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ContinualAI: a Non-Profit Research Organization on Continual Learning for AI

Vincenzo Lomonaco, Keiland Cooper, Natalia Díaz Rodríguez, Timothée Lesort, German I. Parisi, Davide Maltoni, Lorenzo Pellegrini, Giacomo Bartoli, Jidin Dinesh, Manish Agnihotri, Martin Mundt

Fig. 1: ContinualAI official website at
Fig. 2: Growing interest in CL as shown by the number of papers published each year on the subject. Based on Scopus indexed papers, limiting the category to Computer Science and searching for the exact keywords in the legend.

ContinualAI: From 1 to 400+ Members!

Fig 3: ContinualAI, an non-profit research organization and open community on Continual Learning for AI.
Fig 4: ContinualAI slack channel statistics.

Board Members and Top Contributors

ContinualAI Board Members.

Advisory Board

Fig. 6: ContinualAI Advisory Board.

Fundamental Goals

  • Connect: Despite the rise of the open-source adoption in the AI community, research labs work pretty much in isolation. Staying informed is also becoming more and more difficult given the speed of research in this area. Connecting researchers working on continual learning for AI is one of the reason ContinualAI exists today and one of the main goal of the organization, stimulating the exchange of information, ideas and resources on a daily basis.
  • Research: In the last few years we have witnessed a renewed and steadily growing interest in the ability to learn continuously from high-dimensional data. At ContinualAI we work to create a distributed and inclusive virtual research lab on Continual Learning, where anyone can contribute and learn more about this fascinating topic, while producing cutting edge research results. While in recent years the AI community has started open-sourcing the final research products (e.g. paper and/or code), research is still conducted within closed doors in small research labs. We believe in a more inclusive approach where research is conducted openly at every stage with huge benefits for the community.
  • Educate: Researchers should not only produce awesome research results but should be also concerned in making them accessible to anyone. This is why many projects are underway at ContinualAI to disseminate research in Continual Learning and related areas.

ContinualAI Open Initiatives

  • ContinualAI VirtualLab
    A collection of tools and resources that let researchers communicate and collaborate better remotely on a daily basis. Up to date, the ContinualAI Virtual Lab is based on Gitter and Slack, G Suit for Non Profit, a GitHub organization account, all the major social platforms, a Slack and a Twitter bot and much more.
  • ContinualAI Wiki
    A collaboratively maintained central hub of information and didactic/dissemination materials for Continual Learning and AI, hosted on Github pages and available at
  • ContinualAI Avalanche
    A comprehensive framework for Continual Learning Research. It aims at unifying a set of popular CL baselines, environments and benchmarks to help algorithm prototyping and experimenting, with flexibility, reproducibility, efficiency and maintainability in mind.
  • ContinualAI Colab
    A number of notebooks and scripts (for demo, showcasing & tutorials) which can be directly imported into Google Co-laboratory and are related to Continual Learning.
  • ContinualAI Medium Publication
    At ContinualAI we value scientific dissemination. We think that to advance science it is important to promote cutting-edge research and make it accessible to a larger audience of people. This is why we maintain a Medium Publication where we try to distill and simplify the continual learning ideas with as little technical details as possible.
  • ContinualAI News Mailing list and Newsletter
    For people on-the-run, we also thought about a monthly newsletter with all the major breakthrough and news within this area (write us if you’d like to contribute or see your next article published!). We also maintain a Google group for the exchange of information within and outside the ContinualAI community.
  • ContinualAI Papers Database
    Given the amount of papers published each month on the topic within different venues, it is starting to become very difficult to keep track of the progress and the different ideas emerging in this topic. This is why one of the main projects at ContinualAI is a public papers database, with a rich-full set of attributes to help researchers find interesting papers related to their research.
  • ContinualAI Short-Science
    We love short-science! this is why we plan to contribute to it with a short description of as many continual learning papers as possible. Join us to be always updated about recent continual learning research progresses.

Over 500 Members and Beyond!



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Vincenzo Lomonaco

AI & Continual Learning Assistant Professor @ Unipi | Co-Founding President & Lab Director @ | Personal Website: