This is the 4th workshop in the workshop series that started Oct 2021, motivated by recent advances in the rapidly developing area of large-scale neural network models (e.g., GPT-3, CLIP, DALL-e, Gato, StableDiffusion and many others), 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 - what one could call a "transformation of quantity into quality" or an "emergent behavior". 

Rapid increase in generalization capabilities of such models is an important step towards a long-standing objective of achieving Artificial General Intelligence (AGI). By AGI here we mean literally a "general", i.e. broad, versatile AI capable of quickly adapting to a wide range of situations and tasks, both novel and those encountered before - i.e. achieving a good stability (memory) vs plasticity (adaptation) trade-off, using the continual learning terminology. 

The goal of the workshop series is to provide a forum for discussion of the most recent advances in large-scale pretrained models, focusing specifically on empirical scaling laws of such systems' performance, with increasing compute, model size, and pretraining data (power laws, "broken" power laws, "phase transitions", etc). We will also explore the trade-off between the increasing AI capabilities and AI safety/alignment with human values, considering a range of evaluation metrics beyond the predictive performance. These topics are also closely related to transfer-, continual- and meta-learning, as well as out-of-distribution generalization, robustness and invariant/causal predictive modeling.

This is the 4th workshop in the workshop series that started Oct 2021, motivated by recent advances in the rapidly developing area of large-scale neural network models (e.g., GPT-3, CLIP, DALL-e, Gato, StableDiffusion and many others), 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 - what one could call a "transformation of quantity into quality" or an "emergent behavior". 

Rapid increase in generalization capabilities of such models is an important step towards a long-standing objective of achieving Artificial General Intelligence (AGI). By AGI here we mean literally a "general", i.e. broad, versatile AI capable of quickly adapting to a wide range of situations and tasks, both novel and those encountered before - i.e. achieving a good stability (memory) vs plasticity (adaptation) trade-off, using the continual learning terminology. 

The goal of the workshop series is to provide a forum for discussion of the most recent advances in large-scale pretrained models, focusing specifically on empirical scaling laws of such systems' performance, with increasing compute, model size, and pretraining data (power laws, "broken" power laws, "phase transitions", etc). We will also explore the trade-off between the increasing AI capabilities and AI safety/alignment with human values, considering a range of evaluation metrics beyond the predictive performance. These topics are also closely related to transfer-, continual- and meta-learning, as well as out-of-distribution generalization, robustness and invariant/causal predictive modeling.