Licensing & automating creativity

One of the big areas that we're seeing just kind of explode is, I mean in one sense AI and creativity, but in a sort of broader sense, like an expansion of the things that AI is doing in a creative sphere that is paralleling, you know, a lot of what humans could do. And particularly I think the most obvious one that we've seen recently is this dall e stable diffusion. All of these models that are sort of text to image models. It brings up really interesting questions around the ability of maybe non artistic people like myself, who I'm not a designer, I'm not a painter, but the things that I could do creatively with these tools is amazing and fun from my perspective. There's this sort of really fun, positive element about it.

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Well, welcome to another fully connected episode of Practical AI. This is where Chris and I keep you fully connected with everything that's happening in the AI community. We'll take some time to dissect the latest news in the AI world and dig into some learning resources to help you level up your machine learning game. I'm Daniel Whitenack, I'm a data scientist with SIL International and I'm joined as always by my co host Chris Benson, who is a tech strategist with Lockheed Martin. How are you doing Chris?

I'm very well today, Daniel. It's been an interesting week in various types of AI news and I've noticed some themes that we can jump into along the way of some fairly substantial concerns. I'd say. The world evolves.

Yes. Well, I mean on a slightly less on topic note, but definitely newsworthy. I don't know if you're going to watch Rings of Power today or tomorrow or. You've already watched it.

Not yet.

Yeah, so neither of us have watched it, so you're not going to spoil anything for me because I haven't watched it either. But tomorrow our family, we're going to have a bit of a marathon and watch through Peter Jackson's Lord of the Rings. Oh good. And then we're gonna watch Rings of Power, so it's gonna be an all day affair and I'M sort of basically ignoring anything I'm trying to hear, you know, trying to ignore anything I'm hearing about whether Rings of Power is good or bad. Reserving judgment until. Until I see it. But.

So, yeah, that is. I don't know that we've ever talked about this. Cause it's not directly in the AI world, but that I grew up as a fanatical Tolkien fan. And not only Lord of the Rings and obviously the Hive, but the Sil and all the other stories that most people don't ever want to read. I read them all. I've read them all multiple times. So I'm one of those a little bit slightly nut job types. It's my favorite set of stories in the world. So, yes, here's the conflict. My wife and my daughter aren't as enthusiastic as I am. So they like it, but not like I did.

Neither is anyone in my life, really.

Yeah. So the big thing about Rings of Power coming out was like, do I just watch them or do we do it as a family? And my wife's like, yeah, we'd like to watch it together, but they don't have the urgency. So now it was released last night. Daniel, as we are recording this, I am sitting here waiting. Like, when are they gonna be ready to do this? Part of me wants to selfishly just go watch it. But no, no, I'm gonna be a good family man.

I mean, for someone like you, with that sort of background, you're getting this visual sense of things we haven't seen in other movies, like the Dwarf Kingdoms and such, which is gonna be really interesting. I mean, I hope it's really awesome. But it's different. It's definitely different. So there's a lot they've taken license. Oh, yeah, sure. With the story, which one would probably expect.

I agree. And Peter Jackson, though, he did a great job. He took some license with the story, but he pulled it off pretty well, in my view. They've taken more license at Amazon with this story. So I'm. I'm one of those people who has read the Silmarillion many times, and I know all the detail, and I'm going to be trying so hard not to let it just irritate the crap out of me, but I'm looking forward to it because of the scope and level of quality that it's supposed to have.

Yes, well, speaking of creativity and license and all of those things, I mean, I think. And this is one of the topics you brought up to me, one of the big areas that we're seeing just kind of explode is I would say, I mean in one sense AI and creativity, but in a sort of broader sense, like an expansion of the things that AI is doing in a creative sphere that is paralleling a lot of what humans could do. And particularly I think the most obvious one that we've seen recently is this both Dall E stable diffusion, all of these models that are sort of text to image models.

Yeah.

So yeah, there's this. It brings up really interesting questions around. Well, I think really fun things first of all. Really like positive and fun things around. Like just like the ability of maybe non artistic people like myself, who. I'm not a designer, I'm not a painter necessarily. I mean I took art in high school. Right. But the things that I could do creatively with these tools is amazing and fun and like that's a really. From my perspective, there's this sort of really fun, positive element about it. I don't know. Do you see that side of things?

Yeah. And by the way, I'm looking forward to going and touring the Louvre at some point in future years and seeing a Daniel Whitenack's AI original up there because you know that's going to happen.

Well, what do you think is the attribution of these works? Like how should one attribute one of these AI augmented artworks? I guess so.

I don't know that I have the answer. And I can see both sides as we talk about this because as we are talking about some of these articles that we've seen, one of them which was a little maybe on vice.com is an AI generated artwork won first place at a state fine arts competition and artists are pissed. So I think that's going to happen a lot. I think the question then becomes if you want to talk about artwork, you have to be specific. I mean I'm so biased coming from this AI community. But you know, it's a set of tools, you know, just like Adobe Photoshop, you know, was for many years, you know, in the digital space. And now AI is another set of tools. And I personally see it as perfectly legitimate. But there may have to be competitions that specify by the human hand in a non digital format kind of thing. I don't know. It's not just coming, it's here and it's here in a big way and only more of it.

Yeah, and I think to. I mean we are practical AI here. So people are wondering how they can practically do some of these things. One of the examples is Dall E. You can request Access and use Dall E, which now includes sort of extra features around inpainting and other things. There's also. I think it's funny, Chris, you know, we talked about Dall E recently. It almost seems like we've moved on from Dall E and now stable diffusion is the thing. And you know, you can see how things, fast things move in the AI world now. @ the time we're recording this, stable diffusion is. Is the new new. And you can use. There's actually from. From Stability, there's dreamstudio AI which is you can sign up and do this sort of. Also AI assisted creativity work within that Dream Studio app. So if you're wanting to get hands on in a practical way with these models, it's not that hard at this point. Whether you're doing that on a hugging face space or in Dream Studio or in the Dall E interface, there's multiple on ramps to.

This. Yeah, I agree. And it's interesting. I mean, I do think that this is going to open up the spaces in a big way. Can you talk a little bit? Because I know we've talked. Talked about Dall? E quite a lot over recent episodes and so listeners probably are fairly familiar with it if they've been with us for a short while at least. Can you talk a little bit about stable diffusion at a high level? Like what do you know of that's different? I know it may not be your.

Specialty. Yeah, it isn't really my specialty. I think that there are differences with the sort of modeling technique and the data and the way they did it. I think though, one of the interesting things is with stable diffusion being more, I guess more of an open approach, where Dall E was sort of released in a very, very guarded way with the sort of waitlist type thing that we've seen from OpenAI. I mean, like last, last time we talked, I think I used. Just used public hugging face space to generate stable diffusion output. On Hugging Face, there's the Stream Studio thing. So I think it is kind of. It's in the same stream as Dall E and other diffusion models that we've seen. So we're still in this sort of stream of diffusion models. But I think the power of openness and community is part of what has driven the stable diffusion model to just explode and kind of, in some senses, people move on from Delhi to stable diffusion. So that's a really interesting point in terms of openness and how openness drives adoption of things as well, I.

Guess. Yeah. So to turn a Little bit as we've been talking about kind of this new intrusion, if you will, into artistic output and the fact that the folks that have been doing it the traditional way aren't so keen about it at this point. It has implications in a much larger way than just whether we like the art and whether we care about it being AI driven. It affects people's livelihoods, it affects jobs. And this is a recurring theme that we've talked about over time that I don't think is a theme going away anytime soon. And that's how these things intrude into livelihood that were previously very human centric and start affecting jobs. There was another article that I ran across in the Register. The article's title is Goodbye Humans Call centers could save $80 billion by switching to AI. And I don't think that's news to anybody quite honestly. I think that we've seen automation occurring for quite some time. It would be natural to have AI models moving into those situations. It calls out the fact that all of these things are driven by economics. Is AI is ever increasingly present in the automation of previously human jobs. It's going to be the economics driving that. I don't have anything up in front of me, but I remember in a very different industry there was the thing we talked about briefly about McDonald's automating the food service a while back. So we're going to see it everywhere. So as we do this, I think one of the challenges in front of us is recognizing it's hitting almost every industry. It hits medical doctors. One of the earliest things was radiographs but there's so many other areas. Cancer diagnosis. We've talked about many of these. We've talked about obviously airline pilots, we've talked about fast food. This is really hitting the gamut of industry. There's really no safe.

Place. I was kind of surprised. So as you know, Chris, I'm. My wife and I are vegetarians. So when we're traveling we have our sort of quick spots where we know there's something that is not meaty. And one of those places is White Castle, which has been interesting over time. I've noticed they've sort of adopt. Been early adopters of things like the impossible meats, sort of plant or non meat alternative meats, which was kind of surprising for me when I did that. But the last one of the last times I was coming down from Chicago, I was going through the drive thru and now they have like in the drive thru an AI assistant that does some of the drive thru interactions at Certain times of the day I think, like not at all times, but at certain times of the day. And I was just totally. I shouldn't have been shocked, right, because we talk about this every week. But I was sort of surprised at just the. Yeah, I mean it's, it's there and they're using it. And this is like in rural Indiana, right? Like AI drive.

Thru. It's been happening for a while. It's just gradually trickling through every industry. And it's funny that you say that because I had a fat. I actually went through a chick fil A. Sorry, Chick fil A people. Cause I'm about to say something not nice, but I was in the drive through and they had done this where they have the human teenagers trying to optimize the drive through to handle the traffic and they're walking.

Around. It's like parallel.

Processing. Yeah, yeah. And coming from this that we do, I was sitting in the car going, yeah, you're all going to get automated away pretty soon in my head. I mean I wasn't saying that to them. That's literally what I was thinking in the drive thru. I was like, yeah, this isn't going to last as long as you think. But we're seeing that and we're seeing it everywhere. I mean software developers, we who develop software are now have some tools that are AI driven by GitHub for instance. That is one step toward that. So where do we go from here? Daniel, just to ask a really hard giant global question, at some point people are going to start to.

Notice. I don't know that I have the answer. I think we're still though at a point where yeah, there's certain things that fit within the distributions that models know. Right. Like we talked about sort of apparent coherence in models recently and I think there's things that fit within the distribution of what models know about. So there's probably two things. One is like what are those areas where we don't have good data sets and models don't know the distributions of things to put together? And those are still very, those will be where we shift sort of human focus, I think. But then also even where we do have data sets and we're automating call centers or something, it even stresses more the biases and the openness of data and models because those biases are going to hit really hard when they start affecting people's.

Lives. So another, another thing that I noticed this last week was there was a lot of interesting things having to do with AI interactions That had to do with government. And we're seeing that because, as we have talked about a number of times, the political, social, cultural and legal frameworks that we all live within, which are all have a lot of commonality, even across different countries, they still haven't kept up with the technology. And so we have another big question going forward is what is the role of AI when it comes to the different types of feedback loops that are present in between government and its, its citizens? And how does that affect. And one thing that was sort of humorous, I suppose, if, especially if it's not happening to you, was that there were some articles out about in France they've released, not release, but they're using a model in the French government to identify swimming pools from satellite imagery. And apparently I did not know this, apparently if you're in France and have a pool, that pool is taxed. And so it affects the tax base and it affects how many. And apparently there were tens of thousands of undeclared pools in France. How dare they, how dare they not declare their.

Pool? Dare they.

Swim? So apparently there were more than a few French citizens that were a little surprised to receive a tax notification that we're now taxing your pool that they had not previously declared. And, you know, it's a little funny to me because I don't have a pool and I'm not in France, I'm in the US but, you know, it does raise the question of such tools could be used anywhere by any government and for pretty much anything that they, that they can do with the data set. So that was one thing. What do you think of that? You don't have a pool, do.

You? It is interesting. I think we were just talking about how AI models are affecting people's lives, but these very practical things about how the model's built show up almost immediately. So in one of the articles that we can include in our show notes is talking about this pool model that at first the system confused solar panels for swimming pools with an error rate of 3, 30%. But DGFIP, I'm assuming that's the government agency or whatever that's, that's doing this, says that it has since increased the, the accuracy. So I think that's one of these cases. Where can a model, I mean, in a very practical sense, outside of the implications of it, in a government rule sense, a model can do this, no doubt, but it doesn't do it perfectly. Right. So as a practitioner in the French government, if you are one of those practitioners, how do you anticipate and deal with those biases in the system and your understanding of your data. I would say that if you do some work upfront to sort of understand behavior, also do some human evaluation to understand how the model is behaving. If you analyze the biases in your data set and then you release in a way that is taking into account those behaviors of your model, then people might still be angry, but they're going to be angry that the government is taxing them for their pool. They're not necessarily going to be as angry as hey, the government is taxing me for my pool that doesn't exist and only is thought to exist because I have solar panels on my house and I'm trying to save the environment. You know, that's probably a different scenario, right? So I don't know the full details of what they did in terms of that side of things, but I think as a practitioner there's a lesson here that as you release these models and they are influencing people's lives, the very fact that these won't behave in maybe the way that you always assume that they will, that has to be factored in up.

Front. I agree with you. And not only that, but you know, we, we tend to, as we've been tracking, you know, what's happening in the world over the years we've been doing the show and we tend to gravitate naturally toward kind of the new stuff coming out and, and all that. And what I was struck about this is this is, I don't know the details, but I'm. This looks to me like a, probably a fairly simple by today's standards, old fashioned convolutional model, I would guess, you know, that was doing this. I don't think it's necessarily the most cutting edge, deep learning of 2022, but, and I'm not being critical of those if we happen to have listeners. I'm just saying you're being very practical and that you're using a technology that's been around for a while to do that. But with, you know, that it's really mundane but with some fairly substantial effect. It probably did not cost very much to put this model together and train it and get it better compared to many of the things we talk about now. And yet they Note that a 30 square meter swimming pool will result in about €200 of extra taxes per year. So every pool is another €200 that they can do. And apparently that that has amassed something along the order of 10 million euros in New revenue for the French Government, so you know enough to easily pay for the time of your data. Scientists that are working on that, plus quite a bit more. So I would not be surprised to see a lot.

More. Well, and yeah, I wonder too, the. So if you're generating that amount, I'm thinking of, you know, the model that I'm thinking of, Chris, is I don't know if you had this happen in your town, but the like bird scooters or these electric scooters that like are in cities.

Right. I've used.

Them. I mean, I can't comment on their whole strategy, but my impression of their strategy was we're going to show up overnight in your town and drop hundreds of scooters on the streets. Right. And we'll deal with the legal implications later. So they are prioritizing adoption above the legal implications of the problems that they might see. And I think it worked. Right. The companies that did that, they saw the trend coming and they received a lot of adoption up front and they likely received a bump in revenue versus the legal implications of what they're doing. So maybe in a more pessimistic sense, one could take the perspective, if you're the French government, well, you say your thing's 70% accurate. Well, just release it because we'll get enough revenue that we can take care of any of the legal things that come up from us doing this. So, I mean, that's maybe a more cynical view of the practicalities of it, but it's a business. That's very much a business decision.

Right? It is. It's funny, I think I'm usually the more cynical of the two of us in terms of what we bring, but yet you raised a great point. Speaking of cynical, did you see the Nvidia news over this past week? Nvidia has been having kind of an ongoing, very public conversation with the US government about whether or not they can have a 100 and H100 chips in China, which is relevant because there is a little concealed kind of competition in many areas, economically, militarily, all sorts of implications between China and the west, with the US probably being kind of the center of that. And so for national security reasons, there is this ongoing. Whether or not they can do that, I won't jump. What do you think of that? I'm kind of biased. I'm already in the defense industry, so I have some thoughts on that. But what do you think? How did that strike you when you, when you found out this was going.

On? I mean, I see a variety of perspectives here, I guess I know like the supply chain stuff is all messed up around the world and so to some degree people are just looking for what jurisdictions do I need to partner with business wise to run my business. Right. But there are a lot of other implications there too around security things and that sort of thing. And I know in listening to the IRL podcast from Mozilla recently, talking about sort of balances of power that are happening with different countries around the world, that's something to consider as well. I don't know. What did it trigger in your.

Mind? Well, I can see like you, I can see multiple sides to this. You know, I'm part of this AI community and I certainly, you know, that part of me goes, well, that's not a very sensible thing to, you know, to go do that. And it impacts a lot of livelihoods and all that. The defense side of me is very aware of that competition. And so as a strategic move that I don't think could last if it, if that were to stand, the Chinese would go and create their own capability. But that might take time. So there is a, I can see all sides of the story. I don't know that this particular move, the way it's being done is, is the most effective way for the US to secure its interests in the long term. But I'm not, I wasn't at all surprised that that was, I mean right now we've talked about, you know, multiple shows that AI is a power lever in the world. It is for governments, it is for companies, it is for individuals. It is one of the key ways of filling power vacuums now and changing who has the power. So I think we're going to see all sorts of things all over the place with, you know, across the way and you see the same thing in a non government sense with, between corporations and we saw with artists this week as we talked about a little while.

Ago. Yeah, it is one of the things I think is interesting, I guess this gets to some of the power dynamics. One of the things that was surprising to me and I don't know the stats on Dall E but I saw on Twitter that stable diffusion, the new stable diffusion model they were talking about pricing in terms of the kind of market value of that training and I'm seeing that tweet from, from one of the team is that they use 256A/100 in the cloud for 150k hours. So the mark at market price, stable diffusion costs 600k. So 600k is by no means a small amount of Money for most individual humans. But 600k in, in a government sense or in a large company sense is, is nothing.

Right? Like it's not even a rounding.

Error. Right, exactly. So I do find it interesting that like this sort of, because of how the infrastructure around this is being commoditized in the tooling around workflows and MLOps and distributed training and all of these things is getting better in some degrees. That's a, I see this sort of also a positive shift, I guess, in terms of what's possible maybe for smaller teams that can still make an impact. Release a thing that takes the industry by storm for a number. That's not something that's restricted to nation states before. I think you have these models that were being released where this is out of reach of anyone but big tech and a nation state. And I think that it's very interesting to me that something like stable diffusion, again, not a small amount of money for if I'm myself and I'm an independent researcher, 600k is a lot of.

Money. You don't have that in your checking account right.

Now? I wish I did, but it's reasonable to think, well, if I really wanted to, and maybe I live in the Bay Area, I could sell my house for 600k and train a model. This is like a route. There's a route to that sort of money for, or a small amount of seed funding in terms of a startup or something like.

That. Yeah, right. If you really are motivated, it's.

Attainable. Yeah. And I realize that also I'm kind of revealing my western biases here. That sort of money is not, yeah, is not within reach for basically the majority of the world. But it is encouraging to me that this sort of innovation can happen from small teams and not isn't restricted to sort of nation state actors or big tech. So that's, yeah, that's.

Encouraging. It's a great point that would be expected over time. You know, with all technologies. They come out, they're, they're most expensive and they're most inaccessible and then eventually, you know, they become cheaper and more accessible. And we're seeing that happen.

Sam? Well, Chris, I think there's a number of things that I ran across that are in some way related to the things that we've been talking about in one way or another. One of the interesting articles that I think everyone should take a look at is, was posted on the Hugging face blog in August 31st of this year, 2022. And it's talking about the open rail licensing so toward open and responsible AI licensing frameworks. And I think this is really, really relevant for people and people should take note because this is the licensing mechanism that was used in the recent releases of the big science Bloom model, which is kind of the latest, greatest large language model and stable Diffusion, which we've been talking about the whole episode. So you can actually go in this blog post and you can see the links to the open rail licenses for Bloom and for stable diffusion. And what I think they argue here is basically machine learning models or AI models have in one way or another been licensed under licenses that are mostly have been applied in the past to either software or content. So like software on GitHub I might license as MIT or Apache 2, something like that. There's various implications of that. And if I release content like set up pictures or something, I could do that under Creative Commons, for example, or like a book if I wanted to make it more permissively licensed under Creative Commons, something like that. And over time people are like, well, which of these do I use for models, right? Because a model is sort of like an asset. It's files in some way, it has relations or similarities to content. And in other ways, well, it does have a code code associated with it training. There's a model definition, there's config that's sort of a similarity with code, right? So do one or both of these fit better? And I think there's additional things with models that maybe don't fit either one of those cases, right? This sort of things around biases the use of the model, the limitations of the model, that's really not in common with code, right? Because in code generally if it's deterministic, like you have an API endpoint and it does a thing and you can look at the code and understand what it does, like it does a thing, that's all it does. But with a model, this sort of like behavioral and bias things that aren't in common, I don't know, over time, if you've run across these sorts of licensing things with, with models in terms of the fit of sort of data sets, code models and what's licensed.

Where I have, I mean, and I think a lot of people in the community at large have. It's a natural question to ask you and I are fond of saying you have to have software with your model to get stuff out there and software is pretty well established, as you just pointed out, with the open source models, and you have some choices and I think they're fairly well understood today. Not so. And even, especially with Creative Commons and such as that as well. This has been a big ambiguous area for a lot of people not understanding. So I think it's, I think it's long overdue. Glad to see it came from hugging face because they always put out fantastic stuff as we're often fond of talking about. How would you describe that? A little bit of the differences like recognizing bias. Like how would they approach kind of doing the licensing that accommodates bias for those who haven't had a chance to look at the article. What's, what's different or what's new there they might not have seen in those other categories of open source licensing of some.

Sort. Yeah. So I think that there's commonalities with other open licensing structures or mechanisms that maybe we're used to from the AI world. But there's here, there's really two kind of pieces of what makes this interesting. One is open. So that's like one aspect of it, open access. So that's like an access thing. But then the other side of it is responsible use. And that's really kind of where this rail component comes in. So they build off of this idea of rail or responsible AI licenses and these. You can go to the. Also the rail. There's a rail page which talks about the rail licensing effort which has been talked about in ACM paper behavioral use licensing for responsible AI. But basically these rail licenses say they include behavioral use clauses which grant permissions for specific use cases and. Or restrict certain use cases. So if you think of a model, again it has similarities with open data or open code, but it also has this behavioral aspect to it. And so what the Open Rail license kind of does is it grants sort of permissive access and redistribution and that sort of thing, as you might expect with an open license. But then it has these clauses which talk about responsible use. So for I can give an example here, that'd be.

Good. That's the one thing I'm wondering about is responsible is kind of an.

Ambiguous. It is. Well, and I think that the structure, really the Open Rail license is I think a structure and the Open Rail license that you would adopt for your model. Maybe you could adopt sort of stable diffusion's open rail model if you have a similar model, but other models are going to have other implications. Right. So you have some flexibility in the responsible use case clauses for the stable Diffusion license. I'm just looking here. There's sort of attachment A all the way at the bottom and it talks about use restrictions and this is really this kind of clause that in my understanding is really important here and they talk about you agree to not use the model or derivatives of the model and then they have a bunch of things. So one of those things is in any way that violates applicable laws. That's okay. So kind of boring. But if you go down a little bit, there's very interesting ones, right? You agree to not use the model to defame, disparage or otherwise harass others. And there's other kind of interesting ones maybe specific to behaviors that they anticipated use of the model that they wanted to avoid. So there's one that says you agree to not use the model to provide medical advice and medical results interpretation. So obviously you could generate images with stable diffusion or maybe interpret images with such a model that are medical related. Right. Medical imagery is a type of imagery. Maybe that's a use that they anticipated and had internal discussions about. And so there's all of these use restrictions or clauses that go into the sort of rail part of the Open Rail license. So it's still open in terms of access and distribution type of things. But then there's these clauses around responsibility that are explicitly.

Included. That's interesting. Whereas I know you and I are very focused on kind of AI ethical topics and the responsibilities associated with that, I find the explicit call out potentially short sighted in terms of unexpected outcomes and consequences. So I'll have to go through that in depth after the show and just kind of. And see what I think about some of those ideas and see if I can come up with any that I think maybe weren't what they were thinking just as a fun.

Exercise. Yeah, yeah. And I think that that's always. It's probably. I mean it's a pre existing problem with any licenses. Right. You can only anticipate so much. I'm reading some of the frequently asked questions from the Bloom Open Rail license and they have. One of the explicit frequently asked questions is does the license cover every harmful use case? And so this is their response. Maybe that's useful for this discussion. They say no. We recognize that the list of use based restrictions does not conceivably represent everything one could possibly do with our work. We focus on use cases which could be feasible for the model at this time. The license is a start by us at exploring how such rail licenses could be used to mitigate harm. And we hope that these first set of provisions can evolve into more comprehensive provisions over time with community engagement. So I think they also recognize this explicitly. Right. You can never anticipate Everything. I think in some of the discussions we've had on the podcast over time around responsible use of AI and ethics, I think there's this obligation on the developer's part to reasonably try to anticipate harmful uses of what they're doing with the understanding that you're not going to anticipate anything. But at least if you made an effort to anticipate some things, then some of those things are anticipated and you're not just sort of chucking your thing out into the world and seeing what.

Happens. I think for each item they need to have a little parenthesis behind it and says, and the kinds of things that, you know, I'm referring to, et.

Cetera. Yeah, that sort of thing. Yeah. So, I mean, I think this is.

Really. It's a good.

Start. It's a good start. I think it opens a. Opens dialogue as well, which we're on my team, we're thinking a lot about these things in terms of data sets and models that we release. So it's really good to have some sort of open dialogue around these things. I.

Think. I agree. I think, I think if this is a start and the conversation can kind of evolve, as did open source licenses on the software side, that was an ongoing. That, you know, there was a.

Many. There's still not agreement and there's.

Still no agreement, but it's much more mature than it was, say, early in my career. And so hopefully this is the start of the conversation on the model.

Side at the end here, I think, I mean, what we've been talking about is hopefully very practical, but we can provide people with a couple practical learning resources that have come across our desks over the past week. One that I think fits right into our theme of practical AI and the things that we care about is an upcoming conference that actually started, I think, out of a sort of viral tweet on Twitter called NormConf. This is a completely, as far as I understand, a completely free and online event, but with an amazing list of speakers, like the really, really great list of sort of AI, machine learning, data science, tech speakers. And what they say is, what if there was a conference all about the mundane behind the scenes, how the sausage is made, middlebrow, unsexy, norm core stuff in the data and ML parts of the tech world. And so that's the goal, to sort of talk about all of those things that aren't probably what is the latest diffusion model. But I tried to train this diffusion model and I'm having trouble with my infrastructure and data and can't make it work. Sort of problems. So you can see even people that we've had on the show here are recognizing that. Yeah. Are represented in the list of.

Speakers. And future guests for our show are surely coming from this.

List. I certainly hope future guests. Yeah. If you're, if you're out there and you're speaking at norm Conf, let us know, but just reach out to us. This, I think, is just a great, focused, practical thing that will be happening, and I hope it continues to happen. But I'm certainly excited to listen in and see the set of content that they're putting.

Together. Me, too. I'm all over this. So I'm going to register right.

Now.

Yeah.

Cool. Well, Chris, I. I've enjoyed chatting through things as always with you and hope that you have a good Labor Day. It's about Labor Day here in the US So we've got a long, long weekend. But enjoy the holiday and good to chat as.

Always. You too, Daniel. Have a good weekend.

Take. All right. That is our show for this week, if you dig it. Don't forget to subscribe. Head to PracticalAI FM for all the ways and if Practical AI has benefited your life, pay it forward by sharing the show with a friend or a colleague. Word of mouth is the number one way people find shows like ours. Thanks again to Fastly for fronting our static assets to fly IO for back in our dynamic requests to break master cylinder for the beats, and to you for listening. We appreciate you. That's all for now. We'll talk to you again on the next.