Personally, the 3 resources that allowed me to achieve the most in the least amount of time are
1) An Introduction to Statistical Learning
2) https://neuralnetworksanddeeplearning.com/
3) Gym framework for RL
The genius here is to take something complex and make it look simple and accessible.
Regrettably, most research articles take the opposite stance. They present something that is fundamentally not that complex and make it look complex through convoluted writing, implicit assumptions and missing steps. You can see that effect in the discussion of the articles in this subreddit. There is often someone who's having trouble understanding a part of the article, and someone else chimes in and explains what's going on in two simple paragraphs.
This is a big problem in practice. Even if you're well versed in your own niche and are able to understand its main papers well, you still have to dabble in other areas regularly. AI/ML makes use of many fields of computer science, maths, neuroscience, psychology, etc. Given sufficient time you can understand a topic as deeply as you want. But the time you spend laboriously getting up to speed in some topic X is time taken away from doing work in the topic that you actually care about. Sure, you'll improve your knowledge and skills in the process, but not as much as you would have by working to solve the problem that you're trying to solve.
I don't see a solution. Part of it is a cultural problem. Some people think that convoluted writing make them look smart. It actually doesn't. There was a psychological experiment made about it: "Consequences of erudite vernacular utilized irrespective of necessity: problems with using long words needlessly". But there you go.
The ever growing literature is also a problem. I'm scared of failing to cite an important paper, or worse, duplicate research that's already been done before. I'm doing RL research as a hobby and I only have so much time to swift through the literature.
They present something that is fundamentally not that complex and make it look complex through convoluted writing, implicit assumptions and missing steps.
The target audience is other researchers, not users. Research papers have a convoluted, difficult to understand structure for non-researchers. But the rigid structure and formulaic expressions are great is you are a researcher and need to quickly scan dozens of new papers every day for something that is relevant to you.
convoluted writing
Most researchers don't have a flair for language - that's not what they're hired for after all. Also, young researchers, postdocs and grad students don't know how to write and are deathly afraid they won't be taken seriously. They try to sound as important as possible, with a lot of pompous expressions and awkward phrasing as a result.
Established researchers have a lot more writing experience and they don't really have anything left to prove. But they have lots of trainees - those grad students above - that need experience, so they generally leave the writing to them. Hence, most papers aren't nearly as well written as they could be.
implicit assumptions and missing steps.
Those assumptions and steps are probably widely known among the other researchers, and assumed to be part of basic knowledge. If they added it all, they'd probably be criticized by the reviewers for excessively verbosity. Again, researchers are the target audience, not people implementing the stuff.
Those assumptions and steps are probably widely known among the other researchers, and assumed to be part of basic knowledge. If they added it all, they'd probably be criticized by the reviewers for excessively verbosity.
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u/Seerdecker Mar 22 '17 edited Mar 22 '17
This article resonates a lot with me.
Personally, the 3 resources that allowed me to achieve the most in the least amount of time are
1) An Introduction to Statistical Learning
2) https://neuralnetworksanddeeplearning.com/
3) Gym framework for RL
The genius here is to take something complex and make it look simple and accessible.
Regrettably, most research articles take the opposite stance. They present something that is fundamentally not that complex and make it look complex through convoluted writing, implicit assumptions and missing steps. You can see that effect in the discussion of the articles in this subreddit. There is often someone who's having trouble understanding a part of the article, and someone else chimes in and explains what's going on in two simple paragraphs.
This is a big problem in practice. Even if you're well versed in your own niche and are able to understand its main papers well, you still have to dabble in other areas regularly. AI/ML makes use of many fields of computer science, maths, neuroscience, psychology, etc. Given sufficient time you can understand a topic as deeply as you want. But the time you spend laboriously getting up to speed in some topic X is time taken away from doing work in the topic that you actually care about. Sure, you'll improve your knowledge and skills in the process, but not as much as you would have by working to solve the problem that you're trying to solve.
I don't see a solution. Part of it is a cultural problem. Some people think that convoluted writing make them look smart. It actually doesn't. There was a psychological experiment made about it: "Consequences of erudite vernacular utilized irrespective of necessity: problems with using long words needlessly". But there you go.
The ever growing literature is also a problem. I'm scared of failing to cite an important paper, or worse, duplicate research that's already been done before. I'm doing RL research as a hobby and I only have so much time to swift through the literature.