General AI Challenge
Challenge Update #17 - Results & Evaluation of Round 1: Gradual Learning
After a lot of testing and deliberation from our jury, the results for the first round of the General AI Challenge are in!
The first round of the Challenge asked participants to create AI agents capable of gradual learning (building new knowledge on top of previously learned skills and reapplying existing skills to learn how to solve new problems more efficiently), one of the keys to solving general AI.
We had 13 submissions, which all competed for the qualitative or “best idea” prize, while the eight entries with agents competed in the quantitative category as well.
None of the agents were able to complete the quantitative evaluation curriculum. The most successful agent got close to completing 25% of the tasks. Therefore, the quantitative prize was not awarded in this round.
However, we are excited to announce the qualitative prize! The submissions were analyzed by our jury who concluded that the top four solutions are closely comparable, and more work is required to demonstrate that the authors are on track to robust gradual learning mechanisms.
The jury selected no winner, but to encourage further work on gradual learning and to reward the participants for their considerable efforts, they decided to split the 2nd qualitative prize ($7000) among the four finalists.
The recipients of the joint award are (in alphabetical order):
A former NASA astronaut and a veteran of three space flights, four spacewalks and two trips to the International Space Station. He retired from NASA in 2005 and started his own company, Denbar Robotics that focuses on smart robots and artificial intelligence interfaces, concentrating on assistive devices for people with disabilities. In 2011 he co-founded Fellow Robots, a company that provides robots for retail settings. He has ten patents, over 50 articles in scientific journals and has served on two scientific journal editorial boards.
Andrés del Campo Novales
AI hobbyist passionate about the idea of a general AI. He is a Software Engineer with 15 years of professional experience. He has been working for Microsoft in Denmark for the last 11 years in business applications. Andrés studied computer science engineering at Córdoba and Málaga. He created a chatbot that could learn conversation patterns, context and numerical systems.
Research fellow at the TU Wien where he obtained his habilitation in the field of theoretical physics. His current research is focused on simulating the production of the quark-gluon plasma in heavy ion colliders like the LHC in CERN. After obtaining his PhD, he had postdoctoral fellow positions in Italy and at the Max Planck Institute in Germany. Since his return to TU Wien, he is involved in teaching activities, including lecturing on quantum electrodynamics. Apart from his scientific achievements, he founded the choir of the TU Wien a few years ago, which successfully participates at international choir competitions.
Assistant professor at the University of Miyazaki in Japan, inventor of the MagicHaskeller inductive functional programming system. He has been working on inductive functional programming (IFP) for fifteen years. His research goal is to realize a human-level AI based on IFP.
On top of the split monetary prize, the jury awarded Andreas Ipp with the special GPU prize from our Challenge partner NVIDIA – a GEFORCE GTX 1080 graphics card.
We have listened to feedback from the community and have had great enthusiasm from participants who want to keep working on the issue of gradual learning. Therefore, we plan to continue the round in early 2018.
In the meantime we are also launching the second round of the Challenge in November 2017, Round 2 will focus on AI Race avoidance. With the increasing rate of progress made in the AI field, developers might race towards being the first to achieve general AI and might neglect either safety procedures or agreements with other stakeholders for the sake of first mover advantage. Participants will be asked to come up with a proposal of what practical steps can be taken to avoid the pitfalls of the AI race and advance the development of beneficial general AI.