Open questions in machine intelligence.

We study neural architectures that remain adaptive during operation. Our work explores continuous learning, dynamic plasticity, fast and slow adaptation, and mechanisms for incorporating new experience without repeatedly retraining the entire system.

We investigate memory as an active part of intelligence rather than a passive store of context. This includes short-term adaptation, long-term consolidation, selective retention, and architectures that allow experience to influence future behavior.

We explore systems that operate directly on native representations of speech, vision and other continuous signals. The goal is to preserve information within each modality while allowing shared structure and meaning to emerge across them.

We study architectures that maintain internal representations of entities, events and change, use prediction to compare expectations with observation, and update their internal state as new information arrives.

We investigate systems that perceive, reason, communicate and act as continuous processes. This includes real-time interaction, simultaneous perception and generation, and architectures where human communication is a native modality rather than a wrapper around a text model.

We explore alternative and hybrid neural architectures rather than assuming that greater scale alone is the path to more capable intelligence. Our work examines new computational structures, learning dynamics and representations, while using existing architectures wherever they remain useful.