Monday, February 27, 2017

A blogpost about how the General AI Challenge is different from other AI challenges - by José Hernández-Orallo


The following is a blogpost published by José Hernández-Orallo, Professor at Technical University of Valencia and member of the General AI Challenge scientific advisory board.

I find Jose's article very interesting, especially how he summarizes our approach in reaching general artificial intelligence and how well he describes the benefits of gradual learning.

Below is only a brief preview. If you want to read more, please follow this link.


Preview:

When I was aware that the General AI Challenge was using CommAI-env for their warm-up round I was ecstatic. Participants could focus on RL agents without the complexities of vision and navigation. Of course, vision and navigation are very important for AI applications, but they create many extra complications if we want to understand (and evaluate) gradual learning. For instance, two equal tasks for which the texture of the walls changes can be seen as requiring higher transfer effort than two slightly different tasks with the same texture. In other words, this would be extra confounding factors that would make the analysis of task transfer and task dependencies much harder. It is then a wise choice to exclude this from the warm-up round. There will be occasions during other rounds of the challenge for including vision, navigation and other sorts of complex embodiment. Starting with a minimal interface to evaluate whether the agents are able to learn incrementally is not only a challenging but an important open problem for general AI.

Also, the warm-up round has modified CommAI-env in such a way that bits are packed into 8-bit (1 byte) characters. This makes the definition of tasks more intuitive and makes the ASCII coding transparent to the agents. Basically, the set of actions and observations is extended to 256. But interestingly, the set of observations and actions is the same, which allows many possibilities that are unusual in reinforcement learning, where these subsets are different. For instance, an agent with primitives such as “copy input to output” and other sequence transformation operators can compose them in order to solve the task. Variables, and other kinds of abstractions, play a key role.

Continue reading here.



Thank you,

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.

No comments:

Post a Comment