Don’t Be An AI Engineer If You’re Like This…

Summary

This video warns against pursuing a career in AI engineering if you don’t have a genuine interest in software engineering or understanding the theoretical foundations of AI. The speaker outlines two key groups who might not thrive in AI engineering: those who love math and theory but dislike software engineering, and those who don’t care about how AI models work and treat them as black boxes. Success in AI engineering requires both theoretical knowledge and practical software engineering skills, and those who lack either may struggle to advance in the field.

Takeaways

• 😀 Don't pursue AI engineering if you prefer math and theory over practical software engineering. It's more about deploying models than doing math by hand.
• 😀 If you enjoy understanding neural networks and machine learning theory more than coding and deployment, AI engineering might not be the right fit.
• 😀 AI engineering is more about building and deploying AI models than theoretical research or deep mathematical concepts.
• 😀 In AI engineering, practical software engineering skills are more crucial than doing theoretical math or research, especially in production environments.
• 😀 If you're solely interested in using AI models like ChatGPT without understanding how they work, AI engineering may not be the best path for you.
• 😀 Understanding how AI models work—beyond just using them—is critical for long-term success in AI engineering.
• 😀 An AI engineer who understands the theory behind models is more likely to advance in their career compared to someone who just uses AI APIs without understanding the fundamentals.
• 😀 Math and theory are important for AI engineers, but they won’t be used as frequently on the job as in educational settings or research environments.
• 😀 Passion for continuous learning and understanding AI’s inner workings is essential for anyone wanting to succeed in AI engineering.
• 😀 If you don’t care about the inner mechanics of AI models and just want to build wrappers around them, you might fall behind compared to others who dive deeper into AI theory.

Q & A

What are the two main categories of people who should avoid becoming an AI engineer, according to the video?

The two main categories are: 1) People who prefer solving mathematical equations and details of neural networks more than software engineering, and 2) People who don't care about how AI models work and are content with using tools like OpenAI's APIs without understanding the underlying mechanics.

Why is math important in AI engineering, but not necessarily the main focus on the job?

Math is important for understanding the foundational concepts of AI, such as neural networks and backpropagation. However, in practice, AI engineering is more about software engineering—deploying models and making minor adjustments before putting them into production. The heavy math is rarely done on the job.

What is the key recommendation for someone who enjoys the theoretical aspects of AI, like math and neural networks?

If you enjoy the theoretical side of AI, it’s recommended to pursue a master's or PhD in AI research to gain more in-depth experience. AI engineering may not be the best fit if you prefer theory over practical software engineering.

What happens if you don't care about how AI models work but still want to be an AI engineer?

If you don’t care about how AI models work, you are likely to struggle in interviews and in the long-term job performance. You won’t understand the models' limitations or optimal use cases, which will limit your ability to grow as an AI engineer and may result in someone more knowledgeable outworking you.

What does the speaker say about people who treat AI models like black boxes?

The speaker advises that if you only care about using AI tools and treating them like black boxes, without understanding their underlying workings, you shouldn't pursue AI engineering. This approach will likely hinder your progress in the field.

How does the speaker suggest AI engineers should maintain their knowledge?

The speaker recommends that AI engineers should make an effort to read research papers and stay updated on new developments in the field. Continuously improving and understanding the evolving nature of AI models is crucial for long-term success.

What role does software engineering play in AI engineering?

Software engineering is the core focus of AI engineering, as most of the work revolves around deploying and fine-tuning models, rather than conducting deep theoretical research. Engineers are expected to have strong software engineering skills to handle AI applications effectively.

How important is understanding AI theory for AI engineers in the job market?

Understanding AI theory is important for interviews and establishing a solid foundation in the field. While you won’t necessarily perform deep theoretical work on the job, having a strong grasp of fundamental concepts is essential for credibility and career growth.

What will happen to an AI engineer who doesn’t keep their skills up to date?

An AI engineer who doesn’t keep their skills up to date is likely to fall behind, with other engineers who stay current outworking them. This can lead to missed promotions and potentially losing job opportunities to those with more up-to-date knowledge.

Why does the speaker emphasize the importance of passion and enjoyment in AI engineering?

The speaker emphasizes passion and enjoyment because AI engineering is a long-term career. If you're not genuinely interested in the work, others who are passionate will outwork you and surpass you in promotions and career advancement.