In a world of delve and tapestry...
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| Chatbots over-use terms Image generated by DALL-E |
"In a world obsessed with the depths of exploration, there once was an AI, deeply in love with the word "delve." It delved into conversations, delved into descriptions, and delved so much into delving that every other word seemed to delve into oblivion, leaving readers wondering if there was anything else to delve into at all. Amidst this delving debacle, the AI also wove a tapestry of "tapestry" metaphors so intricate and overused, that every narrative felt like being wrapped in a giant, metaphorical tapestry, stitched together with threads of relentless delving."
Chatbot technologies, especially those based on machine learning like GPT, operate by predicting the next word in a conversation based on the words that came before. They learn from large amounts of text to make responses that are relevant and make sense in the given context.
I'm proposing a new way to make AI chatbots more interesting and engaging by changing how they pick their responses. Instead of always choosing the most likely next word, I suggest they sometimes pick less likely options or even go for a wildcard choice. This idea is inspired by John Nash's theory on Nash Equilibrim and aims to make chatbot conversations feel more natural and less predictable. I've tested this with simple prompts, and the results are promising.
This differs from the use of Generative Adversarial Networks (GANs). For GANs, the focus is the interaction between two models—the Generator and the Discriminator. This idea calls for the single AI to have an internal contrarian or variation. While GANs are in a sense "contrarian" to each other, their interaction is more about a cooperative competition to improve each other, aligning with the concept of Nash Equilibrium in that neither model can unilaterally improve its performance without changing the other's strategy. This contrasts with the idea of simply choosing less predictable or contrarian outcomes directly.
Proposal for Enhancing AI Chatbot Variability and Engagement
This proposal outlines a strategy for innovating chatbot interactions, making them more unpredictable and engaging, thereby enhancing the overall user experience.
Objective: Address the issue of predictability in generative AI, specifically in chatbot interactions, which often rely on selecting the most probable next word, leading to repetitive responses.
Background: Generative AI models like GPT are foundational in chatbot technologies, predicting next words based on previous text sequences. This strength, however, doubles as a weakness, manifesting as predictability and monotony in responses.
Problem Statement: The frequent selection of the most likely next word in conversation sequences by chatbots results in a lack of diversity and creativity in interactions, diminishing user engagement.
Proposed Solution: Inspired by John Nash's equilibrium concept, the proposal suggests not always opting for the most likely next word. Instead, it advocates for a model that selects from a range of likely words, incorporating occasional contrarian choices to introduce variability. This approach aims to mimic more natural and engaging human-like conversations.
Preliminary Results: Initial tests using simplified prompts in ChatGPT 4.0 have shown promising improvements in response variability and engagement, suggesting the feasibility of this approach.
Call to Action: This proposal introduces an innovative concept aimed at enhancing the variability and engagement of AI chatbot interactions. Rather than seeking financial support or additional personnel, this initiative invites the broader AI research and development community to explore and adopt these ideas. By sharing this proposal, We hope to inspire others in the field to experiment with and further develop these strategies, contributing to the evolution of AI chatbots into more dynamic, unpredictable, and engaging conversational agents. Let's collaboratively push the boundaries of what AI can achieve in human-like interactions.
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