Process and Tools

When it comes to R&D for Opentech AI, or R&D in general, there is no single right way to do it and there are plenty of tools to utilize. However, a general 3 step learning and discovery process, and a supporting tools can be identified for Opentech AI R&D.

Here are the 3 R’s process that can be applied in Opentech AI research and development:

  1. READ the state of the art – so that you can know and learn from what others may already have done in the area (e.g. from Arxiv). You may also want to check the performance of the existing solutions to benchmark your results on leaderboards (see Progress page).
  2. REDO an experiment just to learn or to improve it – by retrieving the open source code and data sets (e.g. from GitHub) and by running/reusing/improving those.
  3. REPORT the outcome and results of your experiment (using e.g Jupyter notebook). You may also want to share any modified code as open source and publish your results also in form of a scientific research paper (e.g. in Arxiv).  If you have made an advancement that can be reported on an AI leaderboard, that is especially significant to advance AI progress.

By following this process you can contribute to the progress of the Open AI research community. A listing of Open AI publication forums and tools can be found below:

Open AI publication forums (READ, REPORT):

  • Arxiv – e-Print service for scientific research content, Cornell University
  • EFF AI Metrics – large Jupyter Notebook reporting on a large swath of AI progress.
  • JAIR – Journal of Artificial Intelligence Research (Open Access), AI Access Foundation
  • Artificial Intelligence – An International Journal (Supports Open Access), Elsevier
  • OpenKnowledgeMaps– uses AI tools to improve search of literature on topics, such as AI.
  • …and many more Open Access forums covering topics relevant to R&D of AI.

Open AI code, models and data exchange (REDO):

  • GitHub – Open source code repository service.
  • ONNX – Open source format for AI models.
  • Algorithmia – Open marketplace for algorithms
  • Wikidata – Open knowledge base based on Wikipedia
  • Kaggle Dataset – Service for discovering and sharing open data sets
  • zenodo – Service for sharing research outputs and setting up communities
  • codeocean – Service for discovering and running scientific code
  • – US government’s open data service
  • – Finnish open data service
  • – Connecting distributed data across the web
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