The world is awash in data. There is data coming in from research, phone calls, satellites, phones, fitbits and even your Bluetooth connected fridge. Collecting data isn’t our problem, being able to process it is. Before you can process it though, it needs to get transported. In that sense it’s like any other raw product. Like a piece of iron ore, it needs to be transported to a foundry and dumped into a furnace to be refined so it can be turned into something useful. Data needs to make it from your IOT device to a server where it can be processed and analyzed. Too often, transportation and processing are bottlenecks in the transformation of raw data into useful information.
Think about a highway, you can only increase the volume of cars on the road so much before it descends into chaos. Yet there may still be a need to get even more vehicles, or at least people and products from point A to point B. So you need to come up with new ways to handle the traffic. Data is similar. Most data is still transferred over some kind of copper wire. That wire can handle only so many electrons moving through it, just like a highway only being able to accommodate so many vehicles. For years though, those older copper cables have been getting replaced with fiber optics. Basically long pieces of very thin, flexible glass, fiber optics use photons instead of electrons to transfer data. Immediately, there is a gain since the medium allows for faster movement of data. There are also new fiber optics being developed that allow for speeds up to a 100 times faster than what is currently available. How fast is that? Just for a point of reference, imagine walking at 100 times the pace you do now. Instead of power walking at around 3-4 mph, you would suddenly be able to walk from Chicago to Washington D.C. in less than three hours.
Yet, that presents its own problems. Fiber optics have a massive capacity for data because of their ability to send many signals simultaneously. However, when you get too many signals going through at once it becomes a jumbled mess. It’s similar to how one person’s echo is easy to understand but the echo of a choir singing is indecipherable to the human ear. Thankfully, there are clever software writers out there who can write the necessary algorithms to untangle that mess. In fact, with the new fiber optics that will be coming out soon, the bottleneck won’t be the data transportation, it’ll be the ability to untangle that data into discernable bits of information so it can be analyzed. Essentially, the physical technology is already here, we are just working to bring the software side of things up to the same level.
In a sense the kind of data analytics and processing that TARTLE works with is similar. The standard way of aggregating data from second and third parties has a lot of noise embedded in those signals, even after it has been processed. That is because there is a lot of circumstance and context mixed into the kind of data that is gleaned off monitoring your devices and internet activity. And as it turns out, there is no mere algorithm that can filter out that noise. The only way to get a clearer signal is by doing something the big companies rarely do, go to the source, to you the individual. The answer to “why” you did one thing instead of another is the only algorithm that can truly help decipher that data. It gives the context that is missed when companies only look at your data and never to you as a person. TARTLE provides an avenue to get the answer to “why”, making our system the most efficient way to get clear and accurate data about people and why they do what they do.
What’s your data worth? Sign up and join the TARTLE Marketplace with this link here.
The world is awash in data. There is data coming in from research, phone calls, satellites, phones, fitbits and even your Bluetooth connected fridge. Collecting data isn’t our problem, being able to process it is. Before you can process it though, it needs to get transported.
Speaker 1 (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 (00:27):
It always helps when you cue the mics up.
Jason Rigby (00:28):
Yes. We're not talking, yeah.
Alexander McCaig (00:31):
Just blabbing away, just muted on camera, blah, blah, blah, blah, blah.
Jason Rigby (00:34):
Maybe that would probably be our highest viewed video.
Alexander McCaig (00:37):
The one where we're not talking.
Jason Rigby (00:38):
Yes.
Alexander McCaig (00:39):
People are like, "Those guys look like idiots."
Jason Rigby (00:39):
Yeah, exactly.
Alexander McCaig (00:39):
"It must be nice to be an idiot."
Jason Rigby (00:42):
Yes, it must be nice.
Alexander McCaig (00:45):
So, this is an interesting article, what we have here with data. It's not all about analysis. This is like an Elon Musk problem. Elon Musk is like, "Highways are two dimensional. So, how do we three dimensionalize highways to remove the traffic problem?" Well, we've also got to look at data. There's only so much information that can go through a pipe at once. You have to expand on how you can deal with the volume, and then deal with the noise that comes from it.
Alexander McCaig (01:13):
More cars on the highway means you've got to have a higher sound blockers as you're going through Jersey, because they built homes right on the highway, because there's no space. Stop living there. Jersey is not fun to live in.
Jason Rigby (01:24):
But, isn't his alternative tunnels?
Alexander McCaig (01:26):
No, that's what I'm saying. Yeah. So, when there's a more availability of something, what are the other effects that sit outside of it? So more volume, more noise. That transitions over to what we're looking at with these fiber optic sensors. What is it about them? Give me some-
Jason Rigby (01:40):
Well, there was a new... In ScienceDaily, which I love, I get their newsletter Sciencedaily.com. Shout out, we don't have sponsors or anybody, they had an article about new fiber optic sensors transmit data up to 100 times faster. Think about that, 100. We always say things, but we never really think about that.
Alexander McCaig (01:58):
That's 100 times... Imagine if you walked 100 times faster.
Jason Rigby (02:01):
Yes, yeah.
Alexander McCaig (02:02):
If I walked five times faster, I'd be walking at 20 miles an hour?
Jason Rigby (02:05):
Yes, yeah.
Alexander McCaig (02:06):
I've got huge legs, so I can probably speed walk three or four. Imagine me speed walking at 20 miles an hour. I'm not even running yet.
Jason Rigby (02:13):
Yeah, that's only-
Alexander McCaig (02:14):
If I was running and I was running five times faster, I'd be running at like, I don't know, close to 100 miles an hour.
Jason Rigby (02:20):
Yeah, but think 100 times faster.
Alexander McCaig (02:22):
100 times, I'm walking at 400 miles an hour.
Jason Rigby (02:24):
Yeah, exactly. You're breaking the sound by your walking.
Alexander McCaig (02:28):
The thing is, it's like-
Jason Rigby (02:29):
Or when you would run, you-
Alexander McCaig (02:30):
... I'm going and it's like a cone around me, just a pressure cone, just shattering car windows when you're walking.
Jason Rigby (02:42):
Yeah, exactly. Yeah. What was the one with Will Smith? There was a movie where he would do that, and he was a disgruntled superhero.
Alexander McCaig (02:47):
Oh yeah.
Jason Rigby (02:47):
Remember that? The cars were all-
Alexander McCaig (02:49):
It was one named Hancock.
Jason Rigby (02:51):
Yeah, yeah, exactly. Yeah. Yeah. He would just... Like cars, would burst, and windows would shatter, and he just didn't give to-
Alexander McCaig (02:56):
Whatever.
Jason Rigby (02:58):
Yeah. So EPF engineers, they developed this advanced encoding and decoding system that allows fiber optic sensors to send data up to 100 times faster, and over, this is what I thought was interesting, a wider area. Well, how did they did that? Well, what they talked about is, there's these conventional sensors that take measurements at given points like the monitors. So, you would put a thermometer in your mouth and you would take it at that given point.
Jason Rigby (03:21):
Then what they're doing is saying that, you can take this and it'll take all across the whole tube, the fiber optic tube, it can read data all across. Instead of one at the end or at the beginning, it can read it on a bunch of different touch points.
Alexander McCaig (03:39):
I guess we've got to break that down into a little bit of a metaphor here. In the oil and gas industry, and you'll see it out here in national parks where they shouldn't be, I'm going to be very Patagonia with that comment, their pipelines have to be measured. You've got to make sure you don't have any leaks or there's no over pressurizations in certain areas, you've got to measure flow rates. So, all along their pipe has these sensors. The idea here is that, with a fiber optic line, and fiber optics is this really, really clear piece of glass that's been stretched like a thin web. It's flexible, it's flexible glass, like you find on most phone screens. I have a phone that has flexible glass.
Alexander McCaig (04:26):
The idea is that, light gets admitted through one end, there's a pulse which represents a zero and one, your binary data, and it's received on the other end. What you want to do is, you want it to move at the speed of light. But, there's things that drag that down. It's a function of what it's wrapped in, the quality of the glass, there's a lot of stuff that actually slows down a photon moving through a substance, it's propagation of light through a medium. That's the concept.
Alexander McCaig (04:52):
Now, if we're looking at this, the question is, if I'm sending out on one end and receiving on the other, like you said, I'm going to put these sensors like you find on a pipeline throughout this fiber-optic line. Now, as that sensor picks up on whatever it has to sense, I need it to pump light both ways.
Jason Rigby (05:10):
Right.
Alexander McCaig (05:11):
So, as the increase in data from all these sensors is increasing dramatically, now I need to cut through all the noise with all these signals that are coming out, and they're overlaying one another. The Bose noise canceling headphones or Beats, you have one sign wave going out. What happens is, when you are met with that sign wave into your headphones from just ambient noise that's happening around you, there's another one that comes in and it has the pole... It's the inverse of it.
Jason Rigby (05:41):
Right.
Alexander McCaig (05:41):
So, you neutralize the sound wave coming in. That's a lot of the problem when you have all these sensors on a fiber optic line, they're smashing into one another. So, different waves and different resonances depending on the strength of the signal, they can corrupt all that data. So, how is it that you can give each sensor its own specific identification? So as it comes out, or a lot of things are happening like an earthquake, like if you're running the fiber optic line across land and you want to sense if there's a tremor, well, if it's all coming in at once, how do we decode all of those signals? It'll look like a mismatch, like a bad symphony.
Jason Rigby (06:19):
Yeah.
Alexander McCaig (06:20):
That is the beauty of what is going on here with the data. It's not the fact that it couldn't move 100 times faster, it's the fact that they can process it 100 times faster with a new algorithm, that specifically identifies this wind instrument as from chair one to chair seven. Does that make sense?
Jason Rigby (06:38):
Yeah, no, that makes sense. In the article, they talk about the engineers that designed this. Shout out to Zhisheng Yan.
Alexander McCaig (06:46):
It sounds like China. Is that China?
Jason Rigby (06:49):
Yeah, that sounds like. There was a Simon Zas... This is like a Polish name, I think.
Alexander McCaig (06:55):
Zaslawski?
Jason Rigby (06:56):
Yeah, Zaslawski. They developed this new system of encoding and decoding data. But, I like what they said, "The engineers describe their system is working like an echo. If you shout a single word, you hear the word back. But if you sing out a song, what you hear back is a blend of sounds that are hard to distinguish."
Alexander McCaig (07:11):
Or if you have 1,000 people yelling.
Jason Rigby (07:12):
Yes. "You would need a key to decipher the sounds and make them intelligible. Fiber optic sensors function in a similar manner, except that an instrument sends out light pulses rather than sounds," which is the same if you like it.
Alexander McCaig (07:24):
Light and sound are the same thing.
Jason Rigby (07:25):
Yeah.
Alexander McCaig (07:25):
If we understand our physics properly. Both are a wave.
Jason Rigby (07:29):
So, if you think of it as that echo going along that fiber, and then you're in that canyon, you're singing and you keep singing, then it gets like rambled, if somebody has ever done that, the signals bounce back the fiber, and then the device decodes them, turning the signal into usable data, through a key that they've developed.
Alexander McCaig (07:48):
Yeah. Honestly, I don't know what their algorithm looks like, but I'm going to take a best guess that they probably studied how sonar works with whales.
Jason Rigby (07:56):
Yes.
Alexander McCaig (07:57):
Because, you can actually tell a specific grouping of whales by listening to their symphony in the water, and you can know how many whales it is.
Jason Rigby (08:04):
I didn't know this. This is funny. They were talking about this... Yesterday I was reading an article on this. Orcas have, because I love orcas, there's one right there, and you've got one on your cup. but-
Alexander McCaig (08:17):
Do you know, some time ago, my high school, do you know what our mascot was?
Jason Rigby (08:22):
It was an orca?
Alexander McCaig (08:22):
Yeah, it was a sea wolf.
Jason Rigby (08:23):
Oh, that's cool.
Alexander McCaig (08:24):
The orca, yeah.
Jason Rigby (08:25):
So, they actually have a different... You know how we have like Southern dialect, and it's like, "Hi, how are you doing?"
Alexander McCaig (08:35):
Yeah, I know.
Jason Rigby (08:35):
You know that? The orcas have that, depending on... If they're in Seattle, orcas have a little... They all have the same language, but New Zealand orcas, Seattle orcas, Alaska orcas, they all have a different style of dialect.
Alexander McCaig (08:48):
I love that.
Jason Rigby (08:49):
Isn't that cool?
Alexander McCaig (08:49):
I think that's super cool.
Jason Rigby (08:52):
Their brains are as responsive as, or more they said.
Alexander McCaig (08:56):
Yeah, and they're huge.
Jason Rigby (08:57):
Yeah. Yeah, exactly.
Alexander McCaig (08:58):
But not to get too sidetracked, but the idea, I haven't looked at their algorithm, but I'm assuming, because they talked about genetic... What was the word here?
Jason Rigby (09:06):
I like, they group the light pulses as a sequence, so the signals bounce back with greater intensity. However, they didn't solve the echo problem.
Alexander McCaig (09:12):
Yeah. When it bounces back in sequence, that's when resonance of two things match one another. If you take one resonance and another and combine them, they actually doubles down on the power of that wave.
Jason Rigby (09:23):
Yes. Yeah. They talk about it here. So, how they did it, the algorithm, "The method employs special, genetic optimization algorithms to cope with imperfections."
Alexander McCaig (09:32):
Yeah.
Jason Rigby (09:33):
It said, "Other systems are limited with that."
Alexander McCaig (09:36):
When they talk about genetic, it has that biological approach, so I would assume that the first place you'd go to, to study massive sounds would be whale sounds.
Jason Rigby (09:45):
Yes.
Alexander McCaig (09:45):
Because you can really... There's a lot of audio in the water. So, if you're going to test on how that works, well just test it... The animals have perfected it. They can understand very well who's what in the water, as they're swimming around, because it's pitch black.
Jason Rigby (09:58):
Yeah. What I thought was really interesting was this.
Alexander McCaig (10:00):
I've got an orca on my cup.
Jason Rigby (10:02):
I know, yeah.
Alexander McCaig (10:04):
Is this an aquatic episode?
Jason Rigby (10:05):
This is the sea wolf. I like how they named it sea wolf. We're going to need sea wolf t-shirts.
Alexander McCaig (10:12):
I can get us some. I can get us some.
Jason Rigby (10:12):
Really?
Alexander McCaig (10:13):
Yeah, 100%.
Jason Rigby (10:13):
You're going up there, right?
Alexander McCaig (10:14):
Yeah, I'll go grab us some.
Jason Rigby (10:15):
Yeah, grab me... I want a sea wolf t-shirt. I would love an orca.
Alexander McCaig (10:18):
I can do that.
Jason Rigby (10:19):
Yeah, that would be awesome.
Alexander McCaig (10:19):
No problem.
Jason Rigby (10:21):
But what they said is, instead of having to change everything out, and this is them thinking ahead as engineers, they said that, with their system, you just have to add a software program to your existing equipment.
Alexander McCaig (10:31):
Yeah. A lot of technology that we have is inefficient in the fact of how we process it. Think about AM and FM radio. Did that come out '30s, '40s, maybe earlier? We've only perfected it so now you can get HD signals. The technology has been around forever, but that was a function of algorithm output and receiving that we've changed. It wasn't that we were boosting the signal. It's just like, "No, how do we analyze those airwaves that are coming in?" We've had all the technology, it's all been there. It's just the quality of how we look at it and retune it to get the most value out of it. That's the value add. Don't redo your pipeline or redo your fiber optics, just like, "Why don't we be efficient and just pull in this new algorithm?"
Jason Rigby (11:16):
Speaking of being efficient and pulling a new algorithm, if somebody, because it's Tcast, if somebody wanted to sign up for TARTLE and start getting paid for their data-
Alexander McCaig (11:24):
They would go to Tartle.co. The data's always there, you just need an efficient way of retrieving it.
Jason Rigby (11:31):
Right.
Alexander McCaig (11:31):
Well, the most efficient way to retrieve data from people is to go to TARTLE.co or retrieve data from businesses that are selling their information. That's all there, and it would take you less than 30 seconds to sign up and start to find those answers that you've been looking for.
Jason Rigby (11:45):
From multiple touchpoints.
Alexander McCaig (11:47):
Multiple touchpoints. It's like a huge fiber optic lens, it's a web.
Jason Rigby (11:50):
Yes.
Alexander McCaig (11:50):
We have a web of connected... If the people are these fiber optic sensors, we have a lot of them.
Jason Rigby (11:56):
We have a lot of them, yeah.
Alexander McCaig (11:57):
They're all talking to one another. So how are you going to read those interactions, those interplays?
Jason Rigby (12:01):
That's why you have the software system.
Alexander McCaig (12:04):
Yeah. That's why you need a software system to manage it.
Jason Rigby (12:06):
Picks out the echo.
Alexander McCaig (12:06):
Yeah. We're helping you pick out echoes all day long, like a big whale.
Jason Rigby (12:12):
Yes, exactly.
Alexander McCaig (12:13):
Okay. I'm going to end on that note. Thanks.
Jason Rigby (12:13):
And we're done.
Speaker 1 (12:23):
Thank you for listening to TARTLE Cast with your hosts, Alexander McCain and Jason Rigby, where humanity steps into the future, and the source data defines the path. What's your data worth?
