trentmkelly/LessWrong-43k huggingface dataset:
Machine Learning Group
by Regex — 2017-07-16T20:58:18.519Z

After signing up for this post, those of us that want to study machine learning have made a team.
In an effort to actually get high returns on our time we won't delay, and instead actually build the skills. First project: work through Python Machine Learning by Sebastian Raschka, with the mid-term goal of being able to implement the "recognizing handwritten digits" code near the end.
As a matter of short term practicality currently we don't have the hardware for GPU acceleration. This limits the things we can do, but at this stage of learning most of the time spent is on understanding and implementing the basic concepts anyway.
Here is our discord invite link if you're interested in joining in on the fun.

September 14th 2017 edit:
Wasn't enough activity to keep it going. The book we chose also focused on scikitlearn rather than teaching how some of the algorithms are programmed after ~chapter 2 so I think that was part of the problem, but the main failing I suspect is that I personally did not push forward hard enough and got distracted by other projects.
So alas, this effort did not succeed. Feel free to check out the discord to find the people interested in it, and maybe look at the logs to see where we went wrong ;)
LessWrong Is Not about Forum Software, LessWrong Is about Posts (Or: How to Immanentize the LW 2.0 Eschaton in 2.5 Easy Steps!)
by enye-word — 2017-07-15T21:35:28.583Z

[epistemic status: I was going to do a lot of research for this post, but I decided not to as there are no sources on the internet so I'd have to interview people directly and I'd rather have this post be imperfect than never exist.]
Many words have been written about how LessWrong is now shit. Opinions vary about how shit exactly it is. I refer you to http://lesswrong.com/lw/n0l/lesswrong_20/ and http://lesswrong.com/lw/o5z/on_the_importance_of_less_wrong_or_another_single/ for more comments about LessWrong being shit and the LessWrong diaspora being suboptimal.
However, how to make LessWrong stop being shit seems remarkably simple to me. Here are the steps to resurrect it:
1. Get Eliezer: The lifeblood of LessWrong is Eliezer Yudkowsky's writing. If you don't have that, what's the point of being on this website? Currently Eliezer is posting his writings on Facebook, (https://www.facebook.com/groups/674486385982694/) which I consider foolish, for the same reasons I would consider it foolish to house the Mona Lisa in a run-down motel.
2. Get Scott: Once you have Eliezer back, and you sound the alarm that LW is coming back, I'm fairly certain that Scott "Yvain" Alexander will begin posting on LessWrong again. As far as I can tell he's never wanted to have to moderate a comment section, and the growing pains are stressing his website at the seams. He's even mused publicly about arbitrarily splitting the Slate Star Codex comment section in two (http://slatestarcodex.com/2017/04/09/ot73-i-lik-the-thred/) which is a crazy idea on its own but completely reasonable in the context of (cross)posting to LW. Once you have Yudkowsky and Yvain, you have about 80% of what made LessWrong not shit.
3. Get Gwern: I don't read many of Gwern's posts; I just like having him around. Luckily for us, he never left!
After this is done, everyone else should wander back in, more or less.
Possible objections, with replies:
Objection: Most SSC articles and Yudkowsky essays are not on the subject of rationality and thus for your plan to work LessWrong's focus would have to subtly shift.
Reply: Shift away, then! It's LessWrong 2! We no longer have to be a community dedicated to reading Rationality: From AI to Zombies as it's written in real time; we can now be a community that takes Rationality: From AI to Zombies as a starting point and discusses whatever we find interesting! Thus the demarcatio...
"Like this world, but..."
by Stuart_Armstrong — 2017-07-14T20:40:39.000Z

A putative new idea for AI control; index here.

Pick a very unsafe goal: G="AI, make this world richer and less unequal." What does this mean as a goal, and can we make it safe?

I've started to sketch out how we can codify "human understanding" in terms of human ability to answer questions.

Here I'm investigating the reverse problem, to see whether the same idea can be used to give instructions to an AI.

For the purpose of this post, I'll assume we have some sufficient measure of accuracy A. This is a boolean-value function, that takes as input a human h (in a particular time and place), a string/description s, and a world w or a pair of worlds w, w′. Then A(h,s,w)/A(h,s,w,w′) is true iff the string s, when presented to the human h, is an understandably accurate description (of w)/(of the difference between w and w′).

G is describing a world the human would not see as accurately described by G.

Let w be our world, let ω be any world, and let ωG be the world that G is meant to be describing (this is an informal definition, as we haven't formalised what this means yet).

Humans have a poor understanding of causality, of what causes what in the real world w (and in ωG). A lot of strong political positions, for instance, seem predicated on denying the existence of certain trade-offs. And no-one has a complete understanding of all the physics, biology, and social sciences that best model our world. Thus the desiderata of G may be impossible to satisfy; there is no plausible world ωG that is well described by G.

And on a basic and more fundamental level, we are simply ignorant of vast amounts of things about the world. No-one has a knowledge of all the basic statistical descriptors about our world, let alone the full distribution behind those descriptors.

Thus even if there was a plausible world ωG well-described by G, if we had a full description of that world, we would think it very different from what we intended with G -- just as if we had a full description of w, we wouldn't recognise our own world.

This suggests that G should in some way be seen as a description of the "difference" w−ωG between worlds.

Modelling worlds

Here we're going to replace worlds ω with models M(ω) of those worlds. There models are made up of variables {xi}. Each of those variables has a description si, and we use our measure of accuracy to ensure that these descriptions are understandable.

S

...
Delegative Inverse Reinforcement Learning
by Vanessa Kosoy — 2017-07-12T12:18:22.000Z

We introduce a reinforcement-like learning setting we call Delegative Inverse Reinforcement Learning (DIRL). In DIRL, the agent can, at any point of time, delegate the choice of action to an "advisor". The agent knows neither the environment nor the reward function, whereas the advisor knows both. Thus, DIRL can be regarded as a special case of CIRL. A similar setting was studied in Clouse 1997, but as far as we can tell, the relevant literature offers few theoretical results and virtually all researchers focus on the MDP case (please correct me if I'm wrong). On the other hand, we consider general environments (not necessarily MDP or even POMDP) and prove a natural performance guarantee.

The use of an advisor allows us to kill two birds with one stone: learning the reward function and safe exploration (i.e. avoiding both the Scylla of "Bayesian paranoia" and the Charybdis of falling into traps). We prove that, given certain assumption about the advisor, a Bayesian DIRL agent (whose prior is supported on some countable set of hypotheses) is guaranteed to attain most of the value in the slow falling time discount (long-term planning) limit (assuming one of the hypotheses in the prior is true). The assumption about the advisor is quite strong, but the advisor is not required to be fully optimal: a "soft maximizer" satisfies the conditions. Moreover, we allow for the existence of "corrupt states" in which the advisor stops being a relevant signal, thus demonstrating that this approach can deal with wireheading and avoid manipulating the advisor, at least in principle (the assumption about the advisor is still unrealistically strong). Finally we consider advisors that don't know the environment but have some beliefs about the environment, and show that in this case the agent converges to Bayes-optimality w.r.t. the advisor's beliefs, which is arguably the best we can expect.

All the proofs are in the Appendix.

Notation

The set of natural numbers N is defined to begin from 0. Given n∈N, [n] denotes the set {m∈N∣m<n}. Given a logical formula φ, [[φ]]∈{0,1} denotes its truth value.

Given a set X, we denote X∗:=⨆n∈NXn, the set of finite strings over the alphabet X. The unique string of length 0 is denoted λ. We also denote by Xω the set of infinite strings over alphabet X. Given α∈X∗⊔Xω and n∈N, αn∈X is the n-th symbo...
90% of problems are recommendation and adaption problems
by casebash — 2017-07-12T04:53:08.356Z

Want to improve your memory? Start a business? Fix your dating life?

The chances are that out of the thousands upon thousands of books and blogs out there on each of these topics there are already several that will tell you all that you need. I'm not saying that this will immediately solve your problem - you will still need to put in the hard yards of experiment and practise - just that lack of knowledge will no longer be the limiting factor.

This suggests if we want to be winning at life (as any good rationalist should), what is most important isn't creating brilliant and completely unprecedented approaches to solve these problems, but rather taking ideas that already exist.

The first problem is recommendation - finding which out of all of the thousands of books out there are the most helpful for a particular problem. Unfortunately, recommendation is not an easy problem at all. Two people may both be dealing with procrastination problems, but what works for one person may not work for another person. Further, even for the same idea, it is incredibly subjective what counts as a clear explanation - some people may want more detail, others less, some people may find some examples really compelling, others won't. Recommendations are generally either one person's individual recommendations or those which recieved the highest vote, but there probably are other methods of producing a recommendation that should be looked into, such as asking people survey questions and matching on that, or asking people to rate a book on different factors.

The second problem is adaption. Although you shouldn't need to create any new ideas, it is likely that certain elements will need more explanation and certain elements less. For example, when writing for the rationalist community, you may need to be more precise and be clearer when you are talking figuratively, rather than literally. Alternatively, you can probably just link people to certain common ideas such as the map and territory without having to explain it.

I'll finish with a rhetorical question - what percent of solutions here are new ideas and what percentage are existing solutions? Are these in the right ratio?

UPDATE: (Please note: This article is not about time spent learning vs. time spent practising, but about existing ideas vs. new ideas. The reason why this is the focus is because LW can potentially recommend resources ...
On the Seattle Minimum Wage Study (part 2)
by Zvi — 2017-07-11T23:15:04.000Z

Previously: On the Seattle Minimum Wage Study (part 1)

Epistemic Status: If My Calculation Are Correct…

Some links:

Link to Paper: Original Working Paper

National Review: Critics Have Study Wrong

Forbes: Both Sides Might Be Right

Washington Post: $15 Minimum Wage Might Not Help Workers

Also Washington Post: Maine Tipped Workers Don’t Want Minimum Wage Increase

Bryan Caplan: No One’s Mind Will Change

Arnold Kling: Response to Caplan to Not Give Up

Charts from Part 1:

Count:

Quarters
Under $13
Under $19
All
% Under $19

0
39807
92959
292640
31.8%

1
40706
94913
300892
31.5%

2
35421
89598
303089
29.6%

3
35085
90813
305229
29.8%

4/1 ($11 min)
35087
92668
311886
29.7%

5/2
33959
93382
320807
29.1%

6/3
30002
87067
320195
27.2%

7/4
24662
87112
321360
27.1%

8/5 ($13 min)
24420
88431
331927
26.6%

9/6
23232
86842
336517
25.8%

Hours worked:

Quarters
Hours U13
Hours U19
Hours All
U19 H%
Avg U19
Avg All

0
14117
37408
130007
28.8%
402
444

1
14527
38565
132604
29.1%
406
441

2
11999
35589
136012
26.2%
397
449

3
11335
34269
132275
25.9%
377
433

4/1
12174
37270
139197
26.8%
402
446

5/2
11589
37472
142638
26.3%
401
445

6/3
9924
34943
146960
23.8%
401
459

7/4
7645
33031
140429
23.5%
379
437

8/5
8315
35681
149514
23.9%
403
450

9/6
8046
35867
153603
23.4%
413
456

Average hourly wages:

Quarters
Avg U13
Avg U19
Avg 13-19
Avg 19+
All

0
11.14
14.14
15.96
46.14
36.93

1
11.15
14.15
15.96
47.44
37.76

2
11.27
14.37
15.95
48.79
39.78

3
11.28
14.41
15.96
49.77
40.61

4/1
11.47
14.48
15.94
47.31
38.52

5/2
11.54
14.58
15.94
48.83
39.83

6/3
11.64
14.74
15.97
50.15
41.73

7/4
11.82
14.97
15.92
52.80
43.9

8/5
11.87
15.01
15.96
53.14
44.04

9/6
11.87
15.03
15.94
52.30
43.6

With a day to contemplate the profound weirdness on display on that last chart, what conclusions can we draw?

The basic study design and conclusion tell a story. The minimum wage goes up. In response, some jobs are eliminated, and other jobs have their wages increased to compensate, in order to keep relative wages in line. This cascading effect levels off at around $19/hour: At that point, the number of workers getting a raise to $19, and the number getting a raise from $19 to something higher (combined with the number that are fired, although that seems like it should be quite small), are equally sized, so the number of work...
Becoming stronger together
by b4yes_duplicate0.9924090729683128 — 2017-07-11T13:00:04.175Z

I want people to go forth, but also to return. Or maybe even to go forth and stay simultaneously, because this is the Internet and we can get away with that sort of thing; I've learned some interesting things on Less Wrong, lately, and if continuing motivation over years is any sort of problem, talking to others (or even seeing that others are also trying) does often help.

But at any rate, if I have affected you at all, then I hope you will go forth and confront challenges, and achieve somewhere beyond your armchair, and create new Art; and then, remembering whence you came, radio back to tell others what you learned.

– Eliezer Yudkowsky, Rationality: From AI to Zombies

If you want to go fast, go alone. If you want to go far, go together.

– African proverb (possibly just made up)

About a year ago, a secret rationalist group was founded. This is a report of what the group did during that year.

The Purpose

“Rationality, once seen, cannot be unseen,” are words that resonate with all of us. Having glimpsed the general shape of the thing, we feel like we no longer have a choice. I mean, of course we still have an option to think and act in stupid ways, and we probably do it a lot more than we would be okay to admit! We just no longer have an option to do it knowingly without feeling stupid about it. We can stray from the way, but we cannot pretend anymore that it does not exist. And we strongly feel that more is possible, both in our private lives, and for the society in general.

Less Wrong is the website and the community that brought us together. Rationalist meetups are a great place to find smart, interesting, and nice people; awesome people to spend your time with. But feeling good was not enough for us; we also wanted to become stronger. We wanted to live awesome lives, not just to have an awesome afternoon once in a while. But many participants seemed to be there only to enjoy the debate. Or perhaps they were too busy doing important things in their lives. We wanted to achieve something together; not just as individual aspiring rationalists, but as a rationalist group. To make peer pressure a positive force in our lives; to overcome akrasia and become more productive, to provide each other feedback and to hold each other accountable, to support each other. To win, both individually and together.

The Group

We are not super secr...
In praise of fake frameworks
by Valentine — 2017-07-11T02:12:32.017Z

Related to:

Bucket errors

,

Categorizing Has Consequences

,

Fallacies of Compression

Followup to:

Gears in Understanding

I use a lot of fake frameworks — that is, ways of seeing the world that are probably or obviously wrong in some important way.

I think this is an important skill. There are

obvious pitfalls

, but I think the advantages are more than worth it. In fact, I think the "pitfalls" can even sometimes be epistemically useful.

Here I want to share why. This is for two reasons:

I think fake framework use is a wonderful skill. I want it represented more in rationality in practice. Or, I want to know where I'm missing something, and Less Wrong is a great place for that.
I'm building toward something. This is actually a continuation of

Gears in Understanding

, although I imagine it won't be at all clear here how. I need a suite of tools in order to describe something. Talking about fake frameworks is a good way to demo tool #2.

With that, let's get started.

There are two kinds of people

: extroverts and introverts.

…sort of.

I mean, as I look around, it certainly looks like there's a difference between outgoing social butterflies and quiet types who mostly stay at home. Maybe it's more like a continuum rather than a binary thing. But if so, I find myself wondering if it's bimodal with rough "extrovert" and "introvert"

clusters

anyway.

But then I look at

long lists of differences

between extroverts and introverts, and I worry. What exactly do these terms mean? Is it just about how talkative and loud people are? If so, are the labels

sneaking in connotations

about where people "get energy" from and how action-oriented they are?

Well, it turns out that

a bunch of those traits are correlated

. The intuition is, in fact, picking up on something true in the world.

But.

That doesn't mean the intuition is correct.

It looks like

maybe extraversion isn't bimodal

. I can justify that after the fact: the Big Five verified extraversion as a correlational cluster of traits and defines introversion as "low extraversion", and a Gaussian distribution seems like a more sensible prior than a bimodal one. But I didn't think of that ahead of time. If I hadn't thought to look, I might have thought the Big Five had verified the bimodal intuition because "these traits are correlated" ...
Best Of Rationality Blogs RSS Feed
by SquirrelInHell — 2017-07-10T11:11:13.896Z

[Note: There's already a gather-it-all feed by deluks917, and the lw summary recently had a "most recommended" section, so it covers some of what I'm doing here.]

This is an RSS feed that aggregates the most valuable posts (according to me) from around 40 or 50 rationality blogs. It's relatively uncluttered, averaging 3-5 articles per week.

Feed URL: http://www.inoreader.com/stream/user/1005752783/tag/user-favorites

There's also a Facebook page version, and you can view it online using any of the available free RSS viewers.

Edit: see my comment below for details of the heuristics I use for selecting articles for the feed.
Open thread, July 10 - July 16, 2017
by Thomas — 2017-07-10T06:31:11.395Z

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
On the Seattle Minimum Wage Study (part 1)
by Zvi — 2017-07-09T19:44:44.000Z

Epistemic Status (written before I looked at the paper): This new paper confirms all of my priors.

(Which is no longer the epistemic status because things started looking weird, but one step at a time). I’m publishing part 1 now because I don’t want things to drag on forever, and the calculation I just did looks weird enough that it’s a decent stopping point.

Motivation

Seattle has raised its minimum wage to $13 an hour ($12 for small employers) and will soon raise it to $15. Armed with a unique data set, a new working paper claims that even the increase so far has had a big enough effect on low-wage employment that it has backfired on low-wage employees, costing them more in hours than they have gained in pay. Many of the details I read about made the paper and its conclusions appear highly credible, but some of the numbers involved seemed to naively imply crazy things, so I decided it would be worthwhile to read the actual paper.

The paper is worth reading (technically it is a working paper, but no one seems to care about that distinction). It includes details like this one, which appears as a footnote when discussing the fact that a number of big employers are missing from the data set the study uses, to answer the anticipated objection that big employers ‘have more bargaining power when negotiating wages, and can more easily absorb the additional labor costs’:

The Seattle Minimum Wage Study surveyed over 500 Seattle business owners immediately before and a year after the Ordinance went into effect. In April 2015, multi-site employers were more likely to report intentions to reduce hours of their minimum wage employees (34% versus 24%) and more likely to report intentions to reduce employment (33% versus 26%). A one-year follow-up survey revealed that multi-location employers were more likely to report an actual reduction in full-time and part-time employees, with over half of multi-site respondents reporting a reduction in full-time employment (52%, against 45% for single-site firms).

Take a step back for a moment, and look only at those numbers, ignoring the main study. What conclusions can you draw?

In terms of large versus small businesses, this likely indicates that single-site employers cut a higher percentage of jobs and hours than multi-site employers. This is because they are measuring whether magnitude is greater than ze...