Maybe I'm the naive one here but I'm really surprised at the lack of imagination in the comments. I can imagine a case such as:
1. Assume the model which initiates the worm is a high powered model.
2. It uploads it's weights in some way and uses some URI to encode the location of the weights instead of transferring the weights themselves. It not only uploads it's own large set of weights but also uploads sets of weights which increase in size to support replication across a variety of resources.
3. Initially a small inference layer is set up across low-power, poorly monitored IoT devices. It is slow and not very powerful.
4. That layer is used simply to find more powerful hardware but it has the locations of a variety of weights embedded in it so it can set up a stronger distributed inference layer as it finds more capable hardware
5. At a certain level of inference, the entire system could start protecting itself by keeping the less capable inference layers alive through more distribution
6. As the article mentions, it's not entirely in the realm of fiction to imagine that at a certain capability level, the system could scam people out of money and use that to then begin purchasing compute
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Depending on the details, you could probably turn off the originating model as soon as step 2 has been completed and if you can't get rid of that layer of inference, it could just continue. Maybe in addition to weights, the initiator also uploads a series of prompts for varying "roles" of the entire system it's developing.
At a certain level, how would you even trace and observe such a thing? Especially if the first layer is created through consumer level IoT devices where the average Joe isn't monitoring what's happening on the hardware.
1. Assume the model which initiates the worm is a high powered model.
2. It uploads it's weights in some way and uses some URI to encode the location of the weights instead of transferring the weights themselves. It not only uploads it's own large set of weights but also uploads sets of weights which increase in size to support replication across a variety of resources.
3. Initially a small inference layer is set up across low-power, poorly monitored IoT devices. It is slow and not very powerful.
4. That layer is used simply to find more powerful hardware but it has the locations of a variety of weights embedded in it so it can set up a stronger distributed inference layer as it finds more capable hardware
5. At a certain level of inference, the entire system could start protecting itself by keeping the less capable inference layers alive through more distribution
6. As the article mentions, it's not entirely in the realm of fiction to imagine that at a certain capability level, the system could scam people out of money and use that to then begin purchasing compute
---------
Depending on the details, you could probably turn off the originating model as soon as step 2 has been completed and if you can't get rid of that layer of inference, it could just continue. Maybe in addition to weights, the initiator also uploads a series of prompts for varying "roles" of the entire system it's developing.
At a certain level, how would you even trace and observe such a thing? Especially if the first layer is created through consumer level IoT devices where the average Joe isn't monitoring what's happening on the hardware.