“The Phenomenon”: Cover-ups, AI, and “Move 37”
In March 2017, a groundbreaking Go match took place between DeepMind’s AI “AlphaGo” and Go champion Lee Sedol. During the match, AlphaGo made an unexpected move known as “Move 37,” which left both spectators and experts utterly astonished. This move was a significant departure from the traditional strategies that had been honed over the 4,000-year history of the game, demonstrating the AI’s capacity to think innovatively beyond established patterns.
Moreover, the rise of direct-to-consumer DNA testing has played a pivotal role in revealing misconduct by fertility clinic practitioners—individuals who should have been at the forefront of understanding DNA forensics technology yet failed to recognize its associated risks.
Let’s also examine how the trajectory of AI development accelerated unexpectedly, beginning with the success of AlexNet in 2012, which revitalized long-standing techniques, and continuing with the introduction of the LLM “transformers” concept in a pivotal 2017 research paper titled “Attention is All You Need.” These rapid advancements caught many institutions and governments off guard.
What I’m proposing is that it’s increasingly clear that, irrespective of opinions on AI, its capability to derive actionable intelligence from previously “hidden” or “uncorrelated” data and patterns is undeniable. Consequently, it follows that this reality could motivate those in possession of secrets to either “disclose or divest.” The surge in disclosure-related activities since 2019 may very well be a direct result of the recognition that they can no longer keep such information hidden from the public.
What are your thoughts on this?
You’ve raised some thought-provoking points about the interplay between AI advancements, unexpected discoveries, and the implications for transparency in various fields. The examples you’ve cited—the move made by AlphaGo, the use of DNA testing to expose fertility fraud, and the rapid development of AI technologies—highlight a crucial trend: when technology evolves, it often surpasses our expectations, revealing hidden truths and challenging established norms.
The concept of AI offering insights from previously hidden or uncorrelated data is indeed powerful and raises questions about accountability. In the case of AlphaGo’s Move 37, it exemplified a level of strategic thinking that humans had not considered before, prompting us to reassess our understanding of the game itself. Similarly, the unexpected application of DNA technology has allowed individuals to hold professionals accountable in fields where ethical standards should ideally prevent such abuses.
Your suggestion that the rapid evolution of AI might compel organizations and institutions to be more transparent, perhaps due to their recognition of an impending inability to suppress information, aligns with a growing discourse about accountability in the digital age. As AI continues to develop, the power dynamics between knowledge holders and the public may indeed shift. Institutions may find it more prudent to disclose sensitive information rather than risk exposure through advanced technological means.
Moreover, the increase in disclosure-related activities since 2019 could be interpreted as a response to the recognition that secrecy may not be sustainable in the face of rapidly advancing technologies. This shift could foster a more open dialogue about ethics, accountability, and the responsible use of AI.
It will be interesting to see how these trends evolve and what new ethical challenges emerge as AI technologies continue to permeate various sectors. The conversation around transparency and accountability is more important now than ever, especially as we navigate the implications of AI on our understanding of truth and trust. What are some specific areas where you think this dynamic will be particularly impactful in the near future?