if you get into fashion there is a whole range of expression with suits. with the right cut and materials, you can wear a suit, which looks great as suits ought to, yet is clearly casual and even in Japan would never be perceived as “for work”. expensive hobby but if you’re already doing this, might as well get into it.
Anonymous
Karma: 197
a quite widespread experience right now among normal people, is having their boss tell them to use AI tools in stupid ways that don’t currently work, and then being somewhat held responsible for the failures. (For example: your boss heard about a study saying AI increased productivity by 40% among one group of consultants, so he’s buying you a ChatGPT Plus subscription and increasing all your KPI targets by 40%.)
on the one hand this produces very strong anti-AI sentiment. people are just sick of it. if “Office Space” were made now, Bill Lumbergh would be talking about “AI transformation” and “agents” all the time. that’s politically useful if you’re advocating about x-risk.
on the other hand, it means if you are talking about how AI capabilities are growing fast, this gets an instant negative reaction because you sound like their delusional boss. At the same time they are worried about AI taking their jobs as it gets better.
This isn’t a very internally consistent set of beliefs, but I could summarize what I’ve heard as something like this:
“AI doesn’t really work, it’s all a big scam, but it gives the appearance of working well enough that corporations will use it as an excuse to cut costs, lay people off, and lower quality to increase their profits. The economy is a rigged game anyway, and the same people that own the corporations are all invested in AI, so it won’t be allowed to fail, we will just live in a world of slop.”
I don’t think this is a sufficiently complete way of looking at things. It could make sense when the problem was thought to be “replication crisis via p-hacking” but it turns out things are worse than this.
The research methodology in biology doesn’t necessarily have room for statistical funny business but there are all these cases of influential Science/Nature papers that had fraud via photoshop.
Gino and Ariely’s papers might have been statistically impeccable, the problem is they were just making up data points.
there is fraud in experimental physics and applied sciences too from time to time.
I don’t know much about what opportunities there are for bad research practices in the humanities. The only thing I can think of is citing a source that doesn’t say what is claimed. This seems like a particular risk when history or historical claims are involved, or when a humanist wants to refer to the scientific literature. The spectacular claim that Victorian doctors treated “hysteria” using vibrators turns out to have resulted from something like this.
Outside cases like that, I think the humanities are mostly “safe” like math in that they just need some kind of internal consistency, whether that is presenting a sound argument, or a set of concepts and descriptions that people find to be harmonious or fruitful.
I think the biggest difference is this will mean more people with a wider range of personality types, socially interacting in a more arms-length/professionalized way, according to the social norms of academia.
Especially in CS, you can be accepted among academics as a legitimate researcher even without a formal degree, but it would require being able and willing to follow these existing social norms.
And in order to welcome and integrate new AI safety researchers from academia, the existing AI safety scene would have to make some spaces to facilitate this style of interaction, rather than the existing informal/intense/low-social-distance style.
This community is doing way better than it has any right to for a bunch of contrarian weirdos with below-average social skills. It’s actually astounding.
The US government and broader military-industrial complex is taking existential AI risk somewhat seriously. The head of the RAND Corporation is an existential risk guy who used to work for FHI.
Apparently the Prime Minister of the UK and various European institutions are concerned as well.
There are x-risk-concerned people at most top universities for AI research and within many of the top commercial labs.
In my experience “normies” are mostly open to simple, robust arguments that AI could be very dangerous if sufficiently capable, so I think the outreach has been sufficiently good on that front.
There is a much more specific set of arguments about advanced AI (exotic decision theories, theories of agency and preferences, computationalism about consciousness) that are harder to explain and defend than the basic AI risk case, so would rhetorically weaken it. But people who like them get very excited about them. Thus I think having a lot more popular materials by LessWrong-ish people would do more harm than good, so it was a good move whether intentional or not to avoid this. (On the other hand if you think these ideas are absolutely crucial considerations without which sensible discussion is impossible, then it is not good.)
This is the case for me as well, and I don’t remember when it developed. I have a timeline that starts with the present day on the right, and goes left and slightly up. It gets blurry around 500 BC. I can somewhat zoom in and recenter it if I’m thinking about individual historical periods. I can roughly place some historical events in the correct spots on the timeline, but since I have never needed to formally memorize many historical dates, this is very rough.
You might be interested in reading about experiences in the broad category of synesthesia, and of the really fascinating history of “memory palace” techniques. Also in the linguistic details of how different languages spatially talk about the past and future (e.g. in English the past is behind/future is ahead; in Chinese, past is above/future is below).
Normal, standard causal decision theory is probably it. You can make a case that people sometimes intuitively use evidential decision theory (“Do it. You’ll be glad you did.”) but if asked to spell out their decision making process, most would probably describe causal decision theory.
Fandom people on Tumblr, AO3, etc. really responded to The Last Jedi (because it was targeted to them). Huge phenomenon. There are now bestselling romance novels that started life as TLJ fanfiction. Everything worked just like it does for the Marvel movies, very profitably.
However there was an additional group of Star Wars superfans outside of fandom, who wanted something very different, hence the backlash. This group is somewhat more male and conservative, and then everything polarized on social media so this somehow became a real culture war issue. Of course, Disney did not like the backlash, and tried to make the 3rd movie more palatable to this group.
That kind of fan doesn’t organically exist for most things outside of Star Wars though. For most things, you only get superfans in this network of fan communities which skew towards social justice. And for any new genre story without a pre-existing fanbase, there’s an opportunity to get fandom people excited about it, which is very valuable.
As far as running a media company goes, fandom is extremely profitable, increasingly so in an age where enormous sci-fi/fantasy franchises drive everything. And there’s been huge overlap between fandom communities and social justice politics for a long time.
It’s definitely in Disney’s interest to appeal to Marvel superfans who write fanfiction and cosplay and buy tons of merchandise, and those people tend to also be supporters of social justice politics.
Like, nothing is being forced on this audience—there are large numbers of people who get sincerely excited when a new character is introduced that gives representation for the first time to a new minority group, or something like that.
As with so many businesses, the superfans are worth quite a few normies who might be put off by this. I think this is the main explanation.
The “canonical” rankings that CS academics care about would be csrankings.org (also not without problems but the least bad).
The KataGo paper says of its training, “Self-play games used Tromp-Taylor rules modified to not require capturing stones within pass-alive territory”.
It sounds to me like this is the same scoring system as used in the adversarial attack paper, but I don’t know enough about Go to be sure.
The Sprawl trilogy by William Gibson (starting with Neuromancer) is basically about this, and is a classic for a reason. It’s not exactly hard sci-fi though.
If you don’t signal the expected way then you are, if not being dishonest, at least misleading people — in many cases it is less honest.
Everyone knows your job application is written to puff you up, and they price it in. If you don’t have the correct amount of puffery, you’re misleading people into thinking you’re worse than you are.
It’s a bad way to communicate and a bad race-to-the-bottom equilibrium but not actually dishonest.
You can write “Dear X” on a letter to a person you don’t know. People used to sign off letters “Your obedient servant”. It evolves for weird signaling reasons but is not taken literally.
“Systems that would adapt their policy if their actions would influence the world in a different way”
Does the teacup pass this test? It doesn’t necessarily seem like it.
We might want to model the system as “Heat bath of Air → teacup → Socrates’ tea”. The teacup “listens to” the temperature of the air on its outside, and according to some equation transmits some heat to the inside. In turn the tea listens to this transmitted heat and determines its temperature.
You can consider the counterfactual world where the air is cold instead of hot. Or the counterfactual world where you replace “Socrates’ tea” with “Meletus’ tea”, or with a frog that will jump out of the cup, or whatever. But in all cases the teacup does not actually change its “policy”, which is just to transmit heat to the inside of the cup according to the laws of physics.
To put it in the terminology of “Discovering Agents”, one can add mechanism variables going into the object level variables. But there are no arrows between these, so there’s no agent.
Of course, my model here is bad and wrong physically speaking, even if it does capture crude cause-effect intuition about the effect of air temperature on beverages. However I’d be somewhat surprised if a more physically correct model would introduce an agent to the system where there is none.
There are industry places that will, at least as stated, take you seriously with no PhD as long as you have some publications (many job postings don’t require a PhD or say “or equivalent research experience”), and it’s unusual but not unheard of for people do this.
The thing is, a PhD program is a reliable way to build a research track record. And you don’t see too many PhD dropouts who want to be scientists because if you’ve got a research track record, the extra cost of just finishing your dissertation and graduating is pretty low.
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.
there is a semi-centralized job market for the market for hiring new economics PhD graduates. they allow you to send up to two “signals” to certain job postings. these have no semantic content, but because they are limited, they are taken to indicate serious interest. interesting test case.