Artificial intelligence in real-time communications is enabling a plethora of new features now more than ever. As real-time communication matures and becomes more dependable, developers are taking a step back to see where they can add dynamic, helpful features to their products. This is exemplified by the latest transcription, translation, and emotion detection tools.
The first step of every meeting is assigning a designated notetaker. This person is often unable to contribute as much valuable information as possible, as they have to be meticulous in taking notes that benefit the group and directing the flow of conversation. Using natural language processing, companies like Otter are creating voice assistants to transcribe conversations, including team meetings. This can give teams incredibly valuable insights into their meetings, so they never lose track of what was said and when. By adding a keyword search, teams can identify exactly when an individual person or topic was mentioned, and organize their conversations in a more productive way.
Communicating across languages is largely relegated to bilingual individuals and those who can afford a translator. However, as real-time communication technology evolves, so does the opportunity for individuals to speak across language barriers, even when they are not fluent. Using natural language processing, there is potential for languages to be interpreted and translated in real-time, so individuals with completely different native languages can still communicate and share information. This kind of real-time communication tool has the potential to be valuable for consumers and businesses alike.
Waverly Labs is building a pair of earbuds capable of real-time translation, so anyone can travel to a new city and be able to communicate freely. They are one of many companies big and small pursuing this kind of technology, as companies such as Google are also looking to break into this market.
Similarly, but for a very different environment, Microsoft is integrating real-time inline message translation to Microsoft Teams. Distributed teams will be able to work and communicate effectively in their personal, native languages, without worrying about if their teammates are getting the right message.
Emotion detection, while borderline creepy, can be definitively advantageous for businesses looking to understand their customer base. Instead of solely relying on metrics and focus groups, businesses can look to facial recognition algorithms to better understand how their customers feel.
Specifically, emotion detection can be used to identify when customers are enjoying themselves or not in real-time. For example, Cogito performs in-call voice analysis to identify how customers are feeling about the call. This gives call centers an undeniable edge, as their callers can understand and react to unhappy customers immediately.
What cool artificial intelligence features are you seeing in real-time communication? Let us know in the comments below.
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