A different approach to linguistic and machine learning. Giving enterprises the muscle, flexibility and speed required to develop business-relevant AI applications in record time.
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It’s a common misunderstanding that machine learning systems somehow work completely on their own, without human supervision. Nothing could be further from the truth.
Just as a linguistic-based conversational system requires humans to craft the rules and responses, a machine learning system requires humans to collect, select, and clean the training data.
Teneo overcomes these challenges by manipulating the data using a unique, patented hybrid approach. By combining the best of both linguistic and machine learning models at a native level, Teneo allows enterprises to quickly build conversational AI applications whatever their starting point – with or without data – and use real-life inputs to optimize the application from day one.
The backbone of the Teneo platform is a linguistic-based algorithm with the key ability to embed machine learning algorithms alongside. This is an advantage over purely machine learning systems, which function, as far as the developer is concerned, as a black-box that cannot work without large amounts of curated training data.
Teneo’s linguistic abilities allow for conversational systems to be built even without data and provide transparency in how the system operates. In addition, rules ensure that the system maintains a consistent and correct personality and behavior aligned with business aims.
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Taking a hybrid approach gives some key advantages over purely machine learning systems.
A machine learning conversational system has no consistent personality, because the dialogue answers are all amalgamated text fragments from different sources. From a business point of view, this misses the opportunity to position the company through identifiable brand values.
But the issue of a consistent personality is dwarfed by the problem of semantics. In a linguistic-based conversational system, enterprises can ensure that questions with the same meaning receive the same answer. A machine learning conversational system however might well fail to recognize similar questions phrased in different ways, even within the same conversation.
Teneo’s hybrid approach offers a unique simplifying benefit. The rules and the intelligence architecture behind the conversations can be directly integrated and maintained alongside each other in the same visual interface. This ensures that conversational AI applications properly understand the context of the conversation – every time.
Before Teneo, building conversational AI applications using traditional natural language methods was difficult, resource intensive and frequently prohibitively expensive. With Teneo, enterprises can rapidly develop business-relevant AI applications that can make a difference to the customer experience and the bottom line.