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 use case example of each?"
Lists of subdisciplines like the one provided are largely unhelpful and an indicator that the person who's putting together these lists hasn't done much practical work in AI; these lists are often very theoretical in nature. Theory is fine and important, an essential part of progress and growth, but application is what helps differentiate good theory from bad.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today's episode Annika asks according to some experts, there are 25 sub disciplines of AI.
Within the sub disciplines 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? Melissa 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 payment lists of arbitrary subdivisions, like the one that was provided are largely unhelpful, and an indicator that the person who assembled this list, Nautica, not the person who asked question that the person who assemble lists like this hasn't done much for practical work and AI.
These lists are often very theoretical in nature, they are very speculative, they are not particularly well organized.
And 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 development.
academics have been the source of AI for the last 60 years.
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 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, number 13, is vision.
And number 19, is optical recognition for the purposes of practical applied AI image recognition, visual recognition.
The same thing, right.
on the list is optical recognition.
So that's, that's a straight duplicative.
So clearly, this is a case where the person putting for 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 solve, 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 tech techniques within those four buckets, right, and those combinations are dictated by the machines.
They're not specifically deleted by human whereas in traditional machine learning, is the person writing the code would say, 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.
I 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 supervised versus unsupervised, right, that's really what is classification of prediction or explanation.
Most of the things on this list are classification tasks, vision, pattern recognition.
A lot of natural language processing is a combination of classification and prediction.
knowledge representation, decision support, question answers.
So a lot of this stuff is classification.
classification, something humans are really good at much better than machines.
But we're slow.
Right? So we can't, machine can classify, for example, an image as well as we can, after a lot of training is this a cat is 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, machine cat.
The other side is prediction.
And this is something 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 an understanding of what's likely to happen, or what has happened that in very complex systems, you'd like to get a spreadsheet with 1000 columns of marketing data, could you tell which columns have an impact on the outcome you care about? Probably not would take you days to do 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 then maybe combinations? Which which case you're talking about stuff like deep learning? And then what techniques fall in that bucket? So a couple of these, like, like computational intelligence, and super super intelligence doesn't exist yet super intelligence is after artificial general intelligence, which still doesn't exist.
So yes, it's a sub discipline of AI.
But it's, it's not in the real world.
That's years, if not decades off depends on quantum computing, decision support systems, very practical example.
Decision Support System provides you data to make better decisions, or makes the decision for you, depending on what system using planning analytics is an example of that.
These are all techniques.
They're not even sub disciplines.
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 into marketing? Yeah, 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? It's speech recognition, did somebody say something? And if so, what words did they say if he's language recognition, language processing? How many times was my brand mentioned in this in this thing, and what was 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, our fundamental techniques that make up parts of recipes.
recipes are techniques plus ingredients, right? So every sub discipline that exists of a business because some of them don't exist, every sub discipline that exists has applications and marketing, you just have to figure out whether that recipe can be converted into a marketing recipe.
Can you do multi agent system in marketing? Absolutely you can.
IBM showed a fantastic example that was project debater earlier this year, that was 10 agents all stacked together working, sometimes adversaries, sometimes collectively, to generate near real time, solid speech and some level of reasoning.
robotics sensors robotics is not an AI discipline robotics is a discipline unto itself.
question answering systems, yes, very simple application of natural language processing, and decision trees.
So again, know the categories know the techniques in the categories understanding 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 poured into marketing.
That's the answer.
Every sub discipline that exists can be applied to marketing in some way because marketing is a very broad collection of human experiences.
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