So let's talk about James, dancing Greek statues, had one and a half hour talk with him.
Now he's a master of control nuts inside of stable diffusion.
And Elon Musk replied, James is like we had to reply immediately back.
Ladies and gentlemen, boys and girls, children of all ages, dogs, cats,
robots and everybody in between, especially you first time viewers on YouTube.
That's right. We got a live podcast.
Everybody welcome to HTTTA how to talk to AI.
I am your host in the flesh, the synth buying West and it's all I am joined by the gush,
the gracious, the globe trotting, the galvanizing force of nature herself.
Never ghastly, always graceful, the miss go to go herself.
Gee, how are you?
I'm great. Thank you so much.
You know, I'm always giggling with a huge smile.
You're amazing. Your introductions are fantastic.
I'm excited about this video.
Let's see how that goes.
That's definitely something new.
Yes.
And not entirely new.
You have a little more experience in this realm than me.
Yeah, but it doesn't get easier.
So right to that.
Hey, let's jump right in.
I recall a few months back, you did a video on RunwayML's gen one text to image generation.
Do you want to just fill people in on kind of like your experience using that?
Because we have some news that gen two is out now.
Right. So I tested gen one.
I don't know if we talked about it, but my experience was that it was definitely not
easy with gen one, but it was also beta version in this core.
So to get something consistent, like suddenly, for example, you're prompting something like
a character and suddenly I would get a man with a beard.
And I was like, is it trying to tell me something?
But that being said, gen two came out and that's definitely a little bit different right now.
And everyone is now snapping fingers.
It's so funny.
It is pretty funny.
Runway is offering on their website, a little tutorial video on how to use gen one.
And I think some of the flow, some of the content is even more compelling.
And you see what's the difference because now I think on the paid version, because I
tried free, I'm going to check out paid one too, but there's like sliders, the important
part, this like connectivity with the footage that you prompt.
If you see all these videos, they are all based on original footage.
So you upload a video and then you have a slider on how much of that video it takes
as a reference point.
This is like in a stable diffusion, you know, when you control opacity of how much image
is influencing your final image versus text prompt.
Okay.
I think what is driving this type of results, but you can actually see faces and body
structures or even fingers more accurate as if you just prompted, you have to have like
a video as a reference.
So image to image, video to video, something to kind of give the model.
Yeah.
Some, some lifting.
And you know what?
I will say in prior image generations where I'm looking for very specific details,
someone's holding something a specific way, the lighting conditions need to be such a
way I know in mid journey stable diffusion, you could do an image sample in addition to
your prompt.
And oftentimes the model just goes, okay, I got it.
That, that helps me, you know, not try to have to figure everything out.
You know, it's interesting because with AI video, it's kind of this two paths of the
ones which are using video as a reference of you and kind of overlaying whatever you
want.
And another one is this AI generated avatars where you take the image or the model trained
on your realistic images, and then you feed the text.
It's two different directions of the control of the output.
I mean, it's, it seems like at some point you're going to have to like go, all right,
I have to lean on the model to generate something compelling, but I also as a director, as
producer, as the person creating this, I have a specific vision and I do need some deterministic
lever in the results to achieve that.
I wanted to show that you was kind of back to reality experience.
So let's talk about James, Dancing Greek Statues.
Had one and a half hour talk with him.
He's amazing artist and we were just geeking out.
He was like, I didn't know that you're painting.
He went on my Instagram.
Check out last two weeks newsletters for a little bit more James's work from episode
nine and, and some of Goda's art that we just talked about last week.
Right, so, so we kind of like bonded in this artistic style and he's just, yeah, brilliant
and it's so amazing to hear, you know, that his work took off like that.
But then I was showing his work to everybody.
This is just how I was just like, you have to see this.
And we showed it to our family, the parents, parents-in-law, we were like,
we watched it and they're like, okay, it's nice animation.
And I was like, you don't understand.
This is not animation.
Just Elon Musk replied.
He was like, we had to reply immediately back, but we don't know if Elon Musk saw that,
you know, his version because we did with Elon Musk face on it.
Yep.
You know, that's some good coverage.
Unlike our little video, this is our, our pal James.
We talked about it for a couple of weeks now.
He's a master of control nuts inside of stable diffusion and
just so many possibilities, such, such fun stuff there.
And as we say often on here, this, these kinds of things, they're only going to get better.
This is the worst they'll ever be.
And they're already compelling.
His stuff has been viewed in the last two, three weeks, over 200 million times.
But going back to this kind of, you know, stepping outside of the AI bubble,
it is interesting to me how the outside world perceives these things,
because when at the end of the day, there is going to be value attached to that.
Right.
Yeah.
So while we are geeking out and we're like, oh my God, like I will just pay you as much as possible.
I want something like that.
The public sees this as we said, oh, nice animation as it's like some sort of just,
I don't know, premiere pro package.
Oh, yeah.
That's some frame.
That's the only frame of reference they have.
I don't know.
For me, that was kind of interesting insight because yeah, at the end of the day,
these things will be generalized and actually made available.
So, you know, not to minimize the quality and value of James work.
But I also, because of that, I saw, I think two apps popping up saying that,
oh, I just uploaded a video and we generate AI video.
And it is just kind of, it is this visual effect applied to it.
I really don't believe that they run the whole thing.
Because when we talk to James, like if people think that this is just like
one click prompt easy, so wrong.
Not right now.
No, it is so much work, so much iteration and it costs a lot of money just to run with things.
No one has a concept of for every frame of those little clips of James,
if it's running at 60 frames a second, which some of those are, because they're pretty smooth,
you know, seven second video, that's hundreds and hundreds of frames that individually need
to be rendered and stitched together.
Unless you have a pretty beefy GPU or paying for some cloud server.
Or both.
Yeah, or both.
That takes an inordinately long amount of time.
So talking about GPUs.
We could definitely talk about GPUs.
The biggest transition to that, my video I did about mid journey,
where founder was talking about this two futures of AI.
Did you, I hope you watched it.
No, don't.
Of course, of course I watched your video.
Yes, of course, I am a natural professional.
That's one of the older ones, but interesting thing he had this mention of GPUs and he was
saying there's two realities.
One is that to scale anything, we are just bound by physics as a first principle, just as a data
centers and chips that we can produce physically.
And it will take us seven years to scale.
And that was a, that is a reason why mid journey is not marketed.
You are not going to see ads for mid journey or like a, hey, there is an app for mid journey
because there's simply no ability to scale to sustain such user base.
But when he talked about different path, if we invent new chips or new abilities or integrate
neural nets and chips, it's out of my domain knowledge.
But, and then he said that if this happens, then we scale in a year.
Yeah.
So think what the craziness what's happening right now.
And then if there is next technological breakthrough in the physical department, the
hardware.
Well, speaking of hardware, let's talk.
Yeah.
Since we're talking chips and it is kind of wild to think about like, you know, that's why GPT-4
is constrained in terms of like how fast it generates.
It's literally the compute.
So what's starting to take place is a lot of these big companies are completely rethinking
how compute happens.
You know, I'm sure people, while they may not know the ins and outs of it,
you can conceptualize that all of our stuff, our websites, the apps be like clouds.
They're running in these big data centers.
These data centers are power hungry and inside of each one of these servers is essentially
a different type of computer that is CPU centric, right?
It has a central processing unit.
So one of the things that gets inefficient about these is they spend as much time kind
of moving around data as they do actually like computing, like doing computations.
So that tends to tax and take longer for processes to take place.
But with the onset of some of these generative AI technologies,
the video who's the world's leading chip maker has just put out, it's totally worth the YouTube
watch.
Even if you don't geek out a bunch of this hardware stuff, it is pretty incredible in
terms of some of the things they demo.
So this is the Brace Hopper AI supercomputer.
It's basically a singular GPU, the size of a school bus, 150 miles of fiber optic cable
inside of it.
But this singular row of servers, granted it's a lot of servers, are GPU based.
So it uses something to the tune of like a 44th of the amount of power that to achieve
the same level of compute as this GPU server would, it would need 45 times more power in
our conventional servers right now.
So you can get way more cost effective bang for your buck, so to speak, train a generative
image model, train a language model in fractions of the time, just because of how efficiently
this can handle.
Like this is a 140 terabytes of GPU compute power that is all running in series.
So it works as one.
These are the kind of things that when all these data center transformations happen,
not only will we be able to use AI like we want to, these are the kind of things that
you need to say, okay, computer, supercomputer, we've got 20 of these chained together, please
cure all diseases for us.
And they just can start processing these huge, inconceivably large problems.
I was listening to you.
This is fascinating, but I was checking stock price of any media.
It definitely shot up.
And what's that part is yesterday, I was like, I need to buy some shares of new media.
And then something happened and this is something because it's on mobile.
So it's almost like, oh yeah, let me know.
Now it sounds bad.
It's not like you are just casually buying shares, but I think the media is the place
where it makes sense.
They already control something like 85% of the GPU chip market, which is dangerous.
It is, it totally is.
And I think there's going to be antitrust stuff coming down the pike eventually that,
you know, Microsoft had to face the same thing when they were, you know, this huge software
company in the late nineties, early two thousands.
Yeah, you're going to have this AI arms race that no doubt is going to emerge between these
big players like Microsoft, like Google, like Meta, but they all still need this compute.
So this huge new GPU central server that the videos invented and put together, it has
something like six of these chips inside of each server blade.
The chips cost $200,000 each and there's over a thousand server blades inside of one of
these exa-flock machines.
So that's not something that anybody other than like fortune 50, fortune 500 companies
can even afford to have access to at this point.
But considering we've already blown Moore's law out of the water with some of the, some
of the level of innovations that are happening, you know, it's only going to get cheaper.
I remember I saw this graph learning from history is always a good way to go and was
looking at what happened with semiconductors and SaaS companies, like, you know, big tech
that historically SaaS companies are the ones who win and not the hardware.
Yeah.
But I think when Nvidia case is interesting because as much as they are hardware controlling
market, they are coming with their larger language models like Neo, right?
So it's because we can really attack from all sides and do all this collaborations with
the companies.
It is even like scary to think when we live in a world where all compute is in one company's
hand.
Totally crazy thing.
But I got two computers right here in front of me.
I've got Nvidia GPUs in it.
Right.
Everything else is different and diversified.
The GPUs are both Nvidia.
That's interesting.
I just got Mac.
So remember we were talking about if to get Nvidia and this one has M2 and I'm very happy
with it.
But yeah, that's another thing like maybe Apple, especially with their, you know, Vision
Pro and how much compute that but that's completely two ball games.
Nvidia is on the whole other racetrack.
And what's always made Mac run so efficiently is since they are developing the silicon and
the software in that case, they are able to develop processes, system architectures that
run super efficiently on their chips.
So they're able to kind of optimize both things.
You know, so in many instances, an M2 chip does some of the same things that a big beefy
GPU could do for machine learning purposes.
But it's only going to enable more creativity, more problems being solved.
It's an exciting thing to think about.
You know, on this kind of mass global power.
Now with Sam Altman and you know, the leaders of AI labs on Trophy Cloud, they're on this
world tour for some of them.
And you see these pictures and every time we see it, Sam Altman meeting some leaders
of our presidents or prime minister, those looks like he's a president now.
Didn't it cost you?
He wields a lot of power.
Yeah, and when you look at this picture, you're like, who in this picture is actually the
man of power now?
Yeah.
Speaking of power, one of the things that is an interesting kind of discussion point
as we're talking about more efficient GPUs and chips and all the things that run behind
the scenes to enable these really wonderful AI technological innovations.
I didn't realize this week at the Good Castle came out one of the talks that
Sam Altman's a big investor into fusion power research and maintains even talked about
a little bit.
Some of the recent talks this week, we'll link a few in the description on the newsletter,
talked about how to achieve things like GPT-567 where you're going to need just so much compute,
not only to train, but then to run it, let everybody run it.
It's going to need fusion power to achieve.
So, you know, getting to a point where it's a tenth of the cost of any energy and then
we can make enough of them to go around the world is pretty incredible prospect.
And, you know, it's been a promise of, I've heard it my whole life, fusions 30 years away
or 40 years away, you know, it's going to happen in the future.
But a lot of things that I hear when it comes up, it's like five years away.
Right.
It kind of gives me an idea of it.
What Sam Altman is not part of or invested.
World coin, like scanning your eyeballs, universal basic income.
And probably list goes on.
It's incredible.
The power dynamics are very interesting.
He'll be our main ambassador to the AI overlords later on.
But on the flip side, there is, you know, again, if we look at historically first movers
in the market and not necessarily the ones leading the market at the end of the day.
I'm kind of always running with that thought, but if it's not open AI, who is coming?
Yeah.
I will say though, just having, you know, worked and been exploring the space for six months.
There is something about the first mover advantage.
That's a little just kind of flipped on its head with some, some of these things,
because people are inventing and creating so much and stitching these AI technologies
together to form innovative new products and solutions.
But at the end of the day, it's to solve very similar problems that people can kind of conceive
of themselves that can go, Oh, I see that.
I see that they're not using any, all using the same technology, all using the same language
models.
Okay.
So it's really not a game about whose tech is better.
It's about who can market it better.
So in a lot of instances I've seen, you know, just as long as you're kind of there to the
table first with an idea that might be enough in some instances.
I'll give you an example as it relates to prompt engineering.
You know, I happened to start selling products.
I happened to start selling some prompts on prompt base earlier this year.
Right.
It really didn't start taking off till like March or April, but what that's done because
of things like Amazon and how we online shop before.
Well, what do you do?
The first thing you do when you get on Amazon, Hey, you're looking for a toothbrush.
Hey, let me sort by the five stars with the most sales.
Right.
That's just like our condition.
I want the best thing that's out there.
I do that all the time.
Read reviews.
Right.
Something like this, like I feel like in some instances I've unfairly benefited from that
just because I had plenty of sales before this started taking off a little bit more.
And what do people do?
Hey, what's the top selling in this category?
All right.
You know, by virtue of being to the table first, that probably benefits me, you know,
in that instance, it doesn't, it doesn't for other things.
And I would hope that the creators of prop base or some of these marketplace do things
to encourage new creators to put themselves out there.
That makes me think I had this dilemma when I made the first videos going about
chadgpt and learn prompting and prompt engineering.
And I remember there was this moment I just made a video about how chadgpt works
and explaining, you know, the model behind it.
And it was 3.5.
I released this video in a week and a half where is GPT-4.
And I was like, oh, okay.
So if I make a video about something which is happening, and this is the thing why
I don't like to go news direction because our video is not relevant next week.
Yeah.
But, and I was thinking at the time, like, oh, I need to really, you know,
think about videos which live long, very meaningful.
The thing is that that video plus prompt engineering are getting views every single day.
Like the most.
And I'm like, in my world, I'm like, everyone knows already this.
And I still read comments from people like, oh, I just discovered this.
Wow.
I didn't know this.
I'm like, it feels like not three months, but three years ago.
And that just makes me think this is why, you know, I was sharing this story,
bringing these AI tools outside of the bubble and actually getting real world perspective.
You know, you have to see how the real world actually adapts this fix.
It's still, still trickling out there.
Yeah.
And I think with Chai GPT, when I was doing, you know, the, the keynote in embassy,
three people came to me saying, oh yeah, yeah, I know it.
I tried it, but I'm so disappointed with the results.
Just so basic.
Yep.
And after my presentation, they're like, oh, so it can not like, you can actually
control it and make it interesting and it can write in different things.
And I was like, to me, it was like, yeah, of course, but people had tried and left it off.
And I think it also happens with image tools.
You can get very quickly discouraged.
And I think this learning prompt engineering, that's why I think it's relevant and it's
going to be relevant.
Definitely.
Yeah.
I mean, it kind of boggles my mind that some people don't necessarily see the
possibilities of it.
But I also, people come at this with different needs and different expectations, you know,
like obviously I'm coming at it from someone who's an avid consumer of anything
electronic or technological.
Does one need eight keyboards?
My wife would say no, but I say otherwise.
Yeah.
I mean, I remember the first time my dad tried chat GPT.
He's like, yeah, I put myself into it.
Came up, didn't know anything about me.
It's kind of milk toast.
And I'm like, this is not how large language models work.
Let me tell you about how it's only trained up till September, 2021 and about hallucinations,
you know, so basic things you need to know.
But like, but that's not on the front page at all.
No, it's at the bottom, tiny letters.
Yeah.
This might be totally made up.
Yeah.
Not factual, wrong facts information.
Oh yeah.
That's the whole other thing.
You know, we already talked about this, that there is certain level of responsibility when
you drop a product like that to a mass market.
Yeah.
No information, what it is, how it is, nothing.
It's just like, Hey, chatbot, that's it.
But, but I think some of it is like, Herod, let's let people play around with it and
explore while the stakes are low, as opposed to have this AGI that's ready.
And then it just, they drop it on us and there's this huge shift that needs to happen.
There's probably some value in it slowly, gradually, but it's not like,
granted how slowly can you say it's still the fastest growing app or service in history.
But, you know, it's still probably some value in letting it kind of trickle down.
Yeah.
Who values from that?
I mean, it's probably the altruism of our great ambassador overlord, Sam Altman,
you know, and those kind of investors.
I hope people learned one thing.
Nothing on internet is free.
Yeah.
So if there is something free, it's not free.
Like you are trading off different value.
And I worked in big data analytics company, the amount people pay for data and especially
investment banks, it's insane.
It's millions upon millions.
So I think that's what people don't realize that that just the volume of data from this type of
usage, but what you said, it's a number one, like most trending website or something like that.
Literally vertical growth.
That's if you map it against anything else.
No, but let's talk something prompting.
Yes.
Now that we're leading to that, I wanted to share something with you coming back to the,
my past afterlife in architecture and design.
Yeah.
I saw this design piece, it's foldable sofa, which can fit in the envelope designed by AI.
It's studio 10 in Copenhagen.
I actually know the studio and I think I'm almost sure I've been there, but anyway,
so it's developed closely with IKEA.
So you see this modular system and it's around 10 kilos.
But the interesting thing that they went with first talking with Chad GPT, for example,
one of them is saying, I actually wrote into Chad GPT, could a couch fit into an envelope?
And of course it said no.
And then we went to Midjourney and Delhi and we're spending like hundreds of prompts
testing different words, terminology, and nothing worked really to achieve what they
were looking for. And it was that you on a sofa, you're not front facing like to the TV,
but you're actually sitting in front facing each other.
And the key word which we discovered that actually led to that was conversation pit.
Conversation pit.
Right. So now we build it.
So I would think that something like that is product of a machine learning or a labeling AI
where it takes in tons of different designs and analyzes it and goes, this is the most ergonomic
way to do this, but it's from a language set, generative language model, just kind of going,
this is what I think it would look like.
Yeah, but generative models is actually nothing new in AI space, especially when you look in a
parametric design. So this, like even when I was doing my bachelor thesis, I was actually using
this grasshopper plugins in the rhinos model to do parametric design. And then you can play with some
generative design pieces, but the diffusion models is different. That's something new.
And the generative AI, you have, in a way you have more control because it's like more like
real database. And I could be completely wrong. Like this is just, you know,
I'm kind of surface sliding through this topic right now, but diffusion models in architecture
for render amazing. But then you want to real design vector based because, you know, you need
centimeter millimeter control of the dimensions. That's kind of a different realm. I'm keeping a
close eye on what's happening in architecture space design, because, you know, imagine like
if it goes that route explosion, another explosion.
There's, there's a huge market. I've seen some things for people exploring it from prompting to
some 3d design prompting to vectors. No one has cracked it yet, but you know, there's a few things
I think we brought up on the podcast before we have people that are doing generative AI be a
prompting to create assets for computer games that can be used inside of Unreal five engine.
I'll try to see if I can find them right now, but I know there's a company that does text prompt to
3d STL model. So you can basically just type in a prompt and then have what you would need to
3d print it, you know, throw it into the slicer and then, you know, put it through.
Leonardo AI for anybody looking for a game development, like what the guys behind it,
like I think I told you about Ethan, one of the co-founders, we were, you know, back in the days
before Leonardo, we were just geeking out in the discord channels. It's just incredible. It's,
I think they are reaching millions of users now in such short time. And that also makes me think
a year ago, I made this video, which is who's going to build in metaverse AI or architects.
And in that video, I made completely like just, you know, going down the rabbit hole of this idea.
I was like, in the future, you just prompt and say, I want them desert with flying buildings made
of diamonds, wherever space and boom, AI generates that 3d environment space for you. And now a year
later, we have VR spaces generated by AI. That's almond down the pike. Back then, I remember when
I wrote it, I was like, Oh, probably people will be like, what she's talking about. It's crazy.
Yeah. Well, I mean, we're every day, like I said, it must be, but it felt like to see an airplane
flying the sky for the first time, something so divergent from your experiences up into this date.
But like speaking of things that we haven't seen before, you know, a natural progression,
we've talked about some text to video, we've talked about some of our friends that are,
you know, some luminaries and thought leaders in the space. How long do you think it's going to be
until we see a full on prompted movie? I think it's in the workings right now. Definitely. I mean,
so this was set up question. You're like, come on, I got an agenda here. We gotta, we just gotta
try to be like, you know, professional podcasters here. Yeah. Okay. So I was more thinking about
like full length cinema, but of course this piece from the frost is already like, takes you a step
back to think what is the future of animation and not even animation. You know, I think, look,
make no mistake. There's still some uncanny Valley. Our brains are so used to looking at
images of, you know, not images, but just looking at other people. So anytime their face or hands
are doing something that's not natural to what we're just used to seeing all the time, we notice it.
So that's definitely apparent in these types of things too, but this is a 13 minute completely
generated, you know, AI movie and we're going to include the link to it, but you know, it,
you could follow the narrative structure easily. That's not lost at all. And in some ways, you know,
some of the choices they've made because of the current technological limitations could just be
stylistic ones that, you know, might be said, Hey, we made this deliberately, this kind of blocky
chunky kind of movements and stuff like that. I'd still say like half of these shots, I think the,
the human ones admittedly are hard, but like half of them are still like ones that, you know, I,
I'd be like, all right, that'd be something I'd see in a movie.
I want to kind of take the different camp on this one.
Please do.
Especially as a content creator and editor, my own videos, when you see the composition,
the color grading, the transitions, because I, it was edited by a human, the creative decisions
of the storytelling of the scenes, that central composition leading lines, all this matters
were still decided by a human. Or if you tell me that this was completely just a prompt in the
composition, the mood, like all these things are deliberately there, the color theory, and it's
human who makes those decisions. But of course, for example, Major and he makes incredible images
and you can see this composition decisions just adapted. But do you think in this case was it too?
I mean, I think it probably was in a lot of instances, but you know, you're absolutely
right. The true power here is when you have someone with expertise and creativity,
you know, working alongside AI. Right now, some of these things like this is not,
they wouldn't pass for a Hollywood movie at this point, but it's not too far off.
You know, it looks kind of like 80s or 90s special effects in some degree.
So I'm not saying that it's not good, but I'm still want to think that final creative decisions
is going to be made by human. Or maybe if we go like what I did a year ago, and we say that,
okay, in a year or two, the whole movies are going to be generated by AI. I think in Black
Mirror episode, they teased this moment where you're watching kind of Netflix and seeing
representation of yourself. And the character is like, oh, it's like, she's just like me talks like
me looks like me. So where did they get this data from? Well, kind of on the same vein as you know,
what we're discussing here about creativity, and still needing to have that final creative decision.
One thing I don't think we've brought up on the pod yet is we've touched on it a little bit in terms
of some of the legal implications for these generative models, but they've been trained on.
And if folks aren't aware, there's, you know, it's going to be a fight in a lot of different ways.
And the first one that's going to be the waging the war during the battle is stable diffusion.
This article right here is back from February, but just in the past week,
they released that this is going to trial. But I mean, Getty Images suing stable diffusion for
the landmark amount of 1.8 trillion, the largest lawsuit ever for the 12 million images that they
claim were in the sample database. And they're claiming damages of 150 grand per image.
Yeah. And oh my God, we can dive into this because I was looking into this training data model,
which was stable diffusion, but it just Lyon it's called. And we will include in the newsletter,
there is a link where you can actually upload and check if what is the chance that your image
was used in the training data, but the amounts are staggering. And it's known that they did use
this data. And it's just interesting that I read somewhere that there was shutter stock in Getty
Images was striking a deal with these to get paid. And it just looks to me like that deal maybe
didn't go through. And with the sentiment from art community, they just went full on. If you go in a
law case and you say like, yeah, none of the images, new produced images actually have exactly,
exactly copy of your image. And it's a tricky argument they are using, you know, but there's
an easy solution to that. What's that just develop like a coordinate system or metadata system or
like, you know, licensing or copyright thing in the image and just define the radius that okay,
there is a certain spectrum, everything is packed from nothing is like just true or not. So if my
image and the spectrum, the images get close to my style, to, to my original image, I get a royalty.
I mean, free open source model. It's not quite that simple because no, when you look at that,
when you look at that globe there, right? That is an undirected network. What that means in network
science context. It's not like you start with one thing and it makes these decisions linearly.
The way these diffusion models work is they not only can identify what images are, they understand
how they interact with each other. So it won't necessarily be this linear decision of, okay,
we use a little bit from this sample image, then went over here and use some of this and came back
to this, this neighborhood of cluster, you know, and on the way we have to fill in some stuff that
we, we didn't have any samples for. It's more so, it just kind of does it all at once. So I think
that still being able to track it in a way that's methodical where you can even say that like, hey,
75% of this is, is the same. Well, all right, let's use that argument. So say that's the case.
If I'm not mistaken, I am no legal expert, but I found this kind of surprising in my beloved country
to the North Canada, with my understanding that for using a trademark logo, for example, if you
make one change to that logo, change one color, add something that is now a derivative and it's
a separate thing. Something is 75% the same, but 25% different. That's a derivative. Yeah.
And this is absolutely true. And I'm looking at another law case, which actually this artist,
Carol Heintz, met, and this was in 2016. She sued back then Getty images for 1 billion
because they were using her images and actually send her, this is a funny story. She didn't win,
but the funny story is they send her a bill for license, but she's using license of her own work
illegally. And this is how she found out that they've been stealing her work for years and
licensing and charging people. And now we try to charge author herself. Oops. Yeah. So what,
so she went like 1 billion damages. Yeah. We need to have a lawyer on here. It's such
interesting decisions. I'm hopeful, but also the people that are going to be making these decisions
around the world are not of our generation of conqueror. I mean, these are people that
may not even be able to concede you and understand and have an appreciation for some of these things.
So, I mean, I don't envy them, but it's going to be very challenging kind of decisions and
complexities to sort through. I'm just running in my mind about like all the network of legal.
Even if I know a lawyer, I would be like, does this lawyer know the intricacies of this?
We need an AI lawyer.
This is so new. Yeah. It's funny. Like we have a contact in the patent department. So now I'm
thinking that would be interesting conversation, but yeah, we'll be bringing up guests.
We have a lot of, a lot more of that on the horizon in future weeks of filmed video,
HTTTA. So I think with that, this is a good enough place to call it quits for the week.
So for GoToGo, I am Wes the SynthMind saying happy prompting everybody.
Happy prompting everybody.
Thanks for listening to how to talk to AI with your hosts, GoToGo and Wes the SynthMind.
As always, you can check out the show notes and links at howtotalkto.ai.
That's all for this week's episode. Happy prompting everyone.