In collaboration with academic researchers in China, Alibaba has developed a search engine simulation AI that uses real world data from the ecommerce giant’s live infrastructure in order to develop new ranking models that are not hamstrung by ‘historic’ or out-of-date information.
The engine, called AESim, represents the second major announcement in a week to acknowledge the need for AI systems to be able to evaluate and incorporate live and current data, instead of just abstracting the data that was available at the time the model was trained. The earlier announcement was from Facebook, which last week unveiled the BlenderBot 2.0 language model, an NLP interface that features live polling of internet search results in response to queries.
The objective of the AESim project is to provide an experimental environment for the development of new Learning-To-Rank (LTR) solutions, algorithms and models in commercial information retrieval systems. In testing the framework, the researchers found that it accurately reflected online performance within useful and actionable parameters.