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warns not to give it too much credit – if you ask how to ‘fix the error’ and the error is the timeout, it’s going to try and remove the timeout. I would counter that no, that’s exactly the point.
I think you misunderstand. In the AI Scientist paper, they said that it was "clever" in choosing to remove the timeout. What I meant in writing that: I think that's very not clever. Still dangerous.
I'm still a little confused. The idea that "the better you can do something yourself, the less valuable it is to do it yourself" is pretty paradoxical. But isn't "the better you can do something yourself, the less downside is there in doing it yourself instead of outsourcing" exactly what you'd expect?
Hmmm? How does this support the point that "the better you can do something yourself, the less valuable it is to do it yourself."
I went from being at a normal level of hard-working (for a high schooler under the college admissions pressure-cooker) to what most would consider an insane level.
The first trigger was going to a summer program after my junior year where I met people like @jsteinhardt who were much smarter and more accomplished than me. That cued a senior year of learning advanced math very quickly to try to catch up.
Then I didn't get into my college of choice and got a giant chip on my shoulder. I constantly felt I had to be accomplishing more, and merely outdoing my peers at the school I did wind up going to wasn't enough. Every semester, I'd say to myself "The me of this semester is going to make the me of last semester look like a slacker."
That was not a sustainable source of pressure because, in a sense, I won, and my bio now reads like the kind I used to envy. I still work very hard, but I only have the positive desire to achieve, rather than the negative desire to escape a feeling of mediocrity.
In high school, I played hours of video games every week. That's unimaginable to me now.
My freshman year, I spent most of the day every Saturday hanging out with board game club. Now that seems insanely decadent.
In Chinese, the words for "to let someone do something" and "to make someone do something" are the same, 让 (ràng). My partner often makes this confusion. This one it did not get even after several promptings, up until I asked about the specific word.
Then I asked why both a Swede and a Dane I know say "increased with 20%" instead of "increased by 20%." It guessed that it had something to do with prepositions, but did not volunteer the preposition in question. (Google Translate answered this; "increased by 20%" translates to "ökade med 20%," and "med" commonly translates to "with.")
But then I made up a story based on my favorite cognate*, and it nailed it.
So, 2/4.
* Yes, this is a true cognate. The German word "Gift" meaning "poison" allegedly descends from euphemistic uses of the English meaning of "gift"
More discussion here: https://www.lesswrong.com/posts/gW34iJsyXKHLYptby/ai-capabilities-vs-ai-products
You're probably safe so long as you restrict distribution to the minimum group with an interest. There is conditional privilege if the sender has a shared interest with the recipient. It can be lost through overpublication, malice, or reliance on rumors.
A possible solution against libel is to provide an unspecific accusation, something like "I say that X is seriously a bad person and should be avoided, but I refuse to provide any more details; you have to either trust my judgment, or take the risk
FYI, this doesn't actually work. https://www.virginiadefamationlawyer.com/implied-undisclosed-facts-as-basis-for-defamation-claim/
It does not take luck to find someone who can help you stare into the abyss. Anyone can do it.
It's pretty simple: Get a life coach.
That is, helping people identify, face, and reason through difficult decisions is a core part of what life coaches do. And about all the questions that Ben cobbled together at the end (maybe not "best argument for" — I don't like that one) can be found in a single place: coaching training. All are commonly used by coaches in routine work.
And there are a lot more tools than the handful than the ones Ben found. These questions are examples of a handful of techniques: eliciting alternatives, countering short-term emotion and status-quo bias, checking congruence with dentity. (Many of these have catchy names like "visioning" or less-catchy names like "identity coaching," but I can't find my coach manual right now which has them listed.)
* Noticing flinching or discongruent emotions ("I heard your voice slow when you mentioned your partner, and I'm wondering if there's something behind it")
* Finding unaddressed issues ("Tell me about your last hour. What caused you stress?")
* Helping you elicit and rank your values, and then check the congruence of each choice with your values
* Helping you access your intuition ("Close your eyes and breathe. Now, one day you wake up and everything's changed / put yourself into the shoes of yourself in 10 years and tell me the first thing you see ")
* Many techniques to address negative emotions around such a decision ("If you abandon this path, what does it mean about you? Now suppose a friend did it; what would you think about them?")
* Many techniques to actually make the decision ("If you made this change, what could go wrong? Now, let's take the first thing you said. Tell me 3 ways you could get more information about how likely that is to happen?")
This also implies that, if you want to be able to do it to yourself, you can pick up a coaching book ("Co-Active Coaching" is my favorite, but I've also heard recommended "The Coaching Habit") and try it, although I think it takes a lot of practice doing it on others before you can reliably turn it inward, as it is quite difficult to simultaneously focus on the concrete problem (what the coachee does) and on evaluating and guiding the thinking and feeling (what the coach does).
There have been a number of posts like this about questions to help guide rationalists through tough decisions or emotions. I think the rationality community has a lot to learn from coaching, which in some ways is all about helping people elevate their rationality to solve problems in their own life. I gave a talk on it in 2016; maybe I should write something on it.
Context: I completed coach training in 2017. The vast majority of my work is no longer in "pure" life coaching, but the skills influence me in daily life.
Quote for you summarizing this post:
“A person's success in life can usually be measured by the number of uncomfortable conversations he or she is willing to have.”
— Tim Ferriss
This post culminates years of thinking which formed a dramatic shift in my worldview. It is now a big part of my life and business philosophy, and I've showed it to friends many times when explaining my thinking. It's influenced me to attempt my own bike repair, patch my own clothes, and write web-crawlers to avoid paying for expensive API access. (The latter was a bust.)
I think this post highlights using rationality to analyze daily life in a manner much deeper than you can find outside of LessWrong. It's in the spirit of the 2012 post "Rational Toothpaste: A Case Study," except targeting a much more significant domain. It counters a productivity meme (outsource everything!) common in this community. It showcases economic concepts such as the value of information.
One thing that's shifted since I wrote this: When I went full-time on my business, I had thought that I would spend significant time learning how to run a server out of my closet to power my business, just like startups did 20 years ago. But it turned out that I had too many other things to study around that time, and I discovered that serverless can run most websites for dollars a month. Still a fan of self-hosting; Dan Luu has written that the inability to run servers is a sign of a disorganized company.
I think some of the specific examples are slightly inaccurate. There was some discussion in the comments about the real reason for the difference between canned and homemade tomato sauce. An attorney tells me my understanding of products liability is too simplistic. I'm less confident that a cleaner would have a high probability of cleaning an area you want them to ignore if you told them and they understood; the problem is that they usually have little communication with the host, and many don't speak English. (Also, I wish they'd stop "organizing" my desk and bathroom counter.) I think I shoehorned in that "avocado toast" analogy too hard. Outside of that, I can't identify any other examples that I have questions about. Both the overall analysis and the scores of individuals examples are in good shape.
Rationalists are known to get their hands dirty with knowledge . I remember when I saw two friends posting on Facebook their opinions of the California ballot: the rationalist tried to reason through their effects and looked at primary sources and concrete predictions, while the non-rationalist just looked at who endorsed what. I'd like to see us become known for getting our hands dirty quite literally as well.
Still waiting.
When will you send out the link for tomorrow?
https://galciv3.fandom.com/wiki/The_Galactic_Civilizations_Story#Humanity_and_Hyperdrive
I've hired (short-term) programmers to assist on my research several times. Each time, I've paid from my own money. Even assuming I could have used grant money, it would have been too difficult. And, long story short, there was no good option that involved giving funds to my lab so they could do the hire properly.
Grad students are training to become independent researchers. They have the jobs of conducting research (which in most fields is mostly not coding), giving presentations, writing, making figures, reading papers, and taking and teaching classes. Their career and skillset is rarely aligned with long-term maintenance of a software project; usually, they'd be sacrificing their career to build tools for the lab.
This is a great example of the lessons in https://www.lesswrong.com/posts/tTWL6rkfEuQN9ivxj/leaky-delegation-you-are-not-a-commodity
Really appreciate this informative and well-written answer. Nice to hear from someone on the ground about SELinux instead of the NSA's own presentations.
I phrased my question about time and space badly. I was interested in proving the time and space behavior of the software "under scrutiny", not in the resource consumption of the verification systems themsvelves.
LOL!
I know a few people who have worked in this area. Jan Hoffman and Peng Gong have worked on automatically inferring complexity. Tristan Knoth has gone the other way, including resource bounds in specs for program synthesis. There's a guy who did an MIT Ph. D. on building an operating system in Go, and as part of it needed an analyzer that can upper-bound the memory consumption of a system call. I met someone at CU Boulder working under Bor-Yuh Evan Chang who was also doing static analysis of memory usage, but I forget whom.
So, those are some things that were going on. About all of these are 5+ years old, and I have no more recent updates. I've gone to one of Peng's talks and read none of these papers.
I must disagree with the first claim. Defense-in-depth is very much a thing in cybersecurity. The whole "attack surface" idea assumes that, if you compromise any application, you can take over an entire machine or network of machines. That is still sometimes true, but continually less so. Think it's game over if you get root on a machine? Not if it's running SELinux.
Hey, can I ask an almost unrelated question that you're free to ignore or answer as a private message OR answer here? How good is formal verification for time and space these days?
I can speak only in broad strokes here, as I have not published in verification. My publications are predominantly in programming tools of some form, mostly in program transformation and synthesis.
There are two main subfields that fight over the term "verification": model checking and mechanized/interactive theorem proving. This is not counting people like Dawson Engler, who write very unsound static analysis tools but call it "verification" anyway. I give an ultra-brief overview of verification in https://www.pathsensitive.com/2021/03/why-programmers-shouldnt-learn-theory.html
I am more knowledgable about mechanized theorem proving, since my department has multiple labs who work in this area and I've taken a few of their seminars. But asking about time/space of verification really just makes sense for the automated part. I attended CAV in 2015 and went to a few model checking talks at ICSE 2016, and more recently talked to a friend on AWS's verification team about what some people there are doing with CBMC. Okay, and I guess I talked to someone who used to do model checking on train systems in France just two days ago. Outside of that exposure, I am super not-up-to-date with what's going on. But I'd still expect massive breakthroughs to make the news rounds over to my corner of academia, so I'll give my sense of the status quo.
Explicit state enumeration can crush programs with millions or billions of states, while symbolic model checking routinely handles $10^100$ states.
Those are both very small numbers. To go bigger, you need induction or abstraction, something fully automated methods are still bad at.
Yes, we can handle exponentially large things, but the exponential still wins. There's a saying of SAT solvers "either it runs instantly or it takes forever." I believe this is less true of model checking, though still true. (Also, many model checkers use SAT.)
If you want to model check something, either you check a very small program like a device driver, or you develop some kind of abstract model and check that instead.
I agree with about everything you said as well as several more criticisms along those lines you didn't say. I am probably more familiar with these issues than anyone else on this website with the possible exception of Jason Gross.
Now, suppose we can magic all that away. How much then will this reduce AI risk?
I don't see what this parable has to do with Bayesianism or Frequentism.
I thought this was going to be some kind of trap or joke around how "probability of belief in Bayesianism" is a nonsense question in Frequentism.
I do not. I mostly know of this field from conversations with people in my lab who work in this area, including Osbert Bastani. (I'm more on the pure programming-languages side, not an AI guy.) Those conversations kinda died during COVID when no-one was going into the office, plus the people working in this area moved onto their faculty positions.
I think being able to backtrace through a tree counts as victory, at least in comparison to neural nets. You can make a similar criticism about any large software system.
You're right about the random forest there; I goofed there. Luckily, I also happen to know of another Osbert paper, and this one does indeed do a similar trick for neural nets (specifically for reinforcement learning); https://proceedings.neurips.cc/paper/2018/file/e6d8545daa42d5ced125a4bf747b3688-Paper.pdf
I think you're accusing people who advocate this line of idle speculation, but I see this post as idle speculation. Any particular systems you have in mind when making this claim?
I'm a program synthesis researcher, and I have multiple specific examples of logical or structured alternatives to deep learning
Here's Osbert Bastani's work approximating neural nets with decision trees, https://arxiv.org/pdf/1705.08504.pdf . Would you like to tell me this is not more interpretable over the neural net it was generated from?
Or how about Deep3 ( https://dl.acm.org/doi/pdf/10.1145/3022671.2984041 ), which could match the best character-level language models of its day ( https://openreview.net/pdf?id=ry_sjFqgx ).
The claim that deep learning is not less interpretable than alternatives is not born out by actual examples of alternatives.
I'm a certified life coach, and several of these are questions found in life coaching.
E.g.:
Is there something you could do about that problem in the next five minutes?
Feeling stuck sucks. Have you spent a five minute timer generating options?
What's the twenty minute / minimum viable product version of this overwhelming-feeling thing?
These are all part of a broader technique of breaking down a problem. (I can probably find a name for it in my book.) E.g.: Someone comes in saying they're really bad at X, and you ask them to actually rate their skills and then what they could do to become 5% better.
You want to do that but don't think you will? Do you want to make a concrete plan now?
Do you want to just set an alarm on your phone now as a reminder? (from Damon Sasi)
Do you sort of already know what you're going to do / have your mind made up about this?
These are all part of the "commitment" phase of a coaching session, which basically looks like walking someone through SMART goals.
Do you know anyone else who might have struggled with or succeeded at that? Have you talked to them about it? (from Damon Sasi)
Who do you know who you could ask for help from?
I can't say these are instances of a named technique, but they are things you'd commonly find a coach asking. Helping someone look inside themselves for resources they already have is a pretty significant component of coaching.
There's a major technique in coaching not represented here called championing. Champion is giving someone positive encouragement by reinforcing some underlying quality. E.g.: "You've shown a lot of determination to get this far, and I know you'll be able to use it to succeed at X."
Several of these questions do differ from life coaching in a big way: they suggest a course of action. We call this "advice-giving" as telling someone what to do serves the advice-giver's agenda more than the receiver's, or at least serves what the advice-giver thinks the receiver's agenda should be. The best piece of (irony forthcoming) advice I've received about coaching is to "coach the person, not the problem." Much more effective than to help someone with the task at hand is to help them cultivate the underlying skill. Instead of suggesting courses of action, you instead focus on their ability to come up with and evaluate options.
Recommended reading: Co-active Coaching, https://www.amazon.com/Co-Active-Coaching-Changing-Business-Transforming/dp/1857885678
I realize now that this expressed as a DAG looks identical to precommitment.
Except, I also think it's a faithful representation of the typical Newcomb scenario.
Paradox only arises if you can say "I am a two-boxer" (by picking up two boxes) while you were predicted to be a one-boxer. This can only happen if there are multiple nodes for two-boxing set to different values.
But really, this is a problem of the kind solved by superspecs in my Onward! paper. There is a constraint that the prediction of two-boxing must be the same as the actual two-boxing. Traditional causal DAGs can only express this by making them literally the same node; super-specs allow more flexibility. I am unclear how exactly it's handled in FDT, but it has a similar analysis of the problem ("CDT breaks correlations").
Okay, I see how that technique of breaking circularity in the model looks like precommitment.
I still don't see what this has to do with counterfactuals though.
I don't understand what counterfactuals have to do with Newcomb's problem. You decide either "I am a one-boxer" or "I am a two-boxer," the boxes get filled according to a rule, and then you pick deterministically according to a rule. It's all forward reasoning; it's just a bit weird because the action in question happens way before you are faced with the boxes. I don't see any updating on a factual world to infer outcomes in a counterfactual world.
"Prediction" in this context is a synonym for conditioning. is defined as .
If intervention sounds circular...I don't know what to say other than read Chapter 1 of Pearl ( https://www.amazon.com/Causality-Reasoning-Inference-Judea-Pearl/dp/052189560X ).
To give a two-sentence technical explanation:
A structural causal model is a straight-line program with some random inputs. They look like this
u1 = randBool()
rain = u1
sprinkler = !rain
wet_grass = rain || sprinkler
It's usually written with nodes and graphs, but they are equivalent to straight-line programs, and one can translate easily between these two presentations.
In the basic Pearl setup, an intervention consists of replacing one of the assignments above with an assignment to a constant. Here is an intervention setting the sprinkler off.
u1 = randBool()
rain = u1
sprinkler = false
wet_grass = rain || sprinkler
From this, one can easily compute that.
If you want the technical development of counterfactuals that my post is based on, read Pearl Chapter 7, or Google around for the "twin network construction."
Or I'll just show you in code below how you compute the counterfactual "I see the sprinkler is on, so, if it hadn't come on, the grass would not be wet," which is written
We construct a new program,
u1 = randBool()
rain = u1
sprinkler_factual = !rain
wet_grass_factual = rain || sprinkler_factual
sprinkler_counterfactual = false
wet_grass_counterfactual = rain || sprinkler_counterfactual
This is now reduced to a pure statistical problem. Run this program a bunch of times, filter down to only the runs where sprinkler_factual is true, and you'll find that wet_grass_counterfactual is false in all of them.
If you write this program as a dataflow graph, you see everything that happens after the intervention point being duplicated, but the background variables (the rain) are shared between them. This graph is the twin network, and this technique is called the "twin network construction." It can also be thought of as what the do(y | x -> e) operator is doing in our Omega language.
While I can see this working in theory, in practise it's more complicated as it isn't obvious from immediate inspection to what extent an argument is or isn't dependent on counterfactuals. I mean counterfactuals are everywhere! Part of the problem is that the clearest explanation of such a scheme would likely make use of counterfactuals, even if it were later shown that these aren't necessary.
- Is the explanation in the "What is a Counterfactual" post linked above circular?
- Is the explanation in the post somehow not an explanation of counterfactuals?
The key unanswered question (well, some people claim to have solutions) in Functional Decision theory is how to construct the logical counterfactuals that it depends on.
I read a large chunk of the FDT paper while drafting my last comment.
The quoted sentence may hint at the root of the trouble that I and some others here seem to have in understanding what you want. You seem to be asking about the way "counterfactual" is used in a particular paper, not in general.
It is glossed over and not explained in full detail in the FDT paper, but it seems to mainly rely on extra constraints on allowable interventions, similar to the "super-specs" in one of my other papers: https://www.jameskoppel.com/files/papers/demystifying_dependence.pdf .
I'm going to go try to model Newcomb's problem and some of the other FDT examples in Omega. If I'm successive, it's evidence that there's nothing more interesting going on than what's in my causal hierarchy post.
I'm having a little trouble understanding the question. I think you may be thinking of either philosophical abduction/induction or logical abduction/induction.
Abduction in this article is just computing P(y | x) when x is a causal descendant of y. It's not conceptually different from any other kind of conditioning.
In a different context, I can say that I'm fond of Isil Dillig's thesis work on an abductive SAT solver and its application to program verification, but that's very unrelated.
I'm not surprised by this reaction, seeing as I jumped on banging it out rather than checking to make sure that I understand your confusion first. And I still don't understand your confusion, so my best hope was giving a very clear, computational explanation of counterfactuals with no circularity in hopes it helps.
Anyway, let's have some back and forth right here. I'm having trouble teasing apart the different threads of thought that I'm reading.
After intervening on our decision node do we just project forward as per Causal Decision Theory or do we want to do something like Functional Decision Theory that allows back-projecting as well?
I think I'll need to see some formulae to be sure I know what you're talking about. I understand the core of decision theory to be about how to score potential actions, which seems like a pretty separate question from understanding counterfactuals.
More specifically, I understand that each decision theory provides two components: (1) a type of probabilistic model for modeling relevant scenarios, and (2) a probabilistic query that it says should be used to evaluate potential actions. Evidentiary decision theory uses an arbitrary probability distribution as its model, and evaluates actions by P(outcome |action). Causal decision theory uses a causal Bayes net (set of intervential distributions) and the query P(outcome | do(action)). I understand FDT less well, but basically view it as similar to CDT, except that it intervenes on the input to a decision procedure rather than on the output.
But all this is separate from the question of how to compute counterfactuals, and I don't understand why you bring this up.
When trying to answer these questions, this naturally leads us to ask, "What exactly are these counterfactual things anyway?" and that path (in my opinion) leads to circularity.
I still understand this to be the core of your question. Can you explain what questions remain about "what is a counterfactual" after reading my post?
Oh hey, I already have slides for this.
Here you go: https://www.lesswrong.com/posts/vuvS2nkxn3ftyZSjz/what-is-a-counterfactual-an-elementary-introduction-to-the
I took the approach: if I very clearly explain what counterfactuals are and how to compute them, then it will be plain that there is no circularity. I attack the question more directly in a later paragraph, when I explain how counterfactual can be implemented in terms of two simpler operations: prediction and intervention. And that's exactly how it is implemented in our causal probabilistic programming language, Omega (see http://www.zenna.org/Omega.jl/latest/ or https://github.com/jkoppel/omega-calculus ).
Unrelatedly, if you want to see some totally-sensible but arguably-circular definitions, see https://en.wikipedia.org/wiki/Impredicativity .
"Many thousands of date problems were found in commercial data processing systems and corrected. (The task was huge – to handle the work just for General Motors in Europe, Deloitte had to hire an aircraft hangar and local hotels to house the army of consultants, and buy hundreds of PCs)."
Sounds like more than a few weeks.
Thanks; fixed both.
Was it founded by the Evil Twin of Peter Singer?
https://www.smbc-comics.com/comic/ev
Define "related?"
Stories of wishes gone awry, like King Midas, are the original example.
I've definitely looked at it, but don't recall trying it. My first questions from looking at the screenshots are about its annotation capabilities (e.g.: naming functions, identifying structs) and its UI (IDA highlighting every use of a register when you mouse over it is stupendously useful).
This reminds me of how I did the background reading for my semantic code search paper ( http://www.jameskoppel.com/files/papers/yogo.pdf ). I made a list of somewhat related papers, printed out a big stack of them at a time, and then for each set a 7.5 minute timer for each. By the end of that 7.5 minutes, I should be able to write a few sentences about what exact problem it solves and what its big ideas are, as well as add more cited work / search keywords to expand my list of related papers. I'd often need to give myself a few extra minutes, but I nonetheless worked through it incredibly fast.
The big strategy is that papers are written in a certain format (e.g.: (1) Introduction (2) Overview (3) Detailed technical development (4) Implementation (5) Evaluation (6) Related work (7) Conclusion), so I knew exactly where to look to get the important bits.
A difference between this and your suggestions is that (1) I was already highly knowledgable in this area, having just supervised a master's student building something better than these papers, and (2) the bar of "understand something well enough to discuss in a related work section" is rather low. Still, the end result is what is probably the best ever overview of semantic code search; our paper discusses a full 70 other tools.
++++
Anytime I try a new language, first question is "Is there a JetBrains IDE or plugin for it?"
Bryan Caplan has been creating his "economics graphic novels" using an old "comic creator" software. He has a valid license, but they company that makes it went out decades ago, and the license server no longer exists. So I disabled the license-server check for him.
When I worked in mobile, I did it frequently. Customer would call us and say our SDK isn't working. I'd download their app off the app store, decompile it, and figure out exactly how they're using us.
It's also surprisingly frequent how often I want to step through a library or program that I'm using. If you link to that library as a binary, then (even if the source is available elsewhere) it's often easiest to debug it using a reverse-engineering tool.
Less everyday, but I've also done some larger projects involving REing. I started off in game modding 10 years ago. Last year, I did some election security work that achieved some publicity. https://www.nytimes.com/2020/02/13/us/politics/voting-smartphone-app.html
Software: Omnigraffle
Need: Making figures and diagrams (e.g.: for scientific papers)
Other software I've tried: Sketch, Illustrator, tikz
Omnigraffle has beautiful defaults, and makes it very fast to create shapes and diagrams that connect. It can make crossing edges look pretty and clear instead of a mess. Illustrator gives you a lot more flexibility (e.g.: strokes whose width gradually changes, arbitrary connection points for arrows), but you can be way faster at making figures with Omnigraffle.
Use Illustrator for making art and posters. Use Sketch (or Figma) for mocking up UIs. Use Omnigraffle for making figures.
Software: IDA
Need: Binary reverse-engineering
Other programs I've tried: ghidra, OllyDbg, Hopper
IDA is fast and well-featured. I've had multiple times where my process of having questions about a binary to figuring out the answer took minutes.
Hopper has a nicer UI, but works on fewer executables and does not analyze the binary as well.
IDA gets criticized for "having an interface designed by programmers," but ghidra is much worse in that regard. "A giant Java program written by the government" describes it well. ghidra supposedly has a collaboration mode, but I gave up trying to get it to work after much effort.
OllyDbg is not really comparable, being primarily a binary debugger. But IDA's built-in debugger is quite underrated. And their support is very good. I was among the first to use it for Android-hacking on Android 5.0, and found their Android debugger would not run with the new OS restrictions; they gave me a new version that would within a few days.
I'd appreciate if someone touched on HR software and CRMs for small businesses.
Also, collaborative document editing that isn't owned by Google.
I've been running exercises like the one described here for nearly 5 years as part of my business ( http://jameskoppelcoaching.com/ ). They take the name "design exercises." It's done in both live and canned formats. Chief addition is that, in the live versions, the new features are chosen antagonistically.
Dagon in another thread claims "Making something that's maintainable and extensible over many years isn't something that can be trained in small bites." My long list of testimonials begs to differ.
Am incline to agree, but I want to add that security is all connected. There are several direct causal paths from compromised user data to compromised dev workstation (and vice versa).
Do you think the point of adding nuclear close calls isn't to move public policy into a direction that's less likely to produce a nuclear accident? That's a political purpose. It's not party political but it's political.
Of course I believe it serves that purpose. I also believe that the most recent edit in all of Wikipedia at my time of writing, deleting a paragraph from the article on Leanna Cavanagh (a character from some British TV show I'd never heard of) serves to decrease the prominence of that TV show, which will weaken whatever message or themes it carries (such as bringing attention to Yorkshire, where the show is set).
So, this is an empty criticism.
Similarly, I don't know who "the account behind the edit you point to" is since I linked to two different revisions both of which cover edits by multiple authors, but I checked the edit history of one of them, user Simfish (whose real life identity I shan't reveal at this moment). He has a bunch of edits on the "Timeline of Nordstrom" article, and I don't know what that has to do with EA.
I'm not sure this conversation has any more productive purpose. You keep on harping on a specific defense of Wikipedia culture that any hostility encountered by my peers is justified because we were a paid special interest group. I've stated several reasons why those justifications did not apply at the time hostility was first encountered. I see you continuing to try to find ways to make those criticisms apply. Needless to say, this is a silly battle since I'm the one with all the details.
I can say that this experience is not leaving me any more desirous of editing Wikipedia, so I'm at least one person with whom you've not yet succeeded in your original goal.
Edit: Okay, I just found Simfish (and his real name) on a list of people whom Vipul paid, and found that Vipul Naki's timeframe overlapped with the FLI group. I have to partly retract the details behind my thesis above. I can still make it because I do not recognize anyone else on Vipul's list having a Boston/FLI connection.
Edit 2: Neither of these articles appear on the list of articles sponsored by Vipul.
It was paid-editing for a political agenda. From an EA perspective paying someone to do paid editing or do political lobbying is completley fine. On the other hand you have the money isn't speech side that considers using money to do lobbying or get someone change Wikipedia according to their political interests bad.
Putting aside that a volunteer project by a non-profit is not paid, and I take some issue with arguments that improvements to the page on nuclear close calls is "political":
I mean that some individuals later in this group, before any organized effort by the FLI existed, had dabbled in editing some of these same articles, for exactly the pure motives that you advocate editing for, and encountered entrenched (and perhaps unreasonable) opposition.
From the Wikipedia perspective there's a difference of a Wikipedia user group that does a Wikipedia-editing session together which is great and an organization having a project to change Wikipedia according to their agenda.
Our perspective was that we were merely adding better information, improving accuracy, and giving fair summaries of the arguments.
I expect similar groups would say the same.
You can judge for yourself. Here are some edits from the group:
https://en.wikipedia.org/w/index.php?title=AI_takeover&type=revision&diff=688572046&oldid=687117054
https://en.wikipedia.org/w/index.php?title=Biotechnology_risk&type=revision&diff=713438700&oldid=713369288
The talk of an admin who controlled those pages with an iron fist came from before this project existed, presumably encountered by affiliates who had tried to edit in good faith exactly as you've advocated, but were shut down.
We were far from the first or only group that had Wikipedia-editing sessions. I've walked past signs at my university advertising them for other groups. Ours was quite benign. I'm reading some of the discussion from back then; their list included things like adding links for the page on nuclear close calls.
I've seen articles on hot-button topics where the Wikipedia article is far more slanted to one side than any of the mainstream media articles, and read the talk archives where a powerful few managed to invoke arcane rules to rule out all sources to the contrary. It's stuff like this that makes me want out. I was a happy Wikipedian in high school in a previous decade, but I shall be no longer.
During my stint volunteering with the FLI, I worked on a project to improve Wikipedia's coverage of existential risk. I don't remember the ultimate outcome of the project, but we were up against an admin who "owned" many of those pages, and was hostile to many of FLI's views.
This article, at least by appearances, is an excellent account of the problems and biases of Wikipedia: https://prn.fm/wikipedia-rotten-core/
The underlying thought behind both this and the previous post seems to be the notion that counterfactuals are somehow mysterious or hard to grasp. This looks like a good chance to plug our upcoming ICML paper, w
hich reduces counterfactuals to a programming language feature. It gives a new meaning to "programming Omega." http://www.zenna.org/publications/causal.pdf