Customer-obsessed science

Continued improvement of conversational assistants in knowledge-rich domains like E-Commerce requires large volumes of realistic high-quality conversation data to power increasingly sophisticated LLM chatbots, dialogue managers, response rankers, and recommenders. The problem is worse for multi-modal interactions in realistic conversational product search and recommendation. Here, an artificial sales agent

E-commerce stores typically test changes to ranking algorithms through rigorous A/B testing which requires a change to satisfy some predefined success criteria on multiple metrics. This problem of simultaneously optimization of multiple metrics is multi-objective-optimization (MOO). A common method for MOO is to choose a set of weights to scalarize the multiple metrics into one ranking objective. However

Organizations today are integrating technologies such as cloud computing, and digital twin in their manufacturing and logistical processes. In a capital-intensive logistics industry, Discrete event simulation (DES) plays a crucial role in distribution center design, automation system performance analysis, optimization and operational planning. Developing and deploying DES models demands proficiency in various

State-of-the-art performance has been achieved in recent years on tasks such as search, recommendation and classification using Visuo-Lingual Multi-Modal models. While the pre-trained Vision-Language models like Contrastive Language-Image Pre-training (CLIP) have achieved promising zero-shot performance on several generalized tasks by learning vision-language concepts in a common space, the natural hierarchical

Knowledge graphs (KGs) complement Large Language Models (LLMs) by providing reliable, structured, domain-specific, and up-to-date external knowledge. However, KGs and LLMs are often developed separately and must be integrated after training. We introduce Tree-of-Traversals, a novel zero-shot reasoning algorithm that enables augmentation of black-box LLMs with one or more KGs. The algorithm equips a LLM