When I think about the long- and short-term implications of using machine “assistants” like OpenAI and MSFT CoPilot, they function very similarly to Google. It's like you have these knowledge graphs or flash cards. The taxonomy of information and mental models is significant here. Google has gotten good at having a rigid taxonomy of where everything is on the web, which was the traditional way of doing search engines, while also allowing a folksonomy to emerge.
The concepts of taxonomy and folksonomy hold significant implications, especially in the context of emerging technologies like OpenAI. While traditional taxonomies offer structured hierarchies of knowledge, allowing for a systematic approach to information organization, folksonomies represent a more fluid and emergent way of categorizing information based on user-generated tags and metadata.
Google's search engine exemplifies the transition from rigid taxonomies to adaptable folksonomies, enabling users to ask questions in natural language and receive relevant search results. Similarly, social media platforms like Facebook and Twitter utilize folksonomies through user-generated tags, reflecting their users' present thinking and organizational patterns.
Why should you care?
However, the challenge arises when technological advancements fail to incorporate divergent thinking and promote groupthink through convergent taxonomies. This phenomenon is particularly evident in language models, where developers' linguistic and cultural biases can influence the interpretation and representation of (the dominant) language.
Moreover, the dominance of certain cultural perspectives in machine learning models can marginalize or exclude other cultural or linguistic groups, perpetuating a cycle of normalization and exclusion. This has far-reaching implications for cross-cultural communication and understanding, as nuances and subtleties in language and thinking may be overlooked or misinterpreted.
A Way Forward
It's crucial to consider the broader implications of technology on cultural exchange and innovation. As the business and technology landscape evolves, there's a growing need to prioritize diversity and inclusion in machine learning models to ensure that diverse perspectives and voices are represented and valued.
The interplay between taxonomy and folksonomy in information organization reflects broader societal trends and challenges in technology and culture. In this way, these machine co-workers are merely a mirror of us and our current dilemmas. What remains unresolved in the real world will magnified, as will the unintended and unconsidered consequences. By embracing diverse perspectives and fostering a culture of inclusivity, we can channel the full potential of emerging technologies like OpenAI while mitigating the risks of cultural bias and exclusion.