Microsoft’s GenAI Team to Make Conversational AI Cheaper

Last Updated on February 5, 2024 by SPN Editor

Microsoft has recently embarked on a new project aimed at making conversational AI cheaper. This initiative involves the formation of a dedicated team known as ‘GenAI team’ to develop AI models that require less computational power, thereby reducing costs.

The GenAI team comprises some of Microsoft’s top AI developers. Their mission is to create AI models that not only perform efficiently but also economically, making advanced AI technology more accessible to a wider range of users.

Microsoft has already made significant strides in this area with the development of smaller models, including its Phi system. These models are designed to perform complex tasks while using minimal computational resources. The formation of the GenAI team represents an expansion of these efforts, with a specific focus on conversational systems such as chatbots.

Chatbots, which simulate human conversation through AI, have become increasingly prevalent in various sectors, including customer service, e-commerce, and social media. By making these systems more cost-effective, Microsoft aims to promote their widespread adoption and bring the benefits of AI to more people and businesses.

The GenAI team will be under the leadership of Corporate Vice President Misha Bilenko, who will report directly to Microsoft’s Chief Technology Officer, Kevin Scott. This organizational structure ensures that the team’s efforts align with the company’s broader technological strategy.

Microsoft’s formation of the GenAI team signifies a significant step towards making conversational AI more affordable. By developing models that require less computational power, the company aims to make this advanced technology more accessible, thereby promoting its widespread use and maximizing its potential benefits.

What are the benefits of conversational AI?

Customer Service: Conversational AI, such as chatbots, can provide round-the-clock customer service. They can instantly respond to customer queries, improving customer satisfaction. Unlike human agents, they can handle multiple queries simultaneously, reducing customer wait times. They can also be programmed to handle a wide range of common queries, freeing up human agents to handle more complex issues.

Cost Efficiency: Conversational AI can automate routine tasks, such as answering frequently asked questions or booking appointments, which can significantly reduce operational costs. They also require less human intervention, which can save on labor costs. Over time, these savings can be substantial.

Data Collection: Conversational AI can collect and analyze data from customer interactions. This data can provide valuable insights into customer behavior and preferences, which can be used to improve products and services. For example, if many customers are asking the same question, it might indicate a gap in the information provided on a website.

Personalization: Conversational AI can provide personalized experiences to users. By analyzing past interactions, preferences, and behavior, they can tailor their responses to the individual user. This can enhance the user experience and increase customer loyalty.

Scalability: Conversational AI can easily scale up or down based on demand. During peak times, they can handle a large volume of queries without any decrease in performance. This makes them a flexible solution for businesses of all sizes.

Accessibility: Conversational AI makes services more accessible to users. Users can interact with them using natural language, making them easier to use for people who are not tech-savvy. They can also be used to provide services to customers in different languages or with disabilities.

Productivity: By handling routine tasks, conversational AI allows human employees to focus on more complex and creative tasks. This can increase overall productivity and allow businesses to better utilize their human resources.

However, the effectiveness of conversational AI depends on its design and implementation. It should be user-friendly, understand the user’s intent accurately, and provide relevant responses. It should also be regularly updated and improved based on user feedback and changing business needs.