This is a guest post written by Inference Labs. You can see their version of the post here.
From Web3 and Web2 platforms to traditional brick-and-mortar businesses, every domain we navigate is shaped by rigorously engineered incentive systems that structure trust, value, and participation. Now player 2 has entered the chat — AI Agents. As they join, how do we ensure open and fair participation for all? From “Truth Terminal” to emerging AI Finance (AiFi) systems, the core solution lies in implementing robust verification primitives.
The way AI is trained today creates a black box solution, the author says only the developers of the model know what goes on inside the black box.
This is major pain point in AI, where we are trying to understand it so we can make it better and more reliable. The author mentions that unless AI companies open source their work, it’s impossible for everyone else to ‘debug’ the circuit.
Zero knowledge proofs are how they are trying to combat this, using mathematical algorithms they are trying to verify the output of an AI model in real time, without having to know the underlying intellectual property.
This could be used to train AI further and increase the reliability of AI drastically, so it could be used to make more important decisions and adhere much more easily to the strategies for which they are deployed.
The way AI is trained today creates a black box solution, the author says only the developers of the model know what goes on inside the black box.
This is major pain point in AI, where we are trying to understand it so we can make it better and more reliable. The author mentions that unless AI companies open source their work, it’s impossible for everyone else to ‘debug’ the circuit.
Zero knowledge proofs are how they are trying to combat this, using mathematical algorithms they are trying to verify the output of an AI model in real time, without having to know the underlying intellectual property.
This could be used to train AI further and increase the reliability of AI drastically, so it could be used to make more important decisions and adhere much more easily to the strategies for which they are deployed.
Thanks for the ‘for dummies’ explanation.