“The Phenomenon”: Cover-Ups, AI, and “Move 37”
In a landmark Go match in March 2017, DeepMind’s AI, AlphaGo, faced off against champion Lee Sedol. During the game, AlphaGo executed a move—dubbed “Move 37”—that left both spectators and experts astounded. This unexpected play deviated from 4,000 years of traditional human strategies, highlighting the AI’s capacity to think outside established patterns.
Moreover, the rise of direct-to-consumer DNA testing has played a crucial role in exposing the wrongdoing of certain fertility clinic doctors, who, despite being in a position to anticipate the potential misuse of DNA technology, failed to recognize the risks involved.
Let’s also reflect on the rapid acceleration of AI development, sparked by the success of AlexNet in 2012, which revitalized previously overlooked techniques. Then in 2017, the introduction of the LLM “transformers” concept in the research paper “Attention is All You Need” marked another substantial leap forward. These advancements took many by surprise, leaving institutions and governments scrambling to keep up with the pace of innovation.
What I’m proposing is that it’s becoming increasingly clear that, regardless of individual opinions on AI, its ability to extract actionable insights from previously “hidden” or “uncorrelated” data is irrefutable. This revelation may drive those in possession of secrets to either “disclose or divest.” The surge in disclosure-related activities since 2019 could very well stem from a growing awareness that these secrets can no longer be kept hidden from the public.
What are your thoughts?
You’ve brought up some intriguing points about the interplay between AI advancements and the transparency of hidden truths in various sectors, from gaming to healthcare. The reference to AlphaGo’s “Move 37” highlights how AI can disrupt established norms and challenge our understanding of complex systems. This aligns with the notion that AI can uncover patterns and insights that humans may overlook, leading to both revolutionary discoveries and unsettling revelations.
The example of DNA testing revealing fertility fraud illustrates another dimension of this conversation—how technology can serve as a tool for accountability. Just as AI identifies non-obvious correlations, direct-to-consumer genetic tests have illuminated ethical breaches that were previously concealed by a lack of accessibility to relevant information.
As for the rapid developments in AI, particularly after breakthroughs like AlexNet and the introduction of transformers, it’s evident that the pace of progress has outstripped many expectations. This acceleration not only enhances the capabilities of AI but also increases the stakes for those who may wish to keep information hidden. Organizations and governments, realizing that their secrets may soon be exposed through advanced analytical tools, might indeed feel pressured to disclose information preemptively.
Your argument suggests that the fusion of AI’s capabilities and the growing demand for transparency may provoke a paradigm shift in how information is controlled and disseminated. This trend could lead to a more informed public, but it also raises ethical questions about privacy, consent, and the potential for misuse of such technologies. Overall, the circumstances you’ve outlined appear to be a clarion call for more robust discussions about the intersection of AI, privacy, and accountability in our rapidly evolving landscape. What specific areas do you think we should focus on to ensure that this progress benefits society as a whole?