Looking Up the Ten Steps of a Master

Let's imagine something. Suppose we divide scientists and engineers in the field of deep learning into ten levels. If each level had to be expressed in one sentence describing their work status, Based on your past knowledge and understanding of the world, what would each of these ten levels be like?

Here is a possible imaginative development, from beginner to master level, with a vivid one-sentence description of each stage of a deep learning scientist or engineer’s work:

1. Beginner "I need to understand what these math symbols and codes are doing."

2. Early Exploration "I want to successfully train my first neural network model."

3. Basic Engineer "I need to learn how to optimize parameters and solve convergence problems with the model."

4. Skilled Engineer "I hope to improve the model’s performance and find better training techniques and processes."

5. Advanced Engineer "I’m thinking about how to systematically improve the model architecture and deploy it effectively into production."

6. Senior Researcher "I'm researching how to integrate methods from different fields and propose new techniques."

7. Domain Expert "I want to propose an innovative idea or theory that has the potential to influence the entire research community."

8. Technical Leader "I am leading a team to try and solve key problems that have long been unresolved in the industry or academia."

9. Academic Authority "I hope to establish a new research paradigm and redefine the research direction of the entire field."

10. Thought Leader (Master Level Guru) "I want to explore and clarify the essence of intelligence and learning, laying a new theoretical foundation for the next generation of intelligent systems."

If we use quantified experience values as a metaphor, what would the gap between these ten levels be approximately?

If we consider the differences between these ten levels in terms of quantified experience values, taking into account that the learning curve in deep learning shows "exponential" or "nonlinear" growth, here is an approximate distribution:

🔑 Design Logic:​

• The difference between each level is roughly 5 times that of the previous level, forming exponential growth.
• The gap between the top and bottom levels exceeds a million times, fully illustrating the vast divide between top thinkers and beginners.

With AI tools prevailing, they should be able to narrow the gap for lower-level developers or researchers. If we consider AI tools, what would the corresponding experience values be like?

You’ve made a very good observation.

AI tools (such as ChatGPT, Copilot, AutoML) do indeed significantly lower the threshold for beginners and mid-level developers, boosting their work efficiency and even allowing them to approach engineers several levels higher than themselves.

Considering the additive effect of AI tools, here are the adjusted experience values that reflect reality more accurately:

🔑 Main Logic:​

• Low-level Developers (Levels 1-4): AI tools have the biggest impact, boosting experience by about 20 times (helping them learn faster, solve difficulties, and accomplish tasks they couldn’t previously).
• Mid and High-level (Levels 5-7): AI tools still make a significant difference, but the gap gradually narrows, increasing experience by about 1.5 to 3 times (improving productivity, overcoming bottlenecks, but still requiring solid experience and understanding).
• Top Researchers (Levels 8-10): The influence of AI tools is relatively small, as the decisive factors at this stage come from innovation, deep thinking, intuition, experience, and breakthroughs in theory. AI can only assist.

This adjustment more accurately reflects the actual help provided by AI tools and their impact at different stages. It also clearly explains that the most important gap between top talents in the field still lies in higher-level insight and innovation ability, rather than mere execution efficiency.