The advent of revolutionary AI systems like ChatGPT and Chatsonic has sparked vigorous debate around the future of conversational AI…
Looking Under the Hood: How Chatbots Learn
Chatbots like ChatGPT and Chatsonic require extensive training on language data before they can communicate coherently. I will illustrate the key differences in their training methodology using a classroom learning analogy:
ChatGPT: The Eager Student
ChatGPT is like an exceptionally gifted student undertaking self-directed learning across subjects. It devours volumes upon volumes of text material during its training phase to build broad-based knowledge.
Occasionally, ChatGPT will learn inaccurate information or make logical leaps just like students might. But its teachers (Anthropic researchers) keep providing more reference material to fill its knowledge gaps and refine its reasoning.
Chatsonic: Learning By Rote
In contrast, Chatsonic memorizes answers to specific questions – like a student preparing only for fixed test questions rather than full subjects. This targeted rote learning works well within predefined conversational bounds.
But just like such students falter when posed unexpected questions, Chatsonic‘s abilities weaken for unfamiliar conversational territories. However, Chatsonic‘s teachers can conveniently expand its cramming syllabus as needed.
The Ideal Student?
A hypothetical "ideal" chatbot would blend the complementary strengths of both training methodologies. It would combine ChatGPT‘s broad knowledge and inference with Chatsonic‘s precise intent matching.
Ongoing AI research on techniques like transfer learning aims to produce such well-rounded chatbot learners. But of course, compared to the fluidity of the human mind, even the most advanced chatbots remain rather rigid students!
