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 10, 2021

Why is AI (Artificial Intelligence) Trending on Twitter?

Why is AI Trending on Twitter

Why is AI (Artificial Intelligence) Trending on Twitter?

SHARE: 
BY: TARTLE

AI Twitter   

One of the leading trends on Twitter is Artificial Intelligence (AI). This isn’t surprising given all the buzz these days about anything having to do with machine learning, algorithms, data analysis, etc. But as usual, the trend only scratches the surface. The real question we should be asking is whether or not the fact that AI is getting lots of mentions on social media reflects any genuine understanding of what it is and how best to use it? 

At the very least, we can’t be certain that the trends indicate anything quite so positive as actual understanding of the issue. The very fact the term AI is popular helps drive it getting more mentions. Popularity begets popularity. It’s like this with any popular buzzword. A thing is perceived to be important so a lot of people trying to get attention or appear relevant will just start using it without any real understanding. Even if the person throwing around the buzzword understands it, he probably isn’t helping others to do so. There is a hilarious scene in the movie, New in Town that illustrates how silly this can be. The new manager (played by Renee Zellweger) at a food plant is trying to win over the employees by giving them a typical board room presentation, but at the local bar. They literally turn it into a drinking game, taking a drink every time she uses a buzzword (with a double for every one with a D).

Even the actual use of AI tends to be done in a very superficial, cookie-cutter way. Algorithms are sold, bought, and used with little consideration for whether or not they might actually be useful for the client. An algorithm useful for figuring out the best distribution model for Birkenstocks might not be that helpful in determining the best places to market Twinkies (not that anyone should even market Twinkies, you practically need an AI to decipher the ingredients to those tasty little carcinogen bombs). Your AI is only as useful as the algorithms is useful as the data you put into it. Both should be relevant to the kind of answers you’re trying to get. 

For example, if you want to know the status of the flow in your natural gas pipelines, then it makes good sense to have a bunch of passive sensors to track flow rate, pressure, and the integrity of the pipe itself. That is all relevant and useful data for what a gas company might like to know. However, if a clothing company is trying to develop a new kind of shirt, whether it is a new cut, or a particular line of graphics on the shirt, then it doesn’t make sense to look only at passive data. Such data can only tell you how a similar product has done in certain areas, which is great if you are tracking past or even current trends. However, if you are planning on launching a new product, you’re taking those trends and trying to project them into the future. To put it another way, you’re guessing. Based on that kind of passive data, there is no way to be sure that the market will even be there for what you are planning on selling. Just think of the attempt to sell men’s capris pants back in the late 1990s. Someone clearly needed more relevant data than they had because those were on the clearance racks in droves in a matter of a month or two. 

What if there was a way that you could get active data? Data that’s relevant to you now? That would actually be useful in planning for the future of your company and based on more than guess work? What if you could go right to the source? 

That’s what we are offering at TARTLE, a way for a business to reach out to actual and potential customers to determine not what you hope they want, but what they actually want. Through the TARTLE marketplace, you have access to actual people who can tell you what they think about your proposed product before you spend a ton of money manufacturing and marketing it. Armed with that information, you can then make use of your AI to figure out the best manufacturing process and marketing campaign for products that people actually want, saving you time and money in the end. 

What’s your data worth?

Feature Image Credit: Envato Elements
FOLLOW @TARTLE_OFFICIAL

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

TRANSCRIPT

Speaker 1 (00:07):

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

Alexander McCaig (00:25):

Twitter bird.

Jason Rigby (00:26):

Yes.

Alexander McCaig (00:27):

Tweet tweet.

Jason Rigby (00:28):

Tweet tweet. Twitter.

Alexander McCaig (00:29):

Twitter is all about sentiment.

Jason Rigby (00:33):

Yeah.

Alexander McCaig (00:33):

What're people talking about? What's the buzz?

Jason Rigby (00:40):

What did Justin Bieber say?

Alexander McCaig (00:42):

Yeah. Or all the stock were good. Did somebody tweet about something or Elon Musk with Dogecoin?

Jason Rigby (00:47):

Yes.

Alexander McCaig (00:47):

Come on. Dogecoin-

Jason Rigby (00:50):

What's funny is I had some of that just for fun. And it shot up yesterday.

Alexander McCaig (00:54):

Yeah, it did.

Jason Rigby (00:54):

Because we talked about it.

Alexander McCaig (00:55):

Unbelievable.

Jason Rigby (00:55):

So I looked on there and I was like, "I made a couple hundred bucks. How did I do that?" Beause it's Sunday and the markets closed. And I was like, "Oh my Dogecoin."

Alexander McCaig (01:04):

Oh my Dogecoin went up. And all the guys were like, "To the moon." It's like, no it's not hitting $20K like you think it is.

Jason Rigby (01:10):

Because that's just so fun, cryptocurrency [crosstalk 00:00:01:12]-

Alexander McCaig (01:12):

That's all it was. It's a deviation. It's like here's Bitcoin. Bitcoin uses a SHA-256 encryption. Same standard encryption that the US military uses blah blah blah. It takes a certain amount of machine processing power to clear a block. It takes about 10 minutes. With Dogecoin it uses a [inaudible 00:01:37] called scrypt. It's a different encrypting algorithm. And so the processing time for the work takes about, I don't know, two and a half minutes.

Jason Rigby (01:45):

Oh, wow.

Alexander McCaig (01:45):

I'm not... If I know correctly, I may be wrong. Somebody may correct me on that. And I know Litecoin, which is also another spin off of Bitcoin also has that probably a 10 and a half minute block clearing time using the [Skuveve 00:02:00] scrypt.

Jason Rigby (02:00):

Right, yeah.

Alexander McCaig (02:01):

I'm totally off track.

Jason Rigby (02:02):

No, I love it. Yeah.

Alexander McCaig (02:03):

Oh the Dogecoin, because we were talking about sentiments.

Jason Rigby (02:05):

Dogecoin, yeah, because I actually own some, funny part.

Alexander McCaig (02:08):

Yeah. It's all about the Doge.

Jason Rigby (02:09):

Shut up. I don't know, I just looked yesterday and then I happened to go on Twitter and I saw much joking around about it.

Alexander McCaig (02:15):

It's all about the Shiba Inu.

Jason Rigby (02:16):

Yeah, because it's like 22. Yeah, it is. Yeah. Those are amazing dogs.

Alexander McCaig (02:20):

Oh man.

Jason Rigby (02:21):

They're always very calm and collective and kind of guard. They're like a guard dog.

Alexander McCaig (02:25):

Until they snap.

Jason Rigby (02:26):

Yeah.

Alexander McCaig (02:27):

Like you.

Jason Rigby (02:27):

[crosstalk 00:02:28], yeah.

Alexander McCaig (02:29):

Calm and collected until it snaps.

Jason Rigby (02:32):

There's a great movie out right now. I saw a preview, a trailer of it. It's called Nobody.

Alexander McCaig (02:37):

Nobody?

Jason Rigby (02:37):

Yeah. It's an action movie. And it's a guy that used to be an operative and a super badass and then he had a family. And then he's chill and then somebody comes in and robs his house...

Alexander McCaig (02:47):

Oh then that's it, that's the switch.

Jason Rigby (02:49):

Then he tries to play like, "I'm not..." He doesn't want to create any type of emotion. You know what I mean? Everything has to be, I'm a family man. Well then everybody starts teasing him, his neighbor starts saying, "Well, why didn't you... If it would have been me, I would've hit him."

Alexander McCaig (03:01):

Yeah I bet you would have.

Jason Rigby (03:03):

So then finally he's like, "Screw this." And then he just goes on a tangent.

Alexander McCaig (03:07):

Oh that sounds fun.

Jason Rigby (03:08):

And then people come out of the woodwork from his past. Because they knew he is alive, they thought he was dead.

Alexander McCaig (03:12):

Oh yeah. We know this calling card.

Jason Rigby (03:13):

Yeah, we need to get this guy out. So, it's a pretty cool clip. Enough waste of time here. Speaking of Twitter, big data trends, artificial intelligence leads Twitter mentions in November 2020. The mentions. So #AI, #IOT, #machine learning.

Alexander McCaig (03:35):

This is great. So, how old people in their, their descriptions on Twitter say, "My opinion is my own." It's obviously your damn account. Whatever you say, I mean, you're doing it. No one else is typing it for you. So what I think is funny about it is that all these larger companies, where things are slow moving, very corporate lot of suites, when use a word like that, that's a buzzword.

Jason Rigby (03:58):

Yeah they're buzzwords.

Alexander McCaig (03:59):

The big boss wants to hear, "Well, what are we doing with AI?." Everybody else is using it, and so all these people with all their positions and they have their bio's listed on Twitter, they're all going to talk about it. Because they want to look like they're the forward looking person in their healthcare market.

Jason Rigby (04:17):

They are the futurists.

Alexander McCaig (04:17):

They're the futurist of whatever company, corporate company threat that moves like a sloth. And truly is getting anything done. But it sounds like the company's doing a lot. So there's a lot of buzz around Twitter with artificial intelligence. People are like, "Oh, that also we can get more funding for our projects. And if we do this, we're a forward moving company." And so when you start to look at that analysis, that's why so many people are talking about it. But AI and machine learning, because of everything that's available with online cloud computing, it's becoming commoditized. There's tons of algorithms that are pre-written, you can just kind of pump numbers in and then it spits out an output.

Jason Rigby (04:59):

Well, I've seen it with... I mean, speaking of these sloths companies that just have tons of R&D money. They're just throwing and then hoping something comes back. But the scary part about it is, and I think people need to understand this. You may go talk to a firm and give them permission to have your data. And then you get a report back and then you're going to base your marketing or whatever it may be, your supply chain, you're going to base that all off of that report that you have no substance of how did they come up with that.

Alexander McCaig (05:34):

Wait a minute. So all the guy got was, someone told them, "We had an AI system, come on here, analyze all these numbers we bought from a marketing firm and it told us this is what we need to do. We don't have to hire the marketing firm anymore, we've got all our answers right here for a quarter of the cost." And then the big boss is like, "That's fantastic."

Jason Rigby (05:52):

"We used AI."

Alexander McCaig (05:52):

Yeah. Well actually that's a bad idea. Who's the guy who wrote the algorithm on AI? Is this the angle we should be actually taking?

Jason Rigby (06:02):

Maybe this worked for Nabisco, but it's not working for...

Alexander McCaig (06:05):

Yeah, this is the cookie algorithm, okay, over here, and we can apply that to what we're doing over here. Everybody is very specific. So you got to make sure that the data that's going in, especially if you want to be trendy with your company, you got to make sure you have quality data going into those data sets, especially data that makes sense.

Jason Rigby (06:25):

Yeah. And I love it when you go on Twitter and everybody's using the #AI or... But it gives stats, it says there was 3045 mentions and this is just in November. Machine learning was 3,355. So people are going into that ML a little bit more. IOT, 1780, so we're getting into that. And I know IOT security has become a big concern and issue. Number four, analytics, 1,290.

Alexander McCaig (06:54):

Well, that should actually be number one.

Jason Rigby (06:56):

Yes.

Alexander McCaig (06:56):

They only push that down the list because people are like, "We've had big data for a while. It's not cool anymore. We got to have a robot, some cerebral digital brain figuring it out for us."

Jason Rigby (07:08):

You have all the answers inside your company.

Alexander McCaig (07:10):

All the answers are right there. I don't know, people are always cloned for things on the outside. If you don't have it, if you need an answer, go to your clientele or the people that support your company and get the data from them.

Jason Rigby (07:22):

Your customer, that purchases from you, is the deep learning.

Alexander McCaig (07:26):

They are the deep learning. Why are you spending money on a computer to give you an answer? You're trying to guess? You're a little hunch? No more hunches, okay? Perfect information. That information comes from people, go get it from them.

Jason Rigby (07:38):

And if you want to trend a hashtag, trend TARTLE, T-A-R-T-L-E.

Alexander McCaig (07:42):

Yeah, T-A-R-T-L-E. You want to [inaudible 00:07:43], because it's reasonable.

Jason Rigby (07:45):

Yes and-

Alexander McCaig (07:46):

It's a reasonable use of R&D.

Jason Rigby (07:49):

So what would be the difference between a company coming in, giving their data to an organization then an organization using an AI or machine learning or deep learning, whatever you want to call it, and then getting this report back. How is the difference between TARTLE and a firm like that?

Alexander McCaig (08:05):

A lot of those people get... They're using de-identified data. They're using information where you can't really figure out this is the primary sole source. And so when you come to TARTLE, you can buy identifiable data, you know exactly where it's coming from, when it's coming from it and why it's coming from there. So when we have a full picture over here in TARTLE, that's the information you want to put into your machine learning algorithms. Or your deep learning sets. Those are the things that you want to use to help drive your decisions for your company. Especially if your company is a service or a public product. For other companies maybe if you're a gas pipeline company, yeah obviously if you put machine learning on your pipe sensors for your IOT, that's going to help. There's very small human element involved. But if you have a high human element involved in your company, you need to go get that data from those people, because they're the supporters that allow your business to survive.

Jason Rigby (09:01):

Yeah, it be as simple as asking a question to a pipeline company, you could say, "Do you mind drones flying around to check your meters instead of company [crosstalk 00:09:10]?

Alexander McCaig (09:11):

Or here's a better one, if somebody is planning on screwing up the Alaskan wilderness, in a preserved area, why don't you ask everybody in the United States if they're okay with that.

Jason Rigby (09:22):

Yeah, exactly.

Alexander McCaig (09:23):

And then ask the Inuit people up there too, if they're okay with it. If they're not, don't do it. You shouldn't be allowed to do it. The thing is, there's such a lack of information that moves from a political realm to the public. They're supposed to be in service of public but there's a big separation of it. And we all know that it's obvious.

Jason Rigby (09:41):

But don't you think that polling in a safe matter that cannot be hacked is de-centralization at its best and democracy at its best?

Alexander McCaig (09:52):

De-centralization democracy at its highest. That's a perfect example. And again, that thing is bad as possible with TARTLE. So if I want to get polling data on all these people on a certain thing that might deal with the pipeline going through Alaska, let's ask the United States about it. People's land.

Jason Rigby (10:10):

See what they have to say.

Alexander McCaig (10:10):

See what they have to say. Talk about that, that's a trending Twitter thing right there. Let me give you my sentiment on that.

Jason Rigby (10:17):

We're done. We're out. Twitter.

Alexander McCaig (10:19):

See you Twitter, bye.

Speaker 1 (10:29):

Thank you for listening to TARTLE cast, with your hosts Alexander McCaig and Jason Rigby, Where humanity steps into the future and source data defines the past.

June 10, 2021

Why is AI (Artificial Intelligence) Trending on Twitter?

Why is AI Trending on Twitter

Why is AI (Artificial Intelligence) Trending on Twitter?

SHARE: 
BY: TARTLE

AI Twitter   

One of the leading trends on Twitter is Artificial Intelligence (AI). This isn’t surprising given all the buzz these days about anything having to do with machine learning, algorithms, data analysis, etc. But as usual, the trend only scratches the surface. The real question we should be asking is whether or not the fact that AI is getting lots of mentions on social media reflects any genuine understanding of what it is and how best to use it? 

At the very least, we can’t be certain that the trends indicate anything quite so positive as actual understanding of the issue. The very fact the term AI is popular helps drive it getting more mentions. Popularity begets popularity. It’s like this with any popular buzzword. A thing is perceived to be important so a lot of people trying to get attention or appear relevant will just start using it without any real understanding. Even if the person throwing around the buzzword understands it, he probably isn’t helping others to do so. There is a hilarious scene in the movie, New in Town that illustrates how silly this can be. The new manager (played by Renee Zellweger) at a food plant is trying to win over the employees by giving them a typical board room presentation, but at the local bar. They literally turn it into a drinking game, taking a drink every time she uses a buzzword (with a double for every one with a D).

Even the actual use of AI tends to be done in a very superficial, cookie-cutter way. Algorithms are sold, bought, and used with little consideration for whether or not they might actually be useful for the client. An algorithm useful for figuring out the best distribution model for Birkenstocks might not be that helpful in determining the best places to market Twinkies (not that anyone should even market Twinkies, you practically need an AI to decipher the ingredients to those tasty little carcinogen bombs). Your AI is only as useful as the algorithms is useful as the data you put into it. Both should be relevant to the kind of answers you’re trying to get. 

For example, if you want to know the status of the flow in your natural gas pipelines, then it makes good sense to have a bunch of passive sensors to track flow rate, pressure, and the integrity of the pipe itself. That is all relevant and useful data for what a gas company might like to know. However, if a clothing company is trying to develop a new kind of shirt, whether it is a new cut, or a particular line of graphics on the shirt, then it doesn’t make sense to look only at passive data. Such data can only tell you how a similar product has done in certain areas, which is great if you are tracking past or even current trends. However, if you are planning on launching a new product, you’re taking those trends and trying to project them into the future. To put it another way, you’re guessing. Based on that kind of passive data, there is no way to be sure that the market will even be there for what you are planning on selling. Just think of the attempt to sell men’s capris pants back in the late 1990s. Someone clearly needed more relevant data than they had because those were on the clearance racks in droves in a matter of a month or two. 

What if there was a way that you could get active data? Data that’s relevant to you now? That would actually be useful in planning for the future of your company and based on more than guess work? What if you could go right to the source? 

That’s what we are offering at TARTLE, a way for a business to reach out to actual and potential customers to determine not what you hope they want, but what they actually want. Through the TARTLE marketplace, you have access to actual people who can tell you what they think about your proposed product before you spend a ton of money manufacturing and marketing it. Armed with that information, you can then make use of your AI to figure out the best manufacturing process and marketing campaign for products that people actually want, saving you time and money in the end. 

What’s your data worth?

Feature Image Credit: Envato Elements
FOLLOW @TARTLE_OFFICIAL

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

TRANSCRIPT

Speaker 1 (00:07):

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

Alexander McCaig (00:25):

Twitter bird.

Jason Rigby (00:26):

Yes.

Alexander McCaig (00:27):

Tweet tweet.

Jason Rigby (00:28):

Tweet tweet. Twitter.

Alexander McCaig (00:29):

Twitter is all about sentiment.

Jason Rigby (00:33):

Yeah.

Alexander McCaig (00:33):

What're people talking about? What's the buzz?

Jason Rigby (00:40):

What did Justin Bieber say?

Alexander McCaig (00:42):

Yeah. Or all the stock were good. Did somebody tweet about something or Elon Musk with Dogecoin?

Jason Rigby (00:47):

Yes.

Alexander McCaig (00:47):

Come on. Dogecoin-

Jason Rigby (00:50):

What's funny is I had some of that just for fun. And it shot up yesterday.

Alexander McCaig (00:54):

Yeah, it did.

Jason Rigby (00:54):

Because we talked about it.

Alexander McCaig (00:55):

Unbelievable.

Jason Rigby (00:55):

So I looked on there and I was like, "I made a couple hundred bucks. How did I do that?" Beause it's Sunday and the markets closed. And I was like, "Oh my Dogecoin."

Alexander McCaig (01:04):

Oh my Dogecoin went up. And all the guys were like, "To the moon." It's like, no it's not hitting $20K like you think it is.

Jason Rigby (01:10):

Because that's just so fun, cryptocurrency [crosstalk 00:00:01:12]-

Alexander McCaig (01:12):

That's all it was. It's a deviation. It's like here's Bitcoin. Bitcoin uses a SHA-256 encryption. Same standard encryption that the US military uses blah blah blah. It takes a certain amount of machine processing power to clear a block. It takes about 10 minutes. With Dogecoin it uses a [inaudible 00:01:37] called scrypt. It's a different encrypting algorithm. And so the processing time for the work takes about, I don't know, two and a half minutes.

Jason Rigby (01:45):

Oh, wow.

Alexander McCaig (01:45):

I'm not... If I know correctly, I may be wrong. Somebody may correct me on that. And I know Litecoin, which is also another spin off of Bitcoin also has that probably a 10 and a half minute block clearing time using the [Skuveve 00:02:00] scrypt.

Jason Rigby (02:00):

Right, yeah.

Alexander McCaig (02:01):

I'm totally off track.

Jason Rigby (02:02):

No, I love it. Yeah.

Alexander McCaig (02:03):

Oh the Dogecoin, because we were talking about sentiments.

Jason Rigby (02:05):

Dogecoin, yeah, because I actually own some, funny part.

Alexander McCaig (02:08):

Yeah. It's all about the Doge.

Jason Rigby (02:09):

Shut up. I don't know, I just looked yesterday and then I happened to go on Twitter and I saw much joking around about it.

Alexander McCaig (02:15):

It's all about the Shiba Inu.

Jason Rigby (02:16):

Yeah, because it's like 22. Yeah, it is. Yeah. Those are amazing dogs.

Alexander McCaig (02:20):

Oh man.

Jason Rigby (02:21):

They're always very calm and collective and kind of guard. They're like a guard dog.

Alexander McCaig (02:25):

Until they snap.

Jason Rigby (02:26):

Yeah.

Alexander McCaig (02:27):

Like you.

Jason Rigby (02:27):

[crosstalk 00:02:28], yeah.

Alexander McCaig (02:29):

Calm and collected until it snaps.

Jason Rigby (02:32):

There's a great movie out right now. I saw a preview, a trailer of it. It's called Nobody.

Alexander McCaig (02:37):

Nobody?

Jason Rigby (02:37):

Yeah. It's an action movie. And it's a guy that used to be an operative and a super badass and then he had a family. And then he's chill and then somebody comes in and robs his house...

Alexander McCaig (02:47):

Oh then that's it, that's the switch.

Jason Rigby (02:49):

Then he tries to play like, "I'm not..." He doesn't want to create any type of emotion. You know what I mean? Everything has to be, I'm a family man. Well then everybody starts teasing him, his neighbor starts saying, "Well, why didn't you... If it would have been me, I would've hit him."

Alexander McCaig (03:01):

Yeah I bet you would have.

Jason Rigby (03:03):

So then finally he's like, "Screw this." And then he just goes on a tangent.

Alexander McCaig (03:07):

Oh that sounds fun.

Jason Rigby (03:08):

And then people come out of the woodwork from his past. Because they knew he is alive, they thought he was dead.

Alexander McCaig (03:12):

Oh yeah. We know this calling card.

Jason Rigby (03:13):

Yeah, we need to get this guy out. So, it's a pretty cool clip. Enough waste of time here. Speaking of Twitter, big data trends, artificial intelligence leads Twitter mentions in November 2020. The mentions. So #AI, #IOT, #machine learning.

Alexander McCaig (03:35):

This is great. So, how old people in their, their descriptions on Twitter say, "My opinion is my own." It's obviously your damn account. Whatever you say, I mean, you're doing it. No one else is typing it for you. So what I think is funny about it is that all these larger companies, where things are slow moving, very corporate lot of suites, when use a word like that, that's a buzzword.

Jason Rigby (03:58):

Yeah they're buzzwords.

Alexander McCaig (03:59):

The big boss wants to hear, "Well, what are we doing with AI?." Everybody else is using it, and so all these people with all their positions and they have their bio's listed on Twitter, they're all going to talk about it. Because they want to look like they're the forward looking person in their healthcare market.

Jason Rigby (04:17):

They are the futurists.

Alexander McCaig (04:17):

They're the futurist of whatever company, corporate company threat that moves like a sloth. And truly is getting anything done. But it sounds like the company's doing a lot. So there's a lot of buzz around Twitter with artificial intelligence. People are like, "Oh, that also we can get more funding for our projects. And if we do this, we're a forward moving company." And so when you start to look at that analysis, that's why so many people are talking about it. But AI and machine learning, because of everything that's available with online cloud computing, it's becoming commoditized. There's tons of algorithms that are pre-written, you can just kind of pump numbers in and then it spits out an output.

Jason Rigby (04:59):

Well, I've seen it with... I mean, speaking of these sloths companies that just have tons of R&D money. They're just throwing and then hoping something comes back. But the scary part about it is, and I think people need to understand this. You may go talk to a firm and give them permission to have your data. And then you get a report back and then you're going to base your marketing or whatever it may be, your supply chain, you're going to base that all off of that report that you have no substance of how did they come up with that.

Alexander McCaig (05:34):

Wait a minute. So all the guy got was, someone told them, "We had an AI system, come on here, analyze all these numbers we bought from a marketing firm and it told us this is what we need to do. We don't have to hire the marketing firm anymore, we've got all our answers right here for a quarter of the cost." And then the big boss is like, "That's fantastic."

Jason Rigby (05:52):

"We used AI."

Alexander McCaig (05:52):

Yeah. Well actually that's a bad idea. Who's the guy who wrote the algorithm on AI? Is this the angle we should be actually taking?

Jason Rigby (06:02):

Maybe this worked for Nabisco, but it's not working for...

Alexander McCaig (06:05):

Yeah, this is the cookie algorithm, okay, over here, and we can apply that to what we're doing over here. Everybody is very specific. So you got to make sure that the data that's going in, especially if you want to be trendy with your company, you got to make sure you have quality data going into those data sets, especially data that makes sense.

Jason Rigby (06:25):

Yeah. And I love it when you go on Twitter and everybody's using the #AI or... But it gives stats, it says there was 3045 mentions and this is just in November. Machine learning was 3,355. So people are going into that ML a little bit more. IOT, 1780, so we're getting into that. And I know IOT security has become a big concern and issue. Number four, analytics, 1,290.

Alexander McCaig (06:54):

Well, that should actually be number one.

Jason Rigby (06:56):

Yes.

Alexander McCaig (06:56):

They only push that down the list because people are like, "We've had big data for a while. It's not cool anymore. We got to have a robot, some cerebral digital brain figuring it out for us."

Jason Rigby (07:08):

You have all the answers inside your company.

Alexander McCaig (07:10):

All the answers are right there. I don't know, people are always cloned for things on the outside. If you don't have it, if you need an answer, go to your clientele or the people that support your company and get the data from them.

Jason Rigby (07:22):

Your customer, that purchases from you, is the deep learning.

Alexander McCaig (07:26):

They are the deep learning. Why are you spending money on a computer to give you an answer? You're trying to guess? You're a little hunch? No more hunches, okay? Perfect information. That information comes from people, go get it from them.

Jason Rigby (07:38):

And if you want to trend a hashtag, trend TARTLE, T-A-R-T-L-E.

Alexander McCaig (07:42):

Yeah, T-A-R-T-L-E. You want to [inaudible 00:07:43], because it's reasonable.

Jason Rigby (07:45):

Yes and-

Alexander McCaig (07:46):

It's a reasonable use of R&D.

Jason Rigby (07:49):

So what would be the difference between a company coming in, giving their data to an organization then an organization using an AI or machine learning or deep learning, whatever you want to call it, and then getting this report back. How is the difference between TARTLE and a firm like that?

Alexander McCaig (08:05):

A lot of those people get... They're using de-identified data. They're using information where you can't really figure out this is the primary sole source. And so when you come to TARTLE, you can buy identifiable data, you know exactly where it's coming from, when it's coming from it and why it's coming from there. So when we have a full picture over here in TARTLE, that's the information you want to put into your machine learning algorithms. Or your deep learning sets. Those are the things that you want to use to help drive your decisions for your company. Especially if your company is a service or a public product. For other companies maybe if you're a gas pipeline company, yeah obviously if you put machine learning on your pipe sensors for your IOT, that's going to help. There's very small human element involved. But if you have a high human element involved in your company, you need to go get that data from those people, because they're the supporters that allow your business to survive.

Jason Rigby (09:01):

Yeah, it be as simple as asking a question to a pipeline company, you could say, "Do you mind drones flying around to check your meters instead of company [crosstalk 00:09:10]?

Alexander McCaig (09:11):

Or here's a better one, if somebody is planning on screwing up the Alaskan wilderness, in a preserved area, why don't you ask everybody in the United States if they're okay with that.

Jason Rigby (09:22):

Yeah, exactly.

Alexander McCaig (09:23):

And then ask the Inuit people up there too, if they're okay with it. If they're not, don't do it. You shouldn't be allowed to do it. The thing is, there's such a lack of information that moves from a political realm to the public. They're supposed to be in service of public but there's a big separation of it. And we all know that it's obvious.

Jason Rigby (09:41):

But don't you think that polling in a safe matter that cannot be hacked is de-centralization at its best and democracy at its best?

Alexander McCaig (09:52):

De-centralization democracy at its highest. That's a perfect example. And again, that thing is bad as possible with TARTLE. So if I want to get polling data on all these people on a certain thing that might deal with the pipeline going through Alaska, let's ask the United States about it. People's land.

Jason Rigby (10:10):

See what they have to say.

Alexander McCaig (10:10):

See what they have to say. Talk about that, that's a trending Twitter thing right there. Let me give you my sentiment on that.

Jason Rigby (10:17):

We're done. We're out. Twitter.

Alexander McCaig (10:19):

See you Twitter, bye.

Speaker 1 (10:29):

Thank you for listening to TARTLE cast, with your hosts Alexander McCaig and Jason Rigby, Where humanity steps into the future and source data defines the past.

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