Past Projects
Robust and Flexible Learning
Good generalization means drawing good inferences when tested on new, previously unseen data. ARNI will create neuroscience-inspired approaches to improve learning that are not limited to the training distribution – ones that are robust to perturbations and distribution shifts, and that can transfer across tasks. New theories will be developed to rigorously analyze learning approaches.
Research Projects
Reasoning about Causality and Uncertainty
Achieving a truly reasoning AI remains a grand challenge of AI/ML research. Despite high predictive accuracy, AI lacks causal understanding and can make overconfident, mistaken predictions. This can have disastrous consequences for real-world applications such as self-driving cars. ARNI will develop AI systems that incorporate and infer causal structures, informed by and informing new neurobiological studies.
Research Projects
Neural Mechanisms
The brain’s machinery determines its inductive biases, which define how it generalizes. ARNI will study how biological mechanisms affect cognitive processing, generalization, and continual learning for AI, while our AI studies will produce novel hypotheses for neuroscience.
Research Projects
Language and Vision
The modern era of AI has been defined over the past decade by breakthrough results in two domains: vision and language. ARNI will further these to produce adaptable, self-supervised systems with continual, multimodal input which can deal with uncertainty and causality, be cognizant of cognitive processes, and informed by the state of the art in Neuroscience and Cognitive Science.
Research Projects
Understanding linguistic and visual factors that affect human trust perception of virtual agents
Understanding creative communication and making it accessible
Artificial and natural visual problem solving with resource constraints
A brainwide “universal translator” for neural dynamics at single-cell, single-spike resolution
Advancing SAR target recognition through self-supervised learning: A vision for enhanced ATR systems
Continual Learning
Recent AI models are capable of generalizing to a remarkable range of new tasks, but often contain billions of parameters and rely on batch training with terabytes of data. In contrast, natural learning is continual, online, interactive, and adaptive to new contexts, forming a closed loop between behavior, perception, and learning. ARNI will uncover and apply insights from natural learning, which is data-efficient and leverages the rich structure of unlabeled experiential data.
Research Projects