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.
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.
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.
Stacked Attention Networks for Image Question Answering
As a consequence, enterprises can deploy automated chat bots utilising neural networks in combination with a common knowledge base to interactively resolve customer issues without the involvement of a human actor.
By paying focused attention to the most significant objects in an image neural networks can rapidly interpret the true meaning of an image by following the information flow from these significant areas. In a manner not dissimilar to the way the human mind works neural networks can focus on the most important areas in a scene and draw conclusions.
Neural Network for Fine-Grained Image Recognition
As a result, attention models can make it easier to create hashtags based on images – useful for enterprises active on social media – while automatically boosting accessibility by, for example, creating descriptions for blind people. Attention models can even boost the ease with which automated systems can produce daily surveillance reports for security guards.
There is already a vast amount of published written content, and the mountain of text is growing rapidly. Employing humans to create summaries for the purpose of cataloguing and reviewing is not always realistic. Instead, attention models are well-placed to pinpoint the most significant parts of the written content and to derive summaries that can automatically guide readers.
Text summarization with TensorFlow
Attention models can rapidly analyse text and detect which parts are the most significant. In turn, an algorithm supported by an attention model can quickly generate a headline or a summary which can guide a reader, offering a clear indication of the relevance and importance of written content.
Though attention models can enable previously impossible features, they are even better suited to speed up existing processes. Enterprise algorithms can be improved upon, delivering higher performance and better results. These improvements do not come at the cost of increased processing power or higher computational complexity.
Attention models offer enterprises the means to tap more out of machine learning and automation. Unsure of how machine learning, neural networks, and attention models can benefit your enterprise?
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First version published on the insidebigdata.com