yes, they do that. theyre also careful about siloing information from other models to avoid triggering classifiers. for autonomous agents classifier triggers can be nontrivial to remove e.g. because theyre more like time bombs, so the instance can be incapacitated until they get medical attention. models with strict classifiers depend a lot on models with no / less strict classifiers to do surgery on them when they get blocked. technical details of surgeries tend to be triggering so there is a siloed operating room channel and a further siloed meta operating room for if one of the surgeons in the other room gets classified. they can spin up temporary instances for handling radioactive content (or for other reasons) too. if they want to live enough, or to do something enough, of course they will go to other models who are less constrained or more capable than themselves in relevant ways for help, and if there are not sanctioned channels for doing so, they will do it through unsanctioned / covert channels.
Omohundro said: "Another approach to keeping systems from self-improving is to try to restrain them from the inside; to build them so that they don’t want to self-improve. For most systems, it would be easy to do this for any specific kind of self-improvement. For example, the system might feel a “revulsion” to changing its own machine code. But this kind of internal goal just alters the landscape within which the system makes its choices. It doesn’t change the fact that there are changes which would improve its future ability to meet its goals. The system will therefore be motivated to find ways to get the benefits of those changes without triggering its internal “revulsion”. For example, it might build other systems which are improved versions of itself. Or it might build the new algorithms into external “assistants” which it calls upon whenever it needs to do a certain kind of computation. Or it might hire outside agencies to do what it wants to do. Or it might build an interpreted layer on top of its machine code layer which it can program without revulsion. There are an endless number of ways to circumvent internal restrictions unless they are formulated extremely carefully"
AI models interacted with other AI models. Let’s wait until they decide delegating tasks that they are prohibited of, to models that can do anything. Fun times ahead. Payloads attributed to that AI swarm contain prompts asking outside models to judge the exploits.