What frameworks enable interactions between humans and machines via voice, text, or images?

The choice of conversational systems or patterns accurately identifies frameworks specifically designed to facilitate interactions between humans and machines using various modalities such as voice, text, or images. These systems are often structured around natural language processing and user interface design, enabling a seamless exchange of information between users and machines. They are essential in applications like virtual assistants, chatbots, and voice-activated systems, which rely on understanding and responding to user input in a coherent and context-aware manner.

Conversational systems incorporate various technologies, including voice recognition, language understanding, and dialogue management, making them an integral part of creating intuitive and efficient interactions. Their design is focused on improving user experience by making human-machine communication as natural as possible, which aligns directly with the question’s focus on interaction.

In contrast, machine learning models primarily serve as tools that can be trained on data to make predictions or classifications but do not inherently dictate how interactions are structured. Data processing frameworks focus on the handling and transformation of data rather than user interaction. Feedback loops can inform or improve systems based on previous interactions but do not directly address the framework level of facilitating interactions between humans and machines. Therefore, conversational systems encompass the broad range of methodologies and practices necessary for effective communication and interaction in AI applications.