Many expert level benchmarks totally overestimate the range and diversity of their experts’ knowledge. A person with a PhD in physics is probably undergraduate level in many parts of physics that are not related to his/her research area, and sometimes we even see that within expert’s domain (Neurologists usually forget about nerves that are not clinically relevant).
Relatedly, undergrads are often way below undergrad level in courses that they haven’t taken lately. Like, if you take someone who just graduated with a physics major and get them to retake finals from sophomore physics without prep, I bet they’ll often get wrecked.
As a professional computer scientist, I often feel like my knowledge advantage over recent grads is that I know the content of intro CS classes better than they do.
”Another thing to be aware of is the diversity of mental skills. If by ‘human-level’ we mean a machine that is at least as good as a human at each of these skills, then in practice the first ‘human-level’ machine will be much better than a human on many of those skills. It may not seem ‘human-level’ so much as ‘very super-human’.
We could instead think of human-level as closer to ‘competitive with a human’ - where the machine has some super-human talents and lacks some skills humans have. This is not usually used, I think because it is hard to define in a meaningful way. There are already machines for which a company is willing to pay more than a human: in this sense a microscope might be ‘super-human’. There is no reason for a machine which is equal in value to a human to have the traits we are interested in talking about here, such as agency, superior cognitive abilities or the tendency to drive humans out of work and shape the future. Thus we talk about AI which is at least as good as a human, but you should beware that the predictions made about such an entity may apply before the entity is technically ‘human-level’.
Many expert level benchmarks totally overestimate the range and diversity of their experts’ knowledge. A person with a PhD in physics is probably undergraduate level in many parts of physics that are not related to his/her research area, and sometimes we even see that within expert’s domain (Neurologists usually forget about nerves that are not clinically relevant).
Relatedly, undergrads are often way below undergrad level in courses that they haven’t taken lately. Like, if you take someone who just graduated with a physics major and get them to retake finals from sophomore physics without prep, I bet they’ll often get wrecked.
As a professional computer scientist, I often feel like my knowledge advantage over recent grads is that I know the content of intro CS classes better than they do.
Katja Grace ten years ago:
”Another thing to be aware of is the diversity of mental skills. If by ‘human-level’ we mean a machine that is at least as good as a human at each of these skills, then in practice the first ‘human-level’ machine will be much better than a human on many of those skills. It may not seem ‘human-level’ so much as ‘very super-human’.
We could instead think of human-level as closer to ‘competitive with a human’ - where the machine has some super-human talents and lacks some skills humans have. This is not usually used, I think because it is hard to define in a meaningful way. There are already machines for which a company is willing to pay more than a human: in this sense a microscope might be ‘super-human’. There is no reason for a machine which is equal in value to a human to have the traits we are interested in talking about here, such as agency, superior cognitive abilities or the tendency to drive humans out of work and shape the future. Thus we talk about AI which is at least as good as a human, but you should beware that the predictions made about such an entity may apply before the entity is technically ‘human-level’.
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