You Ask, I Answer: Subdisciplines of AI Applicable to Marketing?

In today's episode, Annika asks According to some experts, there are 25 subdisciplines of AI. Within these subdisciplines, there are different functions of AI. In your opinion, which functions are relevant to marketing, and would you have a real life case? Use case example of each. Uh the list is like cognition, AI knowledge, expert system, knowledge representation, automated planning, computational intelligence, multi-agent system, AI application.

Okay, we're just gonna stop. I have some strong opinions here. Lists of arbitrary subdivisions like the one that was provided are largely unhelpful. Uh, and an indicator that the person who assembled this list, not Annika, not the person who asked the question, um, but the person who assembled lists like this um hasn't done much practical work in AI. These lists are often very theoretical in nature.

Uh they are very speculative, they are not particularly well organized. Um theory is fine. Theory is fine and it's important. It's an important part of progress and growth. It's an important part of uh development.

Academics have been the source of AI for the last sixty years. Uh academic theory, things like neural networks were proposed decades ago. Many, many decades ago. And so these things are important, but what delineates, what differentiates a good theory from bad theory is application. Is the ability to take the theory and turn it into something that is implementable, something that shows that the theory applies to the real world.

This list of stuff is in many cases highly duplicative and redundant. So on this list, uh number 13 is vision, uh, and number 19 is optical recognition. For the purposes of practical applied AI, image recognition, uh, visual recognition, uh, they're the same thing, right? Number 23 on the list is optical recognition. So it's that's just straight duplicative.

So clearly, this is a case where the person putting forward this list just wanted to make a really long, impressive looking list without much consideration of this is what actually happens in AI. Again, if we go back to that matrix of types of problems that AI solves, there are techniques within each supervised versus unsupervised, categorical versus continuous, and then when you have things like reinforcement learning and neural networks, those are combinations of techniques within those four buckets, right? And those combinations are dictated by the machines. Um they're not specifically delineated by a human, whereas in traditional machine learning, uh, I, as the person writing the code would say, you know, machine, I want you to use gradient boosting, or machine, I want you to use linear regression. In a when you're building a neural network, the neural network has the same palette of tools, and it just does it for you and combines those things.

In a lot of these applications, I'm looking at this list. There's two fundamental broad applications of AI, right? It is classification and prediction. When you think about uh supervised versus unsupervised, right? That's really what is classification and prediction or explanation.

Most of the things on this list are classification tasks, vision, pattern recognition. Um a lot of natural language processing is a combination of classification and prediction. Umledge representation, um, decision support, question answers. So a lot of this stuff is classification. Classification is something humans are really good at, much better than machines, but we're slow, right?

So we can't c a machine can classify, for example, an image as well as we can after a lot of training. Is this a cat? Is this a hot dog, etc.? But what makes a machine better is that a machine, once it's been trained, can do that in nanoseconds, right? It can crank out a million recognitions at a time.

We can't do that, we scale poorly. Same for natural language processing. We understand nuance and sarcasm and sentiment and emotion much better than machine can right now. Even the best trained natural language models cannot deal with sarcasm very well. But we can't read a billion books in a day.

We can't read a billion books in our lifetime. A machine can. The other side is prediction, and this is something that humans are bad at. Humans suck at prediction. Machines are very good at mathematical prediction.

So being able to do advanced pattern recognition, being able to do forecasting, being able to do time series, being able to do driver analysis, being able to do uh an understanding of what's likely to happen or what has happened that i in very complex systems. You know, like if you had a spreadsheet with a thousand columns of marketing data, could you tell which columns have uh an impact on the outcome you care about? Probably not. It would take you days to do that. A machine can do it in seconds.

So when you look at lists like this, try to break them down into is this a supervised or unsupervised problem? Fundamentally, and there may be combinations, which in which case you're talking about stuff like deep learning. Um then what techniques fall in that bucket. So a couple of these, like computational intelligence, super and super intelligence superintelligence doesn't exist yet. Superintelligence is after artificial general intelligence, which still doesn't exist.

So, yes, it's a subdiscipline of AI, but it's it's not in the real world. Um that's years, if not decades, off. Depends on quantum computing. Decision support systems, very practical example. Um a decision support system provides you data to make better decisions or makes the decision for you, depending on what system you're using.

Planning analytics is an example of that. Um these are all techniques. They're not even subdisciplines, in a lot of cases, they're collections of techniques. They're like recipes. An omelet recipe, a sushi recipe, a pizza recipe.

Can you apply them to marketing? Yeah, if you in the same way that if you're hungry, make a pizza. If you own an Italian restaurant, make a lot of pizza. Where this goes sideways is these collections of techniques need to be refined down into those fundamentals so that you can understand how to apply it to the marketing problem. If the marketing problem is a classification problem, then you could use vision if it's a visual recognition problem.

Like, is that my logo in an ad? Uh it if it's speech recognition, did somebody say something? Uh and if so, what words did they say? If it is language recognition, language processing, how many times was my brand mentioned in this in this thing? And what was the sentiment around that?

So at the end of the day, it really comes back down to those fundamental building blocks that lead to these collections of recipes, just like boiling, baking, frying, uh, are fundamental techniques that make up parts of recipes. Recipes are techniques plus ingredients, right? So every subdiscipline that exists, often this is because some of them don't exist, every subdiscipline that exists has applications in marketing. You just have to figure out whether that recipe can be converted into a marketing recipe. Can you do uh a multi-agent system in marketing?

Absolutely, you can. Uh IBM showed a fantastic example of that with Project Debater earlier this year. That was 10 AI agents all stacked together, working sometimes adversarily, sometimes collectively, to generate re near real time s uh solid speech and some level of reasoning. I don't even know why robotics is on this. Robotics is not an AI discipline.

Robotics is a discipline unto itself. Um question-answering systems. Yes, very simple application of natural language processing and uh decision trees. So again, know the categories, know the techniques in the categories, understand when it's time to stack those categories together, either as an ensemble or in a deep learning system, and then figure out how those recipes can be ported into marketing. That's the answer.

Every subdiscipline that exists can be applied to marketing in some way because marketing is a very broad collection of human experiences. Um as always, please leave your comments in the comments box below and uh subscribe to the YouTube channel and the newsletter. I'll talk to you soon. Want help solving your company's data analytics and digital marketing problems? Visit TrustInsights.ai today and let us know how we can help you.