Machine learning doesn’t necessarily require a centralized cluster. Usually running those kinds of models is pretty cheap, it’s not an LLM basically. They usually do better than human moderators as well, able to pick up on very minute ‘tells’ these cheats have.
I understand your point about edge cases, but that’s not something the average player cares about much. E-sports is a pretty niche part of any game, especially the higher ranks. You just want to filter out the hackers shooting everyone each game that truly ruin the enjoyment. Someone cheating to rank gold instead of silver or whatever isn’t ruining game experiences; they’re usually detectable too, but if you get a false negative on that it’s not the end of the world. A smurf account of a very highly ranked player probably has a bigger impact on players’ enjoyment.
Depending on the model, inference can be run with CPU only. To distinguish what was originally proposed (a momentary flick consistent with aimbotting), you are either doing ray tracing (really expensive) or analyzing (effectively) video feeds. Both of which tend to put things more into the GPU realm which drastically increases the cost of a server.
But also? The only way these models can work is with constant data. Which means piping feeds back home for training which basically is never inexpensive.
Aside from that: if it was as simple as you are suggesting then this would be a solved problem. Similarly, if people don’t care about hackers outside of e-sports then there would be no reason for games to spend money on anti-cheat solutions when any match that matters would have heavy scrutiny. And yet, studios keep pumping out the cash for EAC and the like.
Machine learning doesn’t necessarily require a centralized cluster. Usually running those kinds of models is pretty cheap, it’s not an LLM basically. They usually do better than human moderators as well, able to pick up on very minute ‘tells’ these cheats have.
I understand your point about edge cases, but that’s not something the average player cares about much. E-sports is a pretty niche part of any game, especially the higher ranks. You just want to filter out the hackers shooting everyone each game that truly ruin the enjoyment. Someone cheating to rank gold instead of silver or whatever isn’t ruining game experiences; they’re usually detectable too, but if you get a false negative on that it’s not the end of the world. A smurf account of a very highly ranked player probably has a bigger impact on players’ enjoyment.
Depending on the model, inference can be run with CPU only. To distinguish what was originally proposed (a momentary flick consistent with aimbotting), you are either doing ray tracing (really expensive) or analyzing (effectively) video feeds. Both of which tend to put things more into the GPU realm which drastically increases the cost of a server.
But also? The only way these models can work is with constant data. Which means piping feeds back home for training which basically is never inexpensive.
Aside from that: if it was as simple as you are suggesting then this would be a solved problem. Similarly, if people don’t care about hackers outside of e-sports then there would be no reason for games to spend money on anti-cheat solutions when any match that matters would have heavy scrutiny. And yet, studios keep pumping out the cash for EAC and the like.