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Researchers publishing in the same conferences, using the same technical jargon, and calling their field Artificial Intelligence can have polar opposite research motivations.Some folks have even suggested different names for the field in an effort to clear things up (e.g.
When the rest of the code has been written, running the visualization scripts will allow me to quickly verify whether my code matches my mental model.
Even more importantly, good visualizations will often make bugs in my thinking or code far more obvious and interpretable than they would be otherwise.
The big three conferences are NIPS, ICML, and ICLR.
Other reputable general-audience conferences include AAAI, IJCAI, and UAI.
Each subdiscipline has more specific conferences too.
For computer vision, there is CVPR, ECCV, and ICCV; for natural language, there is ACL, EMNLP, and NAACL; for robotics, there is Co RL (for learning), ICAPS (for planning, including but not limited to robotics), ICRA, IROS, and RSS; for more theoretical work, there is AISTATS, COLT, and KDD.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.Conferences are by far the dominant venue for publication, but there are journals as well.JAIR and JMLR are the two most prominent journals specific to the field.Deciding what to work on can be the hardest part of research.Some general strategies that I have seen employed by researchers with long track records: The strategy I have come to adopt for writing research code is to start by creating visualization scripts.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.It was many months before I felt comfortable enough with a few colleagues to ask questions, and still my questions were carefully formulated. Now I immediately ask a question when it comes and my confusion is resolved before it compounds.Good papers and researchers will state at the outset their motivation, but often the fundamental impetus is buried.I have found it useful to consider papers through each lens one at a time in case the motivation is not obvious.