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provide state-of-the-art algorithms but little pertinent training data
for many conversational domains, training data may be difficult or impossible to collect
even the smartest data scientists rarely succeed in launching production NLP apps
pre-built domains streamline development but are largely irrelevant for most apps
tools for building custom domains can only handle narrow models and trivial apps
ML capabilities only scratch the surface of what is typically required for production apps
Deep-Domain Conversational AI collects and manages potentially millions of training examples to satisfy the unique requirements of each application.
Deep-Domain Conversational AI harnesses ongoing application usage data to improve accuracy continuously over time.
Deep-Domain Conversational AI leverages state-of-the-art machine learning techniques for NLP and for QA to deliver human-like accuracy for any broad-vocabulary domain.
Deep-Domain Conversational AI employs end-to-end measurement and analytics to ensure optimal performance across the long tail of user interactions.
[MindMeld] has the potential to advance natural- language recognition and push the envelope where Google, Microsoft, and Apple have failed so far.
One of the leading companies in this space is Google- and Samsung-backed [MindMeld].
[MindMeld] named by MIT Technology Review as one of the 50 Smartest Companies of 2014.
The next major step forward in the way content and information are delivered to consumers.
[MindMeld is] advanced enough to enable a whole new approach to using speech recognition in the workspace, at home and in the car.
MindMeld is at the forefront of the kind of technology that will surround us everywhere in a few years.
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