Tartle Best Data Marketplace
Tartle Best Data Marketplace
Tartle Best Data Marketplace
Tartle Best Data Marketplace
Tartle Best Data Marketplace
Tartle Best Data Marketplace
June 11, 2021

Bad Machine Learning

Bad Machine Learning

Bad Machine Learning

SHARE: 
BY: TARTLE

Bad Machine Learning   

We all know how machine learning plays the game. We search for something online, talk about a topic on Facebook, buy something on Amazon or even have a real conversation near our phones and within moments related ads will start popping up. In theory, these ads are based on algorithms that track our interests and present us with items that we might actually be interested in buying. Sometimes, this might well work very well and you’ll see exactly the backpacking tarp you never knew you needed. However, other times it might be clear that the algorithm is either drunk or badly written. For example, if you’re talking about private jets for some reason, it would be silly for your phone to present you with deals on the latest Gulfstream. Yet, exactly this has happened. Clearly there is some context missing. 

Let’s take a less extreme example. We’ve mentioned that TARTLE works on a Sherpa model. We help others achieve their goals, just like the Sherpas at Everest help others make the dangerous and rewarding climb. Let’s say this sparked an interest in Sherpa culture and we were searching eBay for books on the subject. Then, over on the sidebar, up pops an ad for a Sherpa jacket. Seriously? Wanting to learn about Sherpas doesn’t mean you want to dress like one. This just goes to show that artificial intelligence is a lot more artificial than intelligent. 

How does this happen? Quite simply, the algorithms take a lot less into account than you think. They very often work on keywords, focusing on one or two particular data points. They then try to apply that to another type of product that you aren’t already looking at. It’s a cross-marketing strategy designed to get you to spend as much as possible. So in this case, eBay’s algorithms keyed into the word Sherpa and fed that into the different businesses they have data sharing and marketing deals with. Through cross referencing the Sherpa keyword with all the products in the database the best they came up with was a Sherpa jacket. 

Now, based on that model, it’s an understandable error. How could they do better? How could they provide ads within a meaningful context? There are a few ways. One is to provide ads for more books on Sherpas, or maybe a documentary or two. After all, if you’re searching for information, what you want is (wait for it)… information. That’s something eBay can do internally, or maybe with the aid of a deal with a distributor. Or, they could write a better algorithm that takes more variables into account. What sort of variables? Past purchase history is an obvious one. If someone is searching for books on Sherpas, have they ever purchased anything else Sherpa related? Or even the amount of money a person would tend to spend is important. If the customer in question has never spent more than $60 on a single item online then that Sherpa jacket is probably going to be beyond that price point. For that matter, the algorithm should be smart enough to figure out whether or not you even buy clothes online.

What if eBay isn’t the only place you shop online? Plenty of people buy almost everything off Amazon. That would be an excellent source of data to help figure out the best possible ads to put up. However, do we really want these different companies sharing our data like that? If you have been around TARTLE at all you know that we aren’t cool with that. That data is yours and you should be the one deciding whether or not eBay gets to see it. If you are synching your accounts with us then your data is encrypted and stored on our servers, allowing you to choose to share or not. Even better, if eBay is trying to figure out the best way to market additional items to you, then they can actually run some options by you first. Then they would learn quickly that you have no desire to dress like a Sherpa. However, that nice new documentary from NatGeo might look pretty good. That’s what we are doing at TARTLE, connecting businesses with customers in a way that respects and benefits both.

What’s your data worth? Sign up and join the TARTLE Marketplace with this link here.

Feature Image Credit: Envato Elements
FOLLOW @TARTLE_OFFICIAL

For those who are hard of hearing – the episode transcript can be read below:

TRANSCRIPT

Speaker 3 (00:07):

Welcome to TARTLE Cast, with your hosts Alexander McCaig and Jason Rigby, where humanity steps into the future and source data defines the path.

Alexander McCaig (00:25):

Hey everybody. I don't know if you know this, we talk about it quite a bit and if you're a new listener, awesome, thank you, but we take on the model of the Sherpa at TARTLE. And so what that means is that, in a simple format, you're the champion in the journey, you're the one going to the top of the mountain, and we're only there to help teach you and guide you and keep you safe on that journey. Okay? How to find that power and grace within yourself. And you were doing some research online on eBay, reviewing books.

Jason Rigby (00:56):

I was looking at nonfiction books on Sherpa because I wanted to understand and know Sherpa's-

Alexander McCaig (00:59):

Yeah, their cultures, [crosstalk 00:01:01] everything they do...

Jason Rigby (01:01):

Right, everything about them. Yeah.

Alexander McCaig (01:02):

And we want to talk about bad machine learning.

Jason Rigby (01:04):

Yeah, so I get a notification...

Alexander McCaig (01:06):

This is great.

Jason Rigby (01:07):

... on my iPhone here. "Just for you, Sherpa jacket. Because you searched Sherpa." Bad machine learning. I have no desire to buy a Sherpa jacket. Sherpa jackets have nothing related to... Okay, I'm going to click [crosstalk 00:01:21] on it right now.

Alexander McCaig (01:21):

This is so hysterical, their algorithm took one data point.

Jason Rigby (01:25):

Yeah, I have no desire to wear one of those.

Alexander McCaig (01:26):

No, you would look ridiculous in that jacket.

Jason Rigby (01:29):

Yeah, in a Sherpa jacket? Yeah.

Alexander McCaig (01:30):

No, but here's the funny part, their algorithm took one data point.

Jason Rigby (01:33):

Yes.

Alexander McCaig (01:34):

They didn't ask you why you were searching for Sherpa, none of those things. They just came up with some sick sort of assumption that you need this ridiculous looking jacket, when you were looking for non-fiction material on a culture of people in Nepal.

Jason Rigby (01:45):

Yeah. Or say, "Hey, here's a new Sherpa book, though, just got put on eBay." [crosstalk 00:01:51] That would've been... "Oh, he's looking at non-fiction books that are very good or like new."

Alexander McCaig (01:56):

This is why you have to be-

Jason Rigby (01:57):

Because I like to buy used.

Alexander McCaig (02:00):

We always buy used.

Jason Rigby (02:01):

Yeah.

Alexander McCaig (02:02):

This is why this ridiculous sort of machine learning models that have been put in place have racially profiled people, they have swung elections improperly, inundated people with news that they really didn't want to see, and it only took one data point. This thing pushed back some sort of response and it-

Jason Rigby (02:25):

And sends me a notification.

Alexander McCaig (02:27):

It had literally less than 1% of the reasoning for why you were searching this thing. You want to know why? Because that model, that algorithm, that machine learning, had no actual input because there was no true interaction with you.

Jason Rigby (02:41):

But the problem with this is it's not valuing me as a person. Because my notifications are really important to me. Here's where eBay fit in. My son's birthday tomorrow, a package that was delivered to me and then a bunch of product updates from TARTLE.

Alexander McCaig (02:57):

Yeah.

Jason Rigby (02:58):

And then from there some emails, a new YouTube video, an Amazon package and some stocks that are down.

Alexander McCaig (03:07):

Yeah.

Jason Rigby (03:07):

So I really pay attention to my notifications, they do know that. But I don't want a worthless notification.

Alexander McCaig (03:14):

It's totally useless. [crosstalk 00:03:16]

Jason Rigby (03:16):

Give me something of value. There's another company, and I'm not going to mention their name, and I hate this. Here's bad machine learning too. I only buy men's shoes from you. Do not send me emails on women's clearance sales. I have no desire to buy women's shoes. I'm not against women's shoes, I just don't wear them.

Alexander McCaig (03:36):

I've seen your shoe closet.

Jason Rigby (03:37):

Yeah. I had some hidden, maybe some women's shoes, when I'm feeling a little...

Alexander McCaig (03:41):

Yeah, if you want to do Walk a Mile, whatever those fundraisers are.

Jason Rigby (03:45):

Yeah.

Alexander McCaig (03:45):

I'd love to see you in high heels. But no, you've got a ton of shoes in there. But the point is you are wasting your time and resources and advertising dollars to send me something that makes absolutely no sense because you didn't take a little extra effort to go to tartle.co and enhance your machine learning algorithms by pumping them with real data.

Jason Rigby (04:10):

Yes.

Alexander McCaig (04:10):

Sherpa? Are you kidding? You only sent me a notification for your own benefit, eBay.

Jason Rigby (04:16):

Right.

Alexander McCaig (04:17):

It wasn't for my benefit.

Jason Rigby (04:18):

No.

Alexander McCaig (04:19):

Because you really didn't understand what I was looking to benefit from.

Jason Rigby (04:22):

Yes. I needed some education and to learn.

Alexander McCaig (04:25):

Okay. Yeah. All right, I think the rants over.

Jason Rigby (04:27):

We're done.

Alexander McCaig (04:27):

Thanks.

Jason Rigby (04:28):

Bad machine learning.

Speaker 3 (04:35):

Thank you for listening to TARTLE Cast, with your hosts, Alexander McCaig and Jason Rigby. Where humanity steps into the future and the source data defines the path. What's your data worth?

June 11, 2021

Bad Machine Learning

Bad Machine Learning

Bad Machine Learning

SHARE: 
BY: TARTLE

Bad Machine Learning   

We all know how machine learning plays the game. We search for something online, talk about a topic on Facebook, buy something on Amazon or even have a real conversation near our phones and within moments related ads will start popping up. In theory, these ads are based on algorithms that track our interests and present us with items that we might actually be interested in buying. Sometimes, this might well work very well and you’ll see exactly the backpacking tarp you never knew you needed. However, other times it might be clear that the algorithm is either drunk or badly written. For example, if you’re talking about private jets for some reason, it would be silly for your phone to present you with deals on the latest Gulfstream. Yet, exactly this has happened. Clearly there is some context missing. 

Let’s take a less extreme example. We’ve mentioned that TARTLE works on a Sherpa model. We help others achieve their goals, just like the Sherpas at Everest help others make the dangerous and rewarding climb. Let’s say this sparked an interest in Sherpa culture and we were searching eBay for books on the subject. Then, over on the sidebar, up pops an ad for a Sherpa jacket. Seriously? Wanting to learn about Sherpas doesn’t mean you want to dress like one. This just goes to show that artificial intelligence is a lot more artificial than intelligent. 

How does this happen? Quite simply, the algorithms take a lot less into account than you think. They very often work on keywords, focusing on one or two particular data points. They then try to apply that to another type of product that you aren’t already looking at. It’s a cross-marketing strategy designed to get you to spend as much as possible. So in this case, eBay’s algorithms keyed into the word Sherpa and fed that into the different businesses they have data sharing and marketing deals with. Through cross referencing the Sherpa keyword with all the products in the database the best they came up with was a Sherpa jacket. 

Now, based on that model, it’s an understandable error. How could they do better? How could they provide ads within a meaningful context? There are a few ways. One is to provide ads for more books on Sherpas, or maybe a documentary or two. After all, if you’re searching for information, what you want is (wait for it)… information. That’s something eBay can do internally, or maybe with the aid of a deal with a distributor. Or, they could write a better algorithm that takes more variables into account. What sort of variables? Past purchase history is an obvious one. If someone is searching for books on Sherpas, have they ever purchased anything else Sherpa related? Or even the amount of money a person would tend to spend is important. If the customer in question has never spent more than $60 on a single item online then that Sherpa jacket is probably going to be beyond that price point. For that matter, the algorithm should be smart enough to figure out whether or not you even buy clothes online.

What if eBay isn’t the only place you shop online? Plenty of people buy almost everything off Amazon. That would be an excellent source of data to help figure out the best possible ads to put up. However, do we really want these different companies sharing our data like that? If you have been around TARTLE at all you know that we aren’t cool with that. That data is yours and you should be the one deciding whether or not eBay gets to see it. If you are synching your accounts with us then your data is encrypted and stored on our servers, allowing you to choose to share or not. Even better, if eBay is trying to figure out the best way to market additional items to you, then they can actually run some options by you first. Then they would learn quickly that you have no desire to dress like a Sherpa. However, that nice new documentary from NatGeo might look pretty good. That’s what we are doing at TARTLE, connecting businesses with customers in a way that respects and benefits both.

What’s your data worth? Sign up and join the TARTLE Marketplace with this link here.

Feature Image Credit: Envato Elements
FOLLOW @TARTLE_OFFICIAL

For those who are hard of hearing – the episode transcript can be read below:

TRANSCRIPT

Speaker 3 (00:07):

Welcome to TARTLE Cast, with your hosts Alexander McCaig and Jason Rigby, where humanity steps into the future and source data defines the path.

Alexander McCaig (00:25):

Hey everybody. I don't know if you know this, we talk about it quite a bit and if you're a new listener, awesome, thank you, but we take on the model of the Sherpa at TARTLE. And so what that means is that, in a simple format, you're the champion in the journey, you're the one going to the top of the mountain, and we're only there to help teach you and guide you and keep you safe on that journey. Okay? How to find that power and grace within yourself. And you were doing some research online on eBay, reviewing books.

Jason Rigby (00:56):

I was looking at nonfiction books on Sherpa because I wanted to understand and know Sherpa's-

Alexander McCaig (00:59):

Yeah, their cultures, [crosstalk 00:01:01] everything they do...

Jason Rigby (01:01):

Right, everything about them. Yeah.

Alexander McCaig (01:02):

And we want to talk about bad machine learning.

Jason Rigby (01:04):

Yeah, so I get a notification...

Alexander McCaig (01:06):

This is great.

Jason Rigby (01:07):

... on my iPhone here. "Just for you, Sherpa jacket. Because you searched Sherpa." Bad machine learning. I have no desire to buy a Sherpa jacket. Sherpa jackets have nothing related to... Okay, I'm going to click [crosstalk 00:01:21] on it right now.

Alexander McCaig (01:21):

This is so hysterical, their algorithm took one data point.

Jason Rigby (01:25):

Yeah, I have no desire to wear one of those.

Alexander McCaig (01:26):

No, you would look ridiculous in that jacket.

Jason Rigby (01:29):

Yeah, in a Sherpa jacket? Yeah.

Alexander McCaig (01:30):

No, but here's the funny part, their algorithm took one data point.

Jason Rigby (01:33):

Yes.

Alexander McCaig (01:34):

They didn't ask you why you were searching for Sherpa, none of those things. They just came up with some sick sort of assumption that you need this ridiculous looking jacket, when you were looking for non-fiction material on a culture of people in Nepal.

Jason Rigby (01:45):

Yeah. Or say, "Hey, here's a new Sherpa book, though, just got put on eBay." [crosstalk 00:01:51] That would've been... "Oh, he's looking at non-fiction books that are very good or like new."

Alexander McCaig (01:56):

This is why you have to be-

Jason Rigby (01:57):

Because I like to buy used.

Alexander McCaig (02:00):

We always buy used.

Jason Rigby (02:01):

Yeah.

Alexander McCaig (02:02):

This is why this ridiculous sort of machine learning models that have been put in place have racially profiled people, they have swung elections improperly, inundated people with news that they really didn't want to see, and it only took one data point. This thing pushed back some sort of response and it-

Jason Rigby (02:25):

And sends me a notification.

Alexander McCaig (02:27):

It had literally less than 1% of the reasoning for why you were searching this thing. You want to know why? Because that model, that algorithm, that machine learning, had no actual input because there was no true interaction with you.

Jason Rigby (02:41):

But the problem with this is it's not valuing me as a person. Because my notifications are really important to me. Here's where eBay fit in. My son's birthday tomorrow, a package that was delivered to me and then a bunch of product updates from TARTLE.

Alexander McCaig (02:57):

Yeah.

Jason Rigby (02:58):

And then from there some emails, a new YouTube video, an Amazon package and some stocks that are down.

Alexander McCaig (03:07):

Yeah.

Jason Rigby (03:07):

So I really pay attention to my notifications, they do know that. But I don't want a worthless notification.

Alexander McCaig (03:14):

It's totally useless. [crosstalk 00:03:16]

Jason Rigby (03:16):

Give me something of value. There's another company, and I'm not going to mention their name, and I hate this. Here's bad machine learning too. I only buy men's shoes from you. Do not send me emails on women's clearance sales. I have no desire to buy women's shoes. I'm not against women's shoes, I just don't wear them.

Alexander McCaig (03:36):

I've seen your shoe closet.

Jason Rigby (03:37):

Yeah. I had some hidden, maybe some women's shoes, when I'm feeling a little...

Alexander McCaig (03:41):

Yeah, if you want to do Walk a Mile, whatever those fundraisers are.

Jason Rigby (03:45):

Yeah.

Alexander McCaig (03:45):

I'd love to see you in high heels. But no, you've got a ton of shoes in there. But the point is you are wasting your time and resources and advertising dollars to send me something that makes absolutely no sense because you didn't take a little extra effort to go to tartle.co and enhance your machine learning algorithms by pumping them with real data.

Jason Rigby (04:10):

Yes.

Alexander McCaig (04:10):

Sherpa? Are you kidding? You only sent me a notification for your own benefit, eBay.

Jason Rigby (04:16):

Right.

Alexander McCaig (04:17):

It wasn't for my benefit.

Jason Rigby (04:18):

No.

Alexander McCaig (04:19):

Because you really didn't understand what I was looking to benefit from.

Jason Rigby (04:22):

Yes. I needed some education and to learn.

Alexander McCaig (04:25):

Okay. Yeah. All right, I think the rants over.

Jason Rigby (04:27):

We're done.

Alexander McCaig (04:27):

Thanks.

Jason Rigby (04:28):

Bad machine learning.

Speaker 3 (04:35):

Thank you for listening to TARTLE Cast, with your hosts, Alexander McCaig and Jason Rigby. Where humanity steps into the future and the source data defines the path. What's your data worth?