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Here're some resources that once impressed me on the way to computer science research, collected from the web. Some15, 16 of those are also collections of resources, some13, 14 are written by Turing award winner, and some7 are suggestions from research students.

My humble comment

[19] is a collection of experts' answer to four important questions in NLP research, i.e., "What do you think are the three biggest open problems in NLP at the moment?", "What would you say is the most influential work in NLP in the last decade, if you had to pick just one?", "What, if anything, has led the field in the wrong direction?" and "What advice would you give a postgraduate student in NLP starting their project now?" I can also place my humble selection here as for the first and last question.

I think the three biggest open problems in NLP at the moment are:

  • Grounded language learning
  • Language reasoning
  • Learning in low-resource settings (unsupervised, transfer, multitask, meta-learning, prior knowledge, etc)

And I'm going further in the third question now.

While for the suggestion to postgraduate (including me), I want to adopt these four:

  • Read a lot to gain a strong background
  • Be ambitious and novel for the long-term result
  • Publish progress, even in a workshop for the short-term result
  • Spend about 10-20% time to learn to collaboration

[20] includes a series of articles subject to the aptitude of doing research written by Prof. Song-Chun Zhu. Here I put my quotation and inspiration.

There is undeniable a wide spread of utilitarianism in Chinese student and their parent, and it has become even stronger in the recent year since China has been through an economic revival. So it's more difficult and important for our Chinese student to overcome this obstacle. The only way is to make it clear what you want and who are you want to be. Some parent might explain that they just want their children to be happy and enjoy the life, but as the saying goes, the tree wants to be calm and the wind never stops. One need to confront his fate.

Einstein once addressed a speech for Max Planck's sixtieth birthday21 in which he conveyed that there are three various motives leads people thither and dwell in the temple of science: some take to science out of a joyful sense of superior intellectual power; some other come for purely utilitarian purposes. Although they are all responsible for the buildings of the temple of science, if there are only these two kinds of people, the temple would never have come to be, any more than a forest can grow which consists of nothing but creepers. Besides, there is the third kind of odd people who have a finely tempered nature longs to escape from personal life into the world of objective perception and thought. Max Planck absolutely seat in the third group, so as many great scientists including Einstein himself. Whether the other people can become engineers, officers, tradesmen, or scientists depends on circumstances, but for the third kind of people, they mean to be scientists.

Zhu gives two analogies about two traps a researcher can step into. One is named street lamp of research. This story is described in a book written by Michael Arbib:

It's a dark night, you see a man looking for something right under the street lamp when you walk down the street. Then you ask him: "Are you certain about your key is lost here?" "No", he replies. And you go on asking: "So why are you keep looking the key here?" "I don't know, cause here is the only bright place, where else can I find my key?"

It sounds ridiculous, but it's mostly the case we are facing today.

The second analogy called double stampede event. It comes with a story Zhu personally experienced when he was a child. The main point is the unconscious crazy trend of a research hotspot can tear you apart.


[13] is originally a transcription of Dr. Richard Hamming's talk at a Bell Lab seminar. It's a very special talk and is nothing about ordinary run-of-the-mill research, but great world-class research. As of where Hamming stands, he is among a few people who can carry on this kind of study and give some insight. Some points addressed in his speech does intrigue me, for example, the role luck, brain, and ambiguity about a theory play in the way to success.

Reference

[1] Zhihu question about Eric Xing, a professor of CMU

[2] Zhihu question about the status of AI possition in industry in autumn of 2019

[3] Advice for Research Students - Jason Eisner @ JHU

[4] Applying to Ph.D. Programs in Computer Science - Mor Harchol-Balter @ CMU

[5] How to Be a Successful PhD Student in NLP/ML - Mark Dredze @ JHU

[6] Zhihu question "Should undergraduate students major in CS be encouraged to do research?", answered by Dr. Yan Gu @ CMU

[7] Zhihu question "As a sophomore year student, how to prepare to apply CMU?" answered by Anie @ Stanford

[8] Some grad school advice by Noah Smith @ UW

[9] Some advice for undergraduates by Noah Smith @ UW

[10] Advice compiled by Michael Ernst @ UW

[11] How to Succeed in Graduate School - Marie desJardins @ UMBC

[12] Andrej's advice for success - Andrej Karpathy @ Stanford

[13] You and Your Research - Richard Hamming @ UVa

[14] Advice to a Beginning Graduate Student - Manuel Blum @ CMU

[15] Collected Advice on Research and Writing - Mark Leone @ CMU

[16] Grad School Advice - Jason Hong @ CMU

[17] What’s your advice for undergraduate student who aspires to be a research scientist in deep learning or related field one day? - Yann LeCun @ NYU

[18] How I Fail series - VERONIKA CHEPLYGINA @ Eindhoven University of Technology

[19] Frontiers in Natural Language Processing Expert Responses

[20] Research: Are we on the right way? - Song-Chun Zhu @ UCLA

[21] Principles of Research - Albert Einstein

[22] So You Want to Be a Research Scientist - Vincent Vanhoucke @ Google

16年对我来说是很重要的一年。还记得在那年的最后一天,我坐在键盘前,细数一年中经历的点滴,却很难描述彼时的心境。最后也只是赶在新年的钟声敲响之前,写了几句话,列了几个数字,草草了事。

这样的事情同样发生在17年的最后一段日子里,我觉得应该写一点东西了,只要还在春节前,也可以算得上一年总结。