Voice and language recognition software is getting better and better. Once upon a time, it was something that was extremely clunky and unreliable and even the best systems required you to spend far too much time training them while speaking extremely slowly and enunciating like…well…like a computer. However, at last the systems have improved to the point where it’s possible to at least accurately convey meaning through talk to text features without having to clarify every other word. In fact, I know a trucker who does most of his communication using talk to text on his phone. It makes a few mistakes here and there but its accuracy is still pretty impressive considering he’s speaking normally while in a large moving vehicle.
Then there are the voice assistants on our phones. Whether you talk to Siri, Alexa, or Cortana (all four of you, you know who you are) that voice recognition starts out needing a little training but nothing like it used to. And the more you use it to look up local restaurants, find a factoid to settle an argument or to book a hotel room, the more accurate it gets. Now, they are even in the homes of many, listening constantly for you to need their assistance with something – everything from dimming the lights to spinning up your favorite playlist on Spotify.
The improvements in this software hold a lot of potential. It has already been used for years in business to accommodate certain employees who may not be able to speak clearly or who lose the use of their arms. It is also a much more efficient way to record information than the increasingly dated keyboard. Typing is inherently inefficient, creating the possibility for misspellings that need to be corrected lest they convey an unintended meaning. It also requires a keyboard, which adds space, weight and money to your computer. As voice recognition software improves, the keyboard can be replaced with a simple microphone, probably the one on your phone. Imagine being able to compose reliable messages for business, a book, notes on a law case and have them all transcribed without having to take the time to proofread them. The time savings would be impressive. Or perhaps a more mundane situation in which you’re sitting at home and have a craving for pizza, but you can’t quite remember the name of the place you got it from last month. You throw the question out into the air and your device reminds you of the name, the price and asks you if you’d like it to order a pizza for you. If you think about it, Alexa and other smart devices are only a step or two away from that level of functionality.
Another use would be in hospitals. Embedded microphones would record conversations with your doctor, highlighting the important points and recording all of the important information. This would save time and increase efficiency in a number of ways. No more would nurses and admins have to spend hours on data entry, with all the potential transcription errors that entail. Incidentally, that would also save you having to answer the same questions three times every time you go in for a checkup. It also means no one, or at least very few people have to come in contact with the Petri dishes known as keyboards in an environment that should be kept as sterile as possible.
Lectures and presentations could be recorded and transcribed instantly, making information readily available in real time. The possibilities are enormous.
Yet, there are potential problems that arise, namely, who owns all that data getting generated and recorded? Is it the place where the recording happens? The place where they are stored? Some other party? At TARTLE, we believe all the data you generate is yours. So if it’s your information and your data that is being recorded, then you deserve to be the primary beneficiary of sharing it, or of deciding whether you want to share that data or not. These are questions that will be addressed sooner or later in the legislative realm which is why we are encouraging people to sign up at tartle.co to join the TARTLE movement. Together we can help steer that eventual legislation in a direction that will benefit not just a few, but each person who works to generate that data in the first place.
What’s your data worth? Sign up and join the TARTLE Marketplace with this link here.
Voice and language recognition software is getting better and better. Once upon a time, it was something that was extremely clunky and unreliable and even the best systems required you to spend far too much time training them while speaking extremely slowly and enunciating like…well…like a computer.
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 (00:19):
(singing) Natural language processing.
Alexander McCaig (00:27):
Jason, have you ever studied anything other than English?
Jason Rigby (00:31):
Yes, I have.
Alexander McCaig (00:31):
Have you ever stepped out of your natural Southern dialect?
Jason Rigby (00:36):
Yeah, I studied Russian for a little bit and then I studied [crosstalk 00:00:40] Spanish. This isn't Borat, bro. [inaudible 00:00:47] The first one was better than the second one.
Alexander McCaig (00:55):
Yeah, it was just because it was so original.
Jason Rigby (00:58):
Yeah, exactly.
Alexander McCaig (00:59):
Totally edgy.
Jason Rigby (01:00):
Yeah.
Alexander McCaig (01:01):
But yeah, so you have studied other languages?
Jason Rigby (01:03):
Yes. And my brain is like... It's a tough road for it to go down. [crosstalk 00:01:12] I know you're studying Germany. German.
Alexander McCaig (01:16):
Ah, Germany!
Jason Rigby (01:17):
I've been studying Germany. [Crosstalk 00:01:22].
Alexander McCaig (01:22):
Yeah, German's an interesting language. But the process of how we learn a language and how machines learn from us, speaking languages, is interesting. I'm sure when you learned whatever language you had, it was just in a text format or like having a teacher.
Jason Rigby (01:39):
Mm-hmm (affirmative).
Alexander McCaig (01:40):
Okay.
Jason Rigby (01:40):
Listening to CDs or tapes over and over and over again.
Alexander McCaig (01:45):
Let's talk about this point. You are listening to CDs and tapes over and over and over again.
Jason Rigby (01:51):
Right.
Alexander McCaig (01:51):
You get the same input, the same speaker.
Jason Rigby (01:53):
Right. And then trying to say it right.
Alexander McCaig (01:55):
And you're trying to repeat it.
Jason Rigby (01:56):
Right.
Alexander McCaig (01:56):
Okay. So there's been a little turn of the tides here. And I want to say, shout out to Rosetta Stone. Their native speakers and the ability for it to ask you to pronounce something and make sure that you match those intonations and the pieces of the dialect that is natural that to that specific language is quite incredible. And that obviously took the next step, when you have things like Alexa.
Jason Rigby (02:26):
Right.
Alexander McCaig (02:26):
And it's constantly listening to what you're saying. And it has to go back into Amazon servers and analyze all the different dialects, tones, maybe certain nuances of how people speak in a certain area. But still recognize, "Oh, me and my machine machine algorithm, machine learning algorithm is processing what's going on here with this language."
Alexander McCaig (02:50):
So it takes that obvious step where you, in your learning process, I'm going to listen, listen, listen, listen, listen. And now what we've done is we put this into this cloud format where we start pulling in all the state of people speaking, and machines can analyze and listen, listen, listen. And not just one voice, but to millions of voices.
Jason Rigby (03:10):
Yes.
Alexander McCaig (03:11):
So the data sets for these natural language processing algorithms that sit up in the cloud have become very, very advanced. And they're now taking steps like, "Okay, we've done a good job analyzing English. How do we branch this off into more languages now?" First of all, English is a garbage language. You know I get into this all the time.
Jason Rigby (03:33):
Mm-hmm (affirmative).
Alexander McCaig (03:34):
English does a very poor job in terms of its descriptors for describing your world or how you feel. Emotions or pushing multiple concepts together. The best languages in the world for that are German and Sanskrit, hands down. In terms of description. But thanks to British imperialism and everything else, a lot of people speaking English. Yeah. A lot of people speak English all over the globe, even though it's, not very good. It's not great. So, we've gone through that analysis with that natural language processing. And now there's like three fundamental benchmarks that it's starting to hit, where, okay, how are we going to advance these algorithms? How are we going to pull in more languages? And the third one being, you had it on here?
Jason Rigby (04:21):
Yeah. Accuracy. The state-of-the-art. Keep improving. And then second is more pre-trained models become widely available. And number three is better support for under-representative languages.
Alexander McCaig (04:30):
Yeah. So accuracy's huge. Is it really giving you the proper feedback from what I'm saying? Every word I output is data. So in our human context of you and I-
Jason Rigby (04:44):
And we're going to learn this. I want people to hear what Alex just said. Because as Amazon Alexa is just the beginning. Because typing is so inefficient.
Alexander McCaig (04:54):
Typing is incredibly incredibly inefficient.
Jason Rigby (04:54):
When these machine learning algorithms get our voices down perfectly, we're going to say, what was that pizza? We're going to talk to a device and say, what was that pizza ordered a month ago? It was so good.
Alexander McCaig (05:06):
Yes.
Jason Rigby (05:06):
Where was that place at? And they're going to be like, "Duh-nuh-nuh-nuh. It was $28 and 62 cents. Would you like me to order that now for you?"
Alexander McCaig (05:13):
Yeah. And so-
Jason Rigby (05:14):
That's where our advertising marketing is going to.
Alexander McCaig (05:17):
Yeah. It's going to feel like that sort of Jarvis system that Ironman uses, Tony Stark.
Jason Rigby (05:21):
Yes, exactly.
Alexander McCaig (05:22):
It becomes very conversational. It understands how you operate and how-
Jason Rigby (05:25):
Her.
Alexander McCaig (05:26):
Or her and how you like to receive that information back.
Jason Rigby (05:29):
I need one of those.
Alexander McCaig (05:30):
I would love one. Super cool.
Jason Rigby (05:32):
I could be like, I'm feeling a little lonely.
Alexander McCaig (05:34):
Typing is naturally inefficient.
Jason Rigby (05:36):
Yes.
Alexander McCaig (05:36):
And typing also has a lot of mistakes.
Jason Rigby (05:38):
Mm-hmm (affirmative).
Alexander McCaig (05:38):
But we're moving into this world of-
Jason Rigby (05:40):
Especially when I send an email.
Alexander McCaig (05:41):
Oh my gosh. [crosstalk 00:05:46] Smash my head against the wall.
Jason Rigby (05:47):
You could see how quick I type stuff like that. [crosstalk 00:05:52].
Alexander McCaig (05:52):
Watching the dots go on like 10 minutes. And then all I get back is like a seven word sentence. I'm like, "This is unbelievable."
Jason Rigby (06:00):
I literally go... I should probably use the voice [crosstalk 00:06:04]. I wonder if the voice thing would work good with Alexa?
Alexander McCaig (06:07):
It's improved a lot.
Jason Rigby (06:08):
I mean, not Alexa, the Apple part. But is Slack... That would be interesting. So is it using Apple's voice recognition to type the words out or is it using Slack's?
Alexander McCaig (06:17):
No. It's not using Slack. It's using Apple. It's built into their keyboard, their software. And so then it just translates that into script.
Jason Rigby (06:24):
That would probably do a lot better job than my typing, huh?
Alexander McCaig (06:26):
Yeah. Much better, much better. And what's cool about it is that there is a difference between how someone types, their tone, their grammar, their syntax, as opposed to how they're actually speaking. And so when we look at these sort of inputs and the changes, accuracy is going to be one thing because there's so many datasets coming in. There's so many inputs by human beings that it can say, oh, that was accurate or it wasn't.
Alexander McCaig (06:52):
But that's been holding on a human being to come in and say, that's not what I said. So we're here interacting essentially with these models to actually improve that accuracy. And it's important. Like I want to go back to the original concept. Every time you and I are speaking, that is data. In a biological format, every word coming out of my mouth, you receive it as tone through the ears, and then you process it as a thought. And then that thought can go back in inside of you and then you reform it and then spit something out. It's like, you're doing your own biological algorithm in your head, to say, "Oh, I understand what Alex said." I know what my filter is.
Alexander McCaig (07:25):
Was that accurate? Yes. It wasn't, no. Do I need clarification? Do I need to go back out and retest my algorithm? Yes I do. So this interaction that you and I naturally have is something that has being put into these machine learning models. And because there are so many scientists working towards natural language processing, these preset models have already come out. So if you have a startup and you're saying, "I want to get into language processing or create some sort of new product or service", you can go through a whole catalog of different offerings of people that have say, "This is my algorithm, it's used this way or for this specific subject. And it's used in the medical field, legal transcription." Why do I still have a stenographer in court?
Jason Rigby (08:03):
Yeah. That's always interesting.
Alexander McCaig (08:05):
Bye. See you later.
Jason Rigby (08:06):
Yeah, when we have artificial intelligence.
Alexander McCaig (08:08):
If you want to have the least amount of touch points in a hospital, especially to keep places clean, why do you have keyboards? They're disgusting.
Jason Rigby (08:15):
Right.
Alexander McCaig (08:15):
I should be able to walk into a hospital room. It records everything that's being said by the doctors and the nurses. Okay? no one has to worry about typing or writing anything down. And then it's immediately put into script and stored in whatever HIPAA secured database associated with that file. All these things are possible and that's what it's gaming towards. But the efficiency of these models will increase if someone comes and they say-
Jason Rigby (08:38):
No, I'm picturing you right now. Us walking into this medical facility.
Alexander McCaig (08:44):
You feeling it?
Jason Rigby (08:45):
And no. I'm walking into this medical facility and there's like these little robots, and they're just like ultra nice [crosstalk 00:08:54] and everything and is like super sterile.
Alexander McCaig (08:56):
Yeah!
Jason Rigby (08:57):
And just like weird. [crosstalk 00:08:58]
Jason Rigby (08:58):
We were actually just talking about Tom Cruise yelling earlier.
Alexander McCaig (09:03):
About his COVID policy on a set.
Jason Rigby (09:05):
Yeah, you guys should listen to that. But the Minority Report wasn't that-
Alexander McCaig (09:09):
The Minority Report Yes.
Jason Rigby (09:11):
Where it was kind of that whole idea of this world, pushing. And when we get into our voice, this is what I'm afraid of. And this is what I think is going to happen.
Alexander McCaig (09:22):
You have a fear?
Jason Rigby (09:24):
Not really a fear. I'm just concerned-
Alexander McCaig (09:27):
Be careful with your words. My language processing, what you're saying, that's why I'm asking you-
Jason Rigby (09:30):
right.
Alexander McCaig (09:31):
Is it really fear that you have?
Jason Rigby (09:32):
No. A concern of mine, and I think it could turn into a fear for humanity, is that once we begin to... Once these large companies begin to use our voice.
Alexander McCaig (09:45):
Mm-hmm (affirmative).
Jason Rigby (09:47):
And then that information of our voice... Because like music, it's a hard... now I start singing. Now we have a whole different issue. Now I'm giving a speech. Now we have a copyright issue. So how much of a company am I going to allow my voice, my words to... How much of that data would I give to them? If it data because that's what you're saying.
Alexander McCaig (10:08):
It is data. It's a form of unstructured data.
Jason Rigby (10:11):
Right.
Alexander McCaig (10:12):
So just like, you know how DocuSign came out the electronic signature?
Jason Rigby (10:14):
Right.
Alexander McCaig (10:15):
It came from my computer. I touched the document this time. No one else touched it. I own that. Right? I own that signature. You can still put that same sort of electronic signature on your voice profiles. If I have specific intonations that are happening and I carry that, that is only specific to me. And that is one signature that even if someone tried to come out and use it, I'm sorry, but that doesn't match. It's essentially like a forged document.
Jason Rigby (10:40):
Mm-hmm (affirmative).
Alexander McCaig (10:40):
And so if they want that, you would upload whatever these pieces of voice information that's unstructured data to Tartle and then we can expand it. That third pillar is underserved languages? And you can get voice input from every country across the globe. Could you imagine our learning algorithms at that point? And as long as we can say, this person truly owns this voice signature, which is what we do with, blockchain smart contracts and things like DocuSign. There's no reason it can't be done with what's going on in the voice.
Jason Rigby (11:12):
Agreed a hundred percent. What I think is important, especially when we look at languages, and I kind of want to get a little philosophical here, but I think it's really important, is that we understand as we... We get so freaked out when we hear of an endangered species.
Alexander McCaig (11:28):
Yeah. It bums me out.
Jason Rigby (11:28):
Yeah. It bums you out. I was reading an article earlier this morning and they were talking about this one mouse. And we think of as like a mouse, I don't like mice or whatever.
Alexander McCaig (11:39):
A field mouse?
Jason Rigby (11:39):
Yeah. It was a type of mouse, but it just went extinct. And I know like tons of different animals are going extinct all the time. I don't know the rate of-
Alexander McCaig (11:49):
Remember I said in the other episode, 99.6, 8% of all species that hit this planet have gone extinct.
Jason Rigby (11:55):
But I want people... As much as you can see a white rhino or are you look at leopards or tigers in India, however it may be, language is that important. And languages are going extinct. So having machine learning, to me, and being able to understand and catalog these ancient languages, would help humanity in such a tremendous way.
Alexander McCaig (12:19):
I'm so glad you said that. Etymology gets me amped up. You know that. You know I love talking about root words. Because it tells such a phenomenal history about how language has moved and moved with people. And it brings the culture and the feeling and the emotion behind it. And it's awesome to watch that transition and how it develops over time, whether it's a function of simplicity for pronouncing something, or we want to refine what that means to us. As we become more conscious as a species and developed, things change in our perspective. So, when you're woke, it's not like I woke up. It was just like, oh, now I'm aware. I'm truly aware, in that sort of social context, if I got that right. But that is the beauty of it. And when you look at that entomology, the study of language, it's the oldest history we have. Because when we talk about written documents, that doesn't even come close to matching how old, just a story is told.
Jason Rigby (13:25):
Yes.
Alexander McCaig (13:26):
For instance, even like a parable.
Jason Rigby (13:27):
Or they're discovering ancient manuscripts all the time.
Alexander McCaig (13:31):
Yeah.
Jason Rigby (13:31):
What if we have lost that language?
Alexander McCaig (13:33):
Yeah.
Jason Rigby (13:34):
Then what would be the ability to be able to... Because machines can do a great job, and we talked about this, machines could do a great job in language. Because of once the rules are put into the machine, then it can sit there and it can decipher... I mean, we know about and what we're to the Enigma box.
Alexander McCaig (13:52):
The Enigma machine.
Jason Rigby (13:53):
Yeah. And then how Great Britain was translating all these a U-boat codes and everything else. And it kind of shifted the tide of the war because we were able to read that information that was encrypted. So machines do a great job, especially now.
Alexander McCaig (14:07):
Yeah.
Jason Rigby (14:08):
At encryption.
Alexander McCaig (14:09):
The question is how do we want to use that technology?
Jason Rigby (14:11):
That's the part that I think we need to have a concern form.
Alexander McCaig (14:15):
Here's what bums me out. Skype. You ever used it?
Jason Rigby (14:19):
Yeah. Microsoft owns them, right?
Alexander McCaig (14:20):
All of your conversations in Skype, video conversations or calls, are transcribed into their database. Every single one.
Jason Rigby (14:30):
So I want people to get that. They're transcribed-
Alexander McCaig (14:33):
Into the database, into text. Everything you've ever said. Did you ask me if that was cool?
Jason Rigby (14:41):
Well, WhatsApp just... Facebook just changed the policy. Because WhatsApp used to be like Signal. That's why everybody's going over to Signal.
Alexander McCaig (14:48):
Signal's legit!
Jason Rigby (14:49):
And I have that. We're going to be talking about in the next few. We're going to do a whole episode on it. But the privacy policy changed in WhatsApp.
Alexander McCaig (14:55):
Yep.
Jason Rigby (14:56):
And so now they're wanting to record. And we'll get into a little bit more, because I want to actually print out the privacy policy, because I think it'd be a great example, [crosstalk 00:15:06] actually go over a privacy policy with somebody that's from a large tech company. [crosstalk 00:15:12].
Alexander McCaig (15:13):
But the point is they make these changes and they think they're developing on a technology, but it's like, I really never gave you permission to listen to all my conversations. I thought that me talking to another person in some sort of private format would stay private. But you're doing all this backend analysis so that you can then go back and develop a product or an algorithm and sell it to someone else off of my back.
Jason Rigby (15:36):
Mm-hmm (affirmative).
Alexander McCaig (15:36):
Come on.
Jason Rigby (15:37):
That's the same Google did with having us translate [crosstalk 00:15:40].
Alexander McCaig (15:41):
So that it helped their scanning of archive, texts and books.
Jason Rigby (15:46):
Right.
Alexander McCaig (15:47):
That bums me out. It's just not very forthright and the fact that you're just recording me. That's the double-edged sword one. Like what's going on with it.
Jason Rigby (15:54):
It's so easy, too. We're fine with that. I don't really care that much. I'm not as paranoid in that arena. I don't do anything wrong.
Alexander McCaig (16:03):
It's not a paranoia.
Jason Rigby (16:04):
But I mean, it's like if Google would have came and said, "We want to help out Wikipedia. So if you fill out this reCAPTCHA find out how filling out reCAPTCHA will help Wikipedia. And we're going to give, you know, every year we pledge to give a million dollars a month to", which is nothing to them.
Alexander McCaig (16:22):
Nothing.
Jason Rigby (16:22):
"$12 million. We're going to give a million dollars a month to Wikipedia." Okay. Now, we're all in.
Alexander McCaig (16:27):
Now we're cool. But guess what? They don't do that.
Jason Rigby (16:29):
No!
Alexander McCaig (16:29):
Do you know who does do that? Tartle?
Jason Rigby (16:31):
I know. Yes. Exactly.
Alexander McCaig (16:33):
Oh wow! [crosstalk 00:16:34].
Jason Rigby (16:34):
I was teeing you up for that. You caught on!
Alexander McCaig (16:36):
'The company comes in, we'd like to buy your data because this is how we want to analyze it for this specific purpose. Cool. You paid me for the thing that I created and now you're also telling me how it's going to get used. And it's not going to be used like against me.
Jason Rigby (16:50):
No.
Alexander McCaig (16:50):
Thank you very much for being forthright, honest and upfront. I'm cool with sharing that with you.
Jason Rigby (16:56):
But not only... This is the beautiful part. Not only am I able to share the data, I'm able to earn money from it. And then I get to help my world.
Alexander McCaig (17:06):
Is that just the wrong path? Does that seem like absurd?
Jason Rigby (17:12):
So not only am I helping myself with the data that I'm creating, but I'm helping my world.
Alexander McCaig (17:18):
Yes.
Jason Rigby (17:19):
I'm helping the globe.
Alexander McCaig (17:20):
Yeah. I, in my own little locale here in New Mexico, can actually have an effect on something that's happening all over the globe. It's like everything we do on Tartle has it's own little butterfly effect.
Jason Rigby (17:31):
Yes.
Alexander McCaig (17:31):
Not for the negative stances there are going to be hurricanes, but in terms of positively solving these issues for us.
Jason Rigby (17:36):
You have the ability to be able to help an island in Micronesia that just got devastated.
Alexander McCaig (17:41):
Yep.
Jason Rigby (17:42):
Just by sharing your data and you're earning some money.
Alexander McCaig (17:46):
Yeah. It's great.
Jason Rigby (17:47):
I mean, think about it. What better system is there out there?
Alexander McCaig (17:50):
Yeah. What's better than altruistically going out there and also getting paid to be altruistic?
Jason Rigby (17:55):
And this is something, and we'll close in this Alex, because we've been ranting literally ranting for 17 minutes.
Alexander McCaig (18:01):
We're helping the natural language processing algorithms. The more we talk, [crosstalk 00:18:05]. Oh my God. Gobble it up.
Jason Rigby (18:07):
Yeah. Whenever we're looking, and we just had a meeting, a Tartle meeting yesterday, and we're presenting this and we're recognizing that, especially this younger generation. Which is your age and younger. It has this huge concern to help humanity, to help save if there's saving to be had.
Alexander McCaig (18:30):
Saving is to be had.
Jason Rigby (18:31):
The responsibility goes into that generation. Because I think our generation and older is gone. COVID is taking us out, but-
Alexander McCaig (18:41):
We gone!
Jason Rigby (18:42):
Yeah. See ya! Yeah. But I mean, why is it so important? Let me rephrase that because I want you to answer this very thoughtfully. Why is it so important for someone to sign up on Tartle? Not just to earn money, we get that part, but to look at what humanity and this planet has to offer us and what, in our service, to offer it back through Tartle?
Alexander McCaig (19:11):
If we consider these up and coming generations, they are the ones that will take over these essentially positions of power, positions of resource control. And it's important that if they are championing very humanitarian missions, that they're trained with a tool that is effective for that specific purpose. And if they can learn to use and adopt that, this very, very active generation vocal generation, humanitarian generation, that's coming out and you hand them this new sort of thing that says, "This is going to take everything you're doing to the next step and have real impact immediately. And you'll be able to see that impact." That is why it's so important for us to bring those adopters in, that generation. Because in the end game, when they move into these positions of power or resource control, we want to make sure that they're using something that is humanitarian, egalitarian, non-dogmatic, nonpolitical.
Alexander McCaig (20:23):
We are very truthful in how we operate. And the information we use is truthful. Because the effects for those people when they become decision-makers will then come full circle and say, "We've been using the system. We understand the power of it. Let's continue to solve these things that are truly important to us." You know, we've collected the data. We've used it personally.
Jason Rigby (20:44):
Right.
Alexander McCaig (20:45):
We can vouch for this.
Jason Rigby (20:46):
Right.
Alexander McCaig (20:47):
As millions of people, hopefully billions, we can vouch for the value in this, in how we want to solve and protect our future. And if that's 70 years out before the ocean rises five feet, let's try and stop that right this second.
Jason Rigby (21:02):
Maybe there's a 19 year old out there and they're at poverty level. But they have a phone and they're watching YouTube and they're going on Facebook. And they're doing all these great things. You as a 19 year old have so much power with inside of you. And data is that power. And if all the 19 year olds and all the 20 year olds and all the 25 year olds with the amount of data you guys are creating, if we would all come collectively together, it is our opportunity to change humanity. It is our opportunity to change the world.
Alexander McCaig (21:34):
Yeah. If you're going-
Jason Rigby (21:35):
To address each of these specific issues, whether it be climate change, whatever it may be.
Alexander McCaig (21:39):
If there's a video of 20 million views about a baby seal getting clubbed-
Jason Rigby (21:43):
Right.
Alexander McCaig (21:43):
And that's bothering you go over to Tartle and start doing something about it.
Jason Rigby (21:48):
Yes! Yes, exactly.
Alexander McCaig (21:51):
You sit there and watch the video. And then you can talk about it, blah, blah, blah, try create some sort of rally on Facebook or go outside and do a march.
Jason Rigby (21:59):
But if 20 million people that watch that video will go on Tartle and sign up and share their data, now we're doing it something about it.
Alexander McCaig (22:06):
Now we're really doing something! Because here we go. We have input from all of these people. They're sharing data towards a specific cause. The people that are in those positions to do that change, now they can listen to it and really act upon something. We don't have to see any more of those videos.
Jason Rigby (22:20):
And stop relying on politicians. They're just followers, not leaders.
Alexander McCaig (22:23):
We need to be responsible and we need to rely on ourselves.
Jason Rigby (22:28):
Yes! A hundred percent.
Alexander McCaig (22:29):
Once we rely on ourselves and we come together as a self responsible collective, the amount of positive evolutive of change in this world, for me as an individual and everybody else around me and everyone in all those countries, is going to be so dramatic, we're going to be so shook, that in 50 years, we'll be like, now we woke.
Jason Rigby (22:51):
Yes, exactly.
Alexander McCaig (22:52):
That's the moment when you woke, when you've solved all those problems, you realize, wow, why didn't we do this before? That's what we got to keep driving for. And I really hope I use the word woke correctly because it's way out of my nomenclature.
Jason Rigby (23:04):
Right.
Alexander McCaig (23:04):
But that's our focus.
Jason Rigby (23:06):
Yeah. And I love that because when we look at this 50, 70 years we have left that scientists are saying before it's too late, this is the age group. That 19 year old that's sitting there, you have the power. You're holding the power in your hand to be able to change the world, by going on to tartle.co and allowing yourself to be able to be a part of a movement. Tartle is a movement.
Alexander McCaig (23:34):
I don't want to get this to sound morbid, but you and I talk about this.
Jason Rigby (23:38):
Right.
Alexander McCaig (23:38):
And we bring this up with a lot of people that come in to buy data. And we ask them, what is it that you want to look back on your life when you're on your death bed-
Jason Rigby (23:48):
Mm-hmm (affirmative).
Alexander McCaig (23:49):
This is going to sound a little heavy for a second, but this is important. Because unless you've taken the time to reflect on what you want to leave this world with, you're going to find yourself in a rock and a hard spot to be like, wow, I wish I did more. You don't want to be saying that when you're at those last moments.
Jason Rigby (24:07):
Yeah. You don't want to have your Louis Vuitton purse, your fake eyelashes, fake ass, and Lamborghini, and your Chanel belt on-
Alexander McCaig (24:19):
That material world [crosstalk 00:24:21].
Jason Rigby (24:20):
That's not what you're going to take when you die, that stuff just goes away.
Alexander McCaig (24:23):
I want this generation as it phases out of this world, 70 years from now to be like, look what we did. Because we focused on how we wanted to leave it. It allowed us to change our habits and our behaviors to get to that point right now. That's the important part. Let's reflect on what that end game looks for us as a generation. And what can we do actionable to take care of it at this moment?
Jason Rigby (24:50):
Because what you're not realizing, 19 year old, is your grandma's only creating so much data. Not very much. You on the other hand, creating tons and tons of data. And so do you see grandma? Yeah. Grandma can vote on a piece of paper to put a follower in that does absolutely shit for anybody. Politicians, really, whether they're Republican, Democrat, whatever, they may be, whatever country you're from, what have politicians really done for you?
Alexander McCaig (25:17):
Yeah. Focus on that. Now it's time to do something for ourselves.
Jason Rigby (25:21):
Yes!
Alexander McCaig (25:21):
Because that sort of momentum is, it's slow.
Jason Rigby (25:26):
Yes.
Alexander McCaig (25:26):
And it doesn't pay back as fast as it should. We are naturally so efficient. We are so active. And if you are emotional and passionate and you have the vitality to go out there and do something, bring the right tool with you to go do it.
Jason Rigby (25:40):
And that right tool is?
Alexander McCaig (25:42):
And the right tool is Tartle.
Jason Rigby (25:43):
Tartle.co, sign up today. Be a part of the movement.
Alexander McCaig (25:45):
Yeah. Change the world.
Speaker 1 (25:54):
Thank you for listening to Tartle Cast with your hosts, Alexander McCain and Jason Rigby. Where humanity steps into the future and source data defines the path. What's your data of worth?
