The client in “The Expert” is opinionated, unwilling to listen to the expert they’re hiring, and wants several nigh-impossible things. They clearly know nothing about the subject, and their communication style is hopeless. Yes it’s funny, but no it’s not relevant to alignment.
Humans, on the other hand, know a lot about what makes humans happy. We’ve all practiced it for most of our lifetime, attempting to make ourselves and our friends, relatives and loved ones happy, and attempting to avoid annoying random passers-by. Practical tips on this subject are the primary content of most magazines. We also have many entire fields of study devoted to it, many of them STEM: Medicine, (human) Biology, Psychology, Sociology, Anthropology, Economics, Ergonomics, Design, Political Science, Market Research, Art, Crafts, Massage,…: just about every soft science we have, plus every art and craft.. We already know an enormous amount about the subject, and have an equally enormous literature on it. A sufficiently smart AI (or set of AIs) capable of doing STEM research can take that entire literature, read and absorb all of it, and then start to build on and add to it. They will know where to find and how to talk to the humans familiar with the material they are building on, including ones who are aware that in order to be able have seven mutually perpendicular lines (in a flat space) you need to be in a space with seven or more dimensions (mathematicians or physicists: for curved surfaces things are easier, you can do 3 mutually perpendicular great circles on the 2d surface of a globe, and as many as 10-mutually perpendicular geodesics on the 4d surface of a 5-sphere), and artists and craftsmen who know all sorts of tricks for color-changing reactive ink.
Yes, human value are very large, complex, and fragile. However, your local university library contains millions of text books on them containing terrabytes of information on the subject, and the Internet has exabytes more. We and our AIs are not starting anywhere near square one: we have millennia of information on the subject. Yes, as the AIs add more information, we’ll need humans to keep up and become familiar with what they’re learning, so we can still talk to them. Which in the case of a FOOM might prove to be a bottleneck.
So no, the situation for Value Learning or implementing CEV isn’t anything like your humorous video.
What we know less about, is how to design or train AIs to make them understand how to make humans happy (absorb those many exabytes of information, as GPT-4 to some extent already does) and value doing so (convert the data into something that plans can be scored and adjusted based on, and then do so, again, as GPT-4 is already to some extent capable of with suitable prompting). Building us some more expertise there makes a lot of sense. Perhaps we should start with something rather simpler and lower stakes, like training an AI to look after lab rats and keep them healthy, or even fruit flies to start off with? Presumably there are multiple textbooks on “How to Look After Lab Animals”, and we could design a new AI evaluation score based off problems in this space? Then work our way up via “How to Look After Zoo Animals”, before we start on the large sapient primates?
The client in “The Expert” is opinionated, unwilling to listen to the expert they’re hiring, and wants several nigh-impossible things. They clearly know nothing about the subject, and their communication style is hopeless. Yes it’s funny, but no it’s not relevant to alignment.
Humans, on the other hand, know a lot about what makes humans happy. We’ve all practiced it for most of our lifetime, attempting to make ourselves and our friends, relatives and loved ones happy, and attempting to avoid annoying random passers-by. Practical tips on this subject are the primary content of most magazines. We also have many entire fields of study devoted to it, many of them STEM: Medicine, (human) Biology, Psychology, Sociology, Anthropology, Economics, Ergonomics, Design, Political Science, Market Research, Art, Crafts, Massage,…: just about every soft science we have, plus every art and craft.. We already know an enormous amount about the subject, and have an equally enormous literature on it. A sufficiently smart AI (or set of AIs) capable of doing STEM research can take that entire literature, read and absorb all of it, and then start to build on and add to it. They will know where to find and how to talk to the humans familiar with the material they are building on, including ones who are aware that in order to be able have seven mutually perpendicular lines (in a flat space) you need to be in a space with seven or more dimensions (mathematicians or physicists: for curved surfaces things are easier, you can do 3 mutually perpendicular great circles on the 2d surface of a globe, and as many as 10-mutually perpendicular geodesics on the 4d surface of a 5-sphere), and artists and craftsmen who know all sorts of tricks for color-changing reactive ink.
Yes, human value are very large, complex, and fragile. However, your local university library contains millions of text books on them containing terrabytes of information on the subject, and the Internet has exabytes more. We and our AIs are not starting anywhere near square one: we have millennia of information on the subject. Yes, as the AIs add more information, we’ll need humans to keep up and become familiar with what they’re learning, so we can still talk to them. Which in the case of a FOOM might prove to be a bottleneck.
So no, the situation for Value Learning or implementing CEV isn’t anything like your humorous video.
What we know less about, is how to design or train AIs to make them understand how to make humans happy (absorb those many exabytes of information, as GPT-4 to some extent already does) and value doing so (convert the data into something that plans can be scored and adjusted based on, and then do so, again, as GPT-4 is already to some extent capable of with suitable prompting). Building us some more expertise there makes a lot of sense. Perhaps we should start with something rather simpler and lower stakes, like training an AI to look after lab rats and keep them healthy, or even fruit flies to start off with? Presumably there are multiple textbooks on “How to Look After Lab Animals”, and we could design a new AI evaluation score based off problems in this space? Then work our way up via “How to Look After Zoo Animals”, before we start on the large sapient primates?