Data Annotation in Virtual Assistant Technology

Artificial intelligence is regularly integrated into our daily activities. Considering that several day-to-day tasks are repeated, it’s handy to have an assistant to take care of them.

This is why Digital assistants are huge in the tech markets. Everyone has them. Apple has Siri, Amazon has Alexa, Google has Google Assistant, Microsoft has Cortana, and Samsung has Bixby.

This article aims to explore the role of data annotation in the evolution of virtual assistants in the world today.

What is Data Annotation?

Data annotation is defined as the process of familiarizing machines with data. This process can be likened to how humans label things for easy identification. Theoretically, a user can annotate any form of information. Therefore, even seemingly abstract concepts like semantics or sound or solid objects like text, images, or videos can be annotated.

The purpose of Data Annotation is to train machines to identify certain objects to form specific tasks – The computer needs to be instructed to make decisions, as it cannot process information as the brain does. The method used for annotation varies depending on the information a user intends to feed the machine.

Types of Data Annotation include

• Image annotation
• video annotation
• Sound annotation
• Text annotation
• Semantic annotation

Data Annotation in Virtual Assistant Technology

The term virtual assistant refers to independent contractors who take on administrative roles even though they are not present in the office. This role can be carried out by humans, although machines are taking over in many respects. Thanks to data annotation, machines can perform common virtual assistant roles like:

• Prediction: Digital virtual assistants use copious amounts of data to make predictions about certain events. Digital assistants can reasonably predict actions, events, or items that the user is likely to prefer based on data gleaned from users, such as location, texts, and actions that occur at fairly regular periods.
• Speech Recognition: With sound annotations, virtual assistants can detect different users by recognizing speech patterns and tonal disparities between users to perform a myriad of functions such as creating different profiles for different users and protecting sensitive information that can be unlocked using voice commands.
• Data Extraction: Depending on the extent of the assistant’s access, it can extract data from emails and texts to give users more contextual results. Virtual assistants can use complex data algorithms to give users a more personalized experience in searches, recommendations, and the way commands they are given are carried out.
• Task automation: Virtual assistants can carry out simple repeated tasks on behalf of the user. Similar to RPAs, Virtual assistants can perform any myriad of tasks that are ripe for automation.
• Performing non-administrative tasks: Data assistants can carry out preset tasks outside the scope of administrative functions, usually at no extra cost. Mainstream assistants machines like Alexa and Google Assistant can carry out simple tasks like putting on the light or even quirky ones like telling a joke.

Conclusion

The scope of application of Data Annotation is limitless, with new ideas appearing on the horizon daily. With the advancement of technology, the horizon broadens for data annotation. 

While growth is welcome in the spheres of virtual assistant technology, we must also keep ourselves abreast with the price of change and the risks that it portends. Data security threats top the list of these threats. However, as in the illustrious preeminence of William Faulkner. 

“You cannot swim for new horizons until you dare to lose sight of the shore.”

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