We’ve highlighted the incredible benefits machine learning offers at the enterprise level. In fields as diverse as retail and agriculture machine learning can enhance efficiency and profitability. Yet machine learning is continuously evolving. In this article, we illustrate how attention models are promising even more for enterprises at the cutting edge of computing technology.

What are the attention models?

Machine learning often takes inspiration from the way the human mind works. Neural networks have made huge advances in the way natural language and images are processed and attention models – which mimic human behaviour to some degree – are gaining prominence.

In simple terms, attention models selectively process data by focusing on the parts or segments of data that is the most important. Just as the human mind focuses on important facts when evaluating a situation, attention models speed up the processing of large volumes of data by concentrating on the most relevant segments.

Practical examples of attention models

The data science applications for attention models are broad and rapidly evolving. Attention models are turning out to be incredibly practical. Here, we outline a number of ways in which attention models can improve data processing, and what the commercial benefits are.

Interactive chatbots

Automating customer interaction removes the pressure on customer service staff, reduces costs and – if done correctly – improves the customer experience. Natural language processing (NLP) is difficult to achieve but significantly aided by attention mechanisms. An attention model can pick out the most important words even where sentences are long and complex.