“With as easy as it is to do machine learning today, as long as you have good data, there is almost no reason everyone shouldn’t be offering that level of personalization.” explains Zack Pike, head of data at Callahan. What is machine learning and how can a brand get started using its current data set?

In this podcast, Jan-Eric Anderson, president at Callahan, and Zack Pike discuss some of the basics around machine learning including:

• Definition and examples of how machines learn


• Brands currently utilizing machine learning


• How to get started

Listen here:

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Welcome to Callahan’s Uncovering Aha! podcast. We talk about a range of topics for marketing decision-makers, with a special focus on how to uncover insights in data to drive brand strategy and inspire creativity. Featuring Zack Pike and Jan-Eric Anderson.

Jan-Eric Anderson:

Hi, I’m Jan-Eric Anderson, president of Callahan.

Zack Pike:

And I am Zack Pike, head of data at Callahan.

Jan-Eric Anderson:

Welcome back to the podcast, Zack. Today, I’d like to pick your brain on a topic that’s all the rage right now. It’s a very popular term that gets thrown around and personally, I just need to understand it a little bit better. And the topic is machine learning.

Zack Pike:

Yep.

Jan-Eric Anderson:

So there’s been a lot of discussion around machine learning and I think it would be beneficial for me before we get any further into this discussion is just to … If you could help me understand what it is. Can you define for me, what is machine learning?

Zack Pike:

Yeah. So it’s hard, because machine learning is used to describe a lot of different things. And let me start with a little bit of the genesis of why it’s even here. And I think that’ll help explain what it is and kind of how it happens. So if we think back 10 years ago, data started to get so large that it was hard for humans to actually analyze all of it. Right? So if you think about Netflix, there are so many videos being watched on Netflix every day and every hour that it’s too much for a human to even comprehend. So when you put that data in front of an analyst, it’s like, gosh, there’s so much we could do with this, we could never work our way through it.

Zack Pike:

So that’s when people smarter than me started figuring out, okay, well, machines are really good at working through large amounts of data really quickly. Is there a way we can get a machine to help us with this problem? And so that’s where it all started. And really, machine learning’s objective is to make predictions. Okay. So I have a problem. I need to be able to see into the future on how to solve this problem or figure out what’s going to happen if I do X. So we can use machines to do that. So at its core, machine learning’s goal is to predict if this decision happens, what is the likely outcome.

Zack Pike:

And the way you get there is with data. So a machine learning algorithm takes a big set of data. It builds rules and labels around that data. Usually there’s a training dataset that is like, here’s your starter rule-set. And then as time goes on, the machine’s reading that actual data and using that initial rule-set to make further predictions on the data. The coolest thing about machine learning though, is that it reads the results and then it makes everything smarter behind that. So that training dataset just grows over time and makes those predictions smarter on the backend.

Jan-Eric Anderson:

Okay. So getting back to this machine learning, you defined machine learning as driving prediction?

Zack Pike:

Mm-hmm (affirmative).

Jan-Eric Anderson:

Basically it’s a computer where technology, some sort of machine and technology, that’s analyzing data to answer a question or to predict some sort of outcome if a decision were to be made. It’s predicting some sort of decision. So it’s a decision making technology that can replace humans?

Zack Pike:

Exactly.

Jan-Eric Anderson:

In labor, essentially.

Zack Pike:

Today, it’s still reliant on humans to get started. So when I mentioned that training dataset, that’s basically giving the machine initial rules to make decisions around. But from that point forward, as long as it’s done correctly and you’ve got clean data, the machine is what gets smarter on its own. That’s the learning aspect of machine learning. How about we talk about just a really simple example, I think that’ll make it a little bit more concrete.

Zack Pike:

If I have a bunch of pictures and I want to determine if there’s a cat in the picture or a dog in the picture, and that’s all I want to do. I want the machine to predict, okay, this picture has a cat. This picture has a dog. You start with, let’s say a hundred pictures, and a human has gone in and tagged each of those pictures with cat and dog. So you feed that into the algorithm. The machine now says, “Oh, okay. I can look at all these pictures and figure out the attributes inside the picture that tell me if it’s a cat or a dog.”

Zack Pike:

It could be the shape of the animal. It could be the color. It could be the way the fur comes through in the picture and then it takes that dataset. So then you start feeding it new pictures that it hasn’t seen before. The machine, the algorithm, then says, “Okay, I’m going to look at all my training data,” and say, “Okay, I have a 80% confidence that this picture is a cat.” And then it’ll give that result. And the human can then come in and say, “Oh, okay, machine, you were right,” or “You were wrong.”

Zack Pike:

And then the machine looks at that right or wrong answer from the human, and now it makes itself smarter moving forward. So it’s learning as it’s going. And you run a model like that for a year and it gets really smart on the end. Right? It gets to where-

Jan-Eric Anderson:

It no longer has to be taught, right? So it follows a similar teaching model, education model, the traditional education model as teacher and student teacher and pupil.

Zack Pike:

Yeah. Yeah.

Jan-Eric Anderson:

The same thing applies here, the pupil is a machine.

Zack Pike:

Yep. Yep, exactly.

Jan-Eric Anderson:

So to your cat and dog example, I don’t remember what age I was when I was taught the difference between a cat and a dog. But I don’t know how I was taught to know the difference between the two, other than the fact that they look different.

Zack Pike:

Right.

Jan-Eric Anderson:

So in this example, you can show a hundred pictures of a cat and a hundred pictures of the dog. Just like in a human mind, I’m looking at the shape of a cat’s face.

Zack Pike:

Mm-hmm (affirmative). Yep.

Jan-Eric Anderson:

And the shape of the ears.

Zack Pike:

Yep.

Jan-Eric Anderson:

Consistency of the shape of the ears and the nose and the space between the eyes and the length of the tail.

Zack Pike:

Yep.

Jan-Eric Anderson:

And the general body. And the body language that you get from a cat.

Zack Pike:

Yeah.

Jan-Eric Anderson:

And the way that they walk different. All of these visual cues nobody taught me that’s the difference between a cat and dog.

Zack Pike:

Mm-hmm (affirmative). Right.

Jan-Eric Anderson:

I observed this over time. Machines will make the same observations without having to go … A human doesn’t have to go in and tell the machine, “Look, they walk different” or “Look, the eyes are separated and have different separation than that in a dog.”

Zack Pike:

Yep.

Jan-Eric Anderson:

“The tail shape is consistent.” You don’t have to tell the machine that. The machine will figure that out on its own. And at some point, you can stop teaching the difference between cat and dog because the machine has learned it and probably knows more about the differences between cats and dogs than the average human would.

Zack Pike:

Yeah. Yeah.

Jan-Eric Anderson:

Is that fair?

Zack Pike:

That’s fair. And the real benefit of that is, is now once you’ve got something that’s smart enough to make those decisions, you can feed it a million pictures in an hour and it will make all those decisions for you. That would never in