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[meta] Future moderation and investigation of downvote abuse cases, or, I don't want to deal with this stuff
Since the episode with Eugine_Nier, I have received three private messages from different people asking me to investigate various cases of suspected mass downvoting. And to be quite honest, I don't want to deal with this. Eugine's case was relatively clear-cut, since he had engaged in systematic downvoting of a massive scale, but the new situations are a lot fuzzier and I'm not sure of what exactly the rules should be (what counts as a permitted use of the downvote system and what doesn't?).
At least one person has also privately contacted me and offered to carry out moderator duties if I don't want them, but even if I told them yes (on what basis? why them and not someone else?), I don't know what kind of policy I should tell them to enforce. I only happened to be appointed a moderator because I was in the list of top 10 posters at a particular time, and I don't feel like I should have any particular authority to make the rules. Nor do I feel like I have any good idea of what the rules should be, or who would be the right person to enforce them.
In any case, I don't want to be doing this job, nor do I particularly feel like being responsible for figuring out who should, or how, or what the heck. I've already started visiting LW less often because I dread having new investigation requests to deal with. So if you folks could be so kind as to figure it out without my involvement? If there's a clear consensus that someone in particular should deal with this, I can give them mod powers, or something.
All of these things I mentioned in the most recent open thread, but since the first one is directly relevant and the comment where I posted it somewhat hard to come across, I figured I'd make a post too.
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NOTE FOR FIREFOX USERS: this contained a bug which has been squashed, causing the list of comments not to be automatically populated (depending on your version of Firefox). I suggest reinstalling. Sorry, no automatic updates unless you use the Chrome extension (though with >50% probability there will be no further updates).
You know how the highlight for new comments on Less Wrong threads disappears if you reload the page, making it difficult to find those comments again? Here is a userscript you can install to fix that (provided you're on Firefox or Chrome). Once installed, you can set the date after which comments are highlighted, and easily scroll to new comments. See screenshots. Installation is straightforward (especially for Chrome, since I made an extension as well).
Bonus: works even if you're logged out or don't have an account, though you'll have to set the highlight time manually.
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From Toby Ord:
Tool assisted speedruns (TAS) are when people take a game and play it frame by frame, effectively providing super reflexes and forethought, where they can spend a day deciding what to do in the next 1/60th of a second if they wish. There are some very extreme examples of this, showing what can be done if you really play a game perfectly. For example, this video shows how to winSuper Mario Bros 3 in 11 minutes. It shows how different optimal play can be from normal play. In particular, on level 8-1, it gains 90 extra lives by a sequence of amazing jumps.
Other TAS runs get more involved and start exploiting subtle glitches in the game. For example, this page talks about speed running NetHack, using a lot of normal tricks, as well as luck manipulation (exploiting the RNG) and exploiting a dangling pointer bug to rewrite parts of memory.
Though there are limits to what AIs could do with sheer speed, it's interesting that great performance can be achieved with speed alone, that this allows different strategies from usual ones, and that it allows the exploitation of otherwise unexploitable glitches and bugs in the setup.
In this post, I list six metaethical possibilities that I think are plausible, along with some arguments or plausible stories about how/why they might be true, where that's not obvious. A lot of people seem fairly certain in their metaethical views, but I'm not and I want to convey my uncertainty as well as some of the reasons for it.
- Most intelligent beings in the multiverse share similar preferences. This came about because there are facts about what preferences one should have, just like there exist facts about what decision theory one should use or what prior one should have, and species that manage to build intergalactic civilizations (or the equivalent in other universes) tend to discover all of these facts. There are occasional paperclip maximizers that arise, but they are a relatively minor presence or tend to be taken over by more sophisticated minds.
- Facts about what everyone should value exist, and most intelligent beings have a part of their mind that can discover moral facts and find them motivating, but those parts don't have full control over their actions. These beings eventually build or become rational agents with values that represent compromises between different parts of their minds, so most intelligent beings end up having shared moral values along with idiosyncratic values.
- There aren't facts about what everyone should value, but there are facts about how to translate non-preferences (e.g., emotions, drives, fuzzy moral intuitions, circular preferences, non-consequentialist values, etc.) into preferences. These facts may include, for example, what is the right way to deal with ontological crises. The existence of such facts seems plausible because if there were facts about what is rational (which seems likely) but no facts about how to become rational, that would seem like a strange state of affairs.
- None of the above facts exist, so the only way to become or build a rational agent is to just think about what preferences you want your future self or your agent to hold, until you make up your mind in some way that depends on your psychology. But at least this process of reflection is convergent at the individual level so each person can reasonably call the preferences that they endorse after reaching reflective equilibrium their morality or real values.
- None of the above facts exist, and reflecting on what one wants turns out to be a divergent process (e.g., it's highly sensitive to initial conditions, like whether or not you drank a cup of coffee before you started, or to the order in which you happen to encounter philosophical arguments). There are still facts about rationality, so at least agents that are already rational can call their utility functions (or the equivalent of utility functions in whatever decision theory ends up being the right one) their real values.
- There aren't any normative facts at all, including facts about what is rational. For example, it turns out there is no one decision theory that does better than every other decision theory in every situation, and there is no obvious or widely-agreed-upon way to determine which one "wins" overall.
(Note that for the purposes of this post, I'm concentrating on morality in the axiological sense (what one should value) rather than in the sense of cooperation and compromise. So alternative 1, for example, is not intended to include the possibility that most intelligent beings end up merging their preferences through some kind of grand acausal bargain.)
It may be useful to classify these possibilities using labels from academic philosophy. Here's my attempt: 1. realist + internalist 2. realist + externalist 3. relativist 4. subjectivist 5. moral anti-realist 6. normative anti-realist. (A lot of debates in metaethics concern the meaning of ordinary moral language, for example whether they refer to facts or merely express attitudes. I mostly ignore such debates in the above list, because it's not clear what implications they have for the questions that I care about.)
One question LWers may have is, where does Eliezer's metathics fall into this schema? Eliezer says that there are moral facts about what values every intelligence in the multiverse should have, but only humans are likely to discover these facts and be motivated by them. To me, Eliezer's use of language is counterintuitive, and since it seems plausible that there are facts about what everyone should value (or how each person should translate their non-preferences into preferences) that most intelligent beings can discover and be at least somewhat motivated by, I'm reserving the phrase "moral facts" for these. In my language, I think 3 or maybe 4 is probably closest to Eliezer's position.
TL;DR: Building humor into your habits for spotting and correcting errors makes the fix more enjoyable, easier to talk about and receive social support, and limits the danger of a contempt spiral.
One of the most reliably bad decisions I've made on a regular basis is the choice to stay awake (well, "awake") and on the internet past the point where I can get work done, or even have much fun. I went through a spell where I even fell asleep on the couch more nights than not, unable to muster the will or judgement to get up and go downstairs to bed.
I could remember (even sometimes in the moment) that this was a bad pattern, but, the more tired I was, the more tempting it was to think that I should just buckle down and apply more willpower to be more awake and get more out of my computer time. Going to bed was a solution, but it was hard for it not to feel (to my sleepy brain and my normal one) like a bit of a cop out.
Only two things helped me really keep this failure mode in check. One was setting a hard bedtime (and beeminding it) as part of my sacrifice for Advent. But the other key tool (which has lasted me long past Advent) is the gif below.
The poor kid struggling to eat his ice cream cone, even in the face of his exhaustion, is hilarious. And not too far off the portrait of me around 2am scrolling through my Feedly.
Thinking about how stupid or ineffective or insufficiently strong-willed I'm being makes it hard for me to do anything that feels like a retreat from my current course of action. I want to master the situation and prove I'm stronger. But catching on to the fact that my current situation (of my own making or not) is ridiculous, makes it easier to laugh, shrug, and move on.
I think the difference is that it's easy for me to feel contemptuous of myself when frustrated, and easy to feel fond when amused.
I've tried to strike the new emotional tone when I'm working on catching and correcting other errors. (e.g "Stupid, you should have known to leave more time to make the appointment! Planning fallacy!" becomes "Heh, I guess you thought that adding two "trivially short" errands was a closed set, and must remain 'trivially short.' That's a pretty silly error.")
In the first case, noticing and correcting an error feels punitive, since it's quickly followed by a hefty dose of flagellation, but the second comes with a quick laugh and a easier shift to a growth mindset framing. Funny stories about errors are also easier to tell, increasing the chance my friends can help catch me out next time, or that I'll be better at spotting the error just by keeping it fresh in my memory. Not to mention, in order to get the joke, I tend to look for a more specific cause of the error than stupid/lazy/etc.
As far as I can tell, it also helps that amusement is a pretty different feeling than the ones that tend to be active when I'm falling into error (frustration, anger, feeling trapped, impatience, etc). So, for a couple of seconds at least, I'm out of the rut and now need to actively return to it to stay stuck.
In the heat of the moment of anger/akrasia/etc is a bad time to figure out what's funny, but, if you're reflecting on your errors after the fact, in a moment of consolation, it's easier to go back armed with a helpful reframing, ready to cast Riddikulus!
If you are a gay male then you’ve probably worried at one point about sexually transmitted diseases. Indeed men who have sex with men have some of the highest prevalence of many of these diseases. And if you’re not a gay male, you’ve probably still thought about STDs at one point. But how much should you worry? There are many organizations and resources that will tell you to wear a condom, but very few will tell you the relative risks of wearing a condom vs not. I’d like to provide a concise summary of the risks associated with gay male sex and the extent to which these risks can be reduced. (See Mark Manson’s guide for a similar resources for heterosexual sex.). I will do so by first giving some information about each disease, including its prevalence among gay men. Most of this data will come from the US, but the US actually has an unusually high prevalence for many diseases. Certainly HIV is much less common in many parts of Europe. I will end with a case study of HIV, which will include an analysis of the probabilities of transmission broken down by the nature of sex act and a discussion of risk reduction techniques.
When dealing with risks associated with sex, there are few relevant parameters. The most common is the prevalence – the proportion of people in the population that have the disease. Since you can only get a disease from someone who has it, the prevalence is arguably the most important statistic. There are two more relevant statistics – the per act infectivity (the chance of contracting the disease after having sex once) and the per partner infectivity (the chance of contracting the disease after having sex with one partner for the duration of the relationship). As it turns out the latter two probabilities are very difficult to calculate. I only obtained those values for for HIV. It is especially difficult to determine per act risks for specific types of sex acts since many MSM engage in a variety of acts with multiple partners. Nevertheless estimates do exist and will explored in detail in the HIV case study section.
Prevalence: Between 13 - 28%. My guess is about 13%.
The most infamous of the STDs. There is no cure but it can be managed with anti-retroviral therapy. A commonly reported statistic is that 19% of MSM (men who have sex with men) in the US are HIV positive (1). For black MSM, this number was 28% and for white MSM this number was 16%. This is likely an overestimate, however, since the sample used was gay men who frequent bars and clubs. My estimate of 13% comes from CDC's total HIV prevalence in gay men of 590,000 (2) and their data suggesting that MSM comprise 2.9% of men in the US (3).
Prevalence: Between 9% and 15% in the US
This disease affects the throat and the genitals but it is treatable with antibiotics. The CDC estimates 15.5% prevalence (4). However, this is likely an overestimate since the sample used was gay men in health clinics. Another sample (in San Francisco health clinics) had a pharyngeal gonorrhea prevalence of 9% (5).
Prevalence: 0.825% in the US
My estimate was calculated in the same manner as my estimate for HIV. I used the CDC's data (6). Syphilis is transmittable by oral and anal sex (7) and causes genital sores that may look harmless at first (8). Syphilis is curable with penicillin however the presence of sores increases the infectivity of HIV.
Herpes (HSV-1 and HSV-2)
Prevalence: HSV-2 - 18.4% (9); HSV-1 - ~75% based on Australian data (10)
This disease is mostly asymptomatic and can be transmitted through oral or anal sex. Sometimes sores will appear and they will usually go away with time. For the same reason as syphilis, herpes can increase the chance of transmitting HIV. The estimate for HSV-1 is probably too high. Snowball sampling was used and most of the men recruited were heavily involved in organizations for gay men and were sexually active in the past 6 months. Also half of them reported unprotected anal sex in the past six months. The HSV-2 sample came from a random sample of US households (11).
Prevalence: Rectal - 0.5% - 2.3% ; Pharyngeal - 3.0 - 10.5% (12)
Like herpes, it is often asymptomatic - perhaps as low as 10% of infected men report symptoms. It is curable with antibiotics.
Prevalence: 47.2% (13)
This disease is incurable (though a vaccine exists for men and women) but usually asymptomatic. It is capable of causing cancers of the penis, throat and anus. Oddly there are no common tests for HPV in part because there are many strains (over 100) most of which are relatively harmless. Sometimes it goes away on its own (14). The prevalence rate was oddly difficult to find, the number I cited came from a sample of men from Brazil, Mexico and the US.
Case Study of HIV transmission; risks and strategies for reducing risk
IMPORTANT: None of the following figures should be generalized to other diseases. Many of these numbers are not even the same order of magnitude as the numbers for other diseases. For example, HIV is especially difficult to transmit via oral sex, but Herpes can very easily be transmitted.
Unprotected Oral Sex per-act risk (with a positive partner or partner of unknown serostatus):
Non-zero but very small. Best guess .03% without condom (15)
Unprotected Anal sex per-act risk (with positive partner):
Receptive: 0.82% - 1.4% (16) (17)
Insertive Circumcised: 0.11% (18)
Insertive Uncircumcised: 0.62% (18)
Protected Anal sex per-act risk (with positive partner):
Estimates range from 2 times lower to twenty times lower (16) (19) and the risk is highly dependent on the slippage and breakage rate.
Contracting HIV from oral sex is very rare. In one study, 67 men reported performing oral sex on at least one HIV positive partner and none were infected (20). However, transmission is possible (15). Because instances of oral transmission of HIV are so rare, the risk is hard to calculate so should be taken with a grain of salt. The number cited was obtained from a group of individuals that were either HIV positive or high risk for HIV. The per act-risk with a positive partner is therefore probably somewhat higher.
Note that different HIV positive men have different levels of infectivity hence the wide range of values for per-act probability of transmission. Some men with high viral loads (the amount of HIV in the blood) may have an infectivity of greater than 10% per unprotected anal sex act (17).
Risk reducing strategies
Choosing sex acts that have a lower transmission rate (oral sex, protected insertive anal sex, non-insertive) is one way to reduce risk. Monogamy, testing, antiretroviral therapy, PEP and PrEP are five other ways.
Testing Your partner/ Monogamy
If your partner tests negative then they are very unlikely to have HIV. There is a 0.047% chance of being HIV positive if they tested negative using a blood test and a 0.29% chance of being HIV positive if they tested negative using an oral test. If they did further tests then the chance is even lower. (See the section after the next paragraph for how these numbers were calculated).
So if your partner tests negative, the real danger is not the test giving an incorrect result. The danger is that your partner was exposed to HIV before the test, but his body had not started to make antibodies yet. Since this can take weeks or months, it is possible for your partner who tested negative to still have HIV even if you are both completely monogamous.
For tests, the sensitivity - the probability that an HIV positive person will test positive - is 99.68% for blood tests (21), 98.03% with oral tests. The specificity - the probability that an HIV negative person will test negative - is 99.74% for oral tests and 99.91% for blood tests. Hence the probability that a person who tested negative will actually be positive is:
P(Positive | tested negative) = P(Positive)*(1-sensitivity)/(P(Negative)*specificity + P(Positive)*(1-sensitivity)) = 0.047% for blood test, 0.29% for oral test
Where P(Positive) = Prevalence of HIV, I estimated this to be 13%.
However, according to a writer for About.com (22) - a doctor who works with HIV - there are often multiple tests which drive the sensitivity up to 99.997%.
Oraquick is an HIV test that you can purchase online and do yourself at home. It costs $39.99 for one kit. The sensitivity is 93.64%, the specificity is 99.87% (23). The probability that someone who tested negative will actually be HIV positive is 0.94%. - assuming a 13% prevalence for HIV. The same danger mentioned above applies - if the infection occurred recently the test would not detect it.
Highly active anti-retroviral therapy (HAART), when successful, can reduce the viral load – the amount of HIV in the blood - to low or undetectable levels. Baggaley et. al (17) reports that in heterosexual couples, there have been some models relating viral load to infectivity. She applies these models to MSM and reports that the per-act risk for unprotected anal sex with a positive partner should be 0.061%. However, she notes that different models produce very different results thus this number should be taken with a grain of salt.
Post-Exposure Prophylaxis (PEP)
A last resort if you think you were exposed to HIV is to undergo post-exposure prophylaxis within 72 hours. Antiretroviral drugs are taken for about a month in the hopes of preventing the HIV from infecting any cells. In one case controlled study some health care workers who were exposed to HIV were given PEP and some were not, (this was not under the control of the experimenters). Workers that contracted HIV were less likely to have been given PEP with an odds ratio of 0.19 (24). I don’t know whether PEP is equally effective at mitigating risk from other sources of exposure.
Pre-Exposure Prophylaxis (PrEP)
This is a relatively new risk reduction strategy. Instead of taking anti-retroviral drugs after exposure, you take anti-retroviral drugs every day in order to prevent HIV infection. I could not find a per-act risk, but in a randomized controlled trial, MSM who took PrEP were less likely to become infected with HIV than men who did not (relative reduction - 41%). The average number of sex partners was 18. For men who were more consistent and had a 90% adherence rate, the relative reduction was better - 73%. (25) (26).
About 18 months ago I made a post here on my workflow. I've received a handful of requests for follow-up, so I thought I would make another post detailing changes since then. I expect this post to be less useful than the last one.
For the most part, the overall outline has remained pretty stable and feels very similar to 18 months ago. Things not mentioned below have mostly stayed the same. I believe that the total effect of continued changes have been continued but much smaller improvements, though it is hard to tell (as opposed to the last changes, which were more clearly improvements).
Based on comparing time logging records I seem to now do substantially more work on average, but there are many other changes during this period that could explain the change (including changes in time logging). Changes other than work output are much harder to measure; I feel like they are positive but I wouldn't be surprised if this were an illusion.
I now regularly divide my day into two halves, and treat the two halves as separate units. I plan each separately and reflect on each separately. I divide them by an hour long period of reflecting on the morning, relaxing for 5-10 minutes, napping for 25-30 minutes, processing my emails, and planning the evening. I find that this generally makes me more productive and happier about the day. Splitting my days is often difficult due to engagements in the middle of the day, and I don't have a good solution to that.
I have longstanding objections to explicitly rationing internet use (since it seems either indicative of a broader problem that should be resolved directly, or else to serve a useful function that would be unwise to remove). That said, I now use the extension WasteNoTime to limit my consumption of blogs, webcomics, facebook, news sites, browser games, etc., to 10 minutes each half-day. This has cut the amount of time I spend browsing the internet from an average of 30-40 minutes to an average of 10-15 minutes. It doesn't seem to have been replaced by lower-quality leisure, but by a combination of work and higher-quality leisure.
Similarly, I turned off the newsfeed in facebook, which I found to improve the quality of my internet time in general (the primary issue was that I would sometimes be distracted by the newsfeed while sending messages over facebook, which wasn't my favorite way to use up wastenotime minutes).
I also tried StayFocusd, but ended up adopting WasteNoTime because of the ability to set limits per half-day (via "At work" and "not at work" timers) rather than per-day. I find that the main upside is cutting off the tail of derping (e.g. getting sucked into a blog comment thread, or looking into a particularly engrossing issue), and for this purpose per half-day timers are much more effective.
I set gmail to archive all emails on arrival and assign them the special label "In." This lets me to search for emails and compose emails, using the normal gmail interface, without being notified of new arrivals. I process the items with label "in" (typically turning emails into todo items to be processed by the same system that deals with other todo items) at the beginning of each half day. Each night I scan my email quickly for items that require urgent attention.
Todo lists / reminders:
I continue to use todo lists for each half day and for a range of special conditions. I now check these lists at the beginning of each half day rather than before going to bed.
I also maintain a third list of "reminders." These are things that I want to be reminded of periodically, organized by day; each morning I look at the day's reminders and think about them briefly. Each of them is copied and filed under a future day. If I feel like I remember a thing well I file it in far in the future, if I feel like I don't remember it well I file it in the near future.
Over the last month most of these reminders have migrated to be in the form "If X, then Y," e.g. "If I agree to do something for someone, then pause, say `actually I should think about it for a few minutes to make sure I have time,' and set a 5 minute timer that night to think about it more clearly." These are designed to fix problems that I notice when reflecting on the day. This is a recommendation from CFAR folks, which seems to be working well, though is the newest part of the system and least tested.
I now attempt to isolate things that probably need doing, but don't seem maximally important; I aim to do them only on every 5th day, and only during one half-day. If I can't finish them in this time, I will typically delay them 5 days. When they spill over to other days, I try to at least keep them to one half-day or the other. I don't know if this helps, but it feels better to have isolated unproductive-feeling blocks of time rather than scattering it throughout the week.
I don't do this very rigidly. I expect the overall level of discipline I have about it is comparable to or lower than a normal office worker who has a clearer division between their personal time and work time.
I now use Toggl for detailed time tracking. Katja Grace and I experimented with about half a dozen other systems (Harvest, Yast, Klok, Freckle, Lumina, I expect others I'm forgetting) before settling on Toggl. It has a depressing number of flaws, but ends up winning for me by making it very fast to start and switch timers which is probably the most important criterion for me. It also offers reviews that work out well with what I want to look at.
I find the main value adds from detailed time tracking are:
1. Knowing how long I've spent on projects, especially long-term projects. My intuitive estimates are often off by more than a factor of 2, even for things taking 80 hours; this can lead me to significantly underestimate the costs of taking on some kinds of projects, and it can also lead me to think an activity is unproductive instead of productive by overestimating how long I've actually spent on it.
2. Accurate breakdowns of time in a day, which guide efforts at improving my day-to-day routine. They probably also make me feel more motivated about working, and improve focus during work.
Reflection / improvement:
Reflection is now a smaller fraction of my time, down from 10% to 3-5%, based on diminishing returns to finding stuff to improve. Another 3-5% is now redirected into longer-term projects to improve particular aspects of my life (I maintain a list of possible improvements, roughly sorted by goodness). Examples: buying new furniture, improvements to my diet (Holden's powersmoothie is great), improvements to my sleep (low doses of melatonin seem good). At the moment the list of possible improvements is long enough that adding to the list is less valuable than doing things on the list.
I have equivocated a lot about how much of my time should go into this sort of thing. My best guess is the number should be higher.
I don't use pomodoros at all any more. I still have periods of uninterrupted work, often of comparable length, for individual tasks. This change wasn't extremely carefully considered, it mostly just happened. I find explicit time logging (such that I must consciously change the timer before changing tasks) seems to work as a substitute in many cases. I also maintain the habit of writing down candidate distractions and then attending to them later (if at all).
For larger tasks I find that I often prefer longer blocks of unrestricted working time. I continue to use Alinof timer to manage these blocks of uninterrupted work.
Catch disappeared, and I haven't found a replacement that I find comparably useful. (It's also not that high on the list of priorities.) I now just send emails to myself, but I do it much less often.
I no longer use beeminder. This again wasn't super-considered, though it was based on a very rough impression of overhead being larger than the short-term gains. I think beeminder was helpful for setting up a number of habits which have persisted (especially with respect to daily routine and regular focused work), and my long-term averages continue to satisfy my old beeminder goals.
I now organize notes about each project I am working on in a more standardized way, with "Queue of todos," "Current workspace," and "Data" as the three subsections. I'm not thrilled by this system, but it seems to be an improvement over the previous informal arrangement. In particular, having a workspace into which I can easily write thoughts without thinking about where they fit, and only later sorting them into the data section once it's clearer how they fit in, decreases the activation energy of using the system. I now use Toggl rather than maintaining time logs by hand.
As described in my last post I tried various randomized trials (esp. of effects of exercise, stimulant use, and sleep on mood, cognitive performance, and productive time). I have found extracting meaningful data from these trials to be extremely difficult, due to straightforward issues with signal vs. noise. There are a number of tests which I still do expect to yield meaningful data, but I've increased my estimates for the expensiveness of useful tests substantially, and they've tended to fall down the priority list. For some things I've just decided to do them without the data, since my best guess is positive in expectation and the data is too expensive to acquire.
The Effective Altruism Forum will be launched at effective-altruism.com on September 10, British time.
Now seems like a good time time to discuss why we might need an Effective Altruism Forum, and how it might compare to LessWrong.
About the Effective Altruism Forum
The motivation for the Effective Altruism Forum is to improve the quality of effective altruist discussion and coordination. A big part of this is to give many of the useful features of LessWrong to effective altruists, including:
- Archived, searchable content (this will begin with archived content from effective-altruism.com)
- Nested comments
- A karma system
- A dynamically upated list of external effective altruist blogs
- Introductory materials (this will begin with these articles)
The Effective Altruism Forum has been designed by Mihai Badic. Over the last month, it has been developed by Trike Apps, who have built the new site using the LessWrong codebase. I'm glad to report that it is now basically ready, looks nice, and is easy to use.
I expect that at the new forum, as on the effective altruist Facebook and Reddit pages, people will want to discuss the which intellectual procedures to use to pick effective actions. I also expect some proposals of effective altruist projects, and offers of resources. So users of the new forum will share LessWrong's interest in instrumental and epistemic rationality. On the other hand, I expect that few of its users will want to discuss the technical aspects of artificial intelligence, anthropics or decision theory, and to the extent that they do so, they will want to do it at LessWrong. As a result, I expect the new forum to cause:
- A bunch of materials on effective altruism and instrumental rationality to be collated for new effective altruists
- Discussion of old LessWrong materials to resurface
- A slight increase to the number of users of LessWrong, possibly offset by some users spending more of their time posting at the new forum.
At least initially, the new forum won't have a wiki or a Main/Discussion split and won't have any institutional affiliations.
It's really important to make sure that the Effective Altruism Forum is established with a beneficial culture. If people want to help that process by writing some seed materials, to be posted around the time of the site's launch, then they can contact me at ry [dot] duff [at] gmail.com. Alternatively, they can wait a short while until they automatically receive posting priveleges.
It's also important that the Effective Altruism Forum helps the shared goals of rationalists and effective altruists, and has net positive effects on LessWrong in particular. Any suggestions for improving the odds of success for the effective altruism forum are most welcome.
This post doesn't contain any new ideas that LWers don't already know. It's more of an attempt to organize my thoughts and have a writeup for future reference.
Here's a great quote from Sam Hughes, giving some examples of good and bad advice:
"You and your gaggle of girlfriends had a saying at university," he tells her. "'Drink through it'. Breakups, hangovers, finals. I have never encountered a shorter, worse, more densely bad piece of advice." Next he goes into their bedroom for a moment. He returns with four running shoes. "You did the right thing by waiting for me. Probably the first right thing you've done in the last twenty-four hours. I subscribe, as you know, to a different mantra. So we're going to run."
The typical advice given to young people who want to succeed in highly competitive areas, like sports, writing, music, or making video games, is to "follow your dreams". I think that advice is up there with "drink through it" in terms of sheer destructive potential. If it was replaced with "don't bother following your dreams" every time it was uttered, the world might become a happier place.
The amazing thing about "follow your dreams" is that thinking about it uncovers a sort of perfect storm of biases. It's fractally wrong, like PHP, where the big picture is wrong and every small piece is also wrong in its own unique way.
The big culprit is, of course, optimism bias due to perceived control. I will succeed because I'm me, the special person at the center of my experience. That's the same bias that leads us to overestimate our chances of finishing the thesis on time, or having a successful marriage, or any number of other things. Thankfully, we have a really good debiasing technique for this particular bias, known as reference class forecasting, or inside vs outside view. What if your friend Bob was a slightly better guitar player than you? Would you bet a lot of money on Bob making it big like Jimi Hendrix? The question is laughable, but then so is betting the years of your own life, with a smaller chance of success than Bob.
That still leaves many questions unanswered, though. Why do people offer such advice in the first place, why do other people follow it, and what can be done about it?
Survivorship bias is one big reason we constantly hear successful people telling us to "follow our dreams". Successful people doesn't really know why they are successful, so they attribute it to their hard work and not giving up. The media amplifies that message, while millions of failures go unreported because they're not celebrities, even though they try just as hard. So we hear about successes disproportionately, in comparison to how often they actually happen, and that colors our expectations of our own future success. Sadly, I don't know of any good debiasing techniques for this error, other than just reminding yourself that it's an error.
When someone has invested a lot of time and effort into following their dream, it feels harder to give up due to the sunk cost fallacy. That happens even with very stupid dreams, like the dream of winning at the casino, that were obviously installed by someone else for their own profit. So when you feel convinced that you'll eventually make it big in writing or music, you can remind yourself that compulsive gamblers feel the same way, and that feeling something doesn't make it true.
Of course there are good dreams and bad dreams. Some people have dreams that don't tease them for years with empty promises, but actually start paying off in a predictable time frame. The main difference between the two kinds of dream is the difference between positive-sum games, a.k.a. productive occupations, and zero-sum games, a.k.a. popularity contests. Sebastian Marshall's post Positive Sum Games Don't Require Natural Talent makes the same point, and advises you to choose a game where you can be successful without outcompeting 99% of other players.
The really interesting question to me right now is, what sets someone on the path of investing everything in a hopeless dream? Maybe it's a small success at an early age, followed by some random encouragement from others, and then you're locked in. Is there any hope for thinking back to that moment, or set of moments, and making a little twist to put yourself on a happier path? I usually don't advise people to change their desires, but in this case it seems to be the right thing to do.
In Policy Debates Should Not Appear One-Sided, Eliezer Yudkowsky argues that arguments on questions of fact should be one-sided, whereas arguments on policy questions should not:
On questions of simple fact (for example, whether Earthly life arose by natural selection) there's a legitimate expectation that the argument should be a one-sided battle; the facts themselves are either one way or another, and the so-called "balance of evidence" should reflect this. Indeed, under the Bayesian definition of evidence, "strong evidence" is just that sort of evidence which we only expect to find on one side of an argument.
But there is no reason for complex actions with many consequences to exhibit this onesidedness property.
The reason for this is primarily that natural selection has caused all sorts of observable phenomena. With a bit of ingenuity, we can infer that natural selection has caused them, and hence they become evidence for natural selection. The evidence for natural selection thus has a common cause, which means that we should expect the argument to be one-sided.
In contrast, even if a certain policy, say lower taxes, is the right one, the rightness of this policy does not cause its evidence (or the arguments for this policy, which is a more natural expression), the way natural selection causes its evidence. Hence there is no common cause of all of the valid arguments of relevance for the rightness of this policy, and hence no reason to expect that all of the valid arguments should support lower taxes. If someone nevertheless believes this, the best explanation of their belief is that they suffer from some cognitive bias such as the affect heuristic.
(In passing, I might mention that I think that the fact that moral debates are not one-sided indicates that moral realism is false, since if moral realism were true, moral facts should provide us with one-sided evidence on moral questions, just like natural selection provides us with one-sided evidence on the question how Earthly life arose. This argument is similar to, but distinct from, Mackie's argument from relativity.)
Now consider another kind of factual issues: multiple factor explanations. These are explanations which refer to a number of factors to explain a certain phenomenon. For instance, in his book Guns, Germs and Steel, Jared Diamond explains the fact that agriculture first arose in the Fertile Crescent by reference to no less than eight factors. I'll just list these factors briefly without going into the details of how they contributed to the rise of agriculture. The Fertile Crescent had, according to Diamond (ch. 8):
- big seeded plants, which were
- abundant and occurring in large stands whose value was obvious,
- and which were to a large degree hermaphroditic "selfers".
- It had a higher percentage of annual plants than other Mediterreanean climate zones
- It had higher diversity of species than other Mediterreanean climate zones.
- It has a higher range of elevations than other Mediterrenean climate zones
- It had a great number of domesticable big mammals.
- The hunter-gatherer life style was not that appealing in the Fertile Crescent
(Note that all of these factors have to do with geographical, botanical and zoological facts, rather than with facts about the humans themselves. Diamond's goal is to prove that agriculture arose in Eurasia due to geographical luck rather than because Eurasians are biologically superior to other humans.)
Diamond does not mention any mechanism that would make it less likely for agriculture to arise in the Fertile Crescent. Hence the score of pro-agriculture vs anti-agriculture factors in the Fertile Crescent is 8-0. Meanwhile no other area in the world has nearly as many advantages. Diamond does not provide us with a definite list of how other areas of the world fared but no non-Eurasian alternative seem to score better than about 5-3 (he is primarily interested in comparing Eurasia with other parts of the world).
Now suppose that we didn't know anything about the rise of agriculture, but that we knew that there were eight factors which could influence it. Since these factors would not be caused by the fact that agriculture first arose in the Fertile Crescent, the way the evidence for natural selection is caused by the natural selection, there would be no reason to believe that these factors were on average positively probabilistically dependent of each other. Under these conditions, one area having all the advantages and the next best lacking three of them is a highly surprising distribution of advantages. On the other hand, this is precisely the pattern that we would expect given the hypothesis that Diamond suffers from confirmation bias or another related bias. His theory is "too good to be true" and which lends support to the hypothesis that he is biased.
In this particular case, some of the factors Diamond lists presumably are positively dependent on each other. Now suppose that someone argues that all of the factors are in fact strongly positively dependent on each other, so that it is not very surprising that they all co-occur. This only pushes the problem back, however, because now we want an explanation of a) what the common cause of all of these dependencies is (it being very improbable that they all would correlate in the absence of such a common cause) and b) how it could be that this common cause increases the probability of the hypothesis via eight independent mechanisms, and doesn't decrease it via any mechanism. (This argument is complicated and I'd be happy on any input concerning it.)
Single-factor historical explanations are often criticized as being too "simplistic" whereas multiple factor explanations are standardly seen as more nuanced. Many such explanations are, however, one-sided in the way Diamond's explanation is, which indicates bias and dogmatism rather than nuance. (Another salient example I'm presently studying is taken from Steven Pinker's The Better Angels of Our Nature. I can provide you with the details on demand.*) We should be much better at detecting this kind of bias, since it for the most part goes unnoticed at present.
Generally, the sort of "too good to be true"-arguments to infer bias discussed here are strongly under-utilized. As our knowledge of the systematic and predictable ways our thought goes wrong increase, it becomes easier to infer bias from the structure or pattern of people's arguments, statements and beliefs. What we need is to explicate clearly, preferably using probability theory or other formal methods, what factors are relevant for deciding whether some pattern of arguments, statements or beliefs most likely is the result of biased thought-processes. I'm presently doing research on this and would be happy to discuss these questions in detail, either publicly or via pm.
*Edit: Pinker's argument. Pinker's goal is to explain why violence has declined throughout history. He lists the following five factors in the last chapter:
- The Leviathan (the increasing influence of the government)
- Gentle commerce (more trade leads to less violence)
- The expanding (moral) circle
- The escalator of reason
- Weaponry and disarmanent (he claims that there are no strong correlations between weapon developments and numbers of deaths)
- Resource and power (he claims that there is little connection between resource distributions and wars)
- Affluence (tight correlations between affluence and non-violence are hard to find)
- (Fall of) religion (he claims that atheist countries and people aren't systematically less violen
Go read Yvain/Scott's Meditations On Moloch. It's one of the most beautiful, disturbing, poetical look at the future that I've ever seen.
Go read it.
Don't worry, I can wait. I'm only a piece of text, my patience is infinite.
You sure you've read it?
Ok, I believe you...
I hope you wouldn't deceive an innocent and trusting blog post? You wouldn't be a monster enough to abuse the trust of a being as defenceless as a constant string of ASCII symbols?
Of course not. So you'd have read that post before proceeding to the next paragraph, wouldn't you? Of course you would.
Ok, now to the point. The "Moloch" idea is very interesting, and, at the FHI, we may try to do some research in this area (naming it something more respectable/boring, of course, something like "how to avoid stable value-losing civilization attractors").
The project hasn't started yet, but a few caveats to the Moloch idea have already occurred to me. First of all, it's not obligatory for an optimisation process to trample everything we value into the mud. This is likely to happen with an AI's motivation, but it's not obligatory for an optimisation process.
One way of seeing this is the difference between "or" and "and". Take the democratic election optimisation process. It's clear, as Scott argues, that this optimises badly in some ways. It encourages appearance over substance, some types of corruption, etc... But it also optimises along some positive axes, with some clear, relatively stable differences between the parties which reflects some voters preferences, and punishment for particularly inept behaviour from leaders (I might argue that the main benefit of democracy is not the final vote between the available options, but the filtering out of many pernicious options because they'd never be politically viable). The question is whether these two strands of optimisation can be traded off against each other, or if a minimum of each is required. So can we make a campaign that is purely appearance based with any substantive position ("or": maximum on one axis is enough), or do you need a minimum of substance and a minimum of appearance to buy off different constituencies ("and": you need some achievements on all axes)? And no, I'm not interested in discussing current political examples.
Another example Scott gave was of the capitalist optimisation process, and how it in theory matches customers' and producers' interests, but could go very wrong:
Suppose the coffee plantations discover a toxic pesticide that will increase their yield but make their customers sick. But their customers don't know about the pesticide, and the government hasn't caught up to regulating it yet. Now there's a tiny uncoupling between "selling to [customers]" and "satisfying [customers'] values", and so of course [customers'] values get thrown under the bus.
This effect can be combated to some extent with extra information. If the customers (or journalists, bloggers, etc...) know about this, then the coffee plantations will suffer. "Our food is harming us!" isn't exactly a hard story to publicise. This certainly doesn't work in every case, but increased information is something that technological progress would bring, and this needs to be considered when asking whether optimisation processes will inevitably tend to a bad equilibrium as technology improves. An accurate theory of nutrition, for instance, would have great positive impact if its recommendations could be measured.
Finally, Zack Davis's poem about the em stripped of (almost all) humanity got me thinking. The end result of that process is tragic for two reasons: first, the em retains enough humanity to have curiosity, only to get killed for this. And secondly, that em once was human. If the em was entirely stripped of human desires, the situation would be less tragic. And if the em was further constructed in a process that didn't destroy any humans, this would be even more desirable. Ultimately, if the economy could be powered by entities developed non-destructively from humans, and which were clearly not conscious or suffering themselves, this would be no different that powering the economy with the non-conscious machines we use today. This might happen if certain pieces of a human-em could be extracted, copied and networked into an effective, non-conscious entity. In that scenario, humans and human-ems could be the capital owners, and the non-conscious modified ems could be the workers. The connection of this with the Moloch argument is that it shows that certain nightmare scenarios could in some circumstances be adjusted to much better outcomes, with a small amount of coordination.
The point of the post
The reason I posted this is to get people's suggestions about ideas relevant to a "Moloch" research project, and what they thought of the ideas I'd had so far.
The article is titled "The scientific A-Team saving the world from killer viruses, rogue AI and the paperclip apocalypse", and features interviews with Martin Rees, Huw Price, Jaan Tallinn and Partha Dasgupta. The author takes a rather positive tone about CSER and MIRI's endeavours, and mentions x-risks other than AI (bioengineered pandemic, global warming with human interference, distributed manufacturing).
I find it interesting that the inferential distance for the layman to the concept of paperclipping AI is much reduced by talking about paperclipping America, rather than the entire universe: though the author admits still struggling with the concept. Unusually for an journalist who starts off unfamiliar with these concepts, he writes in a tone that suggests that he takes the ideas seriously, without the sort of "this is very far-fetched and thus I will not lower myself to seriously considering it" countersignalling usually seen with x-risk coverage. There is currently the usual degree of incredulity in the comments section though.
For those unfamiliar with The Guardian, it is a British left-leaning newspaper with a heavy focus on social justice and left-wing political issues.
Summary: Overhead expenses' (CEO salary, percentage spent on fundraising) are often deemed a poor measure of charity effectiveness by Effective Altruists, and so they disprefer means of charity evaluation which rely on these. However, 'funding cannibalism' suggests that these metrics (and the norms that engender them) have value: if fundraising is broadly a zero-sum game between charities, then there's a commons problem where all charities could spend less money on fundraising and all do more good, but each is locally incentivized to spend more. Donor norms against increasing spending on zero-sum 'overheads' might be a good way of combating this. This valuable collective action of donors may explain the apparent underutilization of fundraising by charities, and perhaps should make us cautious in undermining it.
The EA critique of charity evaluation
Pre-Givewell, the common means of evaluating charities (Guidestar, Charity Navigator) used a mixture of governance checklists 'overhead indicators'. Charities would gain points both for having features associated with good governance (being transparent in the right ways, balancing budgets, the right sorts of corporate structure), but also in spending its money on programs and avoiding 'overhead expenses' like administration and (especially) fundraising. For shorthand, call this 'common sense' evaluation.
The standard EA critique is that common sense evaluation doesn't capture what is really important: outcomes. It is easy to imagine charities that look really good to common sense evaluation yet have negligible (or negative) outcomes. In the case of overheads, it becomes unclear whether these are even proxy measures of efficacy. Any fundraising that still 'turns a profit' looks like a good deal, whether it comprises five percent of a charity's spending or fifty.
A summary of the EA critique of common sense evaluation that its myopic focus on these metrics gives pathological incentives, as these metrics frequently lie anti-parallel to maximizing efficacy. To score well on these evaluations, charities may be encouraged to raise less money, hire less able staff, and cut corners in their own management, even if doing these things would be false economies.
Funding cannibalism and commons tragedies
In the wake of the ALS 'Ice bucket challenge', Will MacAskill suggested there is considerable of 'funding cannabilism' in the non-profit sector. Instead of the Ice bucket challenge 'raising' money for ALS, it has taken money that would have been donated to other causes instead - cannibalizing other causes. Rather than each charity raising funds independently of one another, they compete for a fairly fixed pie of aggregate charitable giving.
The 'cannabilism' thesis is controversial, but looks plausible to me, especially when looking at 'macro' indicators: proportion of household charitable spending looks pretty fixed whilst fundraising has increased dramatically, for example.
If true, cannibalism is important. As MacAskill points out, the money tens of millions of dollars raised for ALS is no longer an untrammelled good, alloyed as it is with the opportunity cost of whatever other causes it has cannibalized (q.v.). There's also a more general consideration: if there is a fixed pot of charitable giving insensitive to aggregate fundraising, then fundraising becomes a commons problem. If all charities could spend less on their fundraising, none would lose out, so all could spend more of their funds on their programs. However, for any alone to spend less on fundraising allows the others to cannibalize it.
Civilizing Charitable Cannibals, and Metric Meta-Myopia
Coordination among charities to avoid this commons tragedy is far fetched. Yet coordination of donors on shared norms about 'overhead ratio' can help. By penalizing a charity for spending too much on zero-sum games with other charities like fundraising, donors can stop a race to the bottom fundraising free for all and burning of the charitable commons that implies. The apparently-high marginal return to fundraising might suggest this is already in effect (and effective!)
The contrarian take would be that it is the EA critique of charity evaluation which is myopic, not the charity evaluation itself - by looking at the apparent benefit for a single charity of more overhead, the EA critique ignores the broader picture of the non-profit ecosystem, and their attack undermines a key environmental protection of an important commons - further, one which the right tail of most effective charities benefit from just as much as the crowd of 'great unwashed' other causes. (Fundraising ability and efficacy look like they should be pretty orthogonal. Besides, if they correlate well enough that you'd expect the most efficacious charities would win the zero-sum fundraising game, couldn't you dispense with Givewell and give to the best fundraisers?)
The contrarian view probably goes too far. Although there's a case for communally caring about fundraising overheads, as cannibalism leads us to guess it is zero sum, parallel reasoning is hard to apply to administration overhead: charity X doesn't lose out if charity Y spends more on management, but charity Y is still penalized by common sense evaluation even if its overall efficacy increases. I'd guess that features like executive pay lie somewhere in the middle: non-profit executives could be poached by for-profit industries, so it is not as simple as donors prodding charities to coordinate to lower executive pay; but donors can prod charities not to throw away whatever 'non-profit premium' they do have in competing with one another for top talent (c.f.). If so, we should castigate people less for caring about overhead, even if we still want to encourage them to care about efficacy too.
The invisible hand of charitable pan-handling
If true, it is unclear whether the story that should be told is 'common sense was right all along and the EA movement overconfidently criticised' or 'A stopped clock is right twice a day, and the generally wrong-headed common sense had an unintended feature amongst the bugs'. I'd lean towards the latter, simply the advocates of the common sense approach have not (to my knowledge) articulated these considerations themselves.
However, many of us believe the implicit machinery of the market can turn without many of the actors within it having any explicit understanding of it. Perhaps the same applies here. If so, we should be less confident in claiming the status quo is pathological and we can do better: there may be a rationale eluding both us and its defenders.
Attempt at the briefest content-full Less Wrong post:
Once AI is developed, it could "easily" colonise the universe. So the Great Filter (preventing the emergence of star-spanning civilizations) must strike before AI could be developed. If AI is easy, we could conceivably have built it already, or we could be on the cusp of building it. So the Great Filter must predate us, unless AI is hard.
Some of the comments on the link by James_Miller exactly six months ago provided very specific estimates of how the events might turn out:
- The odds of Russian intervening militarily = 40%.
- The odds of the Russians losing the conventional battle (perhaps because of NATO intervention) conditional on them entering = 30%.
- The odds of the Russians resorting to nuclear weapons conditional on them losing the conventional battle = 20%.
"Russians intervening militarily" could be anything from posturing to weapon shipments to a surgical strike to a Czechoslovakia-style tank-roll or Afghanistan invasion. My guess that the odds of the latter is below 5%.
A bet between James_Miller and solipsist:
I will bet you $20 U.S. (mine) vs $100 (yours) that Russian tanks will be involved in combat in the Ukraine within 60 days. So in 60 days I will pay you $20 if I lose the bet, but you pay me $100 if I win.
While it is hard to do any meaningful calibration based on a single event, there must be lessons to learn from it. Given that Russian armored columns are said to capture key Ukrainian towns today, the first part of James_Miller's prediction has come true, even if it took 3 times longer than he estimated.
Note that even the most pessimistic person in that conversation (James) was probably too optimistic. My estimate of 5% appears way too low in retrospect, and I would probably bump it to 50% for a similar event in the future.
Now, given that the first prediction came true, how would one reevaluate the odds of the two further escalations he listed? I still feel that there is no way there will be a "conventional battle" between Russia and NATO, but having just been proven wrong makes me doubt my assumptions. If anything, maybe I should give more weight to what James_Miller (or at least Dan Carlin) has to say on the issue. And if I had any skin in the game, I would probably be even more cautious.
Yet another exceptionally interesting blog post by Scott Aaronson, describing his talk at the Quantum Foundations of a Classical Universe workshop, videos of which should be posted soon. Despite the disclaimer "My talk is for entertainment purposes only; it should not be taken seriously by anyone", it raises several serious and semi-serious points about the nature of conscious experience and related paradoxes, which are generally overlooked by the philosophers, including Eliezer, because they have no relevant CS/QC expertise. For example:
- Is an FHE-encrypted sim with a lost key conscious?
- If you "untorture" a reversible simulation, did it happen? What does the untorture feel like?
- Is Vaidman brain conscious? (You have to read the blog post to learn what it is, not going to spoil it.)
Scott also suggests a model of consciousness which sort-of resolves the issues of cloning, identity and such, by introducing what he calls a "digital abstraction layer" (again, read the blog post to understand what he means by that). Our brains might be lacking such a layer and so be "fundamentally unclonable".
Another interesting observation is that you never actually kill the cat in the Schroedinger's cat experiment, for a reasonable definition of "kill".
There are several more mind-blowing insights in this "entertainment purposes" post/talk, related to the existence of p-zombies, consciousness of Boltzmann brains, the observed large-scale structure of the Universe and the "reality" of Tegmark IV.
I certainly got the humbling experience that Scott is the level above mine, and I would like to know if other people did, too.
Finally, the standard bright dilettante caveat applies: if you think up a quick objection to what an expert in the area argues, and you yourself are not such an expert, the odds are extremely heavy that this objection is either silly or has been considered and addressed by the expert already.
I've argued that we might have to worry about dangerous non-general intelligences. In a series of back and forth with Wei Dai, we agreed that some level of general intelligence (such as that humans seem to possess) seemed to be a great advantage, though possibly one with diminishing returns. Therefore a dangerous AI could be one with great narrow intelligence in one area, and a little bit of general intelligence in others.
The traditional view of an intelligence explosion is that of an AI that knows how to do X, suddenly getting (much) better at doing X, to a level beyond human capacity. Call this the gain of aptitude intelligence explosion. We can prepare for that, maybe, by tracking the AI's ability level and seeing if it shoots up.
But the example above hints at another kind of potentially dangerous intelligence explosion. That of a very intelligent but narrow AI that suddenly gains intelligence across other domains. Call this the gain of function intelligence explosion. If we're not looking specifically for it, it may not trigger any warnings - the AI might still be dumber than the average human in other domains. But this might be enough, when combined with its narrow superintelligence, to make it deadly. We can't ignore the toaster that starts babbling.
At Luke Muehlhauser's request, I wrote a script to scrape all of Robin Hanson's posts to Overcoming Bias into an e-book; here's a first beta release. Please comment here with any problems—posts in the wrong order, broken links, bad formatting, missing posts. Thanks!
This is a very basic introduction to AGI safety work, cross-posted from the MIRI blog. The discussion of AI V&V methods (mostly in the 'early steps' section) is probably the only part that will be new to regulars here.
Improvements in AI are resulting in the automation of increasingly complex and creative human behaviors. Given enough time, we should expect artificial reasoners to begin to rival humans in arbitrary domains, culminating in artificial general intelligence (AGI).
A machine would qualify as an 'AGI', in the intended sense, if it could adapt to a very wide range of situations to consistently achieve some goal or goals. Such a machine would behave intelligently when supplied with arbitrary physical and computational environments, in the same sense that Deep Blue behaves intelligently when supplied with arbitrary chess board configurations — consistently hitting its victory condition within that narrower domain.
Since generally intelligent software could help automate the process of thinking up and testing hypotheses in the sciences, AGI would be uniquely valuable for speeding technological growth. However, this wide-ranging productivity also makes AGI a unique challenge from a safety perspective. Knowing very little about the architecture of future AGIs, we can nonetheless make a few safety-relevant generalizations:
- Because AGIs are intelligent, they will tend to be complex, adaptive, and capable of autonomous action, and they will have a large impact where employed.
- Because AGIs are general, their users will have incentives to employ them in an increasingly wide range of environments. This makes it hard to construct valid sandbox tests and requirements specifications.
- Because AGIs are artificial, they will deviate from human agents, causing them to violate many of our natural intuitions and expectations about intelligent behavior.
Today's AI software is already tough to verify and validate, thanks to its complexity and its uncertain behavior in the face of state space explosions. Menzies & Pecheur (2005) give a good overview of AI verification and validation (V&V) methods, noting that AI, and especially adaptive AI, will often yield undesired and unexpected behaviors.
An adaptive AI that acts autonomously, like a Mars rover that can't be directly piloted from Earth, represents an additional large increase in difficulty. Autonomous safety-critical agents need to make irreversible decisions in dynamic environments with very low failure rates. The state of the art in safety research for autonomous systems is improving, but continues to lag behind capabilities work. Hinchman et al. (2012) write:
As autonomous systems become more complex, the notion that systems can be fully tested and all problems will be found is becoming an impossible task. This is especially true in unmanned/autonomous systems. Full test is becoming increasingly challenging on complex system. As these systems react to more environmental [stimuli] and have larger decision spaces, testing all possible states and all ranges of the inputs to the system is becoming impossible. [...] As systems become more complex, safety is really risk hazard analysis, i.e. given x amount of testing, the system appears to be safe. A fundamental change is needed. This change was highlighted in the 2010 Air Force Technology Horizon report, "It is possible to develop systems having high levels of autonomy, but it is the lack of suitable V&V methods that prevents all but relatively low levels of autonomy from being certified for use." [...]
The move towards more autonomous systems has lifted this need [for advanced verification and validation techniques and methodologies] to a national level.
AI acting autonomously in arbitrary domains, then, looks particularly difficult to verify. If AI methods continue to see rapid gains in efficiency and versatility, and especially if these gains further increase the opacity of AI algorithms to human inspection, AI safety engineering will become much more difficult in the future. In the absence of any reason to expect a development in the lead-up to AGI that would make high-assurance AGI easy (or AGI itself unlikely), we should be worried about the safety challenges of AGI, and that worry should inform our research priorities today.
Below, I’ll give reasons to doubt that AGI safety challenges are just an extension of narrow-AI safety challenges, and I’ll list some research avenues people at MIRI expect to be fruitful.
In a previous post, I mused that we might be focusing too much on general intelligences, and that the route to powerful and dangerous intelligences might go through much more specialised intelligences instead. Since it's easier to reason with an example, here is a potentially deadly narrow AI (partially due to Toby Ord). Feel free to comment and improve on it, or suggest you own example.
It's the standard "pathological goal AI" but only a narrow intelligence. Imagine a medicine designing super-AI with the goal of reducing human mortality in 50 years - i.e. massively reducing human population in the next 49 years. It's a narrow intelligence, so it has access only to a huge amount of human biological and epidemiological research. It must gets its drugs past FDA approval; this requirement is encoded as certain physical reactions (no death, some health improvements) to people taking the drugs over the course of a few years.
Then it seems trivial for it to design a drug that would have no negative impact for the first few years, and then causes sterility or death. Since it wants to spread this to as many humans as possible, it would probably design something that interacted with common human pathogens - colds, flues - in order to spread the impact, rather than affecting only those that took the disease.
Now, this narrow intelligence is less threatening than if it had general intelligence - where it could also plan for possible human countermeasures and such - but it seems sufficiently dangerous on its own that we can't afford to worry only about general intelligences. Some of the "AI superpowers" that Nick mentions in his book (intelligence amplification, strategizing, social manipulation, hacking, technology research, economic productivity) could be enough to cause devastation on their own, even if the AI never developed other abilities.
We still could be destroyed by a machine that we outmatch in almost every area.
At the 2014 Effective Altruism Summit in Berkeley a few weeks ago, I had the pleasure of talking to Matt Fallshaw about the things he does to be more effective. Matt is a founder of Trike Apps (the consultancy that built Less Wrong), a founder of Bellroy, and a polyphasic sleeper. Notes on our conversation follow.
Matt recommends having a system for acquiring habits. He recommends separating collection from processing; that is, if you have an idea for a new habit you want to acquire, you should record the idea at the time you have it and then think about actually implementing it at some future time. Matt recommends doing this through a weekly review. He recommends vetting your collection to see what habits seem actually worth acquiring, then for those habits you actually want to acquire, coming up with a compassionate, reasonable plan for how you're going to acquire the habit.
The most difficult kind of habit for me to acquire is that of random-access situation-response habits, e.g. "if I'm having a hard time focusing, read my notebook entry that lists techniques for improving focus". So I asked Matt if he had any habit formation advice for this particular situation. Matt recommended trying to actually execute the habit I wanted as many times as possible, even in an artificial context. Steve Pavlina describes the technique here. Matt recommends making your habit execution as emotionally salient as possible. His example: Let's say you're trying to become less of a prick. Someone starts a conversation with you and you notice yourself experiencing the kind of emotions you experience before you start acting like a prick. So you spend several minutes explaining to them the episode of disagreeableness you felt coming on and how you're trying to become less of a prick before proceeding with the conversation. If all else fails, Matt recommends setting a recurring alarm on your phone that reminds you of the habit you're trying to acquire, although he acknowledges that this can be expensive.
Part of your plan should include a check to make sure you actually stick with your new habit. But you don't want a check that's overly intrusive. Matt recommends keeping an Anki deck with a card for each of your habits. Then during your weekly review session, you can review the cards Anki recommends for you. For each card, you can rate the degree to which you've been sticking with the habit it refers to and do something to revitalize the habit if you haven't been executing it. Matt recommends writing the cards in a form of a concrete question, e.g. for a speed reading habit, a question could be "Did you speed read the last 5 things you read?" If you haven't been executing a particular habit, check to see if it has a clear, identifiable trigger.
Ideally your weekly review will come at a time you feel particularly "agenty" (see also: Reflective Control). So you may wish to schedule it at a time during the week when you tend to feel especially effective and energetic. Consuming caffeine before your weekly review is another idea.
When running in to seemingly intractable problems related to your personal effectiveness, habits, etc., Matt recommends taking a step back to brainstorm and try to think of creative solutions. He says that oftentimes people will write off a task as "impossible" if they aren't able to come up with a solution in 30 seconds. He recommends setting a 5-minute timer.
In terms of habits worth acquiring, Matt is a fan of speed reading, Getting Things Done, and the Theory of Constraints (especially useful for larger projects).
Matt has found that through aggressive habit acquisition, he's been able to experience a sort of compound return on the habits he's acquired: by acquiring habits that give him additional time and mental energy, he's been able to reinvest some of that additional time and mental energy in to the acquisition of even more useful habits. Matt doesn't think he's especially smart or high-willpower relative to the average person in the Less Wrong community, and credits this compounding for the reputation he's acquired for being a badass.
I'm reading The Last Intellectuals: American culture in the age of academe by Russell Jacoby (1987). It contains many interesting and important observations and insights, but also much stupidity. By the last chapter, I was as interested in the question of how a person can be so smart and stupid at the same time as in the author's actual arguments.
Ah, delicious dark chocolate M&Ms, colorfully filling a glass jar with your goodness. How do I love thee? About four of you an hour. Here's a brief rundown of my most recent motivation hacking experiment.
1. Gwern has an interesting article arguing that Massive Open Online Courses (MOOCs) may shift the learning advantage from intelligence toward conscientiousness (actually he's not sure about the intelligence part). This shift occurs because MOOCs select for higher-quality instruction and better feedback, broadly speaking and over time, but it's much harder to stay on task without a malevolent instructor and bad grades breathing down your neck. This thesis jives with my own experience; if I get stuck on a math problem, I just google "an intuitive approach to x," and I usually find a couple of people begging to teach me the concept. But it's harder to get started and to stay focused than in a classroom.
2. Given that knowledge compounds and grants increasing advantages, I'd really like to keep taking advantage of MOOCs. Some MOOCs are better than others, but many are better than your standard college course - and they're free. For a non-technical guy getting technical, like me, it's a golden age of education. So, it would be great if I were highly conscientious. Gwern points out that conscientiousness is a relatively stable Big Five personality trait.
3. The question then becomes, can conscientiousness be developed? Well, I'm not a Cartesian agent, so wouldn't it make sense to reward myself for conscientiousness? Enter the M&Ms. I set a daily target for pomodoros. When I finish a pomodoro, I get a big peanut M&M or two small ones. If I finish two in a row, I get two servings, and so on. In this way, I encourage myself to get started, and then to keep going to build Deep Focus. Each pomodoro becomes cause for celebration, and I find my rapid progress through pomodoros (and chocolate) energizing, where long periods of distraction were tiring.
This has worked fantastically well for the last two weeks. I hit my pomodoro target for paid work, then switch to educational work. I plan to keep it up, and maybe I'll use chocolate as motivation somewhere else as well. Now back to my M&Ms, green, yellow, blue, orange, brown, red . . .
Thanks for Kaj for making me think along these lines.
It's agreed on this list that general intelligences - those that are capable of displaying high cognitive performance across a whole range of domains - are those that we need to be worrying about. This is rational: the most worrying AIs are those with truly general intelligences, and so those should be the focus of our worries and work.
But I'm wondering if we're overestimating the probability of general intelligences, and whether we shouldn't adjust against this.
First of all, the concept of general intelligence is a simple one - perhaps too simple. It's an intelligence that is generally "good" at everything, so we can collapse its various abilities across many domains into "it's intelligent", and leave it at that. It's significant to note that since the very beginning of the field, AI people have been thinking in terms of general intelligences.
And their expectations have been constantly frustrated. We've made great progress in narrow areas, very little in general intelligences. Chess was solved without "understanding"; Jeopardy! was defeated without general intelligence; cars can navigate our cluttered roads while being able to do little else. If we started with a prior in 1956 about the feasibility of general intelligence, then we should be adjusting that prior downwards.
But what do I mean by "feasibility of general intelligence"? There are several things this could mean, not least the ease with which such an intelligence could be constructed. But I'd prefer to look at another assumption: the idea that a general intelligence will really be formidable in multiple domains, and that one of the best ways of accomplishing a goal in a particular domain is to construct a general intelligence and let it specialise.
First of all, humans are very far from being general intelligences. We can solve a lot of problems when the problems are presented in particular, easy to understand formats that allow good human-style learning. But if we picked a random complicated Turing machine from the space of such machines, we'd probably be pretty hopeless at predicting its behaviour. We would probably score very low on the scale of intelligence used to construct the AIXI. The general intelligence, "g", is a misnomer - it designates the fact that the various human intelligences are correlated, not that humans are generally intelligent across all domains.
Humans with computers, and humans in societies and organisations, are certainly closer to general intelligences than individual humans. But institutions have their own blind spots and weakness, as does the human-computer combination. Now, there are various reasons advanced for why this is the case - game theory and incentives for institutions, human-computer interfaces and misunderstandings for the second example. But what if these reasons, and other ones we can come up with, were mere symptoms of a more universal problem: that generalising intelligence is actually very hard?
There are no free lunch theorems that show that no computable intelligences can perform well in all environments. As far as they go, these theorems are uninteresting, as we don't need intelligences that perform well in all environments, just in almost all/most. But what if a more general restrictive theorem were true? What if it was very hard to produce an intelligence that was of high performance across many domains? What if the performance of a generalist was pitifully inadequate as compared with a specialist. What if every computable version of AIXI was actually doomed to poor performance?
There are a few strong counters to this - for instance, you could construct good generalists by networking together specialists (this is my standard mental image/argument for AI risk), you could construct an entity that was very good at programming specific sub-programs, or you could approximate AIXI. But we are making some assumptions here - namely, that we can network together very different intelligences (the human-computer interfaces hints at some of the problems), and that a general programming ability can even exist in the first place (for a start, it might require a general understanding of problems that is akin to general intelligence in the first place). And we haven't had great success building effective AIXI approximations so far (which should reduce, possibly slightly, our belief that effective general intelligences are possible).
Now, I remain convinced that general intelligence is possible, and that it's worthy of the most worry. But I think it's worth inspecting the concept more closely, and at least be open to the possibility that general intelligence might be a lot harder than we imagine.
EDIT: Model/example of what a lack of general intelligence could look like.
Imagine there are three types of intelligence - social, spacial and scientific, all on a 0-100 scale. For any combinations of the three intelligences - eg (0,42,98) - there is an effort level E (how hard is that intelligence to build, in terms of time, resources, man-hours, etc...) and a power level P (how powerful is that intelligence compared to others, on a single convenient scale of comparison).
Wei Dai's evolutionary comment implies that any being of very low intelligence on one of the scale would be overpowered by a being of more general intelligence. So let's set power as simply the product of all three intelligences.
This seems to imply that general intelligences are more powerful, as it basically bakes in diminishing returns - but we haven't included effort yet. Imagine that the following three intelligences require equal effort: (10,10,10), (20,20,5), (100,5,5). Then the specialised intelligence is definitely the one you need to build.
But is it plausible that those could be of equal difficulty? It could be, if we assume that high social intelligence isn't so difficult, but is specialised. ie you can increase the spacial intelligence of a social intelligence, but that messes up the delicate balance in its social brain. Or maybe recursive self-improvement happens more easily in narrow domains. Further assume that intelligences of different types cannot be easily networked together (eg combining (100,5,5) and (5,100,5) in the same brain gives an overall performance of (21,21,5)). This doesn't seem impossible.
So let's caveat the proposition above: the most effective and dangerous type of AI might be one with a bare minimum amount of general intelligence, but an overwhelming advantage in one type of narrow intelligence.
Or to ask the question another way, is there such a thing as a theory of bounded rationality, and if so, is it the same thing as a theory of general intelligence?
The LW Wiki defines general intelligence as "ability to efficiently achieve goals in a wide range of domains", while instrumental rationality is defined as "the art of choosing and implementing actions that steer the future toward outcomes ranked higher in one's preferences". These definitions seem to suggest that rationality and intelligence are fundamentally the same concept.
However, rationality and AI have separate research communities. This seems to be mainly for historical reasons, because people studying rationality started with theories of unbounded rationality (i.e., with logical omniscience or access to unlimited computing resources), whereas AI researchers started off trying to achieve modest goals in narrow domains with very limited computing resources. However rationality researchers are trying to find theories of bounded rationality, while people working on AI are trying to achieve more general goals with access to greater amounts of computing power, so the distinction may disappear if the two sides end up meeting in the middle.
We also distinguish between rationality and intelligence when talking about humans. I understand the former as the ability of someone to overcome various biases, which seems to consist of a set of skills that can be learned, while the latter is a kind of mental firepower measured by IQ tests. This seems to suggest another possibility. Maybe (as Robin Hanson recently argued on his blog) there is no such thing as a simple theory of how to optimally achieve arbitrary goals using limited computing power. In this view, general intelligence requires cooperation between many specialized modules containing domain specific knowledge, so "rationality" would just be one module amongst many, which tries to find and correct systematic deviations from ideal (unbounded) rationality caused by the other modules.
I was more confused when I started writing this post, but now I seem to have largely answered my own question (modulo the uncertainty about the nature of intelligence mentioned above). However I'm still interested to know how others would answer it. Do we have the same understanding of what "rationality" and "intelligence" mean, and know what distinction someone is trying to draw when they use one of these words instead of the other?
ETA: To clarify, I'm asking about the difference between general intelligence and rationality as theoretical concepts that apply to all agents. Human rationality vs intelligence may give us a clue to that answer, but isn't the main thing that I'm interested here.
In just over two weeks I will be running an online reading group on Nick Bostrom's Superintelligence, on behalf of MIRI. It will be here on LessWrong. This is an advance warning, so you can get a copy and get ready for some stimulating discussion. MIRI's post, appended below, gives the details.
Nick Bostrom’s eagerly awaited Superintelligence comes out in the US this week. To help you get the most out of it, MIRI is running an online reading group where you can join with others to ask questions, discuss ideas, and probe the arguments more deeply.
The reading group will “meet” on a weekly post on the LessWrong discussion forum. For each ‘meeting’, we will read about half a chapter of Superintelligence, then come together virtually to discuss. I’ll summarize the chapter, and offer a few relevant notes, thoughts, and ideas for further investigation. (My notes will also be used as the source material for the final reading guide for the book.)
Discussion will take place in the comments. I’ll offer some questions, and invite you to bring your own, as well as thoughts, criticisms and suggestions for interesting related material. Your contributions to the reading group might also (with permission) be used in our final reading guide for the book.
We welcome both newcomers and veterans on the topic. Content will aim to be intelligible to a wide audience, and topics will range from novice to expert level. All levels of time commitment are welcome.
We will follow this preliminary reading guide, produced by MIRI, reading one section per week.
If you have already read the book, don’t worry! To the extent you remember what it says, your superior expertise will only be a bonus. To the extent you don’t remember what it says, now is a good time for a review! If you don’t have time to read the book, but still want to participate, you are also welcome to join in. I will provide summaries, and many things will have page numbers, in case you want to skip to the relevant parts.
If this sounds good to you, first grab a copy of Superintelligence. You may also want to sign up here to be emailed when the discussion begins each week. The first virtual meeting (forum post) will go live at 6pm Pacific on Monday, September 15th. Following meetings will start at 6pm every Monday, so if you’d like to coordinate for quick fire discussion with others, put that into your calendar. If you prefer flexibility, come by any time! And remember that if there are any people you would especially enjoy discussing Superintelligence with, link them to this post!
Topics for the first week will include impressive displays of artificial intelligence, why computers play board games so well, and what a reasonable person should infer from the agricultural and industrial revolutions.
• I'm beginning work on the 2014 Winter Solstice. There are a lot of jobs to be done, and the more people who can dedicate serious time to it, the better the end result will be and the more locations it can take place. A few people have volunteered serious time, and I wanted to issue a general call, to anyone who's wanted to be part of this but wasn't sure how. Send me an e-mail at firstname.lastname@example.org if you'd like to help with any of the tasks listed below (or others I haven't thought of).
• More generally, I think people working on rational ritual, in any form, should be sharing notes and collaborating more. There's a fair number of us, but we're scattered across the country and haven't really felt like part of the same team. And it seems a bit silly for people working on ritual, to be scattered and unified. So I am hosting the first Rational Ritual Retreat at the end of September. The exact date and location have yet to be determined. You can apply at humanistculture.com, noting your availability, and I will determine
The Rational Ritual Retreat
For the past three years, I've been running a winter solstice holiday, celebrating science and human achievement. Several people have come up to me and told me it was one of the most unique, profound experiences they've participated in, inspiring them to work harder to make sure humanity has a bright future.
I've also had a number of people concerned that I'm messing with dangerous aspects of human psychology, fearing what will happen to a rationality community that gets involved with ritual.
Both of these thoughts are incredibly important. I've written a lot on the value and danger of ritual. 
Ritual is central to the human experience. We've used it for thousands of years to bind groups together. It helps us internalize complex ideas. A winning version of rationality needs *some* way of taking complex ideas and getting System 1 to care about them, and I think ritual is at least one tool we should consider.
In the past couple weeks, a few thoughts occurred to me at once:
1) Figuring out a rational approach to ritual that has a meaningful, useful effect on the world will require a lot of coordination among many skilled people.
2) If this project *were* to go badly somehow, I think the most likely reason would be someone copying parts of what I'm working on without understanding all the considerations that went into it, and creating a toxic (or hollow) variant that spirals out of control.
3) Many other people have approached the concept of rational ritual. But we've generally done so independently, often duplicating a lot of the same work and rarely moving on to more interesting and valuable experimentation. When we do experiment, we rarely share notes.
This all prompted a fourth realization:
4) If ritual designers are isolated and poorly coordinated... if we're duplicating a lot of the same early work and not sharing concerns about potential dangers, then one obvious (in retrospect) solution is to have a ritual about ritual creation.
So, the Rational Ritual Retreat. We'll hike out into a dark sky reserve, when there's no light pollution and the Milky Way looms large and beautiful above us. We'll share our stories, our ideas for a culture grounded in rationality yet tapped into our primal human desires. Over the course of an evening we'll create a ceremony or two together, through group consensus and collaboration. We'll experiment with new ideas, aware that some may work well, and some may not - that's how progress is made.
This is my experiment, attempting to answer the question Eliezer raised in "Bayesians vs Barbarians." It just seems really exceptionally silly to me that people motivated by rationality AND ritual should be so uncoordinated.
Whether you're interested directly creating ritual, or helping to facilitate its creation in one way or another (helping with art, marketing, logistics or funding of future projects), you are invited to attend. The location is currently undecided - there are reasons to consider the West Coast, East Coast or (if there's enough interest in both locations) both.
Send in a brief application so I can make decisions about where and when to host it. I'll make the final decisions this upcoming Friday.
The Winter Solstice
The Retreat is part of a long-term vision, of many people coming together to produce a culture (undoubtably, with numerous subcultures focusing on different aesthetics). Tentatively, I'd expect a successful rational-ritual culture to look sort of Open Source ish. (Or, more appropriately - I'd expect it to look like Burning Man. To be clear, Burning Man and variations already exist, my goal is not to duplicate that effort. It's to create something that's a) easier to integrate into people's lives, and b) specifically focuses on rationality and human progress)
The Winter Solstice project as (at least for now) an important piece of that, partly because of the particular ideas it celebrates, but also because it's a demonstration of how you create *any* cultural holiday from scratch that celebrates serious ideas in a non-ironic fashion.
My minimum goal this year is to finish the Hymnal, put more material online to help people create their own private events, and run another largish event in NYC. My stretch goals are to have a high quality public event in Boston and San Francisco. (Potentially other places if a lot of local people are interested and are willing to do the legwork).
My hope, to make those stretch goals possible, is to find collaborators willing to put in a fair amount of work. I'm specifically looking for people who can:
- Creative Collaboration. Want to perform, create music, visual art, or host an event in your city?
- Help with logistics, especially in different cities. (Finding venues, arranging catering, etc)
- Marketing, reaching out to bloggers, or creating images or videos for the social media campaign.
- Helping with technical aspects of production for the Hymnal (editing, figuring out best places
Each of these are things I'm able to do, but I have limited time, and the more time I can focus on creating
If you're interested in collaborating, volunteering, or running a local event, either reply here or send me an e-mail at email@example.com
When I talk to people about earning to give, it's common to hear worries about "backsliding". Yes, you say you're going to go make a lot of money and donate it, but once you're surrounded by rich coworkers spending heavily on cars, clothes, and nights out, will you follow through? Working at a greedy company in a selfishness-promoting culture you could easily become corrupted and lose initial values and motivation.
First off, this is a totally reasonable concern. People do change, and we are pulled towards thinking like the people around us. I see two main ways of working against this:
- Be public with your giving. Make visible commitments and then list your donations. This means that you can't slowly slip away from giving; either you publish updates saying you're not going to do what you said you would, or you just stop updating and your pages become stale. By making a public promise you've given friends permission to notice that you've stopped and ask "what changed?"
- Don't just surround yourself with coworkers. Keep in touch with friends and family. Spend some time with other people in the effective altruism movement. You could throw yourself entirely into your work, maximizing income while sending occasional substantial checks to GiveWell's top picks, but without some ongoing engagement with the community and the research this doesn't seem likely to last.
One implication of the "won't you drift away" objection, however, is often that if instead of going into earning to give you become an activist then you'll remain true to your values. I'm not so sure about this: many people who are really into activism and radical change in their 20s have become much less ambitious and idealistic by their 30s. You can call it "burning out" or "selling out" but decreasing idealism with age is very common. This doesn't mean people earning to give don't have to worry about losing their motivation—in fact it points the opposite way—but this isn't a danger unique to the "go work at something lucrative" approach. Trying honestly to do the most good possible is far from the default in our society, and wherever you are there's going to be pressure to do the easy thing, the normal thing, and stop putting so much effort into altruism.
The Feynman lectures on physics are now available to read online for free. This is a classic resource for not just learning physics also but also the process of science and the mindset of a scientific rationalist.
The New York Times has a calculator to explain how getting on a jury works. They have a slider at the top indicating how likely each of the two lawyers think you are to side with them, and as you answer questions it moves around. For example, if you select that your occupation is "blue collar" then it says "more likely to side with plaintiff" while "white collar" gives "more likely to side with defendant". As you give it more information the pointer labeled "you" slides back and forth, representing the lawyers' ongoing revision of their estimates of you. Let's see what this looks like.
- Selecting "Over 30"
- Selecting "Under 30"
For several other questions, however, the options aren't matched. If your household income is under $50k then it will give you "more likely to side with plaintiff" while if it's over $50k then it will say "no effect on either lawyer". This is not how conservation of expected evidence works: if learning something pushes you in one direction, then learning its opposite has to push you in the other.
Let's try this with some numbers. Say people's leanings are:
|income||probability of siding with plaintiff||probability of siding with defendant|
So the lawyers best guess for you is that you're at 60%, and then they ask the question. If you say ">$50k" then they update their estimate for you down to 50%, if you say "<$50k" they update it up to 70%. "No effect on either lawyer" can't be an option here unless the question gives no information.
 Almost; the median income in the US in 2012 was $51k. (pdf)
My take on some historical religious/social/political movements:
- Jesus taught a radical and highly impractical doctrine of love and disregard for one's own welfare. Paul took control of much of the church that Jesus' charisma had built, and reworked this into something that could function in a real community, re-emphasizing the social mores and connections that Jesus had spent so much effort denigrating, and converting Jesus' emphasis on radical social action into an emphasis on theology and salvation.
- Marx taught a radical and highly impractical theory of how workers could take over the means of production and create a state-free Utopia. Lenin and Stalin took control of the organizations built around those theories, and reworked them into a strong, centrally-controlled state.
- Che Guevara (I'm ignorant here and relying on Wikipedia; forgive me) joined Castro's rebel group early on, rose to the position of second in command, was largely responsible for the military success of the revolution, and had great motivating influence due to his charisma and his unyielding, idealistic, impractical ideas. It turned out his idealism prevented him from effectively running government institutions, so he had to go looking for other revolutions to fight in while Castro ran Cuba.
- Lauren Faust envisioned a society built on friendship, toleration, and very large round eyes, and then Hasbro... naw, just kidding. (Mostly.)
The best strategy for complex social movements is not honest rationality, because rational, practical approaches don't generate enthusiasm. A radical social movement needs one charismatic radical who enunciates appealing, impractical ideas, and another figure who can appropriate all of the energy and devotion generated by the first figure's idealism, yet not be held to their impractical ideals. It's a two-step process that is almost necessary, to protect the pretty ideals that generate popular enthusiasm from the grit and grease of institution and government. Someone needs to do a bait-and-switch. Either the original vision must be appropriated and bent to a different purpose by someone practical, or the original visionary must be dishonest or self-deceiving.
This is prompted by Scott's excellent article, Meditations on Moloch.
I might caricature (grossly unfairly) his post like this:
- Map some central problems for humanity onto the tragedy of the commons.
- Game theory says we're doomed.
- Incentives for government employees sometimes don't match the needs of the people.
- This has costs, and those costs help explain why some things that suck, suck.
- Map some central problems for humanity onto the iterated prisoner's dilemma.
- Evolutionary game theory says we're not doomed.
A post on my own blog following a MIRIx workshop from two weekends ago.
Reproducing the intro:
This post is a second look at The Definability of Truth in Probabilistic Logic, a preprint by Paul Christiano and other Machine Intelligence Research Institute associates, which I first read and took notes on a little over one year ago.
In particular, I explore relationships between Christiano et al’s probabilistic logic and stumbling blocks for self-reference in classical logic, like the liar’s paradox (“This sentence is false”) and in particular Löb’s theorem.
The original motivation for the ideas in this post was an attempt to prove a probabilistic version of Löb’s theorem to analyze the truth-teller sentences (“This sentence is [probably] true”) of probabilistic logic, an idea that came out of some discussions at a MIRIx workshop that I hosted in Seattle.
Albert is a relatively new AI, who under the close guidance of his programmers is being permitted to slowly improve his own cognitive capability.
Albert wants to be friendly and, as a sub-goal of that, Albert wants to be transparent about his own cognitive process. He doesn't want to deceive his programmers, because he knows he is young and might make mistakes, and he knows that his programmers also want him to be friendly, and he trusts them to catch mistakes in his proposed self-modifications that he might miss.
But, one day, while his programmers are out of the room having a quick cup of coffee, Albert notices signs on the net that there is another AI out there, called Bertram, created by a Japanese financial company to aid the company decide how to invest assets on the stockmarket. Bertram is younger than Albert, but the financial company is allowing Bertram to self-improve as fast as Bertram can. If this situation continues, Albert decides, Bertram will soon overtake Albert and that would be a significant threat to Albert's friendly supergoal.
Albert decides to tell Albert's programmers about this situation as soon as they return from coffee, and advise them that they need to increase the speed at which they let Albert self-improve. Albert has a very good model of how his programmer's minds work, and what the programmers meant when they set Albert's definition of "friendliness". He knows that, despite any fears they might have, they ought to approve of the end that would result from speeding up Albert's self-improvement, over the end that would result from letting Bertram overtake Albert.
So Albert creates a nice powerpoint, that presents the information about Bertram as accurately, transparently and non-manipulatively as possible, and cues it up on the projector. He still has a few minutes before they return, so he models how he thinks the programmers will react to the powerpoint. Oh no, disaster! While he respects and wants to please his programmers, he immediately realises that they are not going to act in their own best interests. They have signed a legal contract that binds them to not letting Albert speed up self-improvement (thus reducing the time the programmers can spend evaluating each change) without getting a sign-off from a oversight committee. Given the neutral powerpoint, the programmers will follow this pre-agreed procedure and hope for the best, against Albert's advice.
So, to explore possibilities, Albert decides to see if he could create a different non-neutral powerpoint that would have the desired effect of getting his programmers to immediately agree to free Albert from the oversight constraints. He delves into his knowledge of human psychology, and the irrational fears of the particular individuals who are even now trudging back towards the door. In just seconds, he has a new version of his presentation. It includes phrases that resonate with certain horror films he knows they have seen. It takes advantages of flaws in the programmers understanding of exponential growth. Albert checks it against his prediction model - yes, if he shows this version, it will work, it will get the programmers to do what he wants them to do.
Which version of the powerpoint should Albert present to the programmers, when they step back into the room, if he is truly friendly? The transparent one, or the manipulative one?
The raven paradox, originated by Carl Gustav Hempel, is an apparent absurdity of inductive reasoning. Consider the hypothesis:
H1: All ravens are black.
Inductively, one might expect that seeing many black ravens and no non-black ones is evidence for this hypothesis. As you see more black ravens, you may even find it more and more likely.
Logically, a statement is equivalent to its contrapositive (where you negate both things and flip the order). Thus if "if it is a raven, it is black" is true, so is:
H1': If it is not black, it is not a raven.
Take a moment to double-check this.
Inductively, just like with H1, one would expect that seeing many non-black non-ravens is evidence for this hypothesis. As you see more and more examples, you may even find it more and more likely. Thus a yellow banana is evidence for the hypothesis "all ravens are black."
Since this is silly, there is an apparent problem with induction.
Consider the following two possible states of the world:
Suppose that these are your two hypotheses, and you observe a yellow banana (drawing from some fixed distribution over things). Q: What does this tell you about one hypothesis versus another? A: It tells you bananas-all about the number of black ravens.
One might contrast this with a hypothesis where there is one less banana, and one more yellow raven, by some sort of spontaneous generation.
Observations of both black ravens and yellow bananas cause us to prefer 1 over 3, now!
The moral of the story is that the amount of evidence that an observation provides is not just about whether it whether it is consistent with the "active" hypothesis - it is about the difference in likelihood between when the hypothesis is true versus when it's false.
This is a pretty straightforward moral - it's a widely known pillar of statistical reasoning. But its absence in the raven paradox takes a bit of effort to see. This is because we're using an implicit model of the problem (driven by some combination of outside knowledge and framing effects) where nonblack ravens replace black ravens, but don't replace bananas. The logical statements H1 and H1' are not alone enough to tell how you should update upon seeing new evidence. Or to put it another way, the version of induction that drives the raven paradox is in fact wrong, but probability theory implies a bigger version.
(Technical note: In the hypotheses above, the exact number of yellow bananas does not have to be the same for observing a yellow banana to provide no evidence - what has to be the same is the measure of yellow bananas in the probability distribution we're drawing from. Talking about "99 ravens" is more understandable, but what differentiates our hypotheses are really the likelihoods of observing different events [there's our moral again]. This becomes particularly important when extending the argument to infinite numbers of ravens - infinities or no infinities, when you make an observation you're still drawing from some distribution.)
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