Daily Dose of English 217
Algorithms
Daily Dose of English 217
Intermediate
Hey everyone, my name is Ben and you're listening to a Daily Dose of English. This is a short, simple podcast that you can listen to every day to improve your English. You can find the transcripts for all episodes and more on benslanguagelab.com. I'm glad you could make it today. In this episode, we're going to be talking a little bit about algorithms. Algorithms are something that I've been thinking about a lot recently. And so I wanted to record a little bit of an episode here talking about my thoughts on algorithms and, well, just that. So let's get into it. First, what is an algorithm? There are lots of ways that we use the word algorithm, but in a more definition sense, it's basically a set of steps, typically mathematical instructions, to change something into something else in a systematic way. So it is a way to do, for example, a conversion or to There's, whatever, that was a, I did not have another thing to say on there, but like if you wanna do a conversion of Celsius to Fahrenheit, you can use a very simple algorithm to find that number. I forget, it's like minus 32 times five divided by nine or something like that. I think those are the numbers. I don't remember the exact order of those operations, but that is a very simple algorithm. Algorithms can be very simple like that, or they can be very, very complicated. They can take in hundreds of data points and do very complicated decisions and mathematical projections and sorts of things. And the way that we use the word algorithm now, like today, is to describe something that's more complicated like that, which will typically show us something, give us a recommendation. A recommendation algorithm is the most common way that we think of an algorithm nowadays. There are also other algorithms out there for solving complicated math problems or even helping diagnose diseases or curing diseases or trying to cure disease. There's a lot of other stuff out there, but I'm mostly focused on these recommendation algorithms because that's what I want to talk about. And so whenever you open an app that has content on it, so for example, YouTube, TikTok, Spotify, pretty much anything that has content that you might engage with on your phone, on your computer, is going to have an algorithm to choose what to show you first. How does it know what you might be interested in or might wanna see? That is essentially the question that an algorithm tries to answer. So for example, I just opened my Kindle app right now. And so I have all my library at the top, all my books. And then under that, it says Kindle unlimited recommendations. And it has a bunch of books that are related to other things that are in my library, right? And so it's trying to recommend me things based on what I've already bought. It's actually not very good. It's recommending a couple of things that I already have, actually. So this is a pretty basic algorithm. But that's what an algorithm is doing. It's trying to show me what I might want to read. And there are other algorithms that are way, way better. which can be a little bit scary sometimes. And that's kind of what I want to talk about. So these recommendation algorithms are absolutely everywhere. And I'm actually reading a book right now called Filter World, which I would recommend if you're interested in this. FilterWorld basically says that algorithms have flattened culture in a lot of ways because these recommendation algorithms, they recommend things that you're more likely to click on because that's what these companies want. They want your attention because you see more ads and thus they make more money. And so they recommend you things that are more likely to keep you on the website, regardless of if necessarily it's the best content for you to actually see or if it's the most interesting. And so a lot of the recommendations end up being the most generic version of something. And I think a really good example comes from an anecdote from this book about a band from the 90s, I think, or a relatively old band that has a bunch of music on Spotify. And they noticed that suddenly one of their songs got a lot more popular than the others. And they're not a super popular band, not super duper well known. Their second biggest song has like nine million plays or something like that. I forget the exact number, but that's a relatively low number for a band that is moderately popular or was at some point. but one of their songs has way more listens because it was picked up by the algorithm. It started to be recommended to a lot more people because it is more generic. It was actually a song they wrote and they recorded as a bit of a joke. It is not like their other music at all. It sounds a lot more generic and it was a sort of a bit of music commentary, if you will. It's kind of making fun of generic heavy metal music, I believe. I forget the exact genre, but they make sort of a lot weirder, a lot more experimental music that most people don't really like, which is totally fine. They know that. They don't necessarily want to be the biggest band in the world, or didn't want to be because it's a relatively old band. but this one song that was made to be kind of a joke and kind of generic got picked up and is more popular than all of their other music because it is more generic and it fits better and less people skip it, if that makes any sense. And that is the whole point of this idea of like recommendation algorithms flattening out culture where there's less like random and interesting things that you're likely to come across or engage in because those don't perform as well in the algorithm. So if you're browsing TikTok, let's say, you're more likely to see the things that other people like, the things that other people enjoyed. And so you end up seeing things that most people enjoy, which you might be okay with. TikTok can be very addictive for that reason, but it misses out on some of the subtlety or the nuance that you might be really interested in. And that to me is a negative impact, the sort of flattening of culture and the losing of this interest that we like of human art and expression, which is too bad. But then the other side of an algorithm is that they can be very good at capturing your attention. They don't necessarily show you things that you're going to be interested in or challenged by, but they're very good at showing you things that will get you to keep clicking and keep watching. And so that's why I've been thinking so much about algorithms and trying to reduce my use of them in pretty much every case that I can, if I can. I'm a person that doesn't really use any social media. Pretty much the only thing that I use that heavily uses an algorithm is YouTube for content. Pretty much everything else that I engage with doesn't really have an algorithm. It's mostly And I've thought about this, I've gone through this. There's like small ones that are definitely very useful, right? For example, spam filtering in your email is an algorithm. It uses algorithmic processes to say this is spam, this is not, this is spam. That's a very useful algorithm that very much improves my life and my mailbox. But I'm trying to be more careful and selective with the other times that I let algorithms sort of drive my consumption or my, yeah, my consumption, really, my attention. So specifically with YouTube, since I don't want to give up YouTube necessarily because I find it very interesting, I really like it, but I'm trying to reduce my just clicking on the next thing time. So trying to be more deliberate, I think is a good word, with my YouTube. So instead of watching four or five videos, which I used to do quite often, I'll try to maybe watch one. or two or something like that. Or I'll fill it and I go, okay, I want to watch YouTube for 15 minutes or something like that. And then I go do something else that's non-algorithmic that lets me kind of have other stuff in my life and figure out things that I want to watch or do. That's how I end up choosing what books to read or movies to watch, is trying to get recommendations from people. They can't even come from things that I might have come through from an algorithm. So I'm not trying to necessarily completely avoid them, but really not let them control my attention, if that makes any sense. I want to be able to discover things that I like, I find interesting, and that I want to engage with. Yeah, those are my thoughts on algorithms more or less. I'm curious to know what you think because you're listening to this on an algorithmic platform, right? And so I'm curious what your thoughts are, if you've ever thought about this before, if you care. I'd be very interested in your opinion if you want to write me a comment down below. I know I ask for comments every single episode, and it's generally because I would like to see what you write, even if you're maybe not the most confident in your English writing, that's okay. You can either write it in bad English, I'll figure it out, or I would actually recommend you write something down, and then you go over it a couple times. You check in with yourself, you ask yourself, okay, does this make sense? Is this correct? Can I improve this writing? You can maybe even ask a tutor if you take English lessons, or you can ask an AI chatbot, which are in some way just really big algorithms, but they're very useful for correcting, especially English. They're very good at English. And so getting corrections there to ask a question or to share your thoughts can be a fantastic exercise in your English ability. If you don't want to, that's also fine. You can just give a little thumbs up emoji and I will respond with a little fire emoji or something. But yeah, that is everything from this episode on algorithms. I'm going to continue thinking about them and my relationship with them because I know they're unavoidable, but they're also not strictly good. So a little bit of moderation I think is important. Anyways, that's all that I have for you today. I hope you enjoyed, maybe learned a little something, a word here and there, but that's all I got. So I'm going to see you again tomorrow. Have a great rest of your day. Goodbye.
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