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New Horizons

February 03, 2022 AI Policy and SocietyResearch

GoodAI's Marek Havrda leaves his role as AI Policy and Social Impact Director to commence a new post in public administration.

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Review of 2021 & plans for 2022

January 24, 2022

GoodAI CEO Marek Rosa reflects on the progress made by GoodAI and Keen Software House in 2021 and outlines the plans for 2022.

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What are the differences between centralized learning (in monolithic systems) and decentralized learning (in multi-agent systems)?

January 18, 2022 ResearchTechnical blogs

Why are we studying social learning in multi-agent systems?

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Open Call for Papers for 2022 ICLR Workshop on Collective Learning

December 23, 2021 ResearchTechnical blogs

The 2022 workshop, “From Cells to Societies: Collective Learning Across Scales,” will examine natural systems to understand how collective interactions can contribute to new learning approaches in artificial learning systems.

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GPU Grant to Support TU Delft’s AI Research

November 30, 2021 ResearchTechnical blogs

Wendelin Böhmer investigates how restricted neural modules, similar to Badger agents, can learn generalizing sub-tasks and how a message passing architecture can reliably compose their output for out-of-distribution generalization.

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GoodAI Contributed to the First Global Agreement on the Ethics of Artificial Intelligence Adopted by 193 UNESCO Member States

November 26, 2021 AI Policy and Society

UNESCO member countries endorse global rules for ‘ethical AI,’ including clear red lines such as banning the use of AI for social scoring and mass surveillance.

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Bridging the Continual Learning Gap with Deep Artificial Neurons

October 26, 2021 ResearchTechnical blogs

Integrating GoodAI’s Badger architecture and principles, new grant recipients Rolando Estrada and Blake Camp aim to develop a continual semi-supervised learning system capable of joint supervised and unsupervised learning.

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Memetic Experiments for an Open-Ended Learning Paradigm

October 05, 2021 ResearchTechnical blogs

GoodAI presents a new paper to trace memetic evolution and investigate whether open-ended learning algorithms can be discovered through the lens of socio-cultural learning models.

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