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I'm very adamant that if you really want to be an AI researcher, it starts with the mathematics. Multi-variable calculus, linear algebra,discrete math, probabil

I'm very adamant that if you really want to be an AI researcher, it starts with the mathematics. Multi-variable calculus, linear algebra,discrete math, probability and statistics are key. The classic books and courses others have suggested are excellent starting points. I'll also say something here that isn't established, so I may take some heat. I believe there are two main paths in AI that will eventually converge to general AI: symbolic and sub-symbolic reasoning. If you go the path of symbolic reasoning, studying functional programming, theory of computation,type theory, natural language processing/compilers are in your future. If you go the path of sub-symbolic reasoning, you will be closer to optimization methods, neural networks, etc. It really depends on what you want to do. Ex: Computer vision is all about sub-symbolic reasoning, while natural language processing is heavily about symbolic reasoning. Of course advanced applications mix both! If you want to go after general AI, you gotta figure out how to tackle both forms of reasoning. In the end, if you are serious about being an AI researcher, you will have to be a great computer scientist and mathematician. This is why it seems so difficult to get into. "Do I focus on working on messing with libraries and algorithms or the mathematical theory? And if I do both, how!?" It's not easy, but just start and keep going. Others have provided great advice. One of my greatest joys in life has been coming to understand the marriage of computer science and mathematics under the banner of AI. It is really something exciting worth living for. [Never give up!](https://www.youtube.com/watch?v=KxGRhd_iWuE https://www.youtube.com/watch?v=KxGRhd_iWuE) Also, I recently gave a talk on getting into AI and my view on the state of things. It also has a resources section that may be of interest. Slides: https://docs.google.com/presentation/d/1pDZLkFTFjuZzM8lIKkuCqgkFHthCVu6vMgNQge92tSY/edit?usp=sharing https://docs.google.com/presentation/d/1pDZLkFTFjuZzM8lIKkuC...

Byzantine_BG 9y ago
One day 40 years after I graduated I "got" linear algebra. The eigenvectors of the inverse of the covariance matrix... Squeeze and twist it into a hypersphere! Mahalabobis distance is generalized z-score! Oh!
- BucketSort 9y ago
  That's the ticket. It's all about building that intuition so you can be creative with mathematics. Linear algebra in particular is an area that continues to teach me more over the years.
abhgh 9y ago
I completely agree with you on knowing the math part. I am kind of emphatic about it too when doling out advice. Without that, getting good results just becomes a blind exercise in tweaking parameters. With that, you can really troubleshoot your models, and know what model(s) to try next; the path to optimality is somewhat meaningful and reasonable.

Byzantine_BG 9y ago

One day 40 years after I graduated I "got" linear algebra. The eigenvectors of the inverse of the covariance matrix... Squeeze and twist it into a hypersphere! Mahalabobis distance is generalized z-score! Oh!

BucketSort 9y ago
That's the ticket. It's all about building that intuition so you can be creative with mathematics. Linear algebra in particular is an area that continues to teach me more over the years.

abhgh 9y ago

I completely agree with you on knowing the math part. I am kind of emphatic about it too when doling out advice. Without that, getting good results just becomes a blind exercise in tweaking parameters. With that, you can really troubleshoot your models, and know what model(s) to try next; the path to optimality is somewhat meaningful and reasonable.