#21 How to Learn 14 Coding Languages and Run a Global Blog

16-year-old Satvik is a massive Marvel fan but it was one of the lesser known characters that took him on a journey that has changed his life.

Jarvis, the artificial intelligence assistant of Tony Stark (aka Iron Man) got Satvik thinking of how he could have his very own version of Jarvis.

Since then, he has learned 14 different coding languages, developed two apps for the Google Assistant, taken more than 50 graduate and undergraduate tech courses and is the founder and head of TechVik - a global tech blog with 75 contributors and an audience of more than a million.

Satvik talks about how he learned so many coding languages and why all students should be learning the fundamentals of coding and AI. He also explains how students from around the world can get involved with Techvik.

• Click here to visit the Techvik website
• Click here to connect with Satvik on LinkedIn

**Download the Ultimate Resource Bank for Science Students with the favourite resources from Satvik and other young scientists featured on the Top of the Class**

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Transcript

Podcast Host  00:17

Hello, and welcome to the top of the class podcast. I'm your host Alex Cork, and in this episode I chat with 16 year old, Satvik Tripathi. Satvik is passionate about computer science and artificial intelligence. We chat about learning 14 different coding languages, being a Google Developer and leading a global student led organization with a mission to bring AI and tech to the world. Let's chat with Satvik Tripathi. Hi, Satvik welcome to the Top of the Class podcast, it's fantastic to have you on the show. Can you tell our listeners who are all around the world a little bit about yourself?

Satvik  00:51

First of all, thank you so much for having me here. I'm Satvik. I'm from Lucknow, India, and I'm a high school senior. And I am very passionate about computer science, artificial intelligence. I have my own nonprofit international organization. I am the head and founder of fit. And I am into research and artificial intelligence app development. And I really look forward to working in some research lab in future as they get into college and hopefully contributing to this world.

Podcast Host  01:24

Well, yeah, are you already are doing some great contributions to the world? And we'll get into that. But how old are you just for our reference?

Satvik  01:30

I'm 16.

Podcast Host  01:31

16 years old, and you're interested in AI and all these amazing things that I don't know too much about? Because I didn't really do that kind of thing. Well, that kind of thing wasn't really around when I was at school. So that's kind of showing my age a little bit. But we want to talk about you and you're interested in AI. Where did it all start from?

Satvik  01:48

It's a very funny story. I am a huge Marvel fan.

Podcast Host  01:51

Me too.

Satvik  01:51

Oh, that's great. Yeah. Iron Man 3000.

Podcast Host  01:55

Yeah, that's right.

Satvik  01:56

So coming from that I am a huge Jarvis fan. I used to sit down for them to TV, and thinking wishing that I could have that Jarvis with me, and bringing the whole possibilities and areas of things I could do with him. And then I've thought of actually, you know, anything you pops into your mind, you just Google it. So I googled how to create jobs. And I got these, you know, long fandom pages. And then, you know, found theories. But that's not what I was looking for. I was looking for something more technical. And then at that point of time, I came across this word artificial intelligence. So this was something which, you know, was my first interaction with artificial intelligence. I tried to, you know, search more about it, learn more about it. And then it was like, a lot of mathematics. Like the first research paper I read, I only got to understand the top four words or five words of the abstract, right. Other than that, I had no clue what was going on. So it took me a lot of time to understand the complexities and topics within AI. I'm currently actually working on a real life job. It's like it's my current project. And it's awesome. It's like on the base level, like, the base level is done, almost done. Maybe in a year or two, it would be like on a basic working.

Podcast Host  03:17

If you were to read that abstract again, how much of them would you understand?

Satvik  03:21

I wrote a similar abstract and a year ago.

Podcast Host  03:24

So yeah, you're not just reading abstracts, you're writing abstracts, and you feel like you've got a real handle on on everything that's involved in AI. But it's I know, it's a super fast developing field. And I feel like teenagers could be on a level playing field compared to someone who is, you know, 30, or 40 years old, because AI is so new, like you've grown up in the age of AI, and a 30, or 40 year old, probably hasn't really grown up and has had to learn it from scratch, and you've learned it from scratch as well. Do you feel like when you interact with the wider community, that you are at a similar kind of level as other people who might be a bit older than you in the field of AI?

Satvik  04:05

That really depends on like, what person I'm talking to you. Like, in general, as a fighter in the world. Everybody knows what artificial intelligence is. Like, one of the most basic definitions like I've got, you heard from like, a layman, who is not a researcher working in artificial intelligence is that anything which computers and humans can do, artificial intelligence can do in a simpler way? Right. So this is what like a simple perception of like most people for artificial intelligence, but as a researcher, or winner as a project manager, when I speak to people, we speak on a whole new level, we speak about algorithms, the, you know, how the data set is formed and how the data is distributed. It's a different world. It's a completely different world. It's more into mathematics. The statics, you know, you sometimes have to think about even the humanities fault, like how is the machine Thinking you have to think about the psychology of it. Because at the very origin, which is something Stanford has, even right now, it's called the symbolic system, which was the combination of artificial intelligence, psychology, linguistic cognitive science to actually understand about how the machine is learning. So yeah, it's really up to the knowledge of the other person. But yeah, on a broader terms, you can obviously explain it to them.

Podcast Host  05:26

What kind of maths is involved in this? I mean, when you say like, it's got a lot of maths and is it something that you would learn at school? Or is it a completely different kind of maths than what you learned at school?

Satvik  05:36

It's a whole lot of math, it's more than calculus, and even calculus, like mostly without any numbers, they are dealing with, like only variables. And like there's a key data. And there's three dimensions of data sets, you know, in n dimension. And then we are finding out about like, the relations between those points. And like, it's a complex mix of the poor mathematics, and the statistics, like understanding the distribution of the data,

Podcast Host  06:05

right? So just slow down a little bit on that one. For me, I've actually never seen an algorithm. I know that sounds like a silly question to us. But what does an algorithm actually look like? Is it one line of code? Or one formula that you apply to a large data set? Or is it you know, a large set of coding data, like I described for me what an algorithm looks like.

Satvik  06:29

I'll walk you through it.

Podcast Host  06:31

Thank you

Satvik  06:31

Think about a data set. It contains a number of data. And the data points are set in a way that you have an x AI, which will give you an output in Y, if you put the P of x is the function P of inside. If you put exci then you will get an output Why am I right? Yes, that's you have a data set D which has all these points, till n and number of points. So now, your job is to do what you have to do is that we now take up a new point, say x comma y. And we asked you that if we put the same this new eggs into the old function, where this why would like, so what we do is that we try to understand the pattern in the previous data sets like how x i and Y are related, right? So say if the you put x equals to one and the y is equals to two, then you put x equals two x two equals two and y two becomes four, x three equals to three, y two, y three becomes eight. Yes. So basically, you're seeing that it's growing exponentially. And it's like two to the power n, that becomes a formula for this data set. So you assume that if x equals to n, then if you put a new number that is x in the data set in this function, then possibly the answer of why would lie into the power x, and we just do it in a whole new complex level. Like this is one of the simplest examples, you will never seen data science, right,

Podcast Host  08:05

Right. So the algorithms, I'm going to guess for the kind of things that are like when people talk about algorithms, changing the world or big data sets, I'm going to guess the formula is extensive and has like a lot of variables and a lot of different things that it's trying to calculate. And if done successfully, or inputted correctly, then the better it can predict the outcome, right of particular behaviour or whatever you're trying to predict.

Satvik  08:32

Exactly. The classified data.

Podcast Host  08:34

Yes. Okay. Okay. Well, I'm glad I understood that. But when you were, you know, 13, and you were looking up how to create Jarvis, and you saw that abstract and you didn't really know what was going on? How did you then start to try and figure out what was going on? Like, where do you start when you're 13? And, you know, obviously, the internet is a source of many resources, but many of them are dead ends, many of them are not the level of, you know, 13 year old set, because as I'm sure you are pretty smart, then as you are now, but where did you go to for the places that you wanted to learn how to code and create something like Jarvis?

Satvik  09:10

Oh, okay. So it contains various parts, like I come from, like an education system where computer science is not, you know, we just start from an early level, like in most American schools, you have your coding classes, right from your like ninth or 10th grade, I guess. We didn't have that in our school. Like we have computer science in our grade 11. slang for junior year. Yes. And that, too, is not a very competitive class. It's barely you could do anything with that. So when I started, I was at the end of my, you know, freshman year, and was going to enter my sophomore year in high school. And I wanted to create this app. So I had to do three things. I first need to understand coding languages because I had no experience in that. Second, I need to understand the mathematics behind it and turn x Understand, what are these terms that we are frequently using like machine learning, or data science or data points like we are using these terms. So what actually these legends mean? So I started with a course, which is like very close to my heart, and I respect it like even till now, that is CS 50 by Harvard, I guess is one of the largest taking course. So, I mean, Professor David J. Milan, I adore him so much. I still like they released the 2020 version, although I'm an alumni of CST, but I still took it again, because they went live. And I actually met Professor Milan in one of the open sessions, and it was really an honor talking to him. So yeah, cs 50 took me from the very scratch, working with a c++ language to taking me to, you know, SQL, Python. And then as I went forward, I took like online courses, I did not pay for any course because I wasn't looking for certification. So I went to open courses, which was like free, then had the same college level, both Mathematics and Computer Science. And they have course logistics as well open for everyone. So I took like Stanford courses, like cs 221, which is artificial intelligence. Then I took cs 229, in CS 230, by Professor Andrew enshi, on deep learning and machine learning. And then I took like, more and more and more courses. And like till now I've taken like, more than 50 courses.

Podcast Host  11:31

Wow. Yeah. All online offering,

Satvik  11:34

like in recent years, like Coursera, and edX, started giving out their scholarships and fee waivers. So I started applying from them. So some are like certified, and I've got them I guess, around 25, or 30, certified something like that.

Podcast Host  11:50

Well, what was the, you know, beyond the Java's and Marvel interest? What continued to draw you in? Because some students, I'm sure, would look at some of the things that you were looking at, in the early days, right, going back to that abstract, and say, Oh, god, this clearly isn't for me. I can't understand anything here. I'll come back to it in a couple years time or, you know, maybe I'll you learned at university, what was your main motivation to be like, No, no, I want to continue learning this, even though it's really hard. And I don't really know much about the topic. You know, Was there anybody mentoring you at that time? Or was there anybody kind of, you know, a role model that you had that you were like, apart from Iron Man? Was there any other role model that you may have had at the time?

Satvik  12:33

No, actually, not. Nobody was like, I guess nobody even knew what I was thinking. Like, even my mom and dad, they're from pharmaceuticals area. I was working on that one. Like, I've got a lot of time for myself, like after school. So these are what I used to after I finished my work. And one of the motivations, as you asked, is, like, when I was searching for artificial intelligence, one program I created I came across was the Google Assistant developer community program. Yes. And since a very young age, almost like since I guess back in 2017, when they first launched Google Assistant in pixel device. I was blown away with that, because it was the nearest possible Redemption of Java's because we have seen theory, but it's not up to that mark. Because what Google Assistant is able to do is far more of the reach of C, because of the, you know, whole data that Google holds. Yes. And it holds all different webpages and stuff in the knowledge it has far more than what Siri has. So I was really intrigued by this program, I went on to read on the documentations. And the same thing happened, as happened with the abstract, I didn't get a word. I started with a few lines and did with a few lines, I got to just that this is something very complicated. And I need to learn about it. So the program I had to made was on a platform called dialogue flow, which is a chatbot making programmer. Like it's a whole developmental suite. It was like pretty tough at first, but YouTube got my back, I was searching like how to do this in dialogue flow, how to do this in dialogue flow, what is dialogue flow? I saw a couple of their examples. So my learning was more in application way. Like I was seeing the code and I was trying to understand, okay, what does this line do? And what if I change to this, what will happen? So more experiment to a learning rather than just understanding things? So we are that really helped me pick a pace. And you know, as I was moving forward, like this is something about computer science. And I guess in most of the fields that once you start getting an output, there's nothing holding you back. Like since the day you start printing hello world on your screen. He has, like you get a few and like I've created something. He has a human being. That's great. feeling to have because as they say that matter can neither be created nor destroyed, it can change form. And here you have created something out of a few lines of code. And it was really fascinating than how much you could hold. And now here I was working on something which could talk to me, it say my name, and you know, chitchat with me. So it was a whole different experience. It was like creating a body out of some line of code and personalizing it as I wanted.

Podcast Host  15:28

Fantastic. Talk to me about the Google Assistant developer, because you became one at the age of 13. How did you do that? Is it something that you had to apply for or you said, you were struggling to read that as well. And it's a bit of an uphill battle, but you ended up being part of the program. So talk us through that.

Satvik  15:44

So it was like I said, coding eligible, like a child learning. It took me like seven or eight months to understand everything, and be proficient with the, you know, API's and everything. It's not like something you have to apply for. It was a limited time period. Program, which was specifically for Google Assistant when it was launched for, like, the Google devices will launch the Google Home mini to Google Home best and all. And it was like, it's basically to create a community to attract more people, and make them more engaged with the Google Assistant and how they could interact. So I started building a kind of app, which kids could have fun with. So my app was automate Marvel trivia, a Marvel guy again? Yes. So it was it was something like it's a game show, like the AI would ask you like a different set of question. It's a trivia questions, and you will have to answer them, the AI would try and count off, you're like, how many correct you got how many wrong you built, and we'll give you a score at the end. And like, it's like even one player could play more than one player could play. And then it's asked like, do you want to play again, and if you want to play again, the level of you know, your questions increases. It's like, it gets harder. And it's really fun. I played it a lot.

Podcast Host  17:02

Yeah, hang on. But don't you program the questions? Or is the AI pulling the questions out of Marvel. Like, where's the questions coming from?

Satvik  17:11

Okay, you have to create a database, or you'll have from a question. I actually there was some website, I don't remember the name I asked. And they had these questions on their website. And I had to ask them, but can I use these questions because they had privacy policies from them? And I spoke to them, can I get these for my app? And they were like, happy? Do you know how the data set contains more than, like, 700 questions? And at a point of time, even though I'm the developer, I don't know the answer to half of them. So what I lost you remember, even though my huge mouth and there's so many small, small details that are way too tough for anybody to you know, remember, as I, you know, went forward, created this app, it was well and functioning. I then got it published on Google Assistant. And I don't know how, like, more and more and more people started using it. And every night, the analytics would change, like it would include the previous day's record. And every night, I used to see like how many people actually played. And I reached 1 million, and within a year, it's something like people reach in three or four years. And I was doing like seven or eight months. So seeing that Google gave me like Google Home device, Google t shirt, $200 per month cloud credit. And like, invite to like all Google io, Google App Fest, and everything.

Podcast Host  18:36

That's awesome. So basically, you created this, like, really cool Marvel trivia game, you know, after seven or eight months, Google's like, Hey, we're taking notice of how many people are playing this game, like here's, you know, some encouragement to keep on developing new apps, right? How many hours would it take to put together something like that? Right, like the Ultimate Marvel trivia, once you've got the data set? How long would it take you to code something like that?

Satvik  19:00

I remember, I was working like 10 hours a day, almost a month. I get Okay.

Podcast Host  19:05

Sorry. That's not like something that you can just sit down with a couple lines. And use Wait, right? There are a lot of like testing that you have to do in between?

Satvik  19:13

Yeah, it's really tough to get published on Google Assistant. Because it's not that you just check it, you send it out for them to check it. Right. And they run your app, they go through various tests. And then they tell you that this is wrong. This is not working. This command is not working. My app actually got like various rejections, like before it actually got published.

Podcast Host  19:36

So that's when you were 14, when you were making this almost almost. Right, right. And what have you learned or continued to learn since then? Because obviously, like that level of coding, I don't know. Was that like beginner level, do you think or is that kind of intermediate level coding? What kind of level was required to make something like that? And what kind of level are you at now do you feel with your skills in that area?

Satvik  20:00

It was close supervision but not very proficient because I couldn't cry all the codes like an understand what am I doing here? Something's like, I think like this program is doing it like that. So I should write this code, right. And I didn't have a complete sense of what I'm doing. But now after two years down the line, yes, I am like proficient in more than 14 languages. And I could write in any other language, and even explain it to you what I'm doing and how I'm doing this. And I could write like, hundreds and 1000s of lines of code. I'm enjoying it now. It's Yes, it's more easy to, for me to write in Julia or five and then writing in English,

Podcast Host  20:41

learning 14 different languages. Is that unusual? And why did you decide to learn so many?

Satvik  20:48

But yeah, it's unusual. Like even like professional developers are like, into three, four or five languages, which they are like proficiently working in. I had no clue what computer sciences, I tried to, you know, discover every aspect of it. And, you know, to discover every aspect, it requires a different skill set a different language. So, as I went into development, I learned a different language, web development, a different language, but then AI, l and a whole bunch of different languages. And you know, I try to work in projects in your real life project, open source projects, and help me understand these languages more, work with them more, proficiently. That's how I like came in contact with them.

Podcast Host  21:29

Does learning one language help you learn other languages? or do some languages have like a closer relationship than others? Because I really don't know much about coding languages at all. But is there like, perhaps even you know, the mother of all languages that gives you an advantage in learning every single other one? Or is it all a little bit different?

Satvik  21:49

paths, like different syntax is like, you have the different way of writing each language. Um, yeah, they are mostly similar. I won't say like, exactly similar. But it's like some languages are just bizarre in writing, like c++, which is like one of the oldest languages. And it's like using terminals, or file sharing, file management systems or operating system but encoded in it. And they are really hard to read, like, what is the quarter trying to do, as the generation moves forward, and you go to a new language, it gets easier for you to understand, like in c++ to just print hello world, it would take at least five lines to write, but in like bytes, and it would take you like one line, just print hello world. That's the difference. It gets easier. It actually motivates you, oh, this is easier than c++. This is so easy to do this.

Podcast Host  22:40

So once you've learned c++, everything looks pretty easy after that, or zero, at least.

Satvik  22:46

Yeah, this is what motivated me like, if you are, you know, proficient with c++, and Java, I guess you would be, you know, good enough with any other language. And even some languages are pretty common. Like if you know, MATLAB, then you almost know Julia. So because Julia actually, is a compilation of all languages, like the developers of Julia said that they wanted to make a language which has the best of everything. So it's like, it has the syntax of different languages like, which is the most efficient way to code being the most powerful at the very same time,

Podcast Host  23:21

yarn, or Julia, you said it was your favorite language to use. And I can see why that's the case. Now, actually, I was falling with Twitter thread, where a student asked, What's everyone's favorite coding language? And no one mentioned Julia yet, because I know it's fairly new. But I was, like I said, If static was here, he would be saying Julia right now. But everyone was like, you know, c++, or Python, or JavaScript, and these kind of things like, I've never learned a coding language, how long would you think it would take me or an average person to learn a new coding language for the very first time to go from like, absolutely knowing nothing at all, to being, you know, fairly proficient in something like c++,

Satvik  24:00

it's up to the dedication, and like the amount of work you're putting in. This is just like maths, you know how to write things. And that's like one class, and then you just try to practice it. Like, there's a course on YouTube, like an eight hour long course. Just, you know, if you do that course, and do another, like five hours of practice with everything that he has taught, that you will be good to go and Pat, like good to go. You will understand everything. And just keep practicing so that you do not forget what this string does or what this command does. It's pretty similar to mathematics.

Podcast Host  24:34

Does knowing 14 languages, though, does that get messy for you in terms of like, does your brain sometimes start trying to write in a different code that's not suited to what you're trying to do? Just because you know, too many?

Satvik  24:47

Yeah, it happens. Like, if you're like doing coding in one language, and you start doing like any, any other language, you know, because you're typing so fast, because you have to write so many lines of code. This kind of gets into your Like, final chord that you have to press this button after you end this line, you say semicolon. And it semicolon doesn't work with every language, they have, like somehow backslash. So you have to put backslash. And even if you you know, missed that even once the whole code would go out of air. So that happens, like if you're working in one language, and you simultaneously if you're working on the project, which has any other language they're working on, it sometimes get a bit messy, but on the longer run, it's very beneficial. Because I know some of the, like companies and like research groups who interview, they say that we are going to give you an example problem, and you have to solve it. And they won't tell you what language it is written in. And they say that it would be in the top 20 languages. And it's like you should know all of them to you know, actually crack it and find what's wrong the code. So in the longer run, I guess it's a good thing to do.

Podcast Host  25:56

Right to Know, all those different codes, right? Yeah, it's a good thing to do. How many coding languages actually are there? I mean, I'm sure new ones are getting developed all the time. But is there an estimate that you have roughly as to how many are out there? Because when I heard 14, I thought that was a lot. But you just said there's a top 20, which implies that is there

Satvik  26:14

a lot. But yeah, they're like new ones coming in every day. And people are actually making new languages as per the system requirements. Also, like, if they're doing a particular task, you will have different language for it. So yeah, people are customizing languages, and using it just for their systems for their networks. And yeah, there's, I guess, hundreds and 1000s of languages? I don't know.

Podcast Host  26:38

Yeah. But yeah, it's probably good to know those, you know, base couple JavaScript, Python, c++. And then from there, there's like variations on a theme and other ones that you can you can do in that kind of stuff. What would be your top two or three tips to learning any new coding language,

Satvik  26:56

I would say start with the basics. That's the most important part like learning anything, regardless of your if you're, you know, learning coding language, or a real language, you know, the basics are the most important part where to put what, like the wall was the consonants, we have syntax, and then try to when you're learning, even when you're learning, try to focus on the neatness of writing the codes, you know, it helps you to understand the codes in a much more comprehensive way. Like if you're doing something just it takes like a few seconds to just write what you just did. And it helps you understand what you're doing and make it makes more sense in the longer run. And once you get proficient with it, you know, you can make acronyms. I know a friend of mine, who uses artists as like his markers, like he says, this part is Bob Marley, this part is Michael Jackson, and then he does his coding. So yeah, this is something I would say be comprehensive while doing the coding and do regular practice. There's like plenty of problems online, there's websites when you could get coding problems, and you can code in there. And they'll you know, tell you that was the code right or wrong, if wrong, like what was wrong in it. So there is like, so many different ways you could approach and even if you don't want to approach it, you could, you know, simply work on your, like daily lives, like, you could make a tic tac toe game, like you want to play with your friend, you could just coat that, or make a calendar or a clock. It's kind

Podcast Host  28:19

of like trying remove some of life's conveniences. And instead of just, you know, looking at a clock or something, as you said, try and code one instead. And that's like, you know, a reason to practice and a reason to practice and a reason to practice. I keep finding those reasons to continue learning. I think it's really good advice. And is there any other tips that you would give like flashcards or that kind of thing? Is that like a memorization type of thing? Or is it just purely practice and getting used to, as you said, that kind of muscle memory type of learning?

Satvik  28:49

Yeah, you can definitely make notes when you're, you know, taking any course or something. But yeah, as you're gonna start applying, and while coding doing practices, I guess you'll learn them, like how to do these operations. I just searched up and they're like, 9000 languages out there.

Podcast Host  29:06

raizy 9000 languages? Well, there you go. So at least you know, 14, have you got plans to learn anymore?

Satvik  29:12

I'm actually thinking too long, a bit more. I'm speaking to a couple friends of mine, and you're thinking to learn more

Podcast Host  29:19

languages? In your experience? Why is there not just one mother language for all coding languages?

Satvik  29:25

Okay, so it's like, let's take a like real life example for it. Um, if say you want to go from point A to point B, and you have various options. You have a bicycle, you'll have a car, you have a Tesla, you have a truck, you have a crane. And then you're asked, like, what would you use? So this is not an easy answer that you say, okay, that Tesla would move the fastest and I'll take the Tesla, but the conditions are very important about the distance between A and B what kind of terrain it is it very hilly or you know, it is Something like it has a lot of traffic or, you know, there could be plenty of conditions. And so basically, this is kind of a puzzle. And you need to find the solution to which you know, vehicle to use to reach there from A to B in the shortest period of time and most efficiently. So the same way it goes for programming languages, that you are given a scenario that you have to develop this thing, you think of the so many languages, and which of the following language would be most convenient, which have the most speed which, and also up to the taste of the programmer, like, Who likes more like, if somebody asked me to develop a new network, I'll probably go with Julia, but there's no harm in doing that the same thing with, you know, Python, either, but it's just that I like Julia more, I enjoy writing it that and also, it's a bit faster, you know, while compiling the code, although it's a fraction of seconds, but when you're compiling a huge amount of data in becomes, you know, quite significant. And then as these people analyze, and they have developed, like each of the languages developed, they have the developers have a mind in mind, they have like, Okay, why vi designing this language. So a language is basically, you know, devil up when they face any problem, right? Like any other invention, they develop, it's like, okay, we are facing this problem, and we need an alternate language to solve it, and so on, you know, we get generations of language, like, at first, like, we had PHP for like, web development, then we got HTML, then we are, you know, concrete working with JavaScript and Java, for your interactive based your web development, like, if you see Apple's website, it's a beautiful website, everything's moving, everything's changing videos and cooperated in it, you know, this was something not available back, even back when Google was founded, but not things are getting changed, and languages are getting more powerful, and, you know, they are moving forward. And the same goes with like, you know, machine learning and your whole statistical analysis of data, like we started with fightin. And now, you know, we are moving forward with Giulia MATLAB, which is used to, you know, make graphs and analyze the data sets, we are using our language, people from economists are using our programming language to analyze the data, you don't make predictions out of it. So it's like, as moving forward, we analyze what thing is useful for what, after all, programming languages are just a tool. And we, as you know, say, it's like, if there's a blade, Smith, and he wants to create a particular type of blade, it's really up to him, like what kind of, you know, tool you would use to make that play. So if he's gonna hammer it, or he's gonna, you know, quench it, or whatever your technique, you would like to make that blade more sharp or more long or short as to what he wants to make, or, and what resources he has, right? Because some languages are like, wood, if you try coding machine learning algorithm in c++, you can do it, it would be robust, it would take you I guess, a lot of time writing, and then a lot of time compiling it. And right, when you have an option, writing that in Python, in Python, we have like libraries, wherein you could import a particular model, right, they have pre written that model, you just have to import it and use it. Like there are a lot of libraries, and you could just borrow the model from there, and you could use them. But if you're, you know, looking for language like c++, you won't get the library and you have to hide everything yourself. So it will be like 500 or 600 lines of code just for one model, and then have to apply it. So it really gets very hard for you. And this is like one of the problems Julia programmers are feeling right now. Because there are not many of the libraries out there. But the ones which are out there are pretty dope, you know, are working refine,

Podcast Host  33:55

you mentioned that people in economics are using some coding languages. In your experience, what do you think would be, or who do you think should be learning some kind of coding language, but they might not be completely aware that that's a skill set that they might need in future,

Satvik  34:11

I guess, like, regardless of what field you are in, if you're learning any kind of skirt, it's going to benefit you. Like if you take example of economics, anywhere where you have to make predictions. And then you want to make new assumption when prediction machine learning is there and helping so there's actually a different field of study, which combines all machine learning and economics, it is called statistical learning. Stanford actually offers a course on that. And its uses our language to analyze the data, apply these machine learning models written in our language to analyze the data. So anybody like a couple of friends of mine are now reaching out to me they are doing comics that can you just send me some of the shots on our because our professor is saying that, you know, this is the next big thing and you guys should know it, and people are moving forward. programming languages, like one of the greatest example would be like the medical field, which has definitely no connection with machine learning in 2015, nobody would have thought that, you know, you could apply computer science and medical now, like drastically changing, it's like, people want to work together and you know, create a better outcome.

Podcast Host  35:20

I think what I'm getting from that is that no matter what field, you're going into, that you would be very, very well served to have at least a base level of knowledge in coding, computer programming, that kind of thing.

Satvik  35:32

Even vice versa. Like, if you're a computer scientist, if you are working with somebody who is in picture of Medical Sciences, you should have a basic background of it. Otherwise, you're just telling you stuff and you just don't get it.

Podcast Host  35:44

But it seems like computer science and AI are going to be and deep learning and those kinds of things are going to be three, or a couple of the main tools that people will be using to solve the problems of the future. Like this is a fairly new technology. And that is increasing in power all the time, and can be applied to complex problems. So if you want to be a part of that problem solving, and if you want to be able to see those connections between the problem and the possible solution, then you really need to have a little bit of a grasp of both. So I think that that's very good advice. And I hope students listening take great analogy.

Satvik  36:25

Yeah, great analogy.

Podcast Host  36:27

As you said, like, even if they don't know when or where they will use it right now, it will be a skill that they can use in the future, almost certainly, it will be a skill as they

Satvik  36:37

even I guess in future of like, artists who do painting would be using AI, because there's this new technique. It's called generative adversarial networks, it is used to generate images, which are not real, like it uses so many different images that like which contains probably 1000s of images, and renders a new image, which is unique, which is very different. And you won't be able to identify whether it's real or not. So people are using this technique to create artworks. And, you know, try to mimic what like Picasso made.

Podcast Host  37:11

I know that that has some very dangerous potential as well. I've seen like deep fakes and those kinds of things.

Satvik  37:17

Exactly. The example of generativity versus networks. It's about how you use your knowledge.

Podcast Host  37:24

I know luid about coding from mainly been on the host of this podcast, actually speaking to other students who are also doing coding, which one would you recommend students should start with? Like, what's the most basic of the languages? And which one do you think has the most possibilities and is perhaps the most advanced of the coding languages that you know?

Satvik  37:44

So yeah, different coding languages are used for different purposes, where if you are someone you know, who is getting into the coding for the first time, I recommend taking c++ because that's a very fundamental coding language, it's very rigorous. It's of no use, nobody uses but what is teaches you is to remember syntaxes, to apply the correct line of code. And then as you move forward, then you choose a path, like you want to be a developer, like an app developer, you choose JavaScript, Java, you want to be like a website designer, you choose HTML, if you want to be an iOS developer, you choose, you know, Android Studio, or like it for iOS, like swift language. But if you want to go into something like data science, machine learning are efficient. So there are like languages like our phyton, like, which is on top of every language right now. But some more languages are like Ruby. And my like, personal favorite is Julia. As of right now, it's a very new language, and it holds so much possibility. It's like, even if you read the documentation of Julia, it's like, we have the syntax of Python, and we have the swiftness of Java. And it's like, a very well composed language, it has the best of everything mushed together. And it's specifically for, you know, machine learning and data science, I felt that this is something revolutionary to me. And I know that when I'll be in my college, like two or three years down the line, when I'm, you know, working at somehow I know, people around me would be using this language more than Python. And I really think if, if there's someone who is really passionate about this field, I really encourage them to take Julia as a language. And even they have their online Academy, Julia Academy where they provide free courses to learn Julia and actually have some, I think, in all of their courses, and it's really fun to you understand this new language, they have great data sets, they have great, you know, what we call libraries to import models. They have amazing, you know, like this data flux, which is really great for deep learning, and it's really fun to Play around.

Podcast Host  40:01

Crazy right? When I was at school, we were told to learn German because it was going to be useful. I don't know why we would learn German because the only reason it would be useful is if you go to Germany, right, I'm yet to go to Germany. So he learning German may or may not have been the most useful thing. But it does sound like this is a super useful language or languages to learn what fascinates you the most about computer science and the I guess the possibilities of computer science,

Satvik  40:28

the possibilities that computer science whole like in not just in computer science field, but in other fields, even like in medical field, computer science and medical have very well mushed together, which was at one time were very separated things like one is engineering, and one is medical. And these were two different centers and very different things. But now, as we are moving forward, we see majors like computational biology, or by informatics, and people are working together, they are learning both sides of biology and computer science. And, you know, applied it, like in one of the conversations with Professor Halliburton, one of the oldest professors at MIT, and he has worked on like, one of the first languages, and he told me that computer science, if you want to work in computer science, you need to think about the ad like computer science and watch. Yes, so you want to move forward in that think about the and that thing has stuck with me forever. Since then, I've been looking for answers. And like, I think at a particular scenario, and I think like how computer science could be, you know, well marched in this location. So I try to, you know, apply things in the real world and see how better world it could be with computer science. It's not like computer science is going to take over. It's about like, if you're using AI, right, and say, another person who is doing podcast is not using the AI, the world for the second person is obviously done more than yours. Yes. And obviously, you will be more efficient, and you will move forward. And that guy would be shocked at what makes difference, that person using AI manages time managers will work in a more efficient way and solving the very basic functionality of computers, to help save time to make it more efficient. And thus remains the same for artificial intelligence. It's not about eating somebody's job, or we hear about like, it will take away jobs and factories. No, it won't, it will always be regulated by humans. And the humans need to be trained with AI.

Podcast Host  42:33

Absolutely. I know, that's a big fear for a lot of people in factories and that kind of thing. But it's going to be replaced. But as long as people take the time to learn things like coding in different languages, and you've created a great community, for people to talk about these kinds of different technologies and what they're doing in that space, which is terrific. And so you've gone from a team of one or two people was you and your two people. And now your ad how many writers

Satvik  43:00

are not just writers, we have a huge team of marketing, graphic designing web designing, outreach, content writing, and we have a team of around 35 people.

Podcast Host  43:11

Wow. So you're like the founder and head of a very fast growing team, which is awesome. So these people are getting paid is it been monetized at all is a not for profit.

Satvik  43:20

It's not for profit, it was in indeed, at first there was no profit, yet later and non non profit. So we started out as like, as I said to people, I paid for the domain name I paid for get the site design, I pay for the VIX that we used to design and I hard coded some of the HTML elements. And then I paid for those things. It was like in dollars, you would say 10 or $15. And then it was nonprofit, obviously. And later on, because the money we were making, first of all, how will it be distributed? amongst like the people, it's not like something I am earning the bus is not just my work, people are reading. Others people work too. And it wasn't really fair to them, if I take all the profit. And then if I take start taking profit, then if say if I get like, a few $100 How am I supposed to distribute that in to like a team of 75 people and everybody getting, say $2 or $3? And that will make any sense? Yep. And then again, if you want it is you get these ugly banners and ads on your website that I really don't appreciate because it's okay to have it to monetize, though. But one you are looking forward to create you know, an impact and working as a nonprofit, it doesn't really gives a good way when you're monetizing.

Podcast Host  44:46

Yes. So that was like you had your initial kind of philosophy of why you wanted to create the website. And even though you probably could monetize it, now you certainly getting enough eyeballs to monetize it, probably not something that you'd want to do because it doesn't really fit well with what your original philosophy was.

Satvik  45:03

Right. And moreover, like, if money is to matter, and we want to do something we would highly, you know, be encouraged to do something like a sponsorship, or to for, you know, program get funded by and foundation, you know, right, something which is more eloquent as I would say. And it's more, you know, in a way, which is benefiting both the organization and us, in a way. So yeah, and that is something I'm looking for, like, I'm currently in contact with MacArthur Foundation. We are speaking over a fund of like, 5000 to $10,000. For tech work, and we are in the final stages, and hopefully, we get it and yeah, move forward.

Podcast Host  45:52

What would that do for the website? If you do get 510 $1,000 to

Satvik  45:56

upgrade? It is not a website? I have huge plans for it. I have like can Campus Ambassador programs, country Ambassador programs, our whole website, redesigning it starting a podcast? Oh, yeah. And then like having events, which is very important. Like we I have people in like most smallest areas of Morocco. We have people from small towns of Italy, I have people from Brazil. And if I could give them this money, and they could host it even there for kids for a young generation, and they could learn something about it.

Podcast Host  46:33

So you're saying like, if you could have tech fake be a vehicle to help rural communities learn about technology more, it could really reinvigorate rural communities, right? So you're empowering young generations of kids in rural areas to be like, Hey, you know, you feel like you can't help out too much on the farm. Well, guess what you actually can, but it might not be like plowing the fields necessarily. It could be creating a code or creating a system or creating some kind of data set for your farm farm or whatever it might be.

Satvik  47:03

Exactly this, like I know a few people that come from a rule background and a vote on the genetics of week, like creating a new week, which is not that prone to cold, not that prone to diseases. This is like a new variety. And they have worked on it.

Podcast Host  47:20

New kind of weight could really change a lot of people's lives all around the world. Now. That's, that's awesome. So I can see like tech Vic, not just being a blog, but being an organization for chatting. Yeah,

Satvik  47:31

it's like it's more than a blog.

Podcast Host  47:32

If our listeners wanted to contribute, potentially, I mean, obviously, they would like to go and visit and we'll put the link in the show notes. But if they wanted to contribute, how would they go back then? Okay,

Satvik  47:43

so we have like options for like permanent positions. We have options for like internship for like three weeks or four weeks internships. And we have options for like one time submission, if you don't want any commitment, and you just have a piece that you want to get published, it's very easy. We are always accepting we are 24, seven, like all 365 days, accepting applications, we are not stopping. And it's a huge family. And you're welcome if you want to join us, because we are all driven by one motive to create an impact door and walk towards something new, which is like a tagline, Admiral era. And yeah, it's a beautiful game to work with. And if you're someone who has a knack for technology, or stem, I'm happy to welcome you to our family.

Podcast Host  48:28

Well, I'm sure we will get a quite a few listeners who are fitting that description very, very well. I mean, a lot of the the episodes that we've done at the moment are quite stem based, how many views Do you get roughly a month or a year or you know what kind of viewership you have.

Satvik  48:43

So far, we have reached over a million people. And we they are growing each month, we have articles which gain like 10,000 or 15,000 views in seconds, we get amazed, we have no idea how this the next cycle is going to go. And all of a sudden we know it got blown. It's like in a city of Morocco, and people are watching from there. And it's really exciting for us to see every day like what happened. It's really interesting. And more importantly, like because of our team, which is so diverse. We have like team for team members from almost all the continents in the world. And most importantly, the team is like a group of high schoolers and college students and a few like masters students and graduates. The most important part is that these people are not here for their welfare. They're here to contribute to the organization, which is very different because we have people from like MIT, Harvard, Stanford, all the ivy League's or technical university Munich, Oxford University. And the thing is that I spoke to a lot of people that Why are actually willing to join us. You know, this spoke about like, this was something I looked for When I was in high school, and I didn't get a chance, because something like this wasn't there, and now you're doing this wonderful job and you know giving a platform to people, and there is no way that I do not contribute to, you know, empower this. But it is very important to apply it for the good, as Ben Parker would say, like, with great power comes great responsibilities. Yes. So it's very important to understand your job as a programmer, as a data scientist or artificial intelligence practitioner, that you need to do something good for this world because you have the opportunity to learn this. You had the privilege to know all this stuff. And now when you do you have to wait for others who do not know about what you have to benefit them also all about living a good footprint. And people who follow that footprint to you know, something better?

Podcast Host  50:52

Well said well said I think it's, it's all well and good to have the skills but even better, if you're able to apply those skills in a way that positively impacts the world. If people wanted to connect with you, what would be the best way to go about it.

Satvik  51:04

Thing me on LinkedIn, I'm always active. First thing I do in the morning is check my LinkedIn. Other than that, if you're not on LinkedIn, you could send me an email, definitely you will get in response immediately. Regardless of any time zones, I am awake, almost like 18 to 20 hours a day. So you're gonna get a response, like in snaps.

Podcast Host  51:25

Awesome. Satvik, thanks so much for joining Top of the Class. It's been fantastic to have you on the show.

Satvik  51:30

It was an honor. And I really thank you for whatever, like podcasts you're doing and the way you're featuring people, it really means a lot to so many people and you're really inspiring people.

Podcast Host  51:41

Well, I hope so. It's not me doing the inspiring, it's you doing the inspiring, and it's all my fantastic guests who are doing the inspiring. So thank you for coming on now, and I look forward to sharing the show far and wide.