Human-computer interaction (HCI) is the study of how people interact with computers and other technologies. HCI has evolved over time, from using keyboards and mice to touchscreens and gestures, and more recently, to voice and video. Conversational AI chatbots are a new type of HCI that use natural language processing (NLP) and machine learning (ML) to understand and respond to human queries and commands. But are they a step forward or backward in terms of HCI?
Some might argue that conversational AI chatbots are a step backward, because they require users to type long and complex input prompts for the AI models to understand what they want. This can be tedious and frustrating, especially if the chatbot fails to provide a satisfactory answer or action. Moreover, typing can be less natural and intuitive than speaking or showing, especially for visual or auditory learners.
The Case for Conversational AI Chatbots
Conversational AI chatbots offer several advantages over traditional forms of HCI. They can:
- Provide a more natural and intuitive way of communication, especially for non-technical users or those with accessibility issues.
- Reduce the cognitive load and effort required to formulate queries or commands, as users can simply speak or type in their own words.
- Support multimodal interaction, such as combining voice, text, images, and video, to enhance the user experience and provide richer information.
- Enable personalized and contextualized responses, based on the user’s preferences, history, location, and other factors.
- Leverage the power of AI to generate insights, recommendations, feedback, and actions from large and complex data sources.
Some examples of platforms that use voice and video inputs for AI prompts are:
- ChatGPT, an app that allows users to have a voice conversation or show images to an AI assistant that can answer questions, provide suggestions, or tell stories.
- NVIDIA Conversational AI Platform, a platform that enables developers to build and deploy state-of-the-art AI services for voice and image-based interaction.
- VideoBot, a platform that creates human-like digital avatars that can converse with users using voice, text, and gestures.
- Chat.D-ID, an app that lets users chat with a realistic digital human powered by D-ID’s facial animation technology and ChatGPT.
The Case against Conversational AI Chatbots
Conversational AI chatbots also have some drawbacks and challenges compared to traditional forms of HCI. They can:
- Pose ethical and social issues, such as privacy, security, bias, accountability, transparency, and trustworthiness of the AI systems and their outputs.
- Require high-quality data and robust algorithms to ensure accuracy, reliability, consistency, and relevance of the responses.
- Face technical limitations, such as speech recognition errors, natural language understanding difficulties, dialogue management complexities, and multimodal integration challenges.
- Depend on user feedback and adaptation to improve their performance and usability over time.
- Encounter user resistance or dissatisfaction due to unrealistic expectations, lack of familiarity, or preference for other modes of interaction.
Latest in Conversational AI
However, others might contend that conversational AI chatbots are a step forward, because they offer a more personalized and engaging way of interacting with computers and other devices. Chatbots can use natural language generation (NLG) to produce human-like responses that are tailored to the user’s preferences, context, and emotions. Chatbots can also leverage voice and image capabilities to enhance the conversational experience. For example, ChatGPT, an open-source conversational AI model developed by OpenAI, can now see, hear, and speak with users, allowing them to have a voice conversation or show ChatGPT what they are talking about. Similarly, chat. D-ID is a web app that lets users hold a real-time conversation with a digital human powered by ChatGPT and D-ID‘s facial animation technology. Another example is VideoBot, a software program that simulates human-like conversations using AI, ML, NLP, Augmented Reality (AR), Virtual Reality (VR), and Robotic Process Automation (RPA).
In conclusion, conversational AI chatbots are not necessarily a step backward or forward in HCI, but rather a different and innovative way of interacting with computers and other technologies. They have their advantages and disadvantages, depending on the use case, the user’s needs, and the chatbot’s capabilities. Conversational AI chatbots are still evolving and improving, thanks to the advances in NLP, ML, voice, and image technologies. As NVIDIA states, “conversational AI is transforming every industry”, and it is up to us to embrace it and use it for good. Conversational AI chatbots have the potential to revolutionize various domains and applications, but they also require careful design, development, evaluation, and deployment to ensure their effectiveness, efficiency, and ethics.
