In natural systems learning and adaptation occurs at multiple levels and often involves interaction between multiple independent agents. Examples include cell-level self-organization, brain plasticity, and complex societies of biological organisms that operate without a system-wide objective. All these systems exhibit remarkably similar patterns of learning through local interaction. On the other hand, most existing approaches to AI, though inspired by biological systems at the mechanistic level, usually ignore this aspect of collective learning, and instead optimize a global, hand-designed and usually fixed loss function in isolation. We posit there is much to be learned and adopted from natural systems, in terms of how learning happens in these systems through collective interactions across scales (starting from single cells, through complex organisms up to groups and societies). The goal of this workshop is to explore both natural and artificial systems and see how they can (or already do) lead to the development of new approaches to learning that go beyond the established optimization or game-theoretic views. The specific topics that we plan to solicit include, but are not limited to: learning leveraged through collectives, biological and otherwise (emergence of learning, swarm intelligence, applying high-level brain features such as fast/slow thinking to AI systems, self-organization in AI systems, evolutionary approaches to AI systems, natural induction), social and cultural learning in AI (cultural ratchet, cumulative cultural evolution, formulation of corresponding meta-losses and objectives, new methods for loss-free learning)