General purpose, always on, conversational systems are rapidly gaining adoption. Interactions with systems such as Apple Siri and Google Now are, slowly but surely, re-training users to a new form of finding answers, one that involves simply stating the question, instead of (re) formulating search queries.
A new class of virtual assistants are building on this and choosing to go beyond Q&A into autonomous actions, task execution. Naturally, designing an agent that responds to specific requests within a well-defined use-case is far more feasible today than delivering general-purpose assistive intelligence. A slimmed, narrower version of the world helps constrain vocabulary and context, which in turn enables the system better interpret requests, perform tasks more accurately and over time learn preferences from usage. We present one such system that helps e-commerce marketers and merchandisers detect trends and act on it.
Jayesh is the Senior Director of Search and Data Science at Salesforce. He joined Salesforce through the acquisition of MinHash, a data science startup he founded to focus on solving problems in entity extraction, topic classification, and trend detection on an enterprise platform that brings together machine learning, search and large scale data processing. He is also the creator of AILA, an AI driven marketing assistant that detects fast growing topics across 1000s of media sources and helps marketers respond quickly to breakout trends.
Before founding MinHash Jayesh held engineering leadership positions at Oracle and more recently was the VP of Collaboration platforms at Avaya, where he led technology initiatives to develop predictive analytics products to better classify customer interactions, automate service agent workflows. He holds a masters in CS from WPI and has authored several patents and publications in the areas of search, complex event processing and P2P computing.