When context is everything, AI models still struggle in the real world: Tencent

Despite rapid advances, today's top AI models are still "brittle" in messy, real-world environments, according to new Tencent research Leading US and Chinese artificial intelligence models are ...
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

Solving a machine-learning mystery

Researchers have explained how large language models like GPT-3 are able to learn new tasks without updating their parameters, despite not being trained to perform those tasks. They found that these ...
VentureBeat

ACE prevents context collapse with ‘evolving playbooks’ for self-improving AI agents

A new framework from Stanford University and SambaNova addresses a critical challenge in building robust AI agents: context engineering. Called Agentic Context Engineering (ACE), the framework ...
technologynetworks

Humans and AI Blend Two Types of Learning

Brown University researchers found that humans and AI integrate two types of learning – fast, flexible learning and slower, incremental learning – in surprisingly similar ways. The study revealed ...