VentureBeat

Google’s ‘Nested Learning’ paradigm could solve AI's memory and continual learning problem

Researchers at Google have developed a new AI paradigm aimed at solving one of the biggest limitations in today’s large language models: their inability to learn or update their knowledge after ...
Science Daily

Resolving ambiguity: How the brain uses context in decision-making and learning

Life can be tricky. We have so many decisions to make. It's a good thing we have an orbitofrontal cortex and hippocampus to help us. These areas of the brain work together to help us sort through ...
Nature

Contextual AI models for context-specific prediction in biology

We developed PINNACLE, a graph-based AI model for learning protein representations across cell-type contexts. These contextualized protein representations enable the integration of 3D protein ...
Nature

AsynDBT: asynchronous distributed bilevel tuning for efficient in-context learning with large language models

With the rapid development of large language models (LLMs), an increasing number of applications leverage cloud-based LLM APIs to reduce usage costs. However, since cloud-based models’ parameters and ...