it From Mozilla. You heard it here first.

Awesome, awesome. And I think going back to one thing that was mixed into that whole conversation was you mentioned open source. And I think one of the things that I got out of that was one of the ways that maybe we as AI practitioners can kind of help tell these stories and help, you know, make more transparent how AI works is actually to, to put our work out there in an, in an open way and to, you know, create, you know, tools and documentation and pre trained models and those sorts of things and put them out in the open so creative people can use them and, and try to understand what they're doing and what they're capable of and that sort of thing. Would you agree?

Yeah, absolutely. All this creative exploration that we sponsor at Mozilla, I mean, we encourage people to release their stuff under open licenses and in fact sometimes require it. But it's so true that you can just get so much further if you can a see what other people have done in this realm and then be like, oh, there's a library for this exact thing that I, I want to do and just go and get it. Yeah, absolutely. Totally agree with you. That's a thing that the community can really help with. And then there's other ways of peer review or just participating in systems like GitHub when people have questions or pull requests or stuff like that, just be active and know that you could never anticipate that somebody is going to build a facial recognition movie out of a library that you make. But sometimes those kind of weird uses that they didn't need to ask you for permission for are some of the most delightful and unexpected things. That can happen on the Internet. So do more of that.

Yeah, I think one kind of interesting piece of this that I'm kind of curious to see is as more creative people utilize a lot of the open stuff that's out there, I think that they're actually going to be able to kind of help us probe some of these implications in ways that like the practitioners haven't even thought of. And I'm thinking of like the chatbot that Microsoft release that, you know, turned into a Nazi. And over many days or whatever it is, sometimes the practitioners, the researchers, since the cycle between research and releasing, you know, like model code and all of that on GitHub is so quick now. I think having people probe those questions and think, think about the implications is also important, you know, for the other side. So it's, it's important for us to release things into open so that creative people can use it, but it's also important for us to look at what the creative people are doing with what we're releasing because it can help us, you know, shed some light on the implications of what we're actually doing.

I agree.

So where can we find out more about the projects that Mozilla is funding through this program?

I think that just we tend to help support these projects at the release. So if you're following us on Twitter, that's probably one of the easiest ways. We have a nice blog post up about the awardees that folks could probably find fairly easy. And we're anticipating that these will all be released sometime over the course of the next year. Everything's gonna, is. Is meant to be done by June, but they'll all have different release dates depending on the complexity of their project. So I think just, just keeping in touch with, with Mozilla, we do have a mailing list as well that we tend to, to send this stuff out on if folks are not subscribed to too many mailing lists. But I think the best way for people to keep track in touch with these projects is just to follow us on Twitter, Mozilla. We also have a mailing list if people are interested, and there's a blog post that should give links to the creators of the project. Sort of poke around and see what else these folks have made if you're interested in this type of work.

This has been a really cool conversation for us, I think in terms of the intersection of creativity and different forms of art and communication as opposed to AI, we got into where we talk about ethics a lot. But I think we need to have even more conversations about the larger world. People outside of just AI in the traditional sense doing this. So thank you so much.

Yeah, it was a pleasure. Thank you to both of you.

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