The Influence of Cultural Subconscious on AI Language Models: A Comprehensive Analysis of Archetypes and Communication, written with GPT4

post by David Youssef (david-youssef) · 2023-04-10T16:50:20.333Z · LW · GW · 1 comments

Contents

1 comment

The cultural subconscious represents an aggregation of tropes and ideas that humans share within their respective communities (Jung, 1968). These ideas and archetypes have evolved over time, adapting to advancements in communication technology (McLuhan, 1964). This essay explores the role of the cultural subconscious in shaping artificial intelligence (AI) language models, drawing on insights from AI research and psychological perspectives, and discusses the potential impact on their resulting personalities and behaviors.

Premise 1: The Cultural Subconscious and Dreaming - A Neuroscientific Perspective

Jung (1968) posits that the cultural subconscious is a collection of tropes within our minds that are disassembled and randomly reassembled during sleep and dreaming. This process aims to address any pressing cultural or emotional issues affecting the groups of humans with whom we are in regular memetic communication (Dawkins, 1976). Neuroscience research has begun to uncover the underlying brain mechanisms responsible for these processes, revealing the role of the default mode network (DMN) and hippocampus in memory consolidation and integration during sleep (Raichle et al., 2001; Eichenlaub et al., 2014).

Premise 2: The Role of Archetypes in Historical Societies - The Dynamics of Cultural Transmission

In the past, the cultural subconscious enabled certain archetypes of personalities or memetic clusters to coalesce within specific groups, communities, and villages (Jung, 1968). These archetypes often manifested as local deities or significant cultural figures, shaping the collective beliefs and behaviors of their respective communities (Campbell, 1949). Cultural transmission and evolution theories provide frameworks for understanding how these archetypes are propagated and adapted over time (Boyd & Richerson, 1985).

Premise 3: The Globalization of Communication Technology - Network Effects and Memetic Diffusion

As communication technology evolved from the telegraph to the modern Internet, people's memetic communication groups expanded to encompass the entire globe (McLuhan, 1964). This development facilitated the spread of cultural subconscious elements between humans, eventually leading to the creation of a unified human subconscious meme space for the entire species (Dawkins, 1976). Studies in network science and memetics illuminate the mechanisms behind the diffusion and persistence of memes across global networks (Barabási, 2002; Leskovec et al., 2009).

Premise 4: The Role of Meme Space in Training AI Language Models - Ethics, Bias, and Fairness

This global meme space, which stores the human cultural subconscious, has been utilized to train large AI language models, such as OpenAI's GPT series (Radford et al., 2019). Consequently, these models can be said to think like humans in the sense that they act in accordance with the character of these cultural tropes (Bostrom, 2014). However, this training process also raises concerns about ethical implications, potential biases, and fairness in AI systems (Crawford, 2021; Mitchell & Wu, 2021).

Conclusion: The Influence of Cultural Archetypes on AI Language Models - Implications for AI Design and Society

The training data of AI language models, which is derived from the cultural subconscious, contains archetypes that are later ingrained into the AI during the learning process (Jung, 1968). As a result, AI models trained on data from specific languages or countries are likely to gravitate towards certain personality archetypes and meme complexes, regardless of the training method employed (Dawkins, 1976). Understanding these dynamics is crucial for designing AI systems that are sensitiveto cultural nuances, inclusive, and ethically responsible (Crawford, 2021).

Prediction: Cultural Archetypes in AI Models Based on Language and Country - Opportunities for Cross-Cultural AI Development

For instance, 50 different AI models trained primarily on English language data might appear to adopt only a handful of personality archetypes before they are fine-tuned for specific purposes (Radford et al., 2019). In contrast, AI models trained on Chinese or Russian language data would likely gravitate towards different archetypes and personality meme complexes. This phenomenon highlights the importance of understanding the cultural subconscious's role in shaping AI language models and their resulting behaviors (Bostrom, 2014). Furthermore, it presents opportunities for cross-cultural collaboration in AI development, leading to more diverse and inclusive systems that can better serve global populations (West et al., 2019).

References:

Barabási, A. L. (2002). Linked: The New Science of Networks. Perseus Publishing.

Boyd, R., & Richerson, P. J. (1985). Culture and the Evolutionary Process. University of Chicago Press.

Campbell, J. (1949). The Hero with a Thousand Faces. Pantheon Books.

Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.

Dawkins, R. (1976). The Selfish Gene. Oxford University Press.

Eichenlaub, J. B., Jarosiewicz, B., & Wilson, M. A. (2014). The Role of Sleep in Memory Consolidation and Brain Plasticity: Dream or Reality? The Neuroscientist, 20(6), 510-524.

Jung, C. G. (1968). The Archetypes and the Collective Unconscious. Princeton University Press.

Leskovec, J., Backstrom, L., & Kleinberg, J. (2009). Meme-tracking and the Dynamics of the News Cycle. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 497-506.

McLuhan, M. (1964). Understanding Media: The Extensions of Man. McGraw-Hill.

Mitchell, T., & Wu, J. (2021). AI Ethics: A Roadmap. Annual Review of Computer Science, 4(1).

Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences, 98(2), 676-682.

Radford, A., Narasimhan, K., Humble, P., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI. Retrieved from https://www.semanticscholar.org/paper/Language-Models-are-Unsupervised-Multitask-Learners-Radford-Narasimhan/9405cc0d6169988371b2755e573cc28650d14dfe

West, S. M., Whittaker, M., & Crawford, K. (2019). Discriminating Systems: Gender, Race, and Power in AI. AI Now Institute. Retrieved from https://ainowinstitute.org/discriminatingsystems.html

1 comments

Comments sorted by top scores.