First round of General AI Challenge just launched: Gradual Learning – Learning like a Human

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SUMMARY:

• First 6-month warm-up round just launched
• Focus on “gradual learning” – because it’s an architecture property that enables gradual accumulation of skills, making learning more efficient
• $50k in prizes in this round; $5mil in total prizes over the multi-year General AI Challenge

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Today, we at GoodAI have launched our first warm-up round of the General AI Challenge.

It’s one of many stepping stones on our mission to develop general artificial intelligence - as fast as possible - to help humanity and understand the universe.

This first round focuses on “gradual learning” – which is the ability to gradually accumulate skills and use existing skills to learn new skills more efficiently.

The reason why we started with “gradual learning” is that we have identified it as an architecture property that will enable the efficient inclusion of additional properties. In other words, if you use your existing knowledge to learn to solve new problems, you should be more efficient than if you always have to start with zero experience.

The gradual learning round is not concerned with how good an agent is at solving a particular task (e.g. highest score in a game). Gradual learning is about how efficient an agent is at learning to solve new and unseen task. Using less training data and fewer computational resources is  among the criteria for better agents.

Gradual learning requires a combination of at least these abilities: compositional learning, meta-learning / learning to learn, continuous learning, life-long learning, learning without forgetting, transfer learning and more. More info about “gradual learning” and other required properties for general AI is available in our framework document.

How this round works:

• Today, teams start developing their AI / AGI agents
• They can develop, test and train their agents on training tasks provided by us
• All these tasks were designed with “graduality” in mind – which means that each task builds on skills acquired in previous tasks; each new task reuses skills learned in previous tasks
• After 6 months, teams will submit their pre-trained agents / models and code
• We will start evaluating the agents on non-public evaluation tasks
• We will test the agent’s ability to learn gradually and to not-forget skills
• The environment that we use for this round is a version of CommAI-Env. It is based on byte inputs and outputs and has text-like properties.
• The training tasks are based on the CommAI-mini set recently proposed by Baroni et al., 2017 (https://arxiv.org/abs/1701.08954).
• It may look like the agent is learning to communicate with the environment, however, our ultimate goal is not to build agents for this kind of environment. We chose it now because this type of environment makes it easier and more intuitive for people to understand why one task builds on top of a previous task. If we chose more complex and noisy environments (e.g. computer games), you would have a much harder time identifying when agents build skills on top of previously acquired skills.
• However, our plan with the General AI Challenge is to scale to this level of complexity sooner or later.
• Another reason for this environment was that during our road-mapping process we had identified “learned communication” as one of the essential skills which can speed up the acquisition of more advanced skills, and therefore increase the efficiency of learning (but this is something for later stages anyway)

Where are we aiming for with the General AI Challenge? We have identified a set of open problems that we consider to be key milestones in achieving general AI. Our teams here at GoodAI are working hard on solving these milestones but we also think that “outsourcing” to the greater community of researchers, programmers, and hackers can both speed up this process and diversify the avenues of research. We are also hoping that we may find new talented colleagues within the participating teams.

What if no team passes the evaluation in 6 months? Well, we will probably restart this same round again, perhaps with modified definitions and rules, or maybe slightly different evaluation tasks. There’s also a hope that if the participants get another chance, they will build on top of experience they gained in the first attempt which would eventually lead to  a solution for gradual learning. In principle, the teams would then be gradually learning how to gradually learn :-)

We have allocated $5mil in total prize money for this multi-year challenge. We plan to distribute this pool of money to the participants of various rounds throughout the following years. We hope that we get to general AI before the money runs out :-)

Thank you for reading!

Marek Rosa
CEO and Founder of Keen Software House
CEO, CTO of GoodAI


For more news:
General AI Challenge: www.general-ai-challenge.org
AI Roadmap Institute: www.roadmapinstitute.org
GoodAI: www.goodai.com
Space Engineers: www.spaceengineersgame.com
Medieval Engineers: www.medievalengineers.com


Personal bio: Marek Rosa is the CEO and CTO of GoodAI, a general artificial intelligence R&D company, and the CEO and founder of Keen Software House, an independent game development studio best known for their best-seller Space Engineers (2mil+ copies sold). Both companies are based in Prague, Czech Republic. Marek has been interested in artificial intelligence since childhood. Marek started his career as a programmer but later transitioned to a leadership role. After the success of the Keen Software House titles, Marek was able to personally fund GoodAI, his new general AI research company building human-level artificial intelligence, with $10mil. GoodAI started in January 2014 and has grown to an international team of 20 researchers.