My understanding is: Bob’s genome didn’t have access to Bob’s developed world model (WM) when he was born (because his WM wasn’t developed yet). Bob’s genome can’t directly specify “care about your specific family” because it can’t hardcode Bob’s specific family’s visual or auditory features.
This direct-specification wouldn’t work anyways because people change looks, Bob could be adopted, or Bob could be born blind & deaf.
[Check, does the Bob example make sense?]
But, the genome does do something indirectly that consistently leads to people valuing their families (say ~80% of people). The bulleted list (e.g. reaction to being scammed, etc) are other extremely common human values & biases that seems improbable for the genome to directly specify, so the alternative hypothesis is the genome set the initial conditions (along with the environment) such that these are generally convergently learned.
The hope is that this is true, the mechanisms of which can be understood, and these mechanism can be applied to AGI convergently learning desired values.
My understanding is: Bob’s genome didn’t have access to Bob’s developed world model (WM) when he was born (because his WM wasn’t developed yet). Bob’s genome can’t directly specify “care about your specific family” because it can’t hardcode Bob’s specific family’s visual or auditory features.
This direct-specification wouldn’t work anyways because people change looks, Bob could be adopted, or Bob could be born blind & deaf.
[Check, does the Bob example make sense?]
But, the genome does do something indirectly that consistently leads to people valuing their families (say ~80% of people). The bulleted list (e.g. reaction to being scammed, etc) are other extremely common human values & biases that seems improbable for the genome to directly specify, so the alternative hypothesis is the genome set the initial conditions (along with the environment) such that these are generally convergently learned.
The hope is that this is true, the mechanisms of which can be understood, and these mechanism can be applied to AGI convergently learning desired values.