Oleksandr Kulkov, or “adamant”, is a competitive programmer from Zaporizhia, Ukraine, and an author of over 60 competitive programming problems. Oleksandr participated in coding competitions since school, and continued doing it in his university years, participating in ICPC regionals and other local contests. Oleksandr is one of two active maintainers of CP-Algorithms, and one of the main organizers of the Osijek Competitive Programming Camp (OCPC), a bi-annual international camp for university students to prepare for ICPC in Osijek, Croatia. Beyond that, Oleksandr also has experience teaching algorithms, data structures and formal languages at university level, and has a notable Codeforces blog which once made him a top-1 Codeforces contributor.

Can you tell us about your early interest in competitive programming and how you got started?

I started around my 8th or 9th grade in school. I’ve been enjoying Mathematics and Physics since a very young age and participated in local Olympiads. But I also really liked computers! I suppose it started from being fascinated by video games and wanting to develop them when I grow up. And then at some point I had a course teaching to code in C++, and I liked it so much! It’s somewhat hard to put into words, but the idea that you can control such a powerful and versatile machine as a computer by giving it precise logically structured commands, and the extent of just how much you can achieve by doing so really struck me.

Then, since I was doing math and physics Olympiads anyway, I also got in informatics Olympiads, and I really enjoyed the tasks in it, and I was also fascinated by the underlying theory of algorithms and data structures. My school wasn’t particularly strong in informatics, so I spent a lot of time studying on my own, mostly solving tasks from previous years, solving some local archives and reading tutorials from https://informatics.msk.ru. It allowed me to advance to national level, where I learnt about Codeforces for the first time, and well, participating in Codeforces rounds at that time was like starting anew for me, since it brought everything up to the whole new level.

What are some of the biggest challenges you faced while competing, and how did you overcome them?

The biggest challenge for me was psychological. Generally, when I compete, I’m mostly motivated by pursuing a certain sense of excitement. For me, in competitions this sense stems from two things:

– Doing something that I particularly enjoy.

– Standing out among the others.

And these 2 are actually pretty bad stimuli if your target is to score top in competitions. The main reason for this is, by “standing out” here I mostly mean doing something unusual, rather than simply achieving a high place. So, solving a problem that others typically do not solve would bring me more excitement than solving the same problems as everyone else, and achieving a higher rank from doing so.

And “doing something I particularly enjoy” also means that I would tend to be narrowly specialized in a selection of “favorite” unpopular topics (string algorithms, geometry, etc), while being somewhat mediocre in all the others, especially the popular ones, such as DP. And it would also mean that in actual contests I might focus on the problems that I “like” leaving easier problems that I “dislike” unattended. Oh, and of course, it also made me averse to training, as I would perceive it as a mostly boring and tedious activity which is not that exciting.

The situation above is a bit less of an issue with a proper team composition, as you may try to just drop unfavorable problems on your teammates, while trying to score some harder ones, which your teammates may be less likely to solve. But even in such scenarios, it entails a heavy risk, and serious competitors are not very likely to take it, so ultimately it was probably part of the reason why I wasn’t particularly popular as a teammate in my university years. Besides, it bites you back much stronger in individual competitions, which would mean that e.g. my rating on Codeforces wouldn’t be particularly prominent, decreasing my credibility as a teammate even further.

Now, how did I overcome this? Well, in some sense I didn’t, as I never made it to either IOI or ICPC WF.

On the other hand, the problems caused by these factors were getting me an enormous deal of stress, and as years went by, I learnt to accept this part of me, and simply allowed myself to keep doing what I like and avoid doing stuff that I don’t like. It might not be the best course of actions if your goal is a high competitive performance, but at the very least it greatly helps me to keep some inner peace, and I believe that this approach is what allows me to still enjoy competitive programming and keep it as a part of my life, while a lot of people whom I was competing against and losing to in my school and university years moved on from competitions and are not very active anymore.

As a grandmaster on CodeForces, what strategies and practices do you find most effective for performing well in contests?

Heh, in the meantime I’ve lost my Grandmaster rank again 🙂

I think a general piece of advice that would be relevant for myself is to avoid getting stuck in trying one particular approach to a problem. If you ever find yourself far behind everyone else, it’s a pretty strong indicator that your general approach to the problem is very far from intended, and you missed some simple observation. And the advice for such cases would be to try to maintain several potential solution directions, and explore them concurrently, rather than focusing on trying one approach.

For me personally, it is hard, and I still struggle to avoid getting tunnel visioned into just trying to do the same thing over and over again, hoping that this time, in this formulation it will work. No, it never works, you just need to unload your head and go in a direction that you didn’t consider before somehow completely. It’s much easier said than done, of course.

What motivated you to become one of the main organizers of the Osijek Competitive Programming Camp? 

I used to participate in competitive programming camps a lot when I was a university student, and it was one of the most fascinating experiences in my whole life. I even kept trying to join with people I know online to further participate in Petrozavodsk camps when I graduated. And I participated at least twice, but since 2022, when Russia started an invasion against Ukraine, I really didn’t want to have any economic relations with Russian state entities, such as Petrozavodsk University. I figured that I might not be alone in this reasoning, but at the same time I really liked the format of programming camps, so I dropped the idea in a competitive programming Discord server, found a co-organizer, found a guy with connections to a potential host university, and then we somehow managed to make it happen.

Can you describe the goals and structure of the Osijek Competitive Programming Camp?

There are 2 people, me and Tähvend Uustalu at the organizational top of the camp. Usually, we also have some organizational support from the host university (e.g. they often manage payments and site organization for us). Besides that, each camp we have a team of authors spread across 7 different contests, who change from one camp to another. We usually reach out to potential authors on competitive programming platforms, such as Codeforces and various programming-related Discord servers. We also work with various sponsors who provide us with funds for the camp.

Speaking of our goals, I described my main initial motivation above, but on a broader level, there are a bunch of things that we want to address:

– We want to provide a quality experience for our participants. As we participated in a lot of camps ourselves, we generally have a good understanding of what we enjoyed and found useful in the camps and competitions and what we did not enjoy, and we’re trying to shape the camp in accordance with this vision, of course, also taking into consideration the feedback from our participants.

– We want to keep the camp accessible to the general audience. I think a lot of camps in the region had some issues with this in one way or another, for example setting large participation fees, or being invitational only. In OCPC, we maintain the camp open to everyone who wants to participate and strive to keep participation fees at a reasonable and affordable level, so that students can participate even if they don’t have a sponsor university to cover the fee.

– We want our camp to be a platform for problem authors to showcase their original ideas and get decently compensated for it. From the very beginning of the camp, we decided that, as a matter of principle, author compensation would be our priority. And for me personally, it’s not only to attract more authors (though it is of course a desirable result), but also to give proper respect where it is due, as preparation of ICPC level contests is very hard and demanding work. At the same time, we adhere to the spirit of Petrozavodsk camp of offering authors a lot of freedom to how they want to shape their contest. We trust our authors to use their best judgment, and so far, we believe that it benefited the camp, even when individual contests ended up somewhat different from more standard ones.

As a special guest at the upcoming CodeIT programming competition, what are you most looking forward to?

I’m not very well versed in marathon style competitions, though I participated in a few whiles in university. I’m a bit anxious because I wasn’t very good at them, and usually was better at more standard algorithmic challenges, but it’s nice to leave your comfort zone every once in a while, so I’m looking to see what the challenge is and what strategies everyone would use to solve it 🙂

What message or advice do you hope to convey to the participants of CodeIT?

Don’t be afraid to leave your comfort zone and explore things you never thought you would!

How do you see the future of competitive programming evolving, and what role do you hope to play in that future?

I think there is a certain tendency in competitive programming to focus more on ad-hoc problems and avoid any problems that may benefit those who are good in implementation or have a lot of advanced computer science knowledge. While it makes sense that problem styles change alongside the community, I’m personally not a big fan of the change, as I usually enjoy the problems in which you may gain or utilize some systematic knowledge about the problem’s topic.

From my side, I hope that not being very restrictive in problem styles for the Osijek camp will help people to remember that good problems come in all shapes and sizes, not necessarily in the strict adherence to whatever the current competitive programming meta is 🙂

“It’s the largest entertainment and business network in America as it stands today, so it’s of great interest and great value,” said Mr. Kevin O’Leary.

According to data from PitchBook, any deal for the $220 billion platform will result in the exclusion of algorithms that are driven by user preferences. According to the chairman of O’Leary Ventures, it is thanks to these algorithms that the platform is so successful.

“This is the most complex deal ever in social media, and I have to build a new algorithm,” O’Leary shared.

According to Mr. Kevin O’Leary, the Chinese government will not sell the algorithms because of the high value of the company, as well as because the platform is used by 170 million users.

Any potential buyer would need to recreate these algorithms with US code and turn the platform from “TikTok China” to “TikTok USA”. These are the reasons for the decline in the company’s valuation.

The House of Representatives has voted in favor of a bill to force ByteDance, the company that owns TikTok, to give up its position as the leader of the global platform. If the company does not give up its dominant position, the platform will be banned.

There is still no draft law related to TikTok to be debated in the Senate. Additionally, there is no word on whether the Chinese government will allow ByteDance to sell TikTok to an American buyer.

Kevin O’Leary supposes that there is a 50% chance that the Senate will vote to ban the platform, as well as introduce an act to force the sale of the platform early next year after the US presidential election. He prepares and waits for this opportunity.

Any deal to buy the platform would need express Senate approval due to national security reasons.

The Chairman of O’Leary Ventures also said that he is in talks with interested parties who want to join the syndicate to buy the Chinese platform.
Former US Treasury Secretary Steve Mnuchin has also said he is interested in the topic of buying TikTok.

Since 1991, Obradovic has been working at Sombor Gymnasium, except for a brief period from 1993 to 1997 when he worked as a programmer in a software company. He has a strong focus on competitive algorithmic programming, achieving significant success with his students. Several of his former students now work for renowned companies like Microsoft and Nordeus.

His accolades include leading his school team to victory in the High School Programming League 2012/13 and consistently participating in all 15 Microsoft “Bubble Cup” Finals. Obradovic has been recognized for his outstanding contributions to education, receiving prestigious awards such as the “Svetosavska nagrada” from the Serbian Minister of Education and the “Charter of the City” from Sombor.

Despite his programming achievements, Obradovic also prioritizes outdoor activities. For the past 18 years, he has organized nature expeditions for his students, involving canoeing, mountain climbing, and wilderness camping, covering over 1000 km per season. Additionally, Obradovic participates annually in the international programming competition CodeIT, where he not only guides his students but also competes alongside them, showcasing his dedication to both their development and his own continuous learning.

With a strong commitment to both programming and outdoor education, Obradovic continues to inspire and lead his students, fostering their talents in various domains.

For DevStyleR, the man who learned to code without a computer – Dusko Obradovic shares more about the importance of programming in his life, reveals to us the secret of a successful and beloved leader, and what it feels like to stand next to his students during CodeIT.

What does programming mean to you and how did your love for it develop?

At first, programming was just my job. Fortunately1, my programming career lasted for only 3 years. I had been working in a firm which made programs for accounting, and those challenges faded very quickly. For the following 3 years, I was working in Hungary as an entrepreneur in retail. Since 1998 until today I have been working in Grammar School in Sombor as an IT teacher. Ever since then, for me programming is pure pleasure.

You mentioned that you learned to program without a computer. How is that possible?

I suppose that today it would be very difficult. Almost impossible. Not because today’s children couldn’t do it, but because today’s technology makes the process of learning programming easier. At that time, there was no other way. I wrote my first program on a programmable calculator after 15 months of learning from a notebook and a board. I wrote my first program on a computer after 2,5 years, in 1984 in PASCAL on Honeywell. Soon after that, the first personal computers appeared on my faculty, but we didn’t have access like today – one or two times a week, for a few hours, mostly at night. There was no internet for us, mere mortal students.

The school team you lead in Serbia is the only one in the world that has participated in all 16 finals of the Microsoft “Bubble Cup”. How do you achieve such success?

We could say that at the beginning it was just a lucky set of circumstances, which in time grew into something inevitable. Experiences of older teams and their stories year after year inspire new generations to take part in qualification competitions. In the past 8 years, CodeIT qualifications have had the same priority with us as well.

These two competitions, and especially the marathons assignments, are priceless in the process of my students’ advance. Knowing that they have 20 days to solve the problem makes most of them to learn new algorithms and structures in that period so that they would make their placement better.

The fact that you don’t have to catch the beginning of the competition, that even less efficient solutions are also valued and the lack of time penalties all brought to the fact that longer qualification rounds are more popular in my school than the sprint rounds. Even if we put aside those who are satisfied with even getting a shirt, we can say that the fever of CodeIT and Bubble Cup is always present in our school.

You have received numerous awards in Serbia, including the “Svetosavskа Award” and the “Povelja grada”.

Both awards are not just the product of success in programming. Over a month which I spend in nature with my students has equally contributed, if not even more, to receiving those awards. I divide my year into programming season: from the finals of Bubble Cup at the end of September, over winter and spring, until the end of the finals of CodeIT at the beginning of June; and on the adventurous season: during the summer. This other includes camping, fishing and ecological-rowing expeditions. During those, we compensate nine months of cyberspace with the life in a complete natural environment without any comfort.

What is the secret to building a cohesive community that competes among itself in various competitions?

Today’s way of life, which is considered as a contemporary value, and extreme influence of mass media led to the fact that in Serbia there are no more colleagues ready to do something like that. As far as I know, it is similar in Bulgaria as well.
The other reasons are, that those who have the knowledge to pass on to new generations will rather work somewhere else for 2000+ EUR instead for 750EUR, which is the salary for teachers in Serbia.

I wouldn’t call my way of work a secret.

• – With my students I don’t have a typical teacher – student relationship. We are all pals, and in many programming competitions and on CodeIT in finals we compete against each other. (This does not apply when I have to mark them).
• Experiences of earlier generations from their faculties and jobs in IT companies, through the students themselves, their friends and relatives in one small town are easily spread, so many elementary school pupils are coming to my extra classes even before they enroll Grammar School.
• My extra-curricular classes are open to students from other schools as well.
• There are no divisions on gender, classes, religions… so, except on competitions, you can always count on help of those who are around you.
• Many of those students enjoy the natural beauties of Serbia with me, next to some water during a big part of summer in the adventurous season.
• In the programming season I do about 5 hours of extra-curricular classes a week. On the contrary to other professions in Serbia, doing extra hours in teaching is not paid.

How do you prepare students for Olympiads and competitions, and what is the best advice you give them before each event?

Before competitions in our country there is a list of advice that competitors should acquire. That list was based on hundreds of points that previous students had lost on their competitions, and that led to a difference in points on their expense.
Choice of the adequate type of data structures and their sizes.
Obligatory winning the partial points, if there are some.
Narrowing down of the possible ways of solving the tasks based on given limitations.
And many more…

Nemanja Majski is my only student who has achieved this so far. And for more than a year he has been working alone and going to Belgrade at weekends, where he and a few more students have been prepared for the international competitions. This, of course, makes me proud.

Talent or hard work – which one is the key to success?

It depends on what you consider a success. If success is getting a job in an IT company, it doesn’t matter. If the aim is to work in a better firm with a bigger salary, it is possible to achieve with less talent and much work or vice versa.

If success is reaching a level where you can choose where you want to work, you need both talent and a lot and a lot of work.

Now that you have mentioned this, I have to say that it is a big pity that in our two countries a good system doesn’t exist. It all depends on the individual. There are talented children everywhere. The proof for that are not so big towns like Sombor (SRB) and Shumen (BG). Whether they will be found and start their way in which their talent will be fully used, depends on luck as well. You should be born in a town like this or its surrounding at the time when there live enthusiasts who put long-term strategic aims in front of their own personal material ones.

You have been competing alongside your students at CodeIT for years. What do you feel and think when you stand next to them during a competition?

Since the codes are public after the competition, I cannot hide the ways I approach solving of the marathon tasks. Honestly, that is not my aim at all. But during earlier competitions that was my advantage. Tempo in which I type and test, comparing to my students, is certainly my handicap. Even reading the text of the task takes a considerable amount of my time. I still manage to compensate that by experience, but it is getting more and more difficult. It happens more and more often that one or even more of them beat me on the same competition. The last sprint round on CodeIT was a disaster for me. I didn’t manage to solve not one task in given time. My ages are getting me. Luckily, I have no problem with the fact that my students are beating me.

What advice would you give to future participants in CodeIT?

The first piece of advice is to be persistent and patient. I have recently tickled the CodeIT officials to, besides presentation of the Great Guru, organize one of the little Guru, i.e. me, where I would, at least to younger participants, convey a few very useful pieces of advice when solving marathon tasks. If they get lucky and that really happens, in future their results in marathon tasks will certainly be better.

Besides this, play computer games less. That is a waste of time and you have no use from it, but a current satisfaction. Successfully finished combat with the solving of a programming problem can give you a similar feeling, with the difference that this will definitely help you in your future profession.

Today, companies of all sizes are using this method to manage costs, reduce risk, and also help improve the quality of their products and services.

Machine learning is a subfield of artificial intelligence that focuses on developing systems that improve their performance based on the data they receive. Machine learning and artificial intelligence often go hand-in-hand, and the terms are sometimes used interchangeably even though they don’t mean the same thing. An important distinction is that while all machine learning is AI, not all AI is machine self-learning.

And if the topic of machine learning is interesting to you, stay with us because we’re going to immerse you in this very world and show you the TOP 10 YouTube channels for machine learning, according to Analytics Insight.

Channels that cover a wide range of topics from basic concepts to advanced techniques, engaging tutorials, expert opinions and discussions for both beginners and seasoned professionals in this field.

Top 10 Machine Learning YouTube Channel

Siraj Raval
This channel has successfully assembled a PyTorch AI model for local operation on iOS and Android platforms. This approach prioritizes privacy, cost-effectiveness, and adherence to data compliance standards such as GDPR and HIPAA. The tutorial offers a comprehensive guide on replicating this process used in their ongoing healthcare project, titled Doctor Dignity (previously referred to as DoctorGPT), enabling viewers to achieve a similar feat with any AI model, especially Large Language Models.

3Blue1Brown
Renowned for its exceptional ability to deliver visually engaging and profoundly captivating explanations, this channel has acquired acclaim for its unique talent in simplifying and elucidating intricate mathematical and machine learning concepts. It has established itself as an invaluable resource due to its visually stimulating approach, ensuring the comprehension of complex subject matter in a profoundly comprehensive and engaging manner, a distinctive trait that separates it from other educational channels.

Sentdex
This YouTube channel exhibits a laser-like focus on instructing Python programming, machine learning, and artificial intelligence, providing a treasure trove of practical coding tutorials. With a specialized emphasis on these multifaceted and crucial fields, the channel meticulously crafts content that caters to learners seeking in-depth understanding and hands-on application. Its commitment to delivering detailed, comprehensive tutorials within the realms of Python, machine learning, and AI sets it apart, making it an invaluable educational hub for enthusiasts aspiring to expand their knowledge and skill set in these domains.

Andrew Ng
This YouTube channel boasts a collection of comprehensive and highly acclaimed lectures delivered by an esteemed expert in the field of artificial intelligence. These lectures serve as an invaluable resource, encompassing an extensive array of machine learning topics, curated to offer a profound understanding of diverse concepts within the realm of AI. The lecturer’s authoritative knowledge and adept articulation set the channel apart, making it an indispensable learning hub for individuals seeking in-depth insights into the intricate landscape of artificial intelligence and machine learning.

Two Minute Papers
This channel has carved a niche for itself by consistently offering concise, yet profoundly informative updates on the most recent research papers in the expansive domain of machine learning. Renowned for its commitment to delivering up-to-date insights, it meticulously curates’ content that presents a thorough analysis of cutting-edge research within the field. Its specialized focus on disseminating the latest advancements and discoveries in the ever-evolving landscape of machine learning distinguishes it as an invaluable source for enthusiasts and professionals seeking a comprehensive understanding of emerging trends and breakthroughs in the field.

StatQuest with Josh Starmer
This channel excels in its unique ability to elucidate and demystify complex statistical concepts, particularly those interwoven with the intricate fabric of machine learning, rendering them comprehensible to a wide audience. Renowned for its adeptness in simplifying the complexities inherent in statistical theory and its application in machine learning, the channel offers an accessible and engaging learning experience. Its commitment to breaking down intricate statistical intricacies and their relevance to machine learning principles sets it apart, making it an indispensable educational resource for learners seeking a thorough grasp of these challenging concepts.

Krish Naik
This YouTube channel stands out for its provision of practical and hands-on tutorials covering an extensive array of machine learning models, programming practices, and in-depth data analysis. Renowned for its commitment to offering step-by-step guidance, it serves as an invaluable resource for individuals seeking practical and applicable knowledge in the realms of machine learning, programming methodologies, and the intricate processes involved in comprehensive data analysis. Its dedication to delivering comprehensive tutorials that bridge theory with practical application makes it an indispensable hub for enthusiasts and professionals striving to enhance their expertise in these critical domains.

Lex Fridman
This channel takes you through explicit emphasis on data science, encompassing a diverse range of topics, including the intricate fabric of machine learning algorithms, the dynamic language of Python, and comprehensive data analysis. Renowned for its comprehensive coverage and in-depth exploration of these multifaceted areas, the channel has established itself as an invaluable resource for learners seeking a profound understanding of data science and its integral components. Its dedication to unraveling the complexities of machine learning algorithms, imparting insights into Python’s versatile applications, and delving into the intricacies of data analysis positions it as an indispensable educational platform for enthusiasts and professionals aspiring to elevate their expertise in these pivotal fields.

Data School
This distinct YouTube channel is dedicated to the multifaceted world of data science, offering a rich tapestry of topics that span the intricate domain of machine learning algorithms, the versatile programming language Python, and the nuanced techniques of comprehensive data analysis. Distinguished for its meticulous approach and extensive exploration of these critical subjects, the channel serves as an invaluable wellspring of knowledge for learners seeking a deep and comprehensive understanding of the expansive field of data science and its interlinked elements. Its commitment to unpacking the complexities of machine learning algorithms, unraveling the diverse applications of Python, and navigating the intricacies of data analysis positions it as an indispensable educational platform for both enthusiasts and professionals aiming to fortify their expertise in these essential areas.

Deep Lizard
This YouTube channel is well-regarded for providing in-depth tutorials covering the broad areas of machine learning, deep learning, and the intricate aspects of neural networks. Its comprehensive approach and thorough exploration of these vital topics make it a valuable resource for individuals seeking a deep understanding of artificial intelligence. The channel’s dedication to explaining the complexities of machine learning, delving into the nuances of deep learning, and exploring the detailed architecture of neural networks positions it as an essential educational platform for those looking to enhance their expertise in these crucial areas.

Its creator developed C++ because he felt constrained by existing programming languages that were not ideal for large-scale projects. He used C to build what he wanted because C was already a general-purpose language that was efficient and fast in its operations.

The free, multi-paradigm and usually compiled programming language is best suited for intermediate level programmers. However, those looking to start their programming journey with C++ will find that they can quickly pick up programming concepts that will take them far in their career.

C++ is All Around Us
Around 4.4 million developers worldwide use this language. A number of search engines, virtual reality applications, databases, operating systems, web browsers, and even movie productions are powered by C++ code. Adobe products, the Unreal Engine, and the Chrome and Firefox browsers are all built with C++. It is quite preferred in performance-critical areas due to its high speed.

C++ Plus Its Top Benefits

Speed
Did you know that low-level programming languages are much faster than higher-level programming languages like Java and Python, for example? Since C++ is an extended version of C, which in turn is a low-level programming language, it is one of the fastest programming languages at the moment

Efficiency
C++ has a standard template library (STL) that can be used extensively in programming. These libraries provide efficient algorithms that allow the programmer to save time and effort that would otherwise be taken in creating code.

Freedom to choose programming styles
Do you know why they define C++ as multi-paradigm? Because it supports about seven different coding styles. In this sense, you can choose a style that suits your use case without having to use objects.

Ability to break down complex problems into smaller groups
C++ is partly an object-oriented programming language, which means you can solve complex problems intuitively by creating objects. In addition, C++ features allow you to break code into logical blocks that are clear, easy to understand, and easy to test and modify.

Ability to catch errors before execution
C++ is statically typed, which means that the compiler makes no assumptions about what the programmer is typing. The advantage is that the compiler can catch errors and omissions before execution, which can save time in the long run.

It also has a number of other features and additional capabilities. C++ plays an important role in many modern and state-of-the-art systems, such as databases, web browsers, operating systems, and others that have used C++ in at least some part of their databases.

In this article we will present to you top 5 interesting facts about C++, presented by TechWibe.

Top 5 Curious Facts About C++ You Didn’t Know

It was originally known as “The New C”.
Did you know that computer programmer Rick Massity actually gave the name to C++? His reasoning was that the ++ in the name represented an improvement over the original C programming language.

C++ is most inspired by C. C++ is also built on the same framework, but of course, new features and capabilities have been added to the language.

Has a rich library support
The C++ language has the STL or standard template library. If you don’t know, the STL is extremely useful as it helps to quickly prepare codes when needed. It basically consists of four components namely iterators, functions, containers and algorithms.

Iterators help in dealing with a sequence of values. Functors allow to customize the operation of the associated function using the required support of the passed parameters.

Containers have classes in them to implement various data structures that are commonly used, such as vectors, lists, sets, maps, etc. Finally, the algorithms can be of different kinds, such as search, sort, etc.

A steep learning curve
Several programmers join the world of C++ or C, but they fail to keep mastering and to learn these languages well. It is mostly because they feel irked in catering to low-level programming elements, like the multi-thread data races, memory storage model, pointers, templates expansion, address alignment, and others. When these elements are not handled well, the application has a possibility of crashing. This can be pretty frustrating for a new programmer.

It powers the world
The truth is that C and C++ are everywhere. These two languages alone power more technology than we give them credit for. The kernels of as many operating systems as Android, iOS, Mac, Linux, and Windows are coded in C. In addition, modern game engines are also widely available and coded in C++ or C, such as Unreal Engine, Unity 3D, cocos2d-x, and others.

C++ has over thirty-five operators
Did you know that C++ has more than thirty-five operators. They range from comparisons to logical operations, bit handling, arithmetic, and more. Virtually any of these thirty-five operators can be overloaded for certain types.

This wide variety of operators makes C++ programming language user definitions more like built-in types.

Today we’ve chosen to bring you the top 5 suitable programming languages for AI developers according to Cointelegraph.

Top 5 Programming Languages for AI Developers

Python
Python is the preferred choice for Artificial Intelligence (AI) development due to its simplicity, understandability and flexibility. It has a rich collection of libraries and frameworks for machine learning, natural language processing, and data analysis, including TensorFlow, Keras, PyTorch, Scikit-learn, and NLTK.

With these tools, you can create and train neural networks, work with huge data sets, interpret natural language, and much more. Python is a very well-liked language for research and training in artificial intelligence. Its ease of use and community support lead to the availability of many online tutorials and courses for people who want to get started in artificial intelligence development.

Java
Java is a general-purpose programming language. It is often used in the development of large-scale enterprise artificial intelligence applications. Because of Java’s reputation for security, reliability, and scalability, it is often used to create complex AI systems that need to manage massive amounts of data.

Deeplearning4j, Weka and Java-ML are some of the AI development libraries and frameworks available in Java. Using these tools, you can create and train neural networks, process data, and work with machine learning algorithms.

Java is the preferred alternative for creating AI applications that run on multiple devices or in a distributed context. This is due to the platform’s freedom and support for distributed computing. Because of Java’s adoption in enterprise development, a significant Java developer community and rich materials are available for those who wish to begin AI development in Java.

Lisp
Lisp is a programming language created in the late 1950s, making it one of the oldest programming languages still in use today. Lisp is known for its unique syntax and powerful support for functional programming.

Because it was used to create some of the earliest artificial intelligence systems, Lisp has traditionally had a significant impact on the field of artificial intelligence. Lisp is a good choice for AI research and development because it supports symbolic computation and can process code as data.

Although Lisp is not as commonly used as some of the other languages discussed previously in AI development, it still maintains a devoted following among AI experts. Many AI researchers and developers appreciate Lisp’s expressiveness and ability to handle complexity. Common Lisp Artificial Intelligence (CLAI) and Portable Standard Lisp (PSL) are two well-known AI frameworks and libraries that have been implemented in Lisp, for example.

C++
In artificial intelligence development, C++ is a high-performance programming language that is often used, especially when creating algorithms and models that need to be fast and efficient. Because of its well-known low-level hardware control, C++ is often used to create AI systems that need precise control over memory and CPU resources.

TensorFlow, Caffe and MXNet are just a few of the libraries and frameworks for AI development available in C++. With the help of these tools, you may create and train neural networks, process data, and work with machine learning algorithms.

R
R is widely used in the field of artificial intelligence development, especially for statistical modeling and data analysis. A popular choice for developing and exploring machine learning models due to its strong support for statistical analysis and visualization.

Caret, mlr, and h2o are just some of the libraries and frameworks available in R for AI development. Building and training neural networks, using machine learning methods, and processing data are all made possible by these technologies.

In addition, researchers who want to perform sophisticated data analysis or create forecasting models often use it for its user-friendly interface and strong statistical analysis capabilities.

Last month, a young woman went to work at Sarzamineh Shadi, or Land of Happiness, an indoor amusement park east of the Iranian capital Tehran. After a photo of the woman without a hijab went viral on social media, the amusement park was shut down, according to multiple reports in Iranian media. The Tehran prosecutor’s office has reportedly launched an investigation, Wired wrote on the subject.

Shutting down a business to enforce Iran’s strict women’s dress laws is a familiar tactic for Shaparak Shajarizadeh. She stopped wearing the hijab in 2017 because she considered it a symbol of government repression, and recalls how restaurant owners, fearful of the authorities, pressured her to cover her head.

After Iranian lawmakers proposed last year that facial recognition be used to monitor the hijab law, the head of the Iranian government agency that enforces the morality law said in an interview in September that the technology would be used “to identify inappropriate and unusual movements,” including “non-compliance with hijab laws.” Individuals could be identified by checking their faces against a national identity database to levy fines and make arrests, he said in the interview.

Two weeks later, a 22-year-old Kurdish woman named Gina Mahsa Amini died after being detained by Iran’s morality police for not wearing her hijab tightly enough. Her death sparked historic protests against women’s dress codes that led to some 19,000 arrests and more than 500 deaths.

Catherine Grote, a human rights analyst at Freedom House, a US government-backed NGO, says she has noticed a shift in Iran in recent years, from relying on informants and physical patrols to forms of automated digital surveillance targeting critics.

Facial recognition has become a coveted tool for authoritarian regimes around the world as a way to suppress dissent, Grote says, though many lack the necessary technical infrastructure.

“Iran is a case where they have both the governmental will and the physical capabilities.”

she says.

Some of the facial recognition devices used in Iran today are the work of Chinese camera and artificial intelligence company Tiandy. Its dealings in Iran were included in a December 2021 report by IPVM, a company that monitors the surveillance and security industry.

Tiandy is one of the world’s largest manufacturers of security cameras, but its sales are mostly in China, the report’s author says, and the company appears to have seized the opportunity to expand into Iran.

In the future, self-learning algorithms combined with new sensors should ensure better traffic flow and shorter waiting times, while providing improved safety for pedestrians at crossings.  Arthur Müller, project manager and scientist at the Fraunhofer IOSB-INA, explains the DRL approach:

“We used a junction in Lemgo, where our testing is carried out, to build a realistic simulation and trained the AI on countless iterations within this model. Prior to running the simulation, we added the traffic volume measured during rush hour into the model, enabling the AI to work with real data. This resulted in an agent trained using deep reinforcement learning: a neural network that represents the light’s control.”

The sensors that are currently in use — induction loop technology embedded in the road surface — provide only a rough impression of the actual traffic situation. The researchers at Fraunhofer IOSB-INA are working to address these problems. Instead of conventional sensors, they are using high-resolution cameras and radar sensors to more precisely capture the actual traffic situation, according to the release.

This allows the number of vehicles waiting at a junction to be determined accurately in real time. The technology also detects the average speed of the cars and the waiting times. The real-time sensors are combined with artificial intelligence, which replaces the usual rigid control rules. The AI uses deep reinforcement learning (DRL) algorithms, a method of machine learning that focuses on finding intelligent solutions to complex control problems.

The AI algorithms run in an edge computer in the control box at the junction. One advantage of the algorithms is that they can be tested, used and scaled up to include neighboring lights that form a wider network.

It means that if you want to start a career in software engineering, this is the right time to do it. 

It’s true that software developers are in such high demand. The main reason for that is the push for innovation in the technology sector. Here are some of the future trends which are expected to change the current landscape.

• Moving to Cloud Services 

Among the positive benefits of cloud-based service are significant cost savings, increased security, ease of use, increased flexibility, and the ability to seamlessly collaborate. Many companies today are transitioning to cloud-based services and that makes demand higher for cloud engineers.

• Use of Artificial Intelligence

We see AI in the form of voice assistants, chatbots, and other devices designed to make our daily lives more convenient. However, the technology hasn’t yet reached the full potential of AI.

• The Blockchain Technology

This kind of technology establishes trust between parties using transparency and security, hence its appeal to the banking industry, and that is its strength. Its applications go far beyond the financial sector.

• Cybersecurity 

When it comes to the companies’ top priority – it is necessary to protect valuable data from hackers and cybercriminals, which is why the cybersecurity industry remains a popular field for developers.

• Advanced Algorithms Driving Automation

According to Grand View Research, the global marketing automation market was valued at $4.06 billion in 2019 and was expected to grow by 9.8% annually to 2027. This demand opens up opportunities for aspiring software engineers.