In May I attended Zuzalu's AI x Crypto mini-conference and got more into AI x-Risk. I thought about a potential solution. I know it might be naive, but looked for something like this and didn't find anything related. So I decided to share some notes here to get some feedback. It's written as a post directed to laymen readers.

Alignment is especially hard because understanding AGI's inner workings is not only challenging because we currently don't understand it fully, but also because of the complexity and potentially opaque nature of AGI itself.

Between the ideas that surfaced, one that caught my attention is that we should NOT use censorship-resistant tech, also known as blockchain to train AIs, since this can make it difficult to unplug them. As an outsider in the field, this was a new concept. Unplugging an AGI is of the measures that can be taken to stop it. This concept is better known as off-switch.

To put it simply there are two types of approaches to this type of problem. The main one is focused on embedding the alignment into the AI, which for this specific case is called a Shutdown utility function (https://arbital.com/p/shutdown_utility_function/). But there are also external solutions, that try to limit desalignment. Both cases should be combined for an optimal solution. Here I’m focusing on the external safeguard.

But if an AI goes rogue and gets to exterminate humans, we won’t be able to turn it off. This leads me to think: what if we use a dead-man switch? This is usually implemented in case a person dies, to trigger a pre-defined action. Usually, it's a beacon that doesn’t activate while it receives a signal. For the alignment case, the trigger would unplug all AI servers and the ones that could be used to train a new one. This brings some problems to be solved, let’s explore them:

Potential resistance from AGI: there are many potential resistance points. The most obvious is that it could try to make a copy that wakes up and restarts servers to keep it running.
Corrigibility: AGI's willingness to allow its objectives to be corrected by humans without resistance. An off-switch is a kind of corrigibility, but designing an AGI that remains corrigible as it self-improves is a nontrivial task.
Reward Hacking: an AGI might find a way to "hack" its reward system to receive maximal rewards without actually following the intended spirit of its pr

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