Hey Habr!
We want to talk about such an unusual form of earnings for teenagers as participation in hackathons. This is both financially beneficial and allows you to put into practice the knowledge gained at school and through reading smart books.
A simple example is last year's hackathon of the Academy of Artificial Intelligence for schoolchildren. Its participants had to predict the outcome of the Dota 2 game. Alexander Mamaev, a tenth-grader from Chelyabinsk, became the winner of the competition then. His algorithm most accurately determined the winning team of the bout. Thanks to this, Alexander received a solid prize money - 100 thousand rubles.
How Alexander Mamaev disposed of the prize money, what knowledge the student lacks to work with ML, and what direction in the field of AI he considers the most interesting - the student told in an interview.
β Tell us about yourself, how did you get interested in AI? Was it difficult to enter the topic?
- I am 17 years old, this year I am finishing school, and I recently moved from Chelyabinsk to Dolgoprudny, it is near Moscow. I study at the Kapitsa Phystech-Lyceum, this is one of the best schools in the Moscow region. I could rent an apartment, but I live in a boarding school at the school, so it's better and easier to communicate with people from the lyceum.
The first time I heard about AI and ML was in 2016, probably when Prisma appeared. Then I was in the 8th grade and was engaged in olympiad programming, attended some olympiads and found out that we had ML meetups in our city. It was interesting for me to figure it out, to understand how it works, and I started going there. There I first learned the basics, then I began to study it on the Internet, in different courses.
At first, there was only a course in Russian from Konstantin Vorontsov, and the manner of his teaching is tough: it contains many terms, and there are many formulas in the descriptions. It was very difficult for an eighth grader, but now, just because I went through such a school at the beginning, the terms do not present difficulties for me in practice in real problems.
β How much do you need to know mathematics to work with AI? Enough knowledge from the school curriculum?
- In many ways, ML is based on the basic concepts of the 10-11th grade school, basic linear algebra and differentiation. If we are talking about production, about technical tasks, then in many ways mathematics is not needed there, many tasks are solved by a trite trial and error method. But if we talk about research, when new technologies are created, then there is nowhere without mathematics. Mathematics is needed at a basic level, at least to know how to make a matrix application or, relatively speaking, calculate derivatives. There is no escape from mathematics here.
- In your opinion, can any student with a natural-analytical mindset solve problems in ML?
- Yes. If a person knows what ML is based on, if he knows how the data is arranged and understands basic tricks or hacks, he will not need matan, because many tools for work have already been written by other people. It all comes down to finding patterns. But everything, of course, depends on the task.
β What is the most difficult thing in solving ML problems and cases?
β Each new task is something new. If the problem had already existed earlier in the same form, it would not have to be solved. There is no universal algorithm. There is a huge community of people who train their problem solving skills, tell how they solved problems, describe the stories of their victories. And it is very interesting to follow their logic, their ideas.
β What cases and tasks are you most interested in solving?
β I specialize in computational linguistics, I am interested in texts, classification tasks, chat bots, and more.
β How often do you participate in AI hackathons?
β Hackathons are, in fact, a different system of olympiads. The Olympiad has a set of closed problems with known answers that the participant must guess. But there are people who are not strong in closed tasks, but tear everyone in open ones. So you can test your knowledge in different ways. In open problems, technologies are sometimes created from scratch, products are developed quickly, and even the organizers often do not know the correct answer. We often participate in hackathons, thanks to this you can earn money. This is interesting.
- How much can you earn on this? And how do you spend the prize money?
β My friend and I participated in the VKontakte hackathon, where we made an application for searching for paintings in the Hermitage. A set of emojis and emoticons was displayed on the phone screen, it was necessary to find a picture using this set, the phone pointed at the picture, it was recognized using neural networks and, in case of a correct answer, points were awarded. We were pleased and interested that we managed to make an application that allowed us to recognize a picture on a mobile device. We were preliminary in first place, but due to a legal formality, we missed the prize of 500 rubles. It's a shame, but that's not the point.
In addition, he participated in the Sberbank Data Science Journey competition, where he took 5th place and earned 200 rubles. For the first there they paid a million, for the second 500 thousand. Prize funds are different, now they are increasing. Being in the top, you can get from 100 to 500 thousand. I save the prize money for education, this is my contribution to the future, the money that I spend in everyday life, I earn myself.
- What is more interesting - individual or team hackathons?
- If we are talking about the development of a product, then it should be a team, one person cannot do it. He gets tired, he needs support. But if we are talking, for example, about the AI ββAcademy hackathon, then the task there is limited, there is no need to create a product. There, the interest is in something else - to overtake another person who is also developing in this area.
- How do you plan to develop further? How do you see your career?
- Now the main goal is to prepare my serious scientific work, research, so that it appears at the leading conferences like NeurIPS or ICML - ML conferences that take place in different countries of the world. As for the career, the question is open, look at how ML has been developing over the past 5 years. It is rapidly changing, now it is difficult to predict what will happen next. And if we talk about ideas and plans outside of scientific work, then perhaps I would see myself in some kind of my own project, a startup in the field of AI and ML, but this is not certain.
β In your opinion, what are the limitations of AI technology?
- Well, in general, if we talk about AI as a thing that has some kind of intelligence, processes data, then, in the near future, this is some kind of awareness of the world around us. If we are talking about neural networks in computational linguistics, for example, we are trying to locally model something, such as a language, without giving the model an understanding of the context about our world. That is, if we can put this into AI, then we will be able to create dialogue models, chat bots that will not only know language models, but will also have an outlook, know scientific facts. And this is what I would like to see in the future.
By the way, the Academy of Artificial Intelligence is now recruiting students for a new hackathon. The prize money is also solid, and this year's challenge is even more interesting - you will need to build an algorithm that predicts the player's experience based on the statistics of one Dota 2 match. For details, go to .
Source: habr.com