Natural language

This talk surveys the components of modern diffusion language models, beginning with masked prediction and extending to iterative refinement, controlled generation and variable-length inference. It examines how parallel generation and repeated revision change inference-time computation. Examples from natural language, proteins and DNA illustrate both practical model designs and uses of fine-grained control in scientific sequence generation.

…recognition, autonomous vehicles, natural language, and many other domains—are coming so quickly that many observers predict that the long-elusive goal of “Artificial General Intelligence” (AGI) is within our grasp. However, we still cannot build a machine capable of building a nest, stalking prey, or loading a dishwasher. I will describe how evolution may have shaped the algor

Biological neural networks have evolved to produce complex social behavior such as communication and cooperation. Social communication only works if cues provided by one individual influence the neural activity and neural representations of the environment in another - particularly internal abstractions in planning and learning new tasks. How this translation works at the level of neural networks is still unclear. We hypothesize that synthesizing and learning to use a state representation that is common across several neural networks is critical to understanding the brain’s function underlying

Natural language is strongly context-dependent and can be perceived through different sensory modalities. For example, humans can easily comprehend the meaning of complex narratives presented through auditory speech, written text, or visual images. To understand how complex language-related information is represented in the human brain there is a necessity to map the different linguistic and non-linguistic information perceived under different modalities across the cerebral cortex. To map this information to the brain, I suggest following a naturalistic approach and observing the human brain p

…concept trained with supervision from natural language. We then constructed voxelwise encoding models to explain whole brain responses arising from viewing natural images from the Natural Scenes Dataset (NSD) - a large-scale fMRI dataset collected at 7T. Our results reveal that CLIP, as compared to convolution based image classification models such as ResNet or AlexNet, as well as language models such as BERT, gives rise to representations that enable better prediction