Today I’m writing with good news from GoodAI – we have published the first iteration of our Framework and Roadmap, and are looking forward to feedback from the scientific community and public as a whole.
In this post, learn more about
Framework: describes how we understand intelligence, and provides tools for studying, measuring, and testing various skills/abilities
Roadmap: an ordered list of skills/abilities (some are intrinsic, other are learned) our general AI needs to accumulate in order to achieve human level intelligence
Where to download these documents and how to send us your feedback
GoodAI has several areas of concentration in addition to the Framework and Roadmap, including the School for AI, Growing Topology Architecture, Arnold Simulator, AI Roadmap Institute, GoodAI Consulting, and more. Read about them here.
Our work takes inspiration from a number of sources across a variety of disciplines, from AI Safety to Machine Learning and more. Read more about our research inspirations here.
Our main building principles include the gradual acquisition of skills, recursive self-improvement, and curriculum learning – teaching an agent the right skills in a gradual and guided manner.
We view intelligence as a tool for searching for solutions to problems. The guiding principles of our AI research revolve around an AI which can accumulate skills gradually and in a self-improving manner (where each new skill can be reused and improved in the accumulation of further skills). Each new skill works like a heuristic that helps to guide and narrow the search for problem solutions. Some heuristics even increase the efficiency of the search for additional heuristics.
These principles have inspired our framework document, which describes how we understand intelligence and which provides tools for studying, measuring, and testing various skills and abilities. The framework itself aims to be as implementation agnostic as possible, without regard to particular learning methods or environments. It provides an analytic, systematic, and scalable way to generate hypotheses that are possibly relevant in the search for general AI.
The research roadmap is an ordered list of skills and abilities (some are intrinsic, other are learned) 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.
The roadmap has two parts – architecture and curricula. You can find more information in the roadmap diagram.
The roadmap is a living document which 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.
Download the first iteration of our Roadmap
Goals of Public Release
We’re reaching out today to AI researchers, scientists, and the public as a whole in the hopes of opening conversation about general AI and persuading others of the importance of big picture thinking in the AI research field. In the past year, the GoodAI team has focused on a wider scope of AGI research, and publishing our Framework and Roadmap is one way we aim to increase transparency and cooperation in the AI and general AI community.
We look forward to receiving your input! Please don’t hesitate to get in touch at email@example.com
Read all about GoodAI’s latest achievements, technology, and initiatives on our About page: www.goodai.com/about
CEO, CTO of GoodAI
CEO of Keen Software House