You Ask, I Answer: Machine Learning vs. AI?

In today's episode of Brizio asks, can you make the intro shorter? Yes, the intro is now 60% shorter, uh, from 30 seconds down to 12. Now, Maureen asks, why do people use machine learning and AI, these two terms, uh interchangeably? Well, the reason is because honestly, it's because I think most people don't have a good sense of what either term means, and so they just kind of mix and match. To be clear, artificial intelligence is a an umbrella term that is specific to teaching machines to develop intelligence skills that we have naturally.

So, if you are watching this video and you can distinguish me from the background here, right? You're using vision. If you're hearing uh the audio to this video, and it's not just noise, you're able to distinguish things like uh sounds, different sounds, you're using uh hearing, right? If those sounds get turned into words, you're using what's called language processing. All of these are intelligence skills.

And when we teach computers to do these things, we it's artificial intelligence. It's intelligence skills that we're teaching to a machine, rather than being something that's natural, uh done by humans or other animals too. I mean, obviously, things like uh parrots can certainly learn to repeat words and such. Machine learning is a subset of artificial intelligence. It's part of AI, but it is not all of AI.

And machine learning specifically refers to giving data to machines from which they can write their own software, right? They can build their own code based on the information they're given and a pre uh defined set of tools and algorithms to build their own code. All machine learning is AI, but not all AI is machine learning. So it is a subset. Why people use these two interchangeably, partly as a marketing reason in some ways, uh artificial intelligence has been a buzzword now for about 10 years, and as such, has sort of lost its luster.

Understandably so. Uh, there have been many, many AI projects that have gotten off the ground that didn't really go anywhere. And there have been a number of movies and TV shows and things where consumers and the general population were introduced to the concept of AI, and it created these mistaken perceptions about what machines are capable of. People think of you know the Terminator and killer robots and Star Trek and all of these different types of artificial intelligence that are more properly uh artificial general intelligence, which means uh machines that are essentially sentient. No such things exist uh as of this recording.

And so for distinguishing uh that sort of consumerization of the AI term, a lot of folks have said, okay, well, maybe we should focus in specifically on the machine learning part. Uh given data, we're teaching machines to do other uh intelligence tasks and create their own software. Most of the artificial intelligence that you interact with on a day-to-day basis is machine learning from recommendations that you get from Google Analytics, from uh recommendations you get on Amazon when you're shopping, it says you might also like these three other things that are uh somewhat related to what's in your cart. Every time you fire up Netflix and it suggests another series, or you fire up Spotify and says, consider adding these songs to your playlist. All of that is machine learning.

There's yet another distinction that people like to make, the differences between classical machine learning and deep learning. Uh, deep learning is when machines are not only writing their own software, but they're also choosing their own algorithms based on um all kinds of uh data inputs in these neural networks. The closest analogy I think you could easily make is if machine learning is a chef with a lot of very expensive uh fancy machines that do you know 90% of the prep work, deep learning really is a kitchen that almost completely runs itself. There's very little to no human intervention a lot of the time. The challenge and the reason why you would pick one or the over the other is based on the amount of data you have.

Deep learning requires a lot of data. Uh, we're talking millions of records, millions of samples from for which the machine can create a neural network. Oftentimes, especially in marketing, we don't have millions of examples to train on. So when we have something, for example, like uh the GPT Neo X natural language uh generation models, that's trained on 800 terabytes of text, right? The entirety of Wikipedia, the entirety of Google Books.

There's tons of information to work from. When you're trying to build a model for your ideal customers, you don't have tens of millions of ideal customers, right? If you're a B2B company, you probably have like five ideal customers, right? The CMOs of the Fortune 10 are your ideal customers. And so those are cases where classical machine learning makes a lot more difference, uh, much more of a difference is much more effective than deep learning.

So which term should you use depends on which application. If you were talking about the overall uh teaching of tasks uh that are currently done by humans, vision, listening, language, etc., AI is a perfectly fine term to use. If you are talking about the feeding of data to machines to build uh their own models, you're talking about machine learning. If you're talking about building neural networks out of the very large data sets, you're talking about deep learning. And there's a few other more specialized terms in there, but those are probably not as well recognized outside of the AI field.

So don't worry too much about them right now. The last caution I will offer is buyer beware. A lot of companies will say that they use artificial intelligence or machine learning, and in fact, are not. In 2018, the Financial Times did a survey of 100 companies that were claiming to use AI and did some substantial background investigation, found that 35% of them were just outright lying. They had offshore uh outsourced humans doing the work instead.

So just because something is using AI or machine learning, A, doesn't mean it actually is, and B doesn't mean that it's it's any good, right? I can use machine learning to overcomplicate nearly any problem. Uh it doesn't make the solution better, it just changes what technologies are in the solution. So, really good question.