People sometimes seem to act like unsolved problems are exasperating, aesthetically offensive, or somehow unappealing, so they have no choice but to roll up their sleeves and try to help fix them, because it's just so irritating to see the problem go unsolved. So one can do purely altruistic stuff, but with this selfish posture (which also shifts focus away from motivation and psychology) it won't trip the hypocrisy alarms. It may also genuinely be a better attitude to cultivate, if it helps deflate one's ego a little bit -- I'm not quite sure.
A lot of the AI risk arguments seem to come mixed together with assumptions about a particular type of utilitarianism, and with a very particular transhumanist aesthetic about the future (nanotech, von Neumann probes, Dyson spheres, tiling the universe with matter in fixed configurations, simulated minds, etc.).
I find these things (especially the transhumanist stuff) to not be very convincing relative to the confidence people seem to express about them, but they also don't seem to be essential to the problem of AI risk. Is there a minimal version of the AI risk arguments that are disentangled from these things?
most academic research work is done by grad students, and grad students need incremental, legible wins to put on their CV so they can prove they are capable of doing research. this has to happen pretty fast. an ML grad student who hasn't contributed to any top conference papers by their second or third year in grad school might get pulled aside for a talk about their future.
ideally you want a topic where you can go from zero to paper in less than a year, with multiple opportunities for followup work. get a few such projects going and you have a very strong chance of getting at least one through in time to not get managed out of your program -- and of course, usually more will succeed and you'll be doing great.
I don't think there's anything like this in AI safety research. Section 3.4 seems to acknowledge this a little bit. If you want AI safety to become more popular, you'd hope that an incoming PhD student could say "I want to work on AI Safety" and be confident that in a year or two, they'll have a finished research project that they can claim as a success and submit to a top venue. Otherwise, they are taking a pretty huge career risk, and most people won't take it.
“Does the disease heavily affect career-age people (age 25-65), or frequently leave survivors with lasting disability?”
This is rightly ticked off as “No”, but I think it morally counts as “Yes” if there is more danger to young children. That’s scarier in itself, and from COVID it seems people are also more likely to accept very extreme NPIs to protect children, meaning there might well be a large economic impact.
Historically, scientists would use anagrams to do this. Galileo famously said "Smaismrmilmepoetaleumibunenugttauiras". Later he revealed that it could be unscrambled into "Altissimum planetam tergeminum observavi" which per Wikipedia is Latin for "I have observed the most distant planet to have a triple form", establishing his priority in discovering the rings of Saturn.
Obviously hashing and salting is better, nowadays.
From my limited knowledge, that's definitely one of the purposes Ruism/Confucianism was put to -- especially once the civil service exams were instituted.
In one way, "philosophy of the establishment" seems mostly correct to me, as the Mengzi seemingly makes a core assumption that the current social order is legitimate. But it mostly isn't making excuses for that social order (as philosophy and social science often does), it's challenging rulers to live up to an ideal and serve the people. At one point, Mengzi says that any king who "mutilates benevolence" is a "mere fellow" who can be rightfully executed by the people.
And I don't know enough about history, but it seems like nearly every Chinese philosopher of any school -- even maybe Zhuangzi (??) -- was involved in some kind of government position. Maybe that's what every literate person did. So it's hard to draw a clear "establishment/anti-establishment" line.
Schizophrenia is the wrong metaphor here -- it's not the same disease as split personalities (i.e. dissociative identity disorder). I think it would be clearer and more accurate to rewrite that paragraph without it. I don't intend this as an attack or harsh criticism, it's just that I have decided to be a pedant about this point whenever I encounter it, as I think it would be good for the general public to develop a more accurate and realistic understanding of schizophrenia.
Rubin's framework says basically, suppose all our observations are in a big data table. Now consider the counterfactual observations that didn't happen (i.e. people in the control group getting the treatment) -- these are called "potential outcomes" -- treat those like missing cells in the data table. Then causal inference is just to fill in potential outcomes using missing data imputation techniques, although to be valid these require some assumptions about conditional independence.
Pearl's framework and Rubin's are isomorphic in the sense that any set of causal assumptions in Pearl's framework (a structural causal model, which has a DAG structure), can be translated into a set of causal assumptions in Rubin's framework (a bunch of conditional independence assumptions about potential outcomes), and vice versa. This is touched on somewhat in Ch. 7 of "Causality".
Pearl argues that despite this equivalence, his framework is superior because it's a better tool for thinking. In other words, writing down your assumptions as DAG/SCM is intuitive and can be explained and argued about, while he claims the Rubin model independence assumptions are opaque and hard to understand.
I will give a potted history of Pearl's discovery as I understand it.
In the late 70s/early 80s, people wanted to deal with uncertainty in logic-based AI. The obvious thing to use is probability, but doing a Bayesian update to compute a posterior is exponentially expensive.
Pearl wanted to come up with a good data structure for doing computations over probability distributions in less-than-exponential time.
He introduced the idea of Bayesian networks in his paper Reverend Bayes On Inference Engines where he represents factorized probability distributions using DAGs. Here, the direction of the arrows is arbitrary and there are many DAGs corresponding to one probability distribution.
He was not thinking about causality at all, it was just a problem in data structures. The idea was this would be used for the same sort of thing as an "expert system" or other logic based AI systems, but taking into account uncertainty expressed probabilistically.
Later, people including Pearl noticed that you can and often should interpret the arrows as causal, this amounts to choosing one DAG from many. The fact that there are many possible DAGs is related to the fact that there are seemingly always multiple incompatible causal stories, to explain observations absent making additional assumptions about the world. But if you pick one, you can start using it to see whether your causal question can be answered from observational data alone.
Finally, he realized that the assumptions encoded in a DAG aren't sufficient for fully general counterfactuals, and realized that in full generality you have to specify exactly what functional relationship goes along each edge of the graph.
As someone originally concerned with AI, not with problems in the natural sciences, Pearl is probably unusual. Pearl himself looks back on Sewall Wright as his progenitor for coming up with path diagrams -- he was working in genetics. If you are interested in this, you should also look at Don Rubin's experience -- his causal framework is isomorphic to Pearl's. He was a 100 percent classic statistician, motivated by looking at medical studies.