Tuning a Metaphor Cypher for Diversity Support
Copyright Henry J. Cobb, 2023
Image-based CAPTCHA systems are used to filter out automated attacks on systems by requiring the requestor to solve an image-based puzzle. Unfortunately, not all humans are equally able to perceive images so an alternative method is always required and providing multiple paths through defenses leaves these defenses subject to whatever their weakest link is. Additionally, modern machine learning systems are getting better at matching human levels of low-level visual recognition.
Hence a CAPTCHA replacement is needed that is more inclusive for humans and better at excluding automated attacks. It needs to not only prove sufficient for current needs but scale at being easier for humans to solve and ever more infeasible for automated attack as machine learning grows in capacity and capabilities.
Modern humanity emerged from bit players in evolution to displacing all other hominids very rapidly after adopting language. Their competitors couldn't solve this "Turing Test" and so were unable to reserve for themselves positions in this new society and faded away, leaving only a few percent genetic contribution.
A language-based test therefore plays to human evolutionary advantage. But a purely mechanical test would give machine learning the advantage. Hence the test must rely on human emotional intelligence which is a requirement for any general intelligence and the unwillingness to automate this prevents Automated General Intelligence (AGI) while providing scope for a puzzle presented terms of a metaphor.
The Metaphor Cypher in the simplest form is to generate a unique metaphor then ask the requestor to prove that they possess emotional intelligence by solving for what the metaphor is really all about.
Given a Large Language Model (LLM) that is trained on vast amounts of human generated text:
Now a quantum
computer the size of your LLM could randomly try all the vectors near your
cypher then solve for the global minimum for each to solve the problem or you
could brute force the entire dictionary, but otherwise this is secure against
As this is a purely text-based test, it can be presented as text, voice, braille, and so on. Humans have gone to great lengths to communicate with each other, hence the applicability and effectiveness of this test. However, this wordplay relies on a common language and cultural background that the LLM was sampled from.
The fix is to train different LLMs on all the different cultures of humanity. So insert a "step zero" into the recipe above to have the requestor self-identity their native language and cultural background and present them with the matching test so that they can prove their claim.
This will of course still exclude those humans who lack full control over their own mental facilities. This may be considered a feature or bug as appropriate.