Despite significant advancements in conversational AI, current systems still face several limitations that hinder their ability to fully engage with users in a natural and effective manner. These limitations include:
- Single Modality Focus: Most conversational AI systems are designed to handle either text or speech, but not both. This single modality focus limits their ability to process and understand the rich, multimodal nature of human communication, which often involves visual cues, audio, and textual information simultaneously.
- Contextual Understanding: Current models often struggle with maintaining context over longer conversations. They may provide relevant responses for short exchanges but fail to sustain context and coherence over extended interactions, leading to fragmented and unsatisfactory user experiences.
- Lack of Personalization: Many existing systems lack the ability to personalize responses based on user preferences and previous interactions. This results in generic and impersonal interactions that fail to engage users effectively.
- Limited Knowledge Integration: Current AI systems often struggle to integrate external knowledge seamlessly into conversations. They may provide accurate responses based on predefined data but fail to dynamically incorporate new information from various sources.
- Response Generation Quality: The quality of responses generated by current conversational AI can vary significantly. Issues such as repetitive answers, irrelevant information, and incorrect responses are common, impacting the overall user experience.