This workshop is  motivated by recent advances in the rapidly developing area of foundation models - i.e., large-scale neural network models pretrained in an unsupervised way on very large and diverse datasets. Such models often demonstrate significant improvement in their few-shot generalization abilities, as compared to their smaller-scale counterparts, across a wide range of downstream tasks. One of the main themes  of this particular workshop is on emergent behaviours and phase transitions in deep learning.  Scaling and emergence are of great importance to both AI capabilities and AI Alignment and Safety. We plan to discuss recent developments along both direction.