Technology
DALI's Technology is based on years of research into Human Computer Interaction (HCI), Machine Learning (ML),
Information System (IS) and Scalability. We have pulled forward thinking concepts from academia
to develop targeted solutions with cutting edge implementation. Some of the concepts include
Reinforcement Learning (RL), Latent Semantic Indexing (LSI) and Bulk Synchronous Parallelism (BSP).
Reinforcement Learning
RL is a learning mechanisms that considers the whole problem of a goal-directed agent interacting with an uncertain environment. Leslie Pack Kaelbling, Michael L. Littman and Andrew W. Moore discuss central issues to RL in Reinforcement Learning: A Survey.
Latent Semantic Indexing
LSI is a concept-based information retrieval model developed by Michael W. Berry in Computational Methods for Intelligent Information Access that addresses the two fundamental problems which plague traditional lexical-matching indexing schemes: synonymy and polysemy. LSI information is available at the Latent Semantic Indexing Web Site.
Bulk Synchronous Parallel
The BSP model is a generalization of the widely researched PRAM model and was initially proposed by Prof. L. G. Valiant of Harvard as a Bridging Model for Parallel Computation in Communications of ACM, 33,8, Aug 1990. Since then the BSP Model has been developed at leading institutions including Oxford University.
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