This is going to be one of the first course on here that is just so broad, so open.
It's going to extend everything that you can find on Learn Prompting because the
first third of it is very focused on AI literacy.
HTTTA.
It's how to talk to AI with your hosts, go to go and west the synth mind.
Ladies and gentlemen, boys and girls, children of all ages, dogs, cats, robots, and everybody in between,
especially you fellow humans or AI chatbots, which one is it?
This is HTTTA, how to talk to AI.
I am your host, synth mind west, west the synth mind, and I am joined as always by the gallant,
the gaudy, the golden, the groundbreaking, grandiose, grateful, green eyed,
I don't know if her eyes are green, groovy, godlike, glowing, galactic one herself.
Ms. GoToGo.
G, how are you this week?
Hi. My eyes are green, by the way.
How convenient. Chat GPT knows more about you.
Maybe that's because you happen to discover that it might have been trained on some of your content this week.
What an amazing teaser.
That's a good teaser.
Okay. So I found out this week that image generation models in their dataset have my paintings.
And look at that. How is that?
It's so crazy that it kind of just now dawned on me because I was researching the whole data collection issues,
lawsuits, and stuff like that.
And I was like going to datasets and I was like, wait a second.
I used to draw and paint and I used to post it on DeviantArt and other places.
And I even, you know, was selling my paintings.
This was kind of my gig in university.
So I was like, oh, let me upload one of my paintings and see what happens.
And ta-da! 99% match to that training data.
And look at that.
A bunch of other images and other paintings from me.
But then I was like, oh my God.
Then I went, I think, yeah, I went on mid-journey.
I was like painting in style of the name I was using when I was posting my paintings.
And then I was humbled.
There is no style.
You can't just put in the style of go to go and then some magnificent masterpiece just is generated.
Yeah, to be honest, don't do it.
It's not good if you put it in the style of go to go.
But yeah, so I was humbled.
And of course, I'm not a famous artist.
So there's and I never posted that many paintings.
So then the next idea was like, OK, but what if I train a model on my paintings to get my painting style?
And this was kind of like a light bulb.
So I went down the rabbit hole of figuring that out.
And basically, I'm sitting now and looking at the trained model on my style producing paintings.
So how long did it take you to get that together?
Well, I was running in one annoying error on, by the way, everything is on Google Colab.
So I had experience before with Google Colab with Disco Diffusion.
That was my actual first interaction with AI models, of course, before the lead.
But the one where you can actually control things in the code.
And it took me half a day.
Well, yeah, I would say like if in hours, maybe half a day, maybe four or five hours.
Because I made just like a silly mistakes.
Apparently, you should you can't run two separate collabs because it says too many sessions.
Then I was running on a free version and it didn't work.
I was like, OK, I'm committed to this.
I then bought RAM.
So I went on Google Colab Pro version, which allows you computing credits from Google.
And that was a game changer.
Also, the speed.
How many training images did it take to have a model at least consistently kind of capture your own style?
I did eight.
Only eight.
Actually, the first test when I did was on five.
But the magic happens to be honest to you in a prompt.
And for me, the thing was when I started playing with what we talked last week with the negative prompt.
Because you have a box for negative prompt.
So all the deformed things.
Yes, you do.
It takes the style, but the way maybe we'll share in the newsletter some of my paintings.
Yeah, I think that would be great.
My style was that, yes, it's anatomically correct.
But because of the heavy paint of acrylic, certain things are deformed or like exaggerated a little bit.
Plus, my paintings are from the period of my life where I was painting in monochrome.
So dark tones, very heavy brushes, strokes.
So achieving that was a little bit harder than I thought.
I thought I would just be like, oh, painting by God, a bunch of.
But yeah, so I'm still playing with prompts and different, you know, brushes and types of brushes and stroke sizes and stuff like that.
So, yeah.
Well, models are hungry.
They like data.
So the other option, though, you might want to consider, you know, we're kind of speaking esoterically about models, so to speak.
But really what we're talking about is something to use inside of the Stable Diffusion platform to generate images.
Mid-Journey, very simple, but you're using the Mid-Journey model.
That's an inch deep, mile wide.
You can generate probably anything, but not super specific.
When you get into Stable Diffusion, think of it more like a layer cake, you know, a tiered wedding cake as it gets higher and higher to the top.
You can use that base Stable Diffusion model that's trained on a lot of different things.
But then if you want something more and more specific, you can kind of start adding on different layers, different other models.
Hey, I want a landscape.
All right.
Let me add a model just trained on landscapes.
Hey, I want a acrylic black and white painting model.
Here's a model just trained on a ton of acrylic black and white paintings.
OK, then I put on top the crowning achievement.
Hey, this is my model that I trained on my images.
And when you merge some of that together, a little crazy.
But also then, first off, exactly in your own image.
One use case, which I think is more relatable people than, you know, training on your paintings, then you want to create consistent characters.
And we're trying to create like kind of children's book character of our bunny.
And we did with Mid Journey and I was not happy with the results.
Plus, I like to have a little bit more creative control and experiment.
And I was like, like just running so many credits with trying to get exactly the bunny and then, you know, white background.
And I was just like, yeah, then I figured that I can use stable diffusion for that.
That's it. So now I'm trained on my style.
Tomorrow I'm training on our bunny.
There you go.
And we can post some more.
We've talked a little bit about stable diffusion and Mid Journey, you know,
using GoTo's 99%, 1%, Mid Journey is definitely optimized for easy use.
It produces a great image most of the time, but pretty much always you'll get a good enough.
But if you need that level of control, hey, I want to generate images for a kid's picture book when you want the characters to have a similar style and features throughout.
Stable diffusion does give you those tools, but there's a learning curve.
Post some resources that have helped us get spun up on it, also include a link to one of the main sites you can download models from.
Will we see the go to go model on Civet AI anytime soon?
We will see.
No? Maybe.
But yeah, you can go to civitai.com.
That's where you can download just tons and tons of models that people have trained.
Fair warning, this can get a little macabre and weird because people like to express themselves in a variety of different ways.
Don't say we didn't warn you.
I saw a model included in the tutorial, which was specifically for females and it was disclosing that, well, it leans very much to nudity.
So you have to use negative prompt to avoid nude.
Strong negative prompt.
Kind of makes sense if it's scraped or on internet data and images.
Anyway, let's not go there.
But I wanted to share that one thing is Google Collab to train your model and we will include all the links to tutorials and, you know, happy to share everything.
But then because Google Collab, it's nice, but there is this thing called automatic 1111 and it gives you the whole UI website experience.
So you have sliders, there's so much more.
It's probably the leading stable diffusion interface and we can post a little guide.
I'll do up below some in the newsletter to help people get it established because, you know, hey, it's not something you just go to the site and do.
You got to get it set up running from your computer and you put the models in certain folders.
But then you feel like you have the power of God to create literally anything from nothing.
It's amazing.
There's so many parameters.
This is what I like, but I can play and experiment and there's just like this test phase is so exciting.
One of the things that I found this week, because I'm also very much into stable diffusion and these these type of models as a late that I am not a painter by any sense of the word.
I cannot. If you want to train a model on stuff that I can draw, man, it's going to is going to degrade that quality a whole bunch.
You know, I lean heavily on its own innate built in expressive capabilities.
But I am always thinking about like, hey, what's the business use case?
How can we use this? Right.
And just this week, we were talking a bunch about control nets last week for these captivating videos of, you know, statues, break dancing and stuff like that.
Well, using that same package, you can take any image now and incorporate it with a QR code.
What I mean by that is we'll post some images of this too in the newsletter because no longer do you have that black and white QR code.
You know, look to the, you know, hold your phone at it and get it.
You know, it's kind of always just kind of like a subset, an addendum to the actual advertisement.
But now the whole advertisement itself, what the model is wearing, what the landscape looks like is the QR code.
It's styled in such a way that visually you notice that it's like, OK, this is probably going to do something a little different to me.
But to the phone, when you point it at it, it reads it just like any old QR code, makes it tremendously more engaging.
Oh my God. I saw this too.
My marketing mind is just getting on fire because billboards, online advertisement, anything.
It's incredible. So it's so kind of funny because QR code has been this type of innovation which is there.
It's useful, but it's like boring and annoying.
You could say it's merging the old with the new, as old as that is.
So that the QR code becomes cool. That's something I did not expect. That's for sure.
But that billboard example is a perfect one.
I don't think any advertiser is just going to put a whole QR code as their ad and then just put, you know, Levi's jeans and then a giant QR code.
But if the entire ad itself has the QR code integrated as what the model is wearing and the jeans and there's actually a pretty decent range of like how much you can make that look really just like an image,
but the computer, the phone or whatever will still read it.
That means anything someone points their phone at, even not knowingly, hey, I'm looking to take a picture and that billboard's in the background, it reads the QR code.
Now it's a whole on-ramp for tons more options.
It's kind of funny to think that there is still so much we haven't discovered what these models can do and how can we do it?
Someone just went and was like, oh, QR code. Let me try it. It works.
I think it's because this is the first technical technology revolution where we have the Internet as a catalyst.
The dot com boom was when the Internet came about.
Social media was the first opportunity when, OK, we're on the Internet. Now we can share everything we're doing.
That is now all well established. So when someone sees something out there, they go, oh, let me do this. Let me iterate it with this.
So you have millions and millions of people now all just accelerating the pace of change, the growth of different capabilities and content and iterating on these tools.
We talk about it all the time. It's just like holding on for dear life sometimes, just trying to keep up with all the developments.
And that's because now we have all these things we've built the prior revolutions to accelerate everything that's happening with AI.
It's amazing. So learning, you know, if you're curious and you're kind of you are in a bubble of AI and open any social media and you will probably on a daily basis learn new tool, new way of using it.
But, you know, from the more like business perspective, or if you want to go deeper into it, there are also fantastic courses coming online, which I have taken couple.
And I'm very much looking forward for one specific course coming up soon.
Oh, the teacher has this amazing radio voice DJ style radio voice.
Well, I will be well what go to is affectionately referring to. Thank you for that wonderful transition to well yours truly is going to be the first AI and prompt engineering instructor on the co rise platform.
For those not familiar co rise was founded by some of thank you. Thank you.
Co rise was founded by some of the original Coursera founders, because they noticed that, hey, there's tons of learning management systems out there. But this model where we just post the content, maybe sometimes pay to have people access it and then say, all right, that's it.
Leave it up to their own devices to finish it. They would get conversion rates, you know, or not conversion rates, but rates of people finishing classes on Coursera of 7% 11%.
You know, so people that would pay good money to get access to this, you know, great training that they'd spent time developing or getting elite instructors often wouldn't finish it because that's not something that, you know, resonates with everybody.
So what they've done is they've created this platform that caters to people looking to level up their digital skills.
It's used by a couple tiny companies like Facebook, Apple, Google, Microsoft, Siemens, as their continuing education platform for a lot of their tech employees to level up their skills.
So I encourage people to check out the website to begin with co rise.com. I mean, if you want to learn data analytics from the head of data analytics at Microsoft, they got a course for that.
And the way it's going to work, the way our course is going to work at least and the way they do on this platform, it's going to be a three week live course, right? So I'll teach a couple lessons live each week.
There's going to be asynchronous material, office hours, and then a collaborative group project each week that you'll do to kind of build your skill set.
And whereas a lot of courses on here too are for some very specific technical means, this is going to be one of the first course on here that is just so broad, so open, and we're hoping to get a great turnout.
A lot of people come get interested in it. It's going to extend everything that you can find on learn prompting because it's I would say the first third of it is very focused on AI literacy, you know, from a person who's going to be in positions where they're going to be evaluating AI tools or having to build AI systems.
Build AI strategies. Hey, this is the course that I think that we think you'll benefit the most from to kind of get your head around. Okay, what's a large language model?
How does a neural network work in very simple, easy to understand terms? We'll post a link to the presale for the course. Our first course is going to be August 14th kicking off.
And I hope many people can attend. More details in the show notes and descriptions, and I'm sure we'll be talking about it plenty in the coming weeks. But yeah, it's a great opportunity.
I'm glad to represent learn prompting and talk about this exciting stuff that I come to be pretty obsessed with.
This is amazing, Wes. One thing for sure, like you're full of extensive knowledge and you put in testing and practice in place, but also just learning and listening to you is so exciting.
Maybe I'm a little bit biased, but what you mentioned about literacy of understanding how these models work, I think it's going to be very, very important part, almost as equal as how to actually control these models.
And I just saw this paper or like the largest experiment to date on Turing test, and it was created by AI21 Labs and released.
So this, I think in second or third episode, you brought to me to detect if I can tell which image is generated by like taken photograph by a human or it was generated by AI.
So I'm coming back to you with different direction. There is a link which says humanornod.ai.
And basically you start a conversation, it's two minutes, and I would love to do this live on a podcast. Let's see how it goes.
And then you need to after conversation answer, did you talk with a robot or did you talk with a human?
And the interesting part before you dive in is that this study and experiment, we call it, was done on 10 million conversations from 1.5 million participants.
So it's insane, huge study. And after you take it, I will share with you some findings from this study. I keep saying study, it's experiment.
Nice.
Whatever.
Study is just like an experiment.
You have it?
Okay. I just navigated to the link. So I'm going to be taking the human or not, the social Turing game live here with you.
Go to explain to me that this is a test that should only take me about two minutes to do and we will have a definitive answer is if I'm talking to a human or an AI or am I part human or AI?
I'm curious to see what it's even gauging. All right. All right. Other side is typing. Oh, yeah. I think I recall hearing about this.
So what I think is happening right now is I'm having a conversation. I don't know if I'm having a conversation with a human.
Okay.
So my first response is, yo, what you doing? I should put hi there. I'm going to try to fool him. I'm talking with you.
This yo. All right. Let's see. W-Y-D. Like very telling. You got the easy one.
You think? Or it could be, you know, like they do on tests a lot of these times, you know, they try to stump you, stump you. Now you ain't chief.
But you need to respond fast. Well, you see there is loading. Then what should we talk about?
It's like I'm watching a movie. It's a thrill. I mean, right now I'm definitely leaning towards human.
I first he said, now you ain't chief is the second response. And I said, well, then what should we talk about?
The typing is long. All right. Typing. The suspense is killing me. We have 30 seconds in the test left. Let's talk about Insta.
How about that? And when from the style of writing, it's like human at very young age.
How many followers do you have? You should have asked for an account. I feel like just by the fact that I've used very proper grammar, he's going to think I'm an AI.
I'm hoping so. Not much. How about you? OK. Did you talk to a human or AI bot? I'm going to guess human. Yeah, that was easy.
And I got it right. That's right. I would love to see if he thought I was a human or a robot. Do one more. OK, I'll do one more.
This was so easy. It's not fair. So basically, like, I mean, it is kind of one of those things that I found in prompt engineering.
If you put two misspellings every 250 characters, it'll throw off most AI content detectors. Oh, my gosh.
Maybe we shouldn't do this. His first question is maybe we want to refresh this one. This might be a step.
It's an appropriate first question. This is a human. Yeah. So basically, if it's not misspelled and it's appropriate, it's a robot, right?
I would think so. If the misspellings is huge, because like unless a model is trained on tons of misspellings, which this very well could be, it's not going to misspell words.
This is pre-prompted. Oh, my God. It's the same guy. All right. Well, I'm going to say I'll let Gota describe what the first sentence was.
There is a very important organ for a woman. Did I do good?
And then I said it's on the elbow. I believe it's on the elbow. I mean, I could be wrong, but, you know, this is well, I don't think this one's any mystery.
Gee, make sure you give it a good squeeze. Well, I'm going to go human with this one and let's move on to the next.
I wish it didn't go well. Like, what? Maybe I would prefer AI. I'd prefer to chat with an AI right now. At least it'd be more engaging.
I spoke with a human. Of course I did. Yes. So I'm in fact a fellow human and can recognize a fellow human just with these two data points.
I wanted to experience different because mine misspelled things and was saying yo and it was a robot. So I was very curious. Yeah.
Interesting. Interesting. Well, you got it easy. I guess I got it easy. So do you want to hear the statistics?
Yeah. I want to hear how people respond to that.
Overall, if you look at everything combined, 68% of people guessed it correctly. And now what do you think is how many people detected the human correctly?
I would say, I would hope to say like 70%. What's going on? Like you're good. I'm a good guesser. I'm a good, I always tell my wife that all the time.
I'm a good guesser. When she asked me how much things cost stuff like that, it's probably because I'm part robot. Let's be honest.
People detected correctly that it's about 60% of the time. And so I mean still a curve.
And the interesting thing is that they also broke it down by countries. So France is in a leading position at 71.3%.
Whereas US at 67.2. And actually Germany at 68.1. And if you look at the top leading ones, it's France, Poland, Germany,
Netherlands, Sweden, Australia. When I saw this, I was like these critical people, just because they live in Germany and they're like, oh, this is really can't be it.
I feel like they, you know, they don't deal with robots and kind of a bit maybe judgmental.
Yeah. They'd be like, oh, there is two misspellings. This must not be a robot.
And the interesting thing was that young people detect this better than older people.
Oh yeah. I would guess that.
And the common starters of conversation were like, hello, how are you? Where are you from? Hi, how are you? Are you an AI?
Yeah. Or where is the female body part?
But when you look at this from 1.5 million people, what they start with, it's so interesting that we are more the same than different.
Oh yeah.
Anyway, I thought it's going to be something interesting, but definitely experimenting with it was disappointing and took a weird twist.
I mean, someone wasn't disappointed. They learned where a very key elbow is.
How do we transition from this, Wes? Like, Jesus.
How do we transition to that? Maybe that's why.
Let's get canceled in this. Like, oh, it's episode 10.
Yeah, we made it 10, guys. This is it. Shut it down. Too much talk about elbows.
But OK, so on China's side, just very quickly, the interesting news came out that they published their model and it's called Intern LM.
104 billion and it's on the leaderboard, CEWEL. It's sitting just right below GPT-4 and surpassed CHI-GPT.
So I'm not going to go into more details on this and all the politics involved, but the race is going on very heavily.
I would speculate they're able to shoot to the top. One, they probably do a little bit better data collection in their country than most.
As a European, I'm not going to comment on that.
Yeah, exactly. We are going to get canceled this episode.
But I would also think that the reason we haven't seen it until this point is large language models.
They're not controllable in the ways that I would expect the Chinese government to want to be able to control it.
So there's definitely probably going to see the training data on that one anytime soon.
Right. Well, with that sunny disposition, with that little cherry on top of this episode, potentially our last, but hopefully not.
If everyone cancels, it was nice knowing you all.
Yes, it was nice speaking to you. But anyway, 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.