Conducting innovative research in AI is not straightforward. Researchers should focus on a problem area that they are deeply passionate about, e.g., NLP, multi-modal AI, computer vision, speech, synthetic data, graph-based models etc. For research, the problem could be in the theoretical realm. However, if the problem area is grounded in the real-world, then practitioners can actually test their algorithms on real-world data and learn from the feedback.
The most important skill for doing novel research is to think deeply about a particular problem, and apply the scientific method systematically. This involves coming up with relevant hypotheses and conducting several experiments using the right datasets, algorithms, models etc. to test the validity of the hypotheses. An empiricial, data-driven strategy coupled with creative ideas usually leads to novel research output.
To come up with innovative ideas, you need to know the existing literature and what ideas have previously worked or not worked for a particular problem. Sometimes, it is sufficient to translate existing ideas for your particular use cases as well. Knowing what ideas can generalize and are practically feasible to solve a business problem is a rare skill that distinguishes the best applied researchers from the rest.
👉 Contact for AI coaching
Copyright © 2022, Sundeep Teki
All rights reserved. No part of these articles may be reproduced, distributed, or transmitted in any form or by any means, including electronic or mechanical methods, without the prior written permission of the author.
This is a personal blog. Any views or opinions represented in this blog are personal and belong solely to the blog owner and do not represent those of people, institutions or organizations that the owner may or may not be associated with in professional or personal capacity, unless explicitly stated.