Conversational AI and the Opportunity for Tech Firms
Conversational Artificial intelligence is the use of messaging applications, speakers, and chatbots to automate communication and create large-scale customer experiences. Conversational AI apps allow long-term customer interactions using the most intuitive interface available such as Natural Language Processing (NLP) via text or voice. A conversation is driven by words; whether in full or in a menu, they can support engaging, two-way interactions with private audiences. These interactions can connect people and machines through chatbots with the help of automation and AI.
Wluper, a London-based start-up recently raised $1.3 million in a seed funding to build conversational AI. The NLP used by Wluper is a responsive approach to the unpredictable nature of real conversations. To achieve its objectives, Wluper builds voice assistants for particular tasks, such as navigation, which was originally built by the startup with the help of InMotion Ventures, a VC Company of Jaguar Land Rover.
According to Adobe Analytics, 71 percent of the owners of smart speakers such as Amazon Echo and Google Home use voice assistants at least every day, and 44 percent use them several times a day. In the past year, more than 76 percent of smart speakers increased their use of voice assistants. Google announced that its Google Home assistant could support up to six user accounts and detect unique voices that enable its users to customize many features.
Apache MXNet is a modern open-source deep learning software framework developed by Apache Software Foundation for training and deployment of deep neural networks. Amazon has committed a significant team to work with the MXNet community to evolve the framework, which aided in its acceptance as an incubator project. PyTorch is an open source learning machine library based on the Torch learning library for Python. The PyTorch site describes the library as a profound learning framework for quick and flexible testing. It comes as a Python package with tensor computation with high GPU acceleration and deep neural networks. With more and more research and progress in AI and machine learning, it would not be surprising if the virtual assistants become a necessity.