Academic Researchers Should be Scouts and Explorers
Perhaps being somewhat idealistic, I think academic researchers should act as “scouts” exploring unknown research terrain
Perhaps being somewhat idealistic, I think academic researchers should act as “scouts” exploring unknown research terrain
I went to my first developers conference last week and was impressed, not least by the sensible attitude towards deep learning and other trendy AI technology.
Good software engineering is criticial when building NLG systems, including requirements analysis, design, testing, and support.
If you are writing a scientific paper which presents statistics, please use two-tailed p values unless you **really** know what you are doing.
Some suggestions for people who are new to NLG and want to learn more about it.
I give some high-level advice on handling edge cases in NLG. As with any software system, most of the effort in building NLG systems usually goes into handling edge cases.
I was very sad to hear that the Jewish synagogue in the Scottish town of Dundee is likely to close
There is a lot of hype around deep learning. especially at business-oriented AI events. I suggest some questions to think about for companies who are considering using DL.
Ehud’s guidelines for evaluating AI systems: keep it simple, keep it ethical, be careful, do proper stats, and be skeptical
Managing research projects is not much fun, but it is important. I have seen many research projects which did not reach their potential or even failed completely, because of poor management.