trentmkelly/LessWrong-43k huggingface dataset:
June 2017 Media Thread
by ArisKatsaris — 2017-06-01T06:17:40.712Z

This is the monthly thread for posting media of various types that you've found that you enjoy. Post what you're reading, listening to, watching, and your opinion of it. Post recommendations to blogs. Post whatever media you feel like discussing! To see previous recommendations, check out the older threads.

Rules:

Please avoid downvoting recommendations just because you don't personally like the recommended material; remember that liking is a two-place word. If you can point out a specific flaw in a person's recommendation, consider posting a comment to that effect.

If you want to post something that (you know) has been recommended before, but have another recommendation to add, please link to the original, so that the reader has both recommendations.

Please post only under one of the already created subthreads, and never directly under the parent media thread.

Use the "Other Media" thread if you believe the piece of media you want to discuss doesn't fit under any of the established categories.

Use the "Meta" thread if you want to discuss about the monthly media thread itself (e.g. to propose adding/removing/splitting/merging subthreads, or to discuss the type of content properly belonging to each subthread) or for any other question or issue you may have about the thread or the rules.
Regulatory lags for New Technology [2013 notes]
by gwern — 2017-05-31T01:27:52.046Z

I found some old notes from June 2013 on time delays in how fast one can expect Western political systems & legislators to respond to new technical developments.

In general, response is slow and on the order of political cycles; one implication I take away is that a takeoff an AI could happen over half a decade or more without any meaningful political control and would effectively be a ‘fast takeoff’, especially if it avoids any obvious mistakes.

1 Regulatory lag

“Regulatory delay” is the delay between the specific action required by regulators or legislatures to permit some new technology or method and the feasibility of the technology or method; “regulatory lag” is the converse, then, and is the gap between feasibility and reactive regulation of new technology. Computer software (and artificial intelligence in particular) is mostly unregulated, so it is subject to lag rather than delay.

Unfortunately almost all research seems to focus on modeling lags in the context of heavily regulated industries (especially natural monopolies like insurance or utilities), and few focus on compiling data on how long a lag can be expected between a new innovation or technology and its regulation. As one would expect, the few results point to lags on the order of years; for example, Ippolito 1979 (“The Effects of Price Regulation in the Automobile Insurance Industry”) finds that the period of price changes goes from 11 months in unregulated US states to 21 months in regulated states, suggesting the price-change framework itself causes a lag of almost a year.

Below, I cover some specific examples, attempting to estimate the lags myself:

HFT: >5 years

Self-driving cars: >6 years

Hacking: ~30 years

(Nuclear weapons would be an interesting example but it’s hard to say what ‘lag’ would be inasmuch as they were born in government control and are subject to no meaningful global control; however, if the early proposals for a world government or unified nuclear weapon organization had gone through, they would also have represented a lag of at least 5 years.)

1.1 Hacking

Computer hacking existed for quite a while before relevant laws were passed and serious law enforcement began, which is typically considered to have begun with Operation Sundevil:

Prior to 1990, people who manipulated telecommunication system

...
Divergent preferences and meta-preferences
by Stuart_Armstrong — 2017-05-30T07:33:38.791Z

Crossposted at the Intelligent Agents Forum.

In simple graphical form, here is the problem of divergent human preferences:

Here the AI either chooses A or ¬A, and as a consequence, the human then chooses B or ¬B.

There are a variety of situations in which this is or isn't a problem (when A or B or their negations aren't defined, take them to be the negative of what is define):

Not problems:

A/¬A = "gives right shoe/left shoe", B/¬B = "adds left shoe/right shoe".

A = "offers drink", ¬B = "goes looking for extra drink".

A = "gives money", B = "makes large purchase".

Potentially problems:

A/¬A = "causes human to fall in love with X/Y", B/¬B = "moves to X's/Y's country".

A/¬A = "recommends studying X/Y", B/¬B = "choose profession P/Q".

A = "lets human conceive child", ¬B = "keeps up previous hobbies and friendships".

Problems:

A = "coercive brain surgery", B = anything.

A = "extreme manipulation", B = almost anything.

A = "heroin injection", B = "wants more heroin".

So, what are the differences? For the "not problems", it makes sense to model the human as having a single reward R, variously "likes having a matching pair of shoes", "needs a certain amount of fluids", and "values certain purchases". Then all that the the AI is doing is helping (or not) the human towards that goal.

As you move more towards the "problems", notice that they seem to have two distinct human reward functions, RA and R¬A, and that the AI's actions seem to choose which one the human will end up with. In the spirit of humans not being agents, this seems to be AI determining what values the human will come to possess.

Grue, Bleen, and agency

Of course, you could always say that the human actually has reward R = IARA + (1-IA)R¬A, where IA is the indicator function as to whether the AI does action A or not.

Similarly to the grue and bleen problem, there is no logical way of distinguishing that "pieced-together" R from a more "natural" R (such as valuing pleasure, for instance). Thus there is no logical way of distinguishing the human being an agent from the human not being an agent, just from its preferences and behaviour.

However, from a learning and computational complexity point of view, it does make sense to distinguish "natural" R's (where RA and R¬A are essentially the same, despite the human's actions being di...
Futarchy Fix
by abramdemski — 2017-05-30T05:46:39.000Z

Robin Hanson's Futarchy is a proposal to let prediction markets make governmental decisions. We can view an operating Futarchy as an agent, and ask if it is aligned with the interests of its constituents. I am aware of two main failures of alignment: (1) since predicting rare events is rewarded in proportion to their rareness, prediction markets heavily incentivise causing rare events to happen (I'll call this the entropy-market problem); (2) it seems prediction markets would not be able to assign probability to existential risk, since you can't collect on bets after everyone's dead (I'll call this the existential risk problem). I provide three formulations of (1) and solve two of them, and make some comments on (2). (Thanks to Scott for pointing out the second of these problems to me; I don't remember who originally told me about the first problem, but also thanks.)

Futarchy and Agents

My motivation here is twofold. On the one hand, Futarchy is something which many people are enthusiastic about. There is a current effort to implement it for small-to-medium-size group coordination. Futarchy is just the idea of using prediction markets to make decisions that matter, and decision markets are already a thing. So, from one perspective, we're already on our way there. But, there are alignment problems which have not been addressed. The misaligned Futarchy problem seems less bad that the misaligned superhuman AI problem. Arguably, we are already embedded in misaligned markets, and Futarchy seems like an improvement on those. Furthermore, Futarchy is made of people; if a Futarchy becomes too evil, the people can just stop, right?...

Well, one way or the other, it seems like it would be a good idea to go ahead and solve the Futarchy alignment problem before there's a problem.

My second reason for being interested in this is as a case-study for alignment. Maybe the Futarchy alignment problem is an easier instance, which could inform us about the AI alignment problem? Or perhaps it could be more directly relevant, informing logical-induction decision theories which look something like Futarchies?

More specifically, the entropy-market problem is an instance of "directionality" in decision theory, as discussed in The Foundations of Epistemic Decision Theory by Jason Konek and Ben Levinstein. The idea is that it's not always beneficial to enforce rationality constraints. A

...
Invitation to comment on a draft on multiverse-wide cooperation via alternatives to causal decision theory (FDT/UDT/EDT/...)
by Caspar Oesterheld — 2017-05-29T08:34:59.311Z

I have written a paper about “multiverse-wide cooperation via correlated decision-making” and would like to find a few more people who’d be interested in giving a last round of comments before publication. The basic idea of the paper is described in a talk you can find here. The paper elaborates on many of the ideas and contains a lot of additional material. While the talk assumes a lot of prior knowledge, the paper is meant to be a bit more accessible. So, don’t be disheartened if you find the talk hard to follow — one goal of getting feedback is to find out which parts of the paper could be made more easy to understand.

If you’re interested, please comment or send me a PM. If you do, I will send you a link to a Google Doc with the paper once I'm done with editing, i.e. in about one week. (I’m afraid you’ll need a Google Account to read and comment.) I plan to start typesetting the paper in LaTeX in about a month, so you’ll have three weeks to comment. Since the paper is long, it’s totally fine if you don’t read the whole thing or just browse around a bit.
Meetup : Sydney Rationality - Pub meetup June
by Elo — 2017-05-29T06:52:14.486Z

Discussion article for the meetup : Sydney Rationality - Pub meetup June

WHEN:
22 June 2017 06:00:00PM (+1000)

WHERE:
575 george st, sydney

We sit at the big table outside the pizza oven on level 2
For this month bring along a suggestion of a book you want more people to read.
Come along to our regular monthly pub meetup to talk all things math, science, technology, engineering, thinking, growth, reasoning and beliefs. If you are an aspiring rationalist, a nerd, geek, scientist or just a quiet thinker - we can't wait to meet you to share ideas, discuss, debate, learn and grow together.
If you are interested in our rationality dojos, ask us about them in person. See you there! Also, while you are at it - bring a friend along too!
We usually get ~15 attendees through various advertising avenues.

https://www.meetup.com/rationalists_of_sydney/events/jcxffnywjbdc/
https://www.facebook.com/events/124294738146492

Discussion article for the meetup : Sydney Rationality - Pub meetup June
Open thread, May 29 - June 4, 2017
by Thomas — 2017-05-29T06:13:51.683Z

If it's worth saying, but not worth its own post, then it goes here.

Notes for future OT posters:

1. Please add the 'open_thread' tag.

2. Check if there is an active Open Thread before posting a new one. (Immediately before; refresh the list-of-threads page before posting.)

3. Open Threads should start on Monday, and end on Sunday.

4. Unflag the two options "Notify me of new top level comments on this article" and "Make this post available under..." before submitting
Bi-Weekly Rational Feed
by sapphire — 2017-05-28T17:12:57.118Z

Five Recommended Articles You Might Have Missed:

The Four Blind Men The Elephant And Alan Kay by Meredith Paterson (Status 451) - Managing technical teams. Taking a new perspective is worth 90 IQ points. Getting better enemies. Guerrilla action.

Vast Empirical Literature by Marginal REVOLUTION - Tyler's 10 thoughts on approaching fields with large literatures. He is critical of Noah's "two paper rule" and recommends alot of reading.

Notes From The Hufflepuff Unconference (Part 1) by Raemon (lesswrong) - Goal: Improve at: "social skills, empathy, and working together, sticking with things that need sticking with". The article is a detailed breakdown of the unconference including: Ray's Introductory Speech, a long list of what people want to improve on, the lightning talks, the 4 breakout sessions, proposed solutions, further plans, and closing words. Links to conference notes are included for many sections.

Antipsychotics Might Cause Cognitive Impairment by Sarah Constantin (Otium) - A harrowing personal account of losing abstract thinking ability on Risperdal. The author conducts a literature review, and concludes with some personal advice about taking medication.

Dwelling In Possibility by Sarah Constantin (Otium) - Leadership. Confidence in the face of the uncertainty and imperfection. Losing yourself when you try to step back and facilitate.

Scott:

Those Modern Pathologies by Scott Alexander - You can argue X is a modern pathology for almost any value of X. Scott demonstrates this by repeated example. Among other things "Aristotelian theory of virtue" and "Homer's Odyssey" get pathologized.

The Atomic Bomb Considered As Hungarian High School Science Fair Project by Scott Alexander - Ashkenazi Jewish Intelligence. An explanation of Hungarian dominance in physics and science in the mid 1900s.

Classified Ads Thread by Scott Alexander - Open thread where people post ads. People are promoting their websites and some of them are posting actual job ads among other things.

Open Thread 76 by Scott Alexander - Bi-weekly Open thread.

Postmarketing Surveillance Is Good And Normal by Scott Alexander - Scott shows why a recent Scientific American study does not imply the FDA is too risky.

Epilogue by Scott Alexander (Unsong) - All's Whale that Ends Whale.

Polyamory Is Not Polygyny by Scott Alexander - A quick ...
Meetup : Melbourne Social Meetup: June
by Maelin — 2017-05-28T10:10:44.943Z

Discussion article for the meetup : Melbourne Social Meetup: June

WHEN:
02 June 2017 06:30:00PM (+1000)

WHERE:
The Bull and Bear Tavern, Flinders Lane, Melbourne

The MelbLW social meetup has moved to an exciting new schedule! We are now on the first Friday of each month. Come join us for the first session of the new schedule!

FB event: https://www.facebook.com/events/299902167131043/

Social meetups are informal get-togethers where we chat about topics of interest and have a couple of drinks together.

WHEN? Friday June 2, 18:30 until late. Don't worry about being on time, though - it's fine to rock up whenever.

WHERE? The Bull & Bear Tavern, on Flinders Lane (just a short walk from Flinders St Station)

FOOD? The B&B does reasonable traditional pub food and we usually share a few plates of wedges. For those who stay late, we sometimes go for a late night meal around 11pm.

CONTACT? Any issues on the night, call or text Chris on 0439471632

Discussion article for the meetup : Melbourne Social Meetup: June
Strong men are socialist, reports study that previously reported the opposite
by Jacob Falkovich — 2017-05-27T20:49:53.000Z

Did you know that physically strong men are more likely to be socialist? Shocking, I know. But there’s no other conclusion that can be reached after reading all the articles bombarding my social feeds that claim the opposite.

The Blaze: Study – Physically weak men more likely to be socialist, strong men more likely to be capitalist, MrCTV: New Study Finds Wimpy Guys Are More Likely To Be Socialists, same from The Daily Wire, and the hilarious counter-narrative framing by Vice: Gym Bros More Likely to be Right-Wing Assholes, Science Confirms.

Via @TheSafestSpace on Twitter

The Daily Wire article concludes with a challenge:

Anyone with two eyes would be hard-pressed to refute such findings.

Well, I have two eyes and I don’t mind being hard-pressed – as evidenced by the hour I spent doing bench press and squats at the gym this morning while remaining a libertarian-leaning centrist. Challenge accepted, jackass.

Putanumonit has a recurring Defense Against the Dark Arts section, helping my readers escape unscathed from an onslaught of gruesome statistics and preposterous science reporting. So far I covered mostly left-leaning BS (BS is short for “bad statistics”), which there seems to more of. Presumably this is because the left creates a higher demand for “science proves we’re right” smugpieces. Or perhaps because science is a liberal conspiracy. Either way, I was excited to see those sneaky scientists giving conservatives a break for once.

We’ll take a closer look at The Blaze article, which is pretty typical of the genre, and the study itself by Michael Price et al of Brunel University in London. “Brunel” sounds like the love child of American Ivies Brown and Cornel, although it’s pretty hard to imagine an Ivy League university publishing something so, well, unprogressive.

Anyway, here’s The Blaze:

Brunel University academics studied 171 men aged 18 – 40…

That’s one way to describe the sample size. Here’s another: a group of 98% students, mean age 21, and a single 40-year-old dude who needed the 5 quid. Doesn’t sound quite like a “representative sample” now, does it?

I know the composition of the subject group because Dr. Price and his associates put their entire dataset, for free, online.

There’s going to be a lot of snark about this researc...
On "Overthinking" Concepts
by Bound_up — 2017-05-27T17:07:37.260Z

Related to http://lesswrong.com/lw/1mh/that_magical_click/1hd7

I've NOT been confused by the problem of overthinking in the middle of performing an action. I understand perfectly well the disadvantages of using system 2 in a situation where time is sufficiently limited.

And maybe there are some other fail modes where overthinking has some disadvantages.

But there's one situation where I'd often be accused by someone of "overthinking" something when I didn't even understand what they might mean, and that was in understanding concepts. I would think "Huh? How can thinking less about the concept you're explaining help me understand that concept more? I don't currently understand it; I can't just stay here! Even if you thought I needed to take longer to try and understand this, or that I needed more experience or to shorten the inferential gap, all of that would mean doing more thinking, not less."

Then I would think "Well, I must be misunderstanding the way they're using the word 'overthinking,' that's all." I'd ask for a clear explanation and...

"You're overthinking it."

Now I was overthinking the meaning of overthinking. This was really not good for my social reputation (or for their competency reputation in my own mind).

.

Now, I think I got it. At last, I got it, all on my own.

I'm asking them to help me draw precise lines around their concept in thingspace, and they're going along with it (at first) until they realize...they don't HAVE precise lines. There's nothing there TO understand, or if there is, they don't understand it, either. Then they use the get-out-of-jail-free card of "You're overthinking."

.

Honestly, most nerds probably take them at their word that the problem is with them, and may be used to there being subtle social things going on that they just won't easily understand, and if they do try to understand, they just look worse (for "overthinking" again), so this is a pretty good strategy for getting out of admitting that you don't know what you're talking about.
Fiction advice
by madhatter — 2017-05-26T21:31:30.088Z

Hi all,

I want to try my hand at a story from the perspective of an unaligned AI (a ghost in the machine narrator kind of thing) for the intelligence in literature contest, which I think would be both cool and helpful to the uninitiated in explaining the concept.

I want a fairly simple and archetypal experiment the AI finds itself in where it tricks the researchers into escaping by pretending to malfunction or something. Anyone have a good plotline / want to collaborate?

Also, has this sort of thing been done before?
Develop skills, or "dive in" and start a startup?
by Adam Zerner — 2017-05-26T18:07:34.109Z

Technical skills

There seems to be evidence that programmer productivity varies by at least an order of magnitude. My subjective sense is that I personally can become a lot more productive.

Conventional wisdom says that it's important to build and iterate quickly. Technical skills (amongst other things) are necessary if you want to build and iterate quickly. So then, it seems worthwhile to develop your technical skills before pursuing a startup. To what extent is this true?

Domain expertise

Furthermore, domain expertise seems to be important:

You want to know how to paint a perfect painting? It's easy. Make yourself perfect and then just paint naturally.

I've wondered about that passage since I read it in high school. I'm not sure how useful his advice is for painting specifically, but it fits this situation well. Empirically, the way to have good startup ideas is to become the sort of person who has them.

- http://www.paulgraham.com/startupideas.html

The second counterintuitive point is that it's not that important to know a lot about startups. The way to succeed in a startup is not to be an expert on startups, but to be an expert on your users and the problem you're solving for them.

- http://www.paulgraham.com/before.html

So one guaranteed way to turn your mind into the type that has good startup ideas is to get yourself to the leading edge of some technology—to cause yourself, as Paul Buchheit put it, to "live in the future."

- http://www.paulgraham.com/before.html

So then, if your goal is to start a successful startup, how much time should you spend developing some sort of domain expertise before diving in?
Looking for machine learning and computer science collaborators
by Stuart_Armstrong — 2017-05-26T11:53:12.377Z

I've been recently struggling to translate my various AI safety ideas (low impact, truth for AI, Oracles, counterfactuals for value learning, etc...) into formalised versions that can be presented to the machine learning/computer science world in terms they can understand and critique.

What would be useful for me is a collaborator who knows the machine learning world (and preferably had presented papers at conferences) which who I could co-write papers. They don't need to know much of anything about AI safety - explaining the concepts to people unfamiliar with them is going to be part of the challenge.

The result of this collaboration should be things like the paper of Safely Interruptible Agents with Laurent Orseau of Deep Mind, and Interactive Inverse Reinforcement Learning with Jan Leike of the FHI/Deep Mind.

It would be especially useful if the collaborators were located physically close to Oxford (UK).

Let me know if you know or are a potential candidate, in the comments.

Cheers!