Those considered "classic" will often turn up from following reference trails, or from browsing the reading lists of graduate courses.Another way to discover older papers is to start with a senior professor in the field and find their earlier works, i.e.Tom Silver | About Me | Blog By Tom Silver A friend of mine who is about to start a career in artificial intelligence research recently asked what I wish I had known when I started two years ago. They range from general life lessons to relatively specific tricks of the AI trade. I was initially very intimidated by my colleagues and hesitant to ask basic questions that might betray my lack of expertise. Before I was drowning in a backlog of terms to Google after work.Tags: Path For Chartered AccountantBusiness Plan For Coffee ShopWrite A Essay About YourselfCreative Pieces Of WritingGood College Essays For RutgersTolstoy Essay Shakespeare
the research that paved the path to their professorship.
Also feel free to email those professors to ask for additional references (though don't take offense if they are too busy to reply).
Papers in AI are fairly accessible and often published on ar Xiv.
The sheer number of papers coming out right now is exciting and overwhelming.
There is also something to be said here for self-motivation: when I finish this code, I will have a pretty figure or video to show people!
Coming up with the right visualization for the problem at hand can be tricky. deep learning), plotting loss curves is always a good place to start.
Occasionally high profile papers will also come out in general scientific journals like Nature and Science.
It is equally important but often much harder to find older papers.
Moreover, individual researchers are often driven by more than one of these motivations, which helps glue the field of AI together. I have some friends and colleagues who are distinctly of the "Engineering" bent and others who are primarily interested in "Biology." A paper showing that some clever combination of existing techniques is sufficient to break the state of the art on a benchmark will pique the interest of the engineers but might earn yawns or even scorn from the cognitive scientists.
The reverse will happen towards a paper with only theoretical or toy results but claims of biological plausibility or cognitive connections.