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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Associate Professor Julian Togelius is working on a scalable generative AI benchmark that will allow for the development of open-ended learning, task transfer, and generalization.
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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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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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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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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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We recently held the Badger Seminar titled: Beyond Life-long Learning via Modular Meta-Learning, with participants joining online and at our headquarters in Prague.
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Last week saw the second fully virtual ALIFE conference bringing together nearly 400 leading researchers and practitioners from across the world working on problems related to simulating artificial life.
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