Although there are a few ways to mitigate the risk, the only way to block it is to get AI to differentiate instructions from data, which is impossible today.
We know that some humans can be trained to do that just fine. Humans are natural neuronal networks. That implies, neuronal networks can in principle do it. We just don’t have any human-capability artificial neuronal networks yet.
LLMs might never get there. But humans aren’t LLMs. If we ever manage to properly model a human brain, that probably will be able to do that task with human-level accuracy (which actually is pretty good if you only look at professionals of the filed).
Hopefully, it doesn’t actually need a human brain for the task - because modeling that might still be a century off.
A human brain model would never get us to a super computer, which is their intended goal.
Our brains have about one exaflop of processing. The NVidia B300 has 15 petaflops of processing. Corresponding to 0.015 exa, you’d need 66 of the strongest GPUs on the market to match the processing power of one human brain.
That’s before considering context, current estimations would put the human brain at 2.5 petabytes of storage. The former B300 has 288 gigabytes of VRAM. Corresponding to 0.000288 of one human brain, the earlier expectation of 66 landing flat at 0.019 the storage of a human brain.
Can arguments be made not all brain capacity is at VRAM at all times? Most likely. But context windows is the primary bottleneck, not compute
Our brains are absolute monsters, we’d gain more from understanding how we can have this powerful of a machine on so little space. The bigger breakthrough would be brainbased processing of software, but I’ve not heard of anyone going that route - Probably because it’s not feasible
We know that some humans can be trained to do that just fine. Humans are natural neuronal networks. That implies, neuronal networks can in principle do it. We just don’t have any human-capability artificial neuronal networks yet.
LLMs might never get there. But humans aren’t LLMs. If we ever manage to properly model a human brain, that probably will be able to do that task with human-level accuracy (which actually is pretty good if you only look at professionals of the filed).
Hopefully, it doesn’t actually need a human brain for the task - because modeling that might still be a century off.
A human brain model would never get us to a super computer, which is their intended goal.
Our brains have about one exaflop of processing. The NVidia B300 has 15 petaflops of processing. Corresponding to 0.015 exa, you’d need 66 of the strongest GPUs on the market to match the processing power of one human brain.
That’s before considering context, current estimations would put the human brain at 2.5 petabytes of storage. The former B300 has 288 gigabytes of VRAM. Corresponding to 0.000288 of one human brain, the earlier expectation of 66 landing flat at 0.019 the storage of a human brain.
Can arguments be made not all brain capacity is at VRAM at all times? Most likely. But context windows is the primary bottleneck, not compute
Our brains are absolute monsters, we’d gain more from understanding how we can have this powerful of a machine on so little space. The bigger breakthrough would be brainbased processing of software, but I’ve not heard of anyone going that route - Probably because it’s not feasible