- Getting started in AI Research
Getting started in AI Research
A guide on how to contribute to confirming the reproducibility of some of the most recent papers and join open-search research.
Focus on research in Artificial Intelligence (AI) is nowadays growing more and more every year, particularly in fields such as Deep Learning, Reinforcement Learning and Natural Language Processing (Figure 1).
Figure 1: Growth of research in Artificial Intelligence 
State of the art research in AI is usually carried out in top universities research groups and research-focused companies such as Deep Mind or Open AI, but what if you would like to give your own contribution in your spare time?
In this article, we are going to explore different possible approaches you can take in order to be always up to date with the latest in research and how to provide your own contribution.
The Reproducibility Challenge
One of the main problems which have affected the AI research field is the possible inability to efficiently reproduce models and results claimed in some publications (Reproducibility Challenge).
In fact, many research articles published every year contains just an explanation of the derided topic and model developed but no source code to reproduce their results. Some reasons why researchers might at times omit these kinds of information are: keep a competitive advantage against other institutions, non-disclosure agreements, transform their research into a product, etc…
In order to make research more accessible and have real-world impacts, different competitions have been created in order to encourage the public to study different publications and try to reproduce their results. Two of the most know competitions in this ambit are the NeurIPS and ICLR Reproducibility Challenges. In case you are looking for any practical example, I recently started a GitHub repository about this topic.
Additionally, websites like Papers with Code, have recently been created in order to easily find research publications which already have publically available code. In this way, anyone can use state of the art models for their own projects completely for free!
Season of Docs
Season of Docs is an annual program organised by Google aimed at connecting technical writers with open-source organizations in order to improve libraries documentation.
Figure 2: Season of Docs 
By joining the program, writers will, in fact, be able to contribute to the documentation of open-source organizations such as Julia, Numpy, Matplotlib, Bokeh and many more.
GitHub Open Source Contributions
Many of nowadays most popular Machine Learning and Deep Learning libraries are available on GitHub and most of them are happy to accept help from external contributors. Some examples of popular GitHub repositories with many Issues and Pull Requests which accepts contributors are:
Two Minute Papers
This YouTube channel in fact reviews and summarises for you on a weekly bases some of the most interesting latest publications, providing also demos and example applications.
Finally, other possible ways in order to keep always updated about AI is to:
Take part in conference events such as: NeurIPS (Neural Information Processing Systems), ICLR (International Conference on Learning Representations), ICML (International Conference on Machine Learning) and AAAI (Association for the Advancement of Artificial Intelligence), etc….
If you have any suggestion on any other possible technique which can be added to this list, please just let me know in the comment section!
I hope you enjoyed this article, thank you for reading!
 Artificial Intelligence Index 2018 Annual Report by Yoav Shoham et. al. Accessed at: http://cdn.aiindex.org/2018/AI%20Index%202018%20Annual%20Report.pdf
 Reactome, Season of Docs. Accessed at: https://reactome.org/about/news/136-season-of-docs