The 2021/22 Research Roadmap focuses on four core research areas: learning to learn, lifelong (gradual) learning, open-endedness, and generalization/extrapolation of meta-learned algorithms.
The 2021/22 Research Roadmap links the work of the GoodAI research team and the work of the GoodAI Grants recipients and how it relates to the four core research areas. The document outlines the requirements for the system GoodAI is developing and for the environment in which the system will be trained and tested, and paths towards implementation.
Previous research roadmap
The older research roadmap was released in 2017 and is an ordered list of skills and abilities (research milestones) which our AI will need to be able to acquire in order to achieve human level intelligence. Each skill or ability represents an open research problem and these problems can be distributed among different research groups either internally at GoodAI, or among external researchers and hobbyists.
New skills very often depend (build on) previously acquired skills, and so the research milestones exhibit some intrinsic dependencies. We cannot simply skip to an ability in the middle of the roadmap and start implementing it. Instead, each skill is like a stepping stone to the following skill.
There are two parts to the roadmap:
- a map for open problems
- a map for known and proposed solutions (where each problem may have multiple or branching solutions)
The roadmap is a living document that will be updated as we work towards the milestones and evaluate them within the framework document. The current version of the documents is early-stage and a work in progress. We anticipate that more milestones and research directions will be added to the roadmap as our understanding matures.
There will still be many parts missing, but we feel that it is better to engage with the community as soon as possible.
