Hybrid – making machine learning accessible to the enterprise

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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.

Humans are also required to evaluate and refine the machine learning model, in addition to assessing the final result, because statistical based systems tend to be non-deterministic. The sheer investment required to collect and categorize the amount of data required to train machine learning systems is eye-wateringly high.

Furthermore, 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 problem of a dependable personality is dwarfed by the problem of semantics. In a linguistic-based conversational system, humans can ensure that questions with the same meaning receive the same answer. A neural network conversational system however might well fail to recognize similar questions phrased in different ways, even within the same conversation.

Teneo overcomes these challenges by offering a unique, patented hybrid approach to developing conversational systems that combines the best of linguistic and machine learning models. This flexible approach allows enterprises to quickly build AI applications whatever their starting point – with or without data – and then uses real-life inputs to optimize the application from day one.

While systems that make use of linguistic and machine learning are not new, Teneo takes a unique approach with its hybrid model by providing a native interface between linguistic and machine learning that ensures seamless integration between the two, as opposed to compartmentalizing them as discrete components. This enables both the the linguistic and machine learning systems to be maintained alongside each other in the same visual interface.

Taking a hybrid approach gives a number of key advantages over purely machine learning systems. Besides allowing for conversational systems to be built even without data, they provide transparency in how the system operates, enabling business users to understand the application, and ensures that a consistent personality is maintained and that its behavior is in alignment with business expectations. At the same time, it allows for machine learning integrations to go beyond the realm of linguistic rules, to make smart and complex inferences in areas where a linguistic only approach is difficult, or even impossible, to create.

Building conversational applications using traditional natural language methods is hard, resource intensive and frequently prohibitively expensive. By taking a hybrid approach, enterprises have the muscle, flexibility and speed required to develop business-relevant AI applications that can make a difference to the customer experience and the bottom line.

To find out more about the hybrid approach to building conversational applications download the latest whitepaper from Opus: Taking a Hybrid Approach to Conversational Commerce: Avoiding the “False Choice” Between Linguistic Models and Machine Learning.


Taking a Hybrid Approach to Conversational Commerce

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