You Ask, I Answer: Biggest Misconception about Marketing AI?

In today's episode, Paul asks, What do you think is the biggest misconception about AI? Oh, there's so many to choose from. Fundamentally, and this is something that I say in all the keynote talks I give on the topic, at its core, AI is math, not magic. It is just the application of advanced mathematics to data at a very large scale. When you decompose major algorithms and all these really cool techniques, you're still just doing math.

Something like, for example, extreme gradient boosting. At the end of the day, boils down to some filtering and a regression. Uh so a statistical regression. It's very well done. It is executed on data that is far beyond uh human capacities to do, but it's still just math.

And it's relatively simple math um at that, once you get past all the distillation, past all the uh the gradient descent and such and stuff like that. Take away all of the uh mystique and you're left with a pile of math. And that makes AI no different in many ways than other mathematical tools that we're used to, like calculators and spreadsheets. Right? If you think about AI as a spreadsheet, just a really, really fancy one, then you suddenly i it does take away the the mystique and the the the cool factor, right?

Oh, it's just like a spreadsheet. But then you think, okay, what are the limitations of spreadsheets? They can't do things that aren't math. Right? I mean, yeah, I you I guess you could uh in do some limited things and some like cute illustrations and maybe uh tables of non-math data, but at the end of the day it still is a computation engine.

And that's what AI is. Which means that AI can't do things that are not fundamentally math at their core. If you take the advanced techniques that exist in AI, um natural language processing, at its core, it's it's still math. You take words, assign numbers to them, and then do math on the numbers, and that's what natural language processing is. It's one of the reasons why, even though some of the uh models out there like GPT2 or distill or Excel net or all these things uh are so cool and and can autocomplete paragraphs or even documents, they're still just doing math.

They're still doing probability. And it's one of the reasons why if you were to type in, you know, two plus two equals the words uh into one of these things, they're gonna predict an outcome that shows they don't have any actual understanding of the words. They're just doing math on probability. And so you end up with some pretty lame examples of uh uh of how these things can't reason, they can't understand truly uh the math is just doing forecasting and predictions, statistical probability. If I write the words, you know, what do you believe about, it's gonna come up with probabilities about what the next word in that sentence is going to be or the next sentence.

When you do image recognition, it's turning pixels into math. I mean, technically, pixels are already math. Um there's if you look at uh sensor on a camera, um a digital camera, there are three uh color sensors on each pixel, and they're either lit up or they're not lit up. And again, it's mathematics. And so when you're recognizing an image or looking for a face in a video or a brand logo, still just mathematics.

Even the most advanced image recognition algorithms uh functionally are like uh distillers. Uh I explained this in one of my keynotes as well. If you were to take all that image data and boil it down to a pattern that the machine can recognize, in many ways that's no different than taking a bunch of you know, grain mash and stuff like that, or beer and distilling it over and over again until you get whiskey, right? That's what deep learning is. It's distillation of data, it's not anything magical.

All this means that something that is fundamentally non-mathematical in nature, uh, like emotion or reasoning, uh, or even logic, human logic, not machine logic, is fundamentally out of reach of today's AI. A machine cannot understand how you feel, it can make probabilistic guesses about the words that you use to describe your emotions, but it cannot feel. It cannot understand, and therefore it can't do things like empathy, because it's simply a non-mathematical thing, at least with today's technology. Now that may change in the years ahead when we do have access to vastly larger amounts of computing with stuff like quantum computing, but it is that is still years off uh from from today as I record this. When we understand that AI is nothing more than a spreadsheet, it also means we understand that AI can't do anything not in its training data, right?

If you don't put it in the data to for the AI to learn from, it can't create it. It can't create something from nothing. Now, if you have some of these really large models like the GPT2s of the world, they've trained on a tremendous amount of text and so and much more than you know a single human could ever learn in their lifetime. And that's where you see AI seemingly creating things. They create because they have a much larger knowledge base to draw from, but they're not creating anything new.

They can't create something that has never been seen before. All of AI is currently what's called narrow. Uh narrow in applications, focused on a specific task, because creating a general purpose AI, like artificial general intelligence, there's no model for life. Not today. Finance and virus prediction uh and ROI of marketing.

It would be so difficult to come up with a model that was universal that uh we don't have that capability in machines. We have it as humans because of our our brains are massive parallel computers. Um but machines can't do that. So when we talk about misconceptions people have about AI, it is fundamentally that it is not a system of magic, it can't create something that doesn't exist, um, and it can't do things it wasn't trained to do for the most part outside of a specific domain. It's math, not magic.

Good question. We could go on for quite some time about this topic, so uh let's leave it at that. If you have follow-up questions about it, or uh you have misconceptions of your own or or things you believe about AI that are your own and that you would like some clarification on, leave it in the comments here. I'd be happy to do follow up questions on this topic about more specifics uh of uh misconceptions in in AI. As always, please subscribe to the YouTube channel and the newsletter.

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