Authors: GoodAI Team, and a special thank you to grant recipients for their contributions and comments.


By Nicholas Guttenberg

When I was at the Earth-Life Science Institute in Tokyo, one of the things I learned about was the difficulty of matching up the expected inputs and outputs of research projects in different fields so that those projects could work together. A researcher may work very hard to measure properties or estimate values that are considered central to the questions of their field, only to find that their potential collaborators can’t use that number because their questions operate at a different level of abstraction. Even within disciplines, one of the places that research often falls flat is…

Dr. Kai Arulkumaran



This month The European Commission (EC) released a report on Humans and Societies in the Age of Artificial Intelligence (AI). It is one of the first EC documents with long-term thinking about the impacts of AI and it talks clearly about AGI and safety. We are very pleased to have contributed to this report and believe it makes important contributions towards building a better future with artificial intelligence.

GoodAI’s contribution looked at the future impacts of AI on individuals and society at large. The contribution builds on GoodAI’s research of longer-term impacts, to which apart from Marek Havrda contribute COO…


Researchers at Carnegie Mellon University (CMU)’s School of Computer Science…


GoodAI has awarded a research grant to Pauching Yap, PhD candidate at UCL Centre for Artificial…


Research team L-R: Nathaniel Virgo — ELSI, Martin Biehl — Araya, and Acer Chang — Araya.



If we are to develop artificial intelligence (AI) capable of learning as humans do, it needs to be tested in complex environments just like humans are…

VeriDream members and (Sorbonne University) have been working on a case study that applies quality diversity algorithms to control robotic legs.

Robotic leg control is a standard engineering task. We’re interested in a rapid adaptation to an arbitrary leg shape/number of joints. Quality diversity algorithms offer themselves as an efficient approach to discovering control policies and robot joint configurations that achieve a diverse set of goals and target configurations, respectively. Since these algorithms are inherently easy to parallelize, we can speed up the whole process by running multiple instances of the algorithm at the same time.

The VeriDream consortium is…


Our mission is to develop general artificial intelligence — as fast as possible — to help humanity and understand the universe

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