AI in Video Conferencing
AI for Security
Recent advances in the field of Computer Vision has led to a multitude of applications in the field of facial/fingerprint/voice recognition. Deep neural networks based on open source libraries like Pytorch and TensorFlow can be used to develop highly accurate facial recognition systems. This can be used for adding an extra layer of authentication when customers join a meeting room.
AI for Accessibility
Advances in Natural language processing have made implementation of real-time language translation a reality. State of the art open-source Neural Machine Translation (NMT) systems are able to understand human language and translate them into multiple languages. Open source solutions are available for real-time closed captioning of videos. Advances have also been made in the field of real-time translation of audio to sign language which can help people with hearing challenges.
AI for Efficiency
Efficient communication requires virtual meetings to happen without distractions. Distractions can be in the form of audio or video like background audio noise or visual background of the participant. Visual backgrounds can be removed real-time using deep learning models deployed on the edge devices using TensorFlowJS. Noise is a form of audio distraction, and machine learning algorithms can help in real-time removal of stationary and non-stationary audio noise.
AI for User Experience
AI can be used to incorporate a whole host of features to bring delight to end-users. These include (not limited to),
Automatically capturing minutes & highlights of the meeting in the form of audio, video and text for records and sharing.
Chatbot support to provide minutes of the meeting for absentee participants.
Last updated
Was this helpful?