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Understanding Artificial Intelligence – a conversation with Garik Tate
16 Plays10 days ago
A straightforward explanation of Artificial Intelligence.
Garik Tate is an AI Futurist, and the founder of Valhalla a software company based in the Philippines.
In this episode of the Abeceder podcast The Independent Minds, Garik explains to host Michael Millward the technical discoveries that made the Artificial Intelligence (AI) possible and how AI works.
Although the technology has been around for less than a decade AI is more widespread than you might think. Garik explains why despite how much we interact with AI we tend to ignore it. Michael and Garik discuss how software has been written in the past and how it will be created in the future, and how this may involve changing our perception of software.
Garik expresses his concern that AI may be seen as the solution for everything and explains to Michael how organisations of all kinds can identify how they can best use AI.
Find out more about both Michael Millward, and Garik Tate at Abeceder.co.uk
The Independent Minds is made on Zencastr, because as the all-in-one podcasting platform, on which you can create your podcast in one place and then distribute it to the major platforms, Zencastr really does make creating content so easy.
If you would like to try podcasting using Zencastr visit zencastr.com/pricing and use our offer code ABECEDER.
Matchmaker.fm If you are a podcaster looking for interesting guests or if like Garik, you have something interesting to say Matchmaker.fm is where matches of great hosts and great guests are made. Use our offer code MILW10 for a discount on membership.
Travel
Garik is based in the Philippines. Members of the Ultimate Travel Club, can travel to the Philippines at trade prices on flights, hotels, trains, package holidays and all sorts of other travel purchases. You can become a member at a discounted price by using my offer code ABEC79 when you join-up.
Fit For Work Look after your health and you will be fit for work.
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Visit York Test and use this discount code MIND25.
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Being a Guest
If you would like to be a guest on The Independent Minds, please contact using the link at Abeceder.co.uk.
We recommend that potential guests take one of the podcasting guest training programmes available from Work Place Learning Centre.
We appreciate every like, download, and subscriber.
Thank you for listening.
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Transcript
Introduction to Independent Minds
00:00:05
Speaker
Hello and welcome to the Independent Minds, a series of conversations between Abysida and people who think outside the box about how work works with the aim of creating better workplace experiences for everyone.
00:00:22
Speaker
I'm your host, Michael Millward, the Managing Director of Abysida. As the jingle at the start of this podcast says, The Independent Minds is made on Zencastr.
00:00:34
Speaker
Zencastr is the all-in-one podcasting platform that really does make every stage of the podcast production process including publishing and distribution, so easy.
00:00:46
Speaker
If you would like to try podcasting using Zencaster, visit zencaster.com forward slash pricing and use my offer code, Abbasida. All the details are in the description.
00:00:58
Speaker
Now that I have told you how wonderful Zencaster is for making podcasts, we should make one. One that will be well worth listening to, liking, downloading, and subscribing to.
Purpose and Production of the Podcast
00:01:10
Speaker
As with every episode of the Independent Minds, we won't be telling you what to think, but we are hoping to make you think.
Garrick Tate's Background and Journey
00:01:17
Speaker
Today, my guest, Independent Mind, who met on matchmaker.fm, Garrick Tate, who is an AI, that's artificial intelligence, futurist who's based in the Philippines.
00:01:30
Speaker
Hello, Garrick. Hi, Michael. Glad to be here. I'm really pleased that you're able to join us because this is something that, well, AI, I think is something that everyone is talking about. I think an awful lot of people don't quite understand what it is all about. so I'm hoping to explore that in a lot of detail.
00:01:49
Speaker
But can we please start with you telling us a little bit about who Garrick Tate is and what led you to to being an AI futurist in the Philippines?
00:02:01
Speaker
ah Yeah, absolutely. So a quick background on me. I come from a a family of entrepreneurs and ah originally was born in Northwestern USA around so Seattle, Washington, grew up there.
00:02:16
Speaker
And when I was 18 years old, we I decided to go in with my family business. So my brother and my father were starting up a business in publishing. Little less than a year later, still 18, flew out to the Philippines. because We decided we wanted to put on our our big boy pants and you know really take it seriously. And so ah since then, I've grown three different companies here to about 65 employees across across all three companies. So about 20 each.
00:02:42
Speaker
each And um across all three, we use AI in different ways. So one of them is software development, and that's that's where I was first introduced to AI. i I heard an amazing quote. It was such a gift early on in my career.
00:02:57
Speaker
It was a quote from the... founder of Valve, which was that software development is nothing more than teaching the dumbest thing in the world how to be smart. And that excited me so much that I dedicated all my time to wanting to build smart things.
00:03:15
Speaker
And so... um because it's It's literally true, right?
Overview of Garrick's Companies
00:03:18
Speaker
Like a computer is just ones and zeros. So it's just a one is saying I exist. A zero is saying I don't exist. And so it's, I mean, you add in logic gates, it gets more complex. you You bootstrap it from there, but that's its fundamental building block. So it really is a dumbest thing and and you're making it smart. So came out here to the Philippines 10 years ago, six years ago, started a software company. And now we have a third company as well in outsourcing.
00:03:40
Speaker
And across all three, we we use AI and that's the long of the short. Okay, so what are the names of the three companies that you run? So the the three are Typewriter Publishing.
00:03:52
Speaker
And I should say, actually, I spend pretty much all my time in the software company at this point, have have amazing business partners that that run the run the other's operations. But ah the the publishing company is Typewriter Publishing, the outsourcing company is called 2XU, and the third is Valhalla.team, and that's our software ah company.
00:04:11
Speaker
You're 18 when you arrive in the Philippines. So that's 10 years ago. We can all do the maths.
Basic Computing and AI Concepts
00:04:17
Speaker
And now you've got three companies out there. And I'm intrigued by this idea that it's, yeah, a computer is just, it's either on, it's one, or it's a zero, it's off. yes And yet, the thing is that people seem to be sort assuming that a computer which operates simply on either I exist, I don't exist, I'm on, or I'm off, is going to take over the world. And it's the dumbest thing in the world.
00:04:45
Speaker
but it's going to take over the world. How does how does that happen? ah Well, if if you ah believe that you know human beings...
00:04:57
Speaker
come about from from biological processes and you know that there is obviously some contention in some circles about it. But but fundamentally, if you know parts of your brain turn off, then parts of your intelligence get adjusted.
00:05:10
Speaker
So fundamentally, physical physical building blocks bootstrap up to intelligence. Well, what we're doing is we're replicating that inside of silicon. We're replicating basic building blocks.
00:05:22
Speaker
I shouldn't replicating biology, but you know Ultimately, you can build incredibly complex things from simple simple underlying steps. um So it's it's one of the most interesting games in town. It takes in things from hardware to software to just about everything, um supply chains, et cetera. But but the the race to build intelligence is is a really exciting exciting thing that we're all we're all engaged in. We've got to do it carefully, but it's definitely possible, and it seems like it's working so far.
00:05:54
Speaker
Right. So human intelligence is essentially the same as a computer's intelligence.
Comparing AI and Human Intelligence
00:06:00
Speaker
It's our ability to process information to create knowledge and memories in order to recall that knowledge.
00:06:10
Speaker
at the appropriate time. And what you've got with a computer that has artificial intelligence in it is that all of those ons and offs helps the computer to work in the same way as the human brain.
00:06:26
Speaker
When we're making intelligence inside of computers, there's there's two ways to go about it. um The way that's been the longest has been a top-down approach, which is essentially, let's tell it exactly what to do in every situation that we can imagine, and it's to behave that way, and we're off to the races.
00:06:41
Speaker
um So basically you build an algorithm, if A, then B, so on and so forth. So it's it's all algorithms and it's very top-down. And that is classical programming. And you can build quite intelligent machines doing that kind of stuff.
00:06:56
Speaker
the The new way of going about it is not top-down where you give it the instructions. It's bottom-up where you're where you give it the inputs and the outputs, and then it figures out how to how to take the intermediary steps, which, if you think about it, is really taking Darwinian evolution and and applying it to to this domain. So what we do is we create a series of different configurations of of neural networks.
00:07:26
Speaker
so um So it's it's a... where you create different versions of the same code, the same neural net, and you're testing all of them against the data that you have, which ones can classify the data properly, which ones can generate the best data, ah which ones get the best user feedback.
00:07:44
Speaker
And you're constantly iterating on that, mutating them, having them compete with each other. And the best um neural nets, the best algorithms are going to be the winners. And then those you know propagate forward, you add more mutations, more mutations, so on and so forth.
00:07:59
Speaker
And you get intelligence, um peers, like we're getting intelligence from that process. It's really just harnessing ah Darwinian principles in silicon.
AI Development and Challenges
00:08:10
Speaker
I've never heard it explained in such a clear, concise way before. Thank you very much. I'm sort of like processing a little bit of what you've said to, I understand. Thank you very much. it's It's intriguing.
00:08:27
Speaker
ah It means, of course, now, I suppose, if if the AI is going through that process, then any software system that is using that is never actually finished.
00:08:38
Speaker
You can't actually put it on the shelf or download it and say, here's our software, it's finished, because the software is is almost developing itself and learning more and more, which constantly, hopefully, is improving how this how the software operates.
00:08:56
Speaker
Yeah, you're bringing up a really interesting point here. um You know, the AI, there's there's two there's two versions of this that AI scientists think about, and and maybe this gets a little bit academic, but you can you can overtrain a model where it is...
00:09:13
Speaker
overly optimized on the data that you trained it. So it it gets 100% success rate on the data you trained it. But when you introduce new data that you didn't train it on, um it uses the same rules of thumb and then it starts to make mistakes.
00:09:26
Speaker
And so that's a ah ah version where you could say it is kind of done, but because of that done status, it doesn't actually interact very well the real world. um another Another principle that that you're touching on here is that these AI systems get more and more intelligent the more data that we feed them, which is quite exciting, but also scary. Yes. It gives you a lot of things to think about, about how AI within a computer could actually help us do all sorts of different things because it it could almost preempt.
00:10:03
Speaker
It learns what we need to know before we before we know that we need to know it.
Effective AI Task Definition
00:10:08
Speaker
Well, the the thing I want to throw out there is if people are are working with these systems on a very practical level, um I recommend, and I do this with with all my clients, that the place you want to get started with is get really clear, not just on the data that you want, you know that's part of it, but almost more importantly, get really clear on your inputs and your outputs.
00:10:28
Speaker
um Because you know what we have right now is artificial neurointelligence, not artificial general intelligence. And so artificial general intelligence, here know that would be the idea that they can do everything we can do but better, right?
00:10:39
Speaker
um It's as smart as as a human being. But artificial neurointelligence says you know this can do this one particular task very well. So what is how do you define a task? Well, the task is defined by inputs and outputs.
00:10:51
Speaker
Now, the the thing that for me is very exciting that we're not we're not here yet, the that the frontier that's very exciting for me is when the inputs start to widen, where it doesn't just become, you know, reply to this email or reply to this this comment or recommend the next video based on previous behavior.
00:11:07
Speaker
But instead, what if the AI is taking in the context from your broader life? What if it's like an AI companion that follows you around, consumes the same content that you're consuming, reads all your messages,
00:11:18
Speaker
So you widen out the content and then because the inputs are significantly larger, the outputs can be way more nuanced than we're getting right now.
00:11:31
Speaker
So that's what I'm very excited in the long term. But in in in the short term, I always recommend the clients not to get too ambitious about it, but just focus on inputs and outputs. Yeah. Yeah, I'm thinking about all sorts of different ways in which in everyday life and in my work as an HR person, I could use
AI in Recommendation Systems
00:11:51
Speaker
AI now. Of course, you've explained it.
00:11:53
Speaker
But are there any examples that you could tell tell us about where we're using AI without really realizing it? you know I think that one thing that was really interesting before ai became such a big buzzword was that there would be any area where AI just was working, we stopped calling it AI. We only called it AI when it was doing something fancy or novel. But the second it was just working, we we stopped it. So like the um YouTube recommendation engine or um
00:12:24
Speaker
you know ah traffic control lights, A couple of years ago, you know some of these things, you wouldn't even be thinking about it as AI because it just was part of the background. And that's that's very human of us.
00:12:35
Speaker
We only process so much data. So when something works, very often we just, well, yeah, it works. Of course it's going to work. You only focus on it when it's a problem. And so I think there's a lot of areas where we take AI for granted, but that's probably changed a bit now that it's become the buzzword. So I think now it's it's pretty well in the in the consciousness. That's my two cents.
00:12:54
Speaker
Yeah. I hadn't realized, for example, that when a website like YouTube or so Amazon or any site makes a recommendation, It is using AI technology to look at what I have previously done.
00:13:09
Speaker
So the input is all the little videos that I have watched and then how long I've watched them for, I suppose, and then thinking, okay, what else might I like that because it's linked to or similar to what I have previously watched.
00:13:27
Speaker
Exactly. So the YouTube algorithm, its input is all the analytics from the website. Watch time, if you scroll up and down, if you read the comments, if you leave a comment, if you like and subscribe.
00:13:39
Speaker
Actually, a big one, because what they're optimizing for is time on site. A big one isn't actually whether or watch and like and subscribe or comment, but whether or not from that point you click on the recommended videos. In other words, is that the the video at the end of your watch cycle? or is that the video that begins a watch cycle?
00:13:55
Speaker
So a lot of things that people don't think about are part of the inputs. The output is what's recommended next. And then the the variable it's optimizing for is maximizing um watch time.
00:14:09
Speaker
Yeah, well, I suppose its it's helping me find things that I will like, but it's not exactly exposing me to new things that I haven't thought that I might like.
00:14:20
Speaker
Yeah. and And interestingly enough, generative AI works in in a bit of a similar way. Of course, all AIs work in similar ways, but generative AI, you can think of it through the same framework. you know Its input is a user's prompt, but the other input that people don't realize is it's its input is every other message or every other letter and word it said previously.
00:14:42
Speaker
So it you might have heard that it's just guessing the next word in the sequence, and that's that's actually accurate. What it's doing is it's taking... the context of the wider conversation and everything it said before.
00:14:53
Speaker
And that's guessing the next word in the sequence, just like watching all your analytics on what you've been doing on YouTube.
Generative AI and Life Coaching
00:14:59
Speaker
It then suggests the next video based on it trying to optimize its it's fitness, you know Darwinian fitness. But in YouTube, Darwinian fitness watch time in chat to PT.
00:15:09
Speaker
Darwinian fitness is when you give it the thumbs up and say, this is a useful response. When I'm using a word processing program, for example, and start using a two-word phrase on a regular basis, the AI in the word processing software will start to recognize that and suggest this is the word that you're wanting to use. Yes. That's AI. Yes, wow exactly.
00:15:35
Speaker
You're obviously working on this all the time with Valhalla. And I'm wondering, like... Where would, where might I have seen your work on AI? What sort of things are you doing?
00:15:46
Speaker
Are you able to tell me? I can talk about one, one project ah that we are are working on right now. We have an Australian client and i'm I'm really passionate about this work. The client is a life coach.
00:15:57
Speaker
He certifies other coaches as well. And has some top executives are his clients. from all around the world, he has boiled down his IP into a very simple um set of of steps that we've built an AI to replicate.
00:16:14
Speaker
So we're building an application right now that will be your AI in a pocket, so that sorry, your coach in a pocket ah that will help you unpack your troubling areas in your life and issues in your life and help you unpack the way you're looking at it and then repackage a better set of beliefs, a better set of principles and the feedback we're getting. And this, you know, full credit to, to, to this coach, his system really works. um ah But we, we built an AI that essentially replicates it.
00:16:45
Speaker
And so the the feedback we're getting has been, has been jaw dropping as people are saying, like you know, this is a totally new way of looking at it. I'm blown away and um i'm I'm using this all these different areas. I've, I've improved so much and it's,
00:16:56
Speaker
It's been a very rewarding project. Because coaching is very much a human interaction process, but you've found a way to replicate that, or at least parts of it, using the AI.
00:17:09
Speaker
Yeah, it's my view that there's not really so much special that humans do that AI can't do.
AI vs Human Roles
00:17:15
Speaker
Now, with that being said, I'm actually 100% on team human. Like yeah there's no one more pro human than I, you know, AI is not here to, ah we're not just the gatekeepers of the next species that's gonna rule planet earth. Like, no, like we're <unk> we're here to to work with them and and kind of level ourselves up in this process, not just, you know give birth to the next generation call it a day.
00:17:37
Speaker
But with that being said, i don't so much believe that there's a ton of things that humans can do that AI can't. I think it really boils down to the input. So like if you prefer a accountant, a human accountant over an AI accountant, probably the reason why you do is that the human accountant has more inputs.
00:17:57
Speaker
Not only are they gonna read your P&L statement, they're going to look at your face. You're gonna talk about your hopes and dreams and fears. They're going to get context from you that is not inside your P&L. So they might be a better accountant than than the AI accountant, but that's not because their neural net is better.
00:18:15
Speaker
It's that it was trained on more inputs. But if an AI, if we get clever about it and figure out how to give AI that same context, how do we give that same information? There's no reason to think that an AI that has 10 million clients has become the top 1% in all of their situations couldn't be a better thing than than a human accountant.
00:18:37
Speaker
That's my view of it. Yeah. It's still inputs and outputs. Inputs, outputs. It's the things that if you are an entrepreneur and both entrepreneurs, if you don't know what questions to ask, what inputs to put in, it's always useful to talk to somebody else who's been through the same process. Oh, yeah.
00:18:54
Speaker
But if there is an AI that can ask you the questions that you don't know you need to ask, it could potentially to deliver the same type of results.
Future of AI and Innovations
00:19:09
Speaker
It's really very interesting. ah I know that the pace of change in this industry is moving so fast that that something happens every day, but you're in the industry, you're looking at it, you're thinking about the future of the industry.
00:19:24
Speaker
What are going to be the next so big things that will come out? What's it going to look like over the next you two, three, four, five years? How how far ahead of you think are you thinking?
00:19:35
Speaker
I want to give a two-part answer that question. That's such a good question. The the first I want to give is is on the macro, and then I want to give it a little more a micro answer of of what I'm hoping people who who are listening to this episode, after they like and subscribe and re-watch the episode and re-download it, you know what what will they do after after all that?
00:19:54
Speaker
Got to reward the podcast algorithms, right? Okay. What I see on the macro sense first is that we were getting as much juice as possible out of the squeeze of a particular set of innovations that came from, i believe it was late 2017, the white paper called Attention is All You Need, which invented, essentially invented the transformer.
00:20:15
Speaker
And the transformer, if you know GPT, I think the T in GPT stands for transformer. So the transformer was ah was a massive innovation that allowed us to do much better parallel processing and paved the way to generative AI. So whether that's DALI or Midjourney or Chachiwiti or Ryder Bard or all these different technologies are blowing people's minds. so All of them essentially get enabled by the same innovation that was about six years ago.
00:20:41
Speaker
And if in the next six years we do not get a massive new innovation, we are not going to get something that is is is massively better.
00:20:53
Speaker
um That being said, we are getting better and better at applying these technologies in more nuanced ways. you just you know Developers and entrepreneurs just need time to build new products. so So it's not to say like innovation won't continue to be made, but we're still in the process of just squeezing the juice out of one innovation.
00:21:11
Speaker
It's my hope that the next innovation is one that can replicate better top-down logic rather than bottom-up. you know So far, we've been sticking with Darwinian you know evolution still, like who we said before.
00:21:25
Speaker
But if if we can create an innovation that can do a better job of simulating physics... and and applying physics in a top-down sort of way, that's very exciting to me because then we can start really applying AI to um science in a way that just we' we've we've been making progress on, but I don't think it's anywhere close to what is really possible with with with AI.
00:21:47
Speaker
And so that's the thing I'm most excited about, but that's that's still going to be in academia. And after it gets invented, it's going another like five years before it starts really getting rolled out. um So that's that's a macro answer.
00:21:58
Speaker
Yeah, again, really very interesting. um Didn't realize that we're talking about an um innovation that is six years old. Yep. And, but it is right. The next generation of innovation will, I suppose, sign point to where it is that the potential could be.
00:22:19
Speaker
Um, I'm presuming there's probably people all over the world working on that next innovation, but what about the micro answer to the question?
AI in Business Solutions
00:22:30
Speaker
Yeah, so so the micro answer of how how do like I use AI in my life?
00:22:36
Speaker
or let's let's say you're you're an entrepreneur, you're a businessman. The way you want to go about doing it is not to ask the question, how do I apply AI? i think that your your listeners might know that the phrase, to he who has a hammer, the whole world looks like a nail.
00:22:48
Speaker
So you'll be looking for problems to to use your solution, and that's not what's going to move you forward. But if instead you look at your business, you put on your architect hat, put on your engineer hat, and say, okay, what are the constraints right now in my business?
00:23:03
Speaker
And you can find those constraints probably just by walking through your your user journey. If you work in ah HR, that would be like your recruitment process or onboarding process. Walk through your user journey and find every every Passover point when you know something gets moved from one department or one stage to the next and ask yourself, where are the bottlenecks occurring? Where are things taking longer? Where are errors being added? and Where are there where there issues?
00:23:27
Speaker
And once you find that that constraint, then ask yourself, can I use AI to solve this? And very often, the the initial answer will be no. But if you do a little bit of engineering, then you can you can constrain the inputs outputs be something AI can apply to.
00:23:45
Speaker
so So this was something, I'll give a real example actually related HR, at least with recruitment. um Our outsourcing company, 2XU, I mentioned it earlier, they they ah were stalling out, but not because they couldn't get customers. if fact, they like eight clients are all trying to get VAs and and there was just even more clients in the pipeline.
00:24:05
Speaker
The problem was that we couldn't get the VAs fast enough. It's a very premium service we offer and there's a lot of IP packed into it, but but bottom line is you still need to get amazing quality people. And we were struggling with that, um but not because we weren't getting enough applicants because we couldn't process them fast enough.
00:24:21
Speaker
And so we looked at the recruitment process, found where the constraints were at, and then added in into that, like um like telling the applicants the next stage of process, answering their questions, using um AI to craft the messages, um add in automation, so on and so forth.
00:24:39
Speaker
And sure enough, we were able to triple um our hiring rate and ultimately double the business in just a few months. So that came about from going about with the engineering point of view and and starting with the constraints first, then figuring out how to add AI.
00:24:54
Speaker
I like the analogy of the the person with the hammer sees is looking for a nail and comparing that to we need to find out whether what type of nail it is, what are the restraints to us doing business and if you remove the constraints with a solution that is designed around the constraint and the removal of it rather than the tools that you have, you will end up with a better solution the just treating everything the same.
00:25:23
Speaker
It feels as if the world um in the past and even today it tries to standardize things so that it can create a or use an existing solution to ah solve a problem or puts the problem into a framework which our existing knowledge allows us to solve.
00:25:51
Speaker
What you're talking about yeah is moving the dial slightly so that we're looking at the constraint stroke problem and thinking about how that could be removed rather than actually solved.
00:26:09
Speaker
I think there's a difference between removing a problem or a constraint and solving the problem. it's You have a remedy for the problem, you solve it, or you work it out of the process.
00:26:22
Speaker
yeah Yeah, I like that. you know These tools are so flexible, they can be applied to most any problem if the problem can be defined as input outputs. If it can't, well, then you're kind of screwed there.
00:26:34
Speaker
But if it can, it can be added. But you want to meet the problem on its own terms. you don't want to say, okay, how can we use ChatGPT to solve this? ChatGPT might not be the solution. So you you you want to focus on it.
00:26:46
Speaker
You want to face a problem on its own terms. Yes. Yeah. Garrett, you have certainly got me thinking. Certainly got me thinking. Thank you very much. i really appreciate the time that you've made available to help me make such an interesting episode of The Independent Minds. Thank you.
00:27:04
Speaker
Oh, my heart grew three times that day. Appreciate it. Thank you. I am Michael Millward, the Managing Director of Abbasida, and I have been having a conversation with the independent mind, Garrick Tate, who is an AI futurist.
00:27:19
Speaker
You can find out more about both of us at abbasida.co.uk. There's a link in the description. The Zencastr system has, as always, been very efficient today. But if you're listening to the independent minds on your smartphone and experienced technical issues, you may like to know that 3.0 has the UK's fastest 5G network with unlimited data.
00:27:39
Speaker
So listening on 3.0 means you can wave goodbye to buffering. There is a link and in the description that will take you to more information about business and personal telecom solutions from 3.0 and the special offers available when you quote my referral code.
00:27:55
Speaker
The description is well worth reading. If you've liked this edition of The Independent Minds, please give it a like. And to make sure you don't miss out on future editions, please subscribe.
00:28:07
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I'm Matt Garrick on matchmaker.fm, which is where great hosts and fabulous guests are matched and interesting podcasts are hatched. If you are a podcast host looking for interesting guests or someone one with an interesting story that would make you a great guest, visit abasida.co.uk where you will be able to access lots of learning for hosts and guests and the special deals that listeners to the Independent Minds are eligible for.
00:28:37
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Garrick is based in the Philippines. If you fancy visiting the Philippines, which Garrick told me before we started recording, is like a paradise, the best way to plan your travel is with our sponsor, the Ultimate Travel Club.
00:28:51
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You will find a link and membership discount code in the description. Remember, the aim of all the podcasts produced by Abbasida is not to tell you what to think, but we do hope to have made you think.
00:29:03
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Until the next episode of The Independent Minds, thank you for listening, and goodbye.