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> about a year ago to dive in to AI (that's all this guy needs to become pretty damn proficient). (edit: I'm not claiming he's a field expert in a week guys, ju
by throw231 7y ago
> about a year ago to dive in to AI (that's all this guy needs to become pretty damn proficient). (edit: I'm not claiming he's a field expert in a week guys, just that he can probably learn the basics pretty fast, especially given ML tech shares many base maths with graphics)
To be honest anyone who has a very good working knowledge of Linear Algebra can learn much of ML-math in a day. There really isn't anything mathematically super-sophisticated that is in popular use today.
- eanzenberg 7y agoML is not AGI
- visarga 7y agoIf you know about math you're just as good at ML as a person who read everything about swimming is in swimming. You got to run experiments to see what happens, build an intuition, understand the problem from the inside. Math leaves you with a few pretty formulas and nothing else.
- fooker 7y agoAh, you've almost described the contemporary profession of being a Machine Learning Priest.
- visarga 7y agoThere are lots of ideas floating around. Everyone who has studied the field has ideas. Ideas are cheap, results matter. The problem is we don't know if any of these ideas would work, and proving an idea requires lots of data, simulations and compute. Being good at grasping the theory is just the first step in a thousand mile journey. The problem of AI is not going to be solved with a neat math trick on paper, but with lots of experiments. Nature has taken a similar path towards intelligence.
- Scarblac 7y agoThe field of statistical inference is about mathematically proving how good various statistical ideas are. It's possible to do better than just throw trial and error on a new idea.
- throw2312 7y ago> much of ML-math in a day.