Ground up knowledge is difficult to obtain in any field. How long do you think it would take you to get a complete understanding of a modern car from he ground up?
The opacity between implementation and understanding is large here and many fields. It depends where you want to contribute. I can build (i.e assemble) a computer. I could learn to build a small basic computer out of transistors and logic gates, etc. Theres a difference between a technician, an engineer and an inventor. To be an inventor takes a lot of work and experimentation probably proportional to the novelty of an invention.
Not to overdo analogies but you dont need to rebuild your own internal combustion engine in a unique way to drive a car or to contribute improvements to a car. The more you understand how and why tensorflow works the more you can do with it. It depends whether you want to build on top of that platform and use it, or build on the concepts for something else.
"Ground up" might be the wrong term here. I don't have right words either, but I feel GP is talking about that level between full knowledge and the "I have no idea what I am doing" level of downloading models from Kaggle, stuffing them into TensorFlow and calling yourself a "Deep Learning expert".
Even though I lack the name for that level, here's how I would describe in qualitative terms some of its attributes:
- Knowing the basic lay of the land all the way down. That is, at least knowing most of the black boxes and what they do, even if you don't exactly know how they do it.
- Being able to solve your own problems, instead of running around like a headless chicken every time you hit a speed bump in your work.
- Being able to reason from that first-ish principles. You're able to sketch solutions within the scope of the extended domain, and as you begin implementing it and need to understand various blackboxes in more depth, the basic shape of your solution isn't usually invalidated by gained knowledge.
I disagree that car design is a stable field. Tesla is selling a radically different car design. All car designers have to face the dawn of self-driving cars.
In every field the total knowledge set is always increasing, which is both empowering, because we stand on the shoulders of giants, and diminishing, because there is less low-hanging fruit. There is always more low-hanging fruit though, the trick is to see it hanging there. ML is a wonderful opportunity because the magical api’s can do far more than they’re currently used for.
