Mind your steps

• Be careful with data. Datasets in scientific problems have many problems: it may be highly-screwed, with 99% positive cases and only 1% negative cases, because researchers will not publish their bad results; it may be very small, because much data is hard to generate and collect; it may be very dirty, for example, some experimental results are noisy and not reliable.

• Understand the problems. Scientific concepts are not as easy to understand as classifying cats and dogs in computer vision. Take a humble and respectful manner toward scientific problems and learn more scientific backgrounds (physics, chemistry, biology, etc.) about the problem of interest. Understand the reason for solving the problem and the practical application of the research are the key to success.

• Be patient. “Rome was not built in a day.” Scientific problems are often challenging and taking years to solve. But don’t be afraid if you miss any of the deadlines for NeurIPS/ICML/ICLR, good work will be recognized and published and become impactful eventually.

• Enjoy interdisciplinary collaborations. Good collaborations between AI and Science communities are key to make impactful work both in terms of real-world challenges and methodologies. Be open-minded while talking to people from the other community.