For one, they are decades old, you can inspect exactly how they flow, are (mostly always) deterministic (given error-correction algorithms) but mostly: the theory behind them is well understood - it had to be before building the hardware. Whereas in ML/"AI" theory is about 20 years behind applied practice - because you can implicitly train something on data and rewards, without exactly knowing how its internal works - leaving interpretablitity and determinism.
Same as how do we know our brains can be trusted. If there are no external voices (scitzophrenia or phoning home respectfully) then the computing device is ours. Hardware bugs are possible but with proven absence of help from the vendor at least the bugs are not malfunctional.
For one, they are decades old, you can inspect exactly how they flow, are (mostly always) deterministic (given error-correction algorithms) but mostly: the theory behind them is well understood - it had to be before building the hardware. Whereas in ML/"AI" theory is about 20 years behind applied practice - because you can implicitly train something on data and rewards, without exactly knowing how its internal works - leaving interpretablitity and determinism.
To do that you'd have to watch and verify a chip going through the entire manufacturing process.
Same as how do we know our brains can be trusted. If there are no external voices (scitzophrenia or phoning home respectfully) then the computing device is ours. Hardware bugs are possible but with proven absence of help from the vendor at least the bugs are not malfunctional.
Our brains can be trusted far less than most people think though.
Brains can't be trusted.