Thanks to the constant development of artificial intelligence and machine learning, computers can perform more and more cognitive tasks. As a result, companies can rely on machines for critical operations when automation is considered impossible. In particular, discussion-based artificial intelligence platforms such as chatbots and virtual cognitive factors have enabled organizations in many areas to improve customer support and staffing – and these platforms are only smarter.

Interest in conversation rose to the sky in 2020as well as corporate investment in machine learning platforms. This was largely due to the COVID-19 pandemic, which forced companies in almost every sector to look for ways to do more with less. For example, sudden customer requests from banks, retailers, and airlines revealed the limitations of people’s customer support teams and the urgent need for automated capabilities. In addition, the pandemic has changed our expectations as consumers, which has increased the demand for digital customer experiences.

1. perception of emotions. First, most platforms are still relatively complex in emotion perception. Human communication depends as much on emotion as on language, and a change in tone can completely change the meaning of spoken or written dialogue. To train computers to detect subtle contextual cues, product groups need a lot of information that includes many different human voices. Finding all the information is no small challenge.

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2. Learning new languages. Most of the world’s population does not speak english. Global organizations that hope to use conversational artificial intelligence Interactions with non-US customers require platforms that understand not only different languages ​​but also different regional dialects and cultural differences. Again, this would require large amounts of multilingual speech and audio data from different communities and a wide variety of situations (e.g., TED discussions, discussions, telephone conversations, monologues, etc.), and that the information should cover multiple topics.

3. Recognizing the right sound. Training artificial intelligence to detect a single speaker among many sounds is another challenge that is likely to be familiar to anyone with a home-based smart speaker like Google Home or Amazon’s Alexa. In a congested living room, these platforms may respond to commands that are not intended for them, or they may not be able to separate commands in multiple conversations. This usually causes a little frustration and perhaps some sort of comic relief, but when business transactions involving sensitive customer information are performed with voice commands, it is absolutely essential that the AI does not confuse user accounts.

Despite these obstacles conversational artificial intelligence has huge potential for all types of businesses. Shaip will help you unlock this potential, and it all starts with information. We can provide the product team with hours of written, annotated audio information in over 50 languages. By using our own data acquisition application, we are able to enhance the distribution of data collection tasks to global groups of experienced data collectors. The application interface allows data collection service providers view easily assigned collection tasks, view detailed project instructions, including samples, and quickly send and send information for approval by project inspectors.

Used together ShaipCloud platform, our application is just one of many tools that allow us to acquire, write and mark data at virtually any scale needed to train advanced algorithms in a real customer relationship. Want to learn what else makes us leaders in conversation? Take contactand let the artificial intelligence speak.


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