Topic: Computational efficiency

Join Yasuo Tabei’s Succinct Information Processing Team to research efficient AI systems. Topics include memory and knowledge management for language models and agents; reusable skills from agent execution histories; algorithms that reduce computing and memory requirements; and AI operating under device, edge and communication constraints. The researcher will formulate problems, implement methods, evaluate reliability and efficiency, publish papers and release software. The Tokyo position uses renewable annual contracts, with maximum terms of seven years for research scientists and five for po

Real world datasets commonly contain nonlinearly separable classes, requiring nonlinear classifiers. However, these classifiers are less computationally efficient than their linear counterparts. This inefficiency wastes energy, resources and time. We were inspired by the efficiency of the brain to create a novel type of computationally efficient Artificial Neural Network (ANN) called Rank Similarity Filters. They can be used to both transform and classify nonlinearly separable datasets with many datapoints and dimensions. The weights of the filters are set using the rank orders of features in