NLP vs. Conversational AI


To date, natural language research for use in virtual assistants has been focused on understanding the many ways people say commands like "Set my alarm for 7 am tomorrow" or "Find a good pizza place nearby." To unlock the power of conversational computing, new technology is required that extracts semantics across multi-turn natural language dialogs, and maintains contextual understanding over time. With true conversational AI, people can accomplish complex transactions which would be impossible with today's command-based assistants.

Our AI Components


Semantic Machines is developing a new, language-independent technology platform that goes beyond understanding commands to understanding conversations. Our approach represents a powerful new paradigm, enabling computers to communicate, collaborate, understand goals, and accomplish tasks.

Leveraging extensive proprietary machine learning technology, we are developing the following core components:


Conversation Engine

The Semantic Machines Conversation Engine is a revolutionary approach for modeling human discourse fluidly across speech and text. The engine extracts semantic intent from natural input (voice or text), and then spawns a self-updating learning framework for managing dialog state, context, salience, and end user goals. The Conversation Engine's natural language generation (NLG) technology actively formulates communication with the user based on dialog context.


Deep Learning

Semantic Machines is developing state-of-the-art neural network systems for use across a range of critical functions from semantic analysis to dialog state to acoustic and language models, NLG and speech synthesis.

Speech Synthesis

Speech synthesis is critical for conversational computing. The computer’s voice takes the place of a display providing information users need. Existing speech synthesis technology, particularly the prosodic models, are not sufficient to enable effective conversational computing. Leveraging our extensive natural language and machine learning expertise we are developing technology that will enable computer voices to have contextually appropriate intonation, cadence, and volume.