Introduction In an article yesterday, Alexander "Sasha" Sidorkin wrote about how AI has “invaded” education, making it “increasingly clear that our current educational paradigms and learning theories are no longer sufficient to explain how people now learn.”
We should focus on introducing computer science (CS) and harder quantitative skills training (e.g., data science and analytics) in all majors (especially in STEM fields with underdeveloped CS and data science skills, such as chemistry and biology).
I don't buy Huang's (Nvidia CEO) premise that people need to study biology instead of learning coding (or programming).
To me, that sounds like a strategy to prevent people from achieving the success they can with programming skills, and to keep a subset of scientists and researchers (or other people in general) from making less money and doing the tedious and boring work that is used to train AI models. Telling people not to learn coding and programming limits competition, which actually benefits Nvidia and other tech companies. It also benefits the people currently working at those companies because they can relax a bit and not worry about competition.
The truth is that the practical work of chemists and biologists, such as laboratory and field work, does not even require a Bachelor of Science degree and can be done by a trained technician and eventually by robots, while the "trained" biologists and chemists can eventually work comfortably from a computer and make a lot of money like programmers by helping to design and program AI systems.
It just takes a paradigm shift. The truth is also that the more competition there is, the better the salaries will be, and for the most part, any trivial job, CS-related or not, will be automated by AI, so the people currently working in tech will not have to worry so much about us non-CS majors taking their jobs. They should not worry so much about making their work non-inclusive.
We should also focus more on oral problem solving and research skills instead of closed book exams, essays, and regurgitation.
We should focus on introducing computer science (CS) and harder quantitative skills training (e.g., data science and analytics) in all majors (especially in STEM fields with underdeveloped CS and data science skills, such as chemistry and biology).
I don't buy Huang's (Nvidia CEO) premise that people need to study biology instead of learning coding (or programming).
To me, that sounds like a strategy to prevent people from achieving the success they can with programming skills, and to keep a subset of scientists and researchers (or other people in general) from making less money and doing the tedious and boring work that is used to train AI models. Telling people not to learn coding and programming limits competition, which actually benefits Nvidia and other tech companies. It also benefits the people currently working at those companies because they can relax a bit and not worry about competition.
The truth is that the practical work of chemists and biologists, such as laboratory and field work, does not even require a Bachelor of Science degree and can be done by a trained technician and eventually by robots, while the "trained" biologists and chemists can eventually work comfortably from a computer and make a lot of money like programmers by helping to design and program AI systems.
It just takes a paradigm shift. The truth is also that the more competition there is, the better the salaries will be, and for the most part, any trivial job, CS-related or not, will be automated by AI, so the people currently working in tech will not have to worry so much about us non-CS majors taking their jobs. They should not worry so much about making their work non-inclusive.
We should also focus more on oral problem solving and research skills instead of closed book exams, essays, and regurgitation.