Memetic Experiments for an Open-Ended Learning Paradigm
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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Badger Seminar Summer 2021 Summary: Beyond Life-long Learning via Modular Meta-Learning
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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ALIFE 2021: celebrating the science and art of artificial life
July 27, 2021
Research
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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GoodAI Scientist Nicholas Guttenberg wins the Evocraft Minecraft Open-Endedness Challenge
We are very proud that Nicholas Guttenberg, Senior Research Scientist at GoodAI, recently won the Evocraft 2021 competition - the Minecraft Open-Endedness Challenge!
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GoodAI Research Roadmap 2021/2022
In 2021/22 GoodAI will focus on four core research areas: learning to learn, lifelong (gradual) learning, open-endedness, and generalization / extrapolation of meta-learned algorithms.
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Building a research culture where we share questions and difficulties, not just answers
May 13, 2021
Research
Is sharing where we’re stuck more important to furthering research than sharing the answers we’ve found?
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Using open-ended algorithms to generate video game content in Space Engineers
Dr. Kai Arulkumaran of Araya has received a grant from GoodAI in order to apply open-ended algorithms to generating video game content (such as spaceships and space stations) in the sandbox game Space Engineers.
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Solving generalization and making artificial intelligence curious
Researchers at Carnegie Mellon University (CMU)’s School of Computer Science in Pittsburgh are working on a project aiming to make artificial intelligence (AI) more resourceful and curious.
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Bayesian Online Meta-Learning (BOML) for continual & gradual learning
New project aims to create AI that can continually acquire knowledge in different domains as well as utilize past experiences to quickly adapt to new unseen tasks.
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GoodAI enters into research collaboration to progress meta-learning and combinatorial generalization
Self-improving artificial intelligence that can learn new tasks from small amounts of data is a crucial step for the advancement of strong artificial intelligence.
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