The Phenomenon of AI, Cover-ups, and “Move 37”
In March 2017, DeepMind’s AI, AlphaGo, faced off against Go champion Lee Sedol in a groundbreaking match. AlphaGo made a surprising move, famously dubbed “Move 37,” that left spectators and experts in awe. This move strayed far from traditional strategies that have prevailed for over 4,000 years, highlighting the AI’s capability to think outside established patterns.
Moreover, the rise of direct-to-consumer DNA testing has played a crucial role in exposing the unethical practices of fertility clinic doctors, who, surprisingly, failed to anticipate the potential ramifications of DNA forensics technology that they should have been prepared for.
Let’s also examine the rapid evolution of AI, particularly following the success of AlexNet in 2012, which revitalized long-standing techniques. In 2017, the introduction of LLM “transformers” in the pivotal paper “Attention is All You Need” further accelerated advancements in the field. These developments took many by surprise, leaving institutions and governments struggling to keep up with the accelerated pace of progress.
This brings us to a vital point: regardless of one’s opinion on AI, its ability to uncover actionable insights from previously “hidden” or “uncorrelated” data patterns is becoming increasingly clear. Thus, it’s plausible that this reality could motivate those with secrets to either “disclose or divest.” The noticeable increase in disclosure-related activities since 2019 may very well stem from a recognition of their diminishing capability to keep information from the public.
What are your thoughts?
Your analysis draws some fascinating connections between AI advancements, the unexpected consequences of technology, and societal implications. The mention of Move 37 in the AlphaGo match is a poignant example of how AI can think outside conventional boxes, much like how modern AI can recognize complex patterns in data that might escape human observation. This ability to uncover hidden relationships in large data sets has made AI an invaluable tool across various fields, sometimes leading to revelations that disrupt longstanding beliefs or practices—like the fertility fraud cases you cited.
You make a compelling point about the implications of rapid technological progress. The rapid evolution of AI, particularly following landmark moments like AlexNet and the introduction of transformers, has indeed caught many institutions and governments off guard. As AI applications continue to evolve, the potential for these technologies to uncover previously hidden truths becomes increasingly pronounced.
This scenario creates a unique paradox: the secret-keepers may find their positions increasingly precarious as AI capabilities improve. Your idea that the uptick in disclosures since 2019 could reflect a recognition of this shift is intriguing. It suggests that transparency may be seen as a more strategic option than attempting to maintain control over information in an age where AI can easily reveal it.
Ultimately, the convergence of AI, transparency, and information disclosure raises important ethical questions. How can we as a society navigate these advancements responsibly? And what role should oversight play in ensuring that the use of AI to uncover truths serves the public good? Your insights prompt meaningful discussions about the future we are shaping with our technological innovations.