By Is Programming Language Dead?
The Evolution of Programming:
There was a time when the dream of those who wanted to enter the IT sector was to become a good programmer. They started learning basic computer skills, from computer fundamentals to computer networking, etc. After that, most of the students used to learn programming languages very systematically, starting with C language, and their first task was to print “Hello World.” Writing their first program was an exciting experience.
I know some seniors who have shared stories about a financially disadvantaged person who completed his graduation in computer science when he didn’t have a personal computer or laptop. But he was very good at coding. He used to write code on paper and practice coding this way, and now he is a very established software engineer. This illustrates that it is possible to succeed with willpower and interest, and these experiences demonstrate how exciting learning coding used to be.
Times have changed, and with them, the context has also changed. However, in my opinion, the importance of programming has not diminished at all, even though code is now being generated with AI.
Why Programming Skills Remain Essential in the AI Era
AI can accomplish whatever I want, but I still have to know when and how much to use it, or I have to understand the code that AI writes for me before I can use it. This means that AI is just helping me to complete my project, but AI can never complete my project the way I want it to. Even if AI can verify code, I have to be very proficient in programming. This means that coding is not obsolete, but it is time to update your skills and take yourself to an expert level rather than just knowing the basics. If you can do this, your work can be done smartly and in less time with the help of AI.
This means that we have to learn coding even better, and another advantage is that if you learn any programming language very well, a lot of code can be written in a very short time with the help of Chat GPT or other AI tools. However, currently, the biggest problem is acquiring very advanced knowledge. It has become a challenge. Think about yourself: when have you been able to consistently do one task for an hour straight? This is not possible because you are somehow distracted by your phone. That’s why you are taking a lot of time to learn a programming language and giving up because it’s boring, so you can’t reach a very advanced level.
Here’s a guide on how to effectively survive and thrive while learning programming languages in this AI-regulated environment:
Understand the Basics of AI Regulation
Data Privacy: Familiarize yourself with regulations like GDPR in Europe, CCPA in California, and other local privacy laws. Understanding data handling and privacy concerns is crucial, as these regulations impact how AI applications can use data.
Ethical AI: Learn about ethical guidelines for AI development, including fairness, accountability, and transparency. Regulatory bodies may require AI systems to be explainable and non-discriminatory.
Compliance Requirements: Be aware of industry-specific regulations, such as HIPAA for healthcare or FINRA for financial services, and how they impact AI application development.
Choose the Right Programming Language
Selecting a programming language that aligns with your goals and the current market demand is essential. Here are some programming languages that are particularly relevant in the AI space:
Python: Known for its simplicity and extensive libraries like TensorFlow, PyTorch, and Scikit-learn, Python is ideal for AI, machine learning, and data science.
R: Excellent for statistical analysis and data visualization, R is widely used in data science.
JavaScript: Essential for web development and useful for deploying AI models on the web.
Java: Known for its robustness and portability, Java is often used in enterprise-level applications.
C++: Offers high performance, making it suitable for developing AI models requiring fast computation.
Julia: Emerging as a strong option for high-performance numerical and scientific computing, Julia is gaining traction in AI research.
Develop a Strong Technical Foundation
Data Structures and Algorithms: Mastering data structures and algorithms is essential for efficient programming and problem-solving.
Mathematics and Statistics: A solid understanding of mathematics, especially linear algebra, calculus, and statistics, is crucial for AI development.
Version Control: Learn to use version control systems like Git, which are essential for collaborative development and code management.
Understand AI’s Impact on Various Industries
Healthcare: AI applications in diagnostics, personalized medicine, and healthcare management require compliance with stringent regulations.
Finance: AI in finance involves fraud detection, risk management, and algorithmic trading, subject to financial regulations.
Automotive: AI for autonomous vehicles involves safety standards and regulations around testing and deployment.
