Mind Readings: AI Generation Tools Are Like Vodka

Talk about AI generation tools and vodka. In the world of vodka, a world of vodka marketing, um, vodka is one of those things that if you read the technical definition, it should be a neutral grain spirit, uh, approximately 40% alcohol by volume, give or take, um, with no discernible taste other than you know, as clean as you can make it. Now, there are of course variations. Any flavor of vodka should have the flavor of whatever is you're trying to create. Um, and there are certain uh characteristic vodkas which in have intentional impurities in them to give them a distinctive flavor, like gray goose, for example.

But for the most part, vodka should be, as uh in most mixology things, colorless, tasteless, odorless. It should just be a straight, neutral spirit. Which means that the marketing of vodka is mostly what's what what the bottle looks like, right? The bottle, um, the the marketing around the bottle, the marketing around the brand, because the contents of the bottle are going to be pretty pretty much the same from vodka to vodka. Now there are some like uh you know pop-off or whatever that are they're the bottom shelf ones that um probably have a lot of impurities in them and as a result are uh more likely to give you an unpleasant outcome.

But at the end of the day, this, which is alcohol and water, is all vodka really should be. This is this is 200 proof, 100% alcohol by volume. Grain neutral spirit. This is actually for for laboratories. It is drinkable.

Um, I would not advise drinking it because uh just 560 milliliters, that is the lethal dose for most people. But from a a marketing perspective, what's in that jug is and should be chemically no different than uh any other vodka. Um the quality of the water, maybe it would be a distinguishing characteristic. If the water that was used was not pure or had a lot of contaminants, and then certainly it's going to have a different flavor profile. But the alcohol should be about the same.

So what does this have to do with AI? With the dramatic explosion in open source models, such as GPT Neo X, which is the open source equivalent of the GPT-3 family, with stable diffusion being the open source uh equivalent of the DALI 2 uh model. A lot of AI generation tools now are like vodka. The ingredients, the contents are probably one or more open source models, right? GFP GAN, ESR GAN, uh stable diffusion, you name it.

Under the hood, they're all probably very, very similar, which means that um what's the difference between all these AI generation tools? Yeah, the bottle, the user interface, right? How easy is the tool to use, knowing that the engine that generates the results is gonna be about the same. Um now there are variations on this. If you have the technical capability, or you have uh an agency that does, or you have a vendor that does, you can fine-tune these engines to come up with a very specific distinct flavors, right?

Like a pepper vodka or a tomato vodka. Um, you can add your content to some of these massive AI models to get them to generate stuff that's more like your stuff. But for the most part, out of the box, the way most people use them, they're gonna be like vodka's. So the difference from vendor to vendor is much more going to be like vodka, right? How good is the user interface?

How good is the customer support? How good is the marketing of the tool? But if you're thinking about will a tool generate substantially better results, one versus another, will I get better uh fictional sci-fi artwork out of Dolly 2 versus mid-journey versus stable diffusion? The answer is probably no. The answer is probably they're all going to give you about the same thing, um, depending on how good you are at engineering prompts, right?

Again, it's just like if you were uh mixing drinks, the quality of the vodka only matters to a certain point. And after that, it's the mixologist skill uh to make you a decent uh vodka martini, because we all know that real martini is made with chin. Um so what does this have to do? So what? Why do we care about this?

If you are working with an AI vendor that generates content, A, you should probably know what model they're using, just to get a baseline sense, and B, the value of that vendor is not going to be their model, right? Because these open source models are so good, they're so good that even companies that develop proprietary closed source models are looking at them going, maybe we should just use these open source ones instead. And so the differentiating factor for these things is going to be user interface, ease of use, um, better results, faster, customer support, and so on and so forth, which means that if you are paying a whole bunch of money to a generation company now, take a look around. Do some free trials, do some evaluations, do a bake off of the type of tools you want to be using image generation, video generation, text generation, whatever the case is. Do some shopping around and see which tools are going to be the easiest for you to use, the easiest for you to get results out of, knowing that under the hood, it's all pretty much the same thing.

So that's today's thoughts. Hope you find them helpful, and thanks for watching. If you like this video, go ahead and hit that subscribe button.