trentmkelly/LessWrong-43k huggingface dataset:
Meetup : San Francisco Meetup: Board Games
by rocurley — 2017-01-19T05:39:04.677Z

Discussion article for the meetup : San Francisco Meetup: Board Games

WHEN:
23 January 2017 06:15:00PM (-0800)

WHERE:
1769 15th St, San Francisco, CA

We’ll be meeting to play board (and other) games!

I have Dominion, Suburbia, and standard playing cards. Please feel free to bring other games you’d like to play.

For help getting into the building, please call (or text, with a likely-somewhat-slower response rate): 301-458-0764.

Format:

We meet and start hanging out at 6:15, but don’t officially start doing the meetup topic until 6:45-7 to accommodate stragglers. Usually there is a food order that goes out before we start the meetup topic.

About these meetups:

The mission of the SF LessWrong meetup is to provide a fun, low-key social space with some structured interaction, where new and non-new community members can mingle and have interesting conversations. Everyone is welcome.

We explicitly encourage people to split off from the main conversation or diverge from the topic if that would be more fun for them (moving side conversations into a separate part of the space if appropriate). Meetup topics are here as a tool to facilitate fun interaction, and we certainly don’t want them to inhibit it.

Discussion article for the meetup : San Francisco Meetup: Board Games
Meetup : Washington, D.C.: Fun & Games
by RobinZ — 2017-01-19T04:41:46.168Z

Discussion article for the meetup : Washington, D.C.: Fun & Games

WHEN:
22 January 2017 03:30:00PM (-0500)

WHERE:
Donald W. Reynolds Center for American Art and Portraiture

We will be meeting in the courtyard to hang out, play games, and engage in fun conversation.

Upcoming meetups:

Jan. 29: Meta Meetup

Feb. 5: Typical Mind Fallacy

Discussion article for the meetup : Washington, D.C.: Fun & Games
Meetup : Games in Kocherga club: FallacyMania, Tower of Chaos, Scientific Discovery
by Alexander230 — 2017-01-18T20:36:15.120Z

Discussion article for the meetup : Games in Kocherga club: FallacyMania, Tower of Chaos, Scientific Discovery

WHEN:
25 January 2017 07:40:00PM (+0300)

WHERE:
Moscow, B.Dorogomilovskaya, 5-2

Welcome to Moscow LW community makeshift games! In that games, some rationality skills are involved, so you can practise while you playing!

FallacyMania: it is a game where you guess logical fallacies in arguments, or practise using logical fallacies yourself (depending on team in which you will be).

Tower of Chaos: funny game with guessing the rules of human placement on a Twister mat.

Scientific Discovery: modified version of Zendo with simultaneous turns for all players.

Details about the games: http://goo.gl/Mz2i94

Come to antikafe "Kocherga", ul.B.Dorogomilovskaya, 5-2. The map is here: http://kocherga-club.ru/#contacts . Nearest metro station is Kievskaya. If you are lost, call Sasha at +7-905-527-30-82.

Games begin at 19:40, the length is 3 hours.

Discussion article for the meetup : Games in Kocherga club: FallacyMania, Tower of Chaos, Scientific Discovery
Project Hufflepuff
by Raemon — 2017-01-18T18:57:20.394Z

(This is a crossposted FB post, so it might read a bit weird)

My goal this year (in particular, my main focus once I arrive in the Bay, but also my focus in NY and online in the meanwhile), is to join and champion the growing cause of people trying to fix some systemic problems in EA and Rationalsphere relating to "lack of Hufflepuff virtue".

I want Hufflepuff Virtue to feel exciting and important, because it is, and I want it to be something that flows naturally into our pursuit of both epistemic integrity, intellectual creativity, and concrete action.

Some concrete examples:

- on the 5 second reflex level, notice when people need help or when things need doing, and do those things.

- have an integrated understanding that being kind to people is *part* of helping them (and you!) to learn more, and have better ideas.

(There are a bunch of ways to be kind to people that do NOT do this, i.e. politely agreeing to disagree. That's not what I'm talking about. We need to hold each other to higher standards but not talk down to people in a fashion that gets in the way of understanding. There are tradeoffs and I'm not sure of the best approach but there's a lot of room for improvement)

- be excited and willing to be the person doing the grunt work to make something happen

- foster a sense that the community encourages people to try new events, actively take personal responsibility to notice and fix community-wide problems that aren't necessarily sexy.

- when starting new projects, try to have mentorship and teamwork built into their ethos from the get-go, rather than hastily tacked on later

I want these sorts of things to come easily to mind when the future people of 2019 think about the rationality community, and have them feel like central examples of the community rather than things that we talk about wanting-more-of.
Corrigibility thoughts III: manipulating versus deceiving
by Stuart_Armstrong — 2017-01-18T15:57:36.623Z

This is the first of three articles about limitations and challenges in the concept of corrigibility (see articles 1 and 2).

The desiderata for corrigibility are:

A corrigible agent tolerates, and preferably assists, its operators in their attempts to alter or shut down the agent.

A corrigible agent does not attempt to manipulate or deceive its operators.

A corrigible agent has incentives to repair safety measures (such as shutdown buttons, tripwires, or containment tools) if they break, or at least notify its operators in the event of a breakage.

A corrigible agent preserves its corrigibility, even as it creates new sub-systems or sub-agents, even if it undergoes significant self-modification.

In this post, I'll be looking more at some aspects of point 2. A summary of the result will be:

Defining manipulation simply may be possible, but defining deception is a whole other problem.

The warning in this post should always be born in mind, of course; it's possible that we me might find a semi-formal version of deception that does the trick.

Manipulation versus deception

In the previous post, I mentioned that we may need to define clearly what an operator was, rather than relying on the pair: {simple description of a value correction event, physical setup around that event}. Can we define manipulation and deception without defining what an operator is?

For manipulation, it seems we can. Because manipulation is all about getting certain preferred outcomes. By specifying that the AI cannot aim to optimise certain outcomes, we can stop at least certain types of manipulations. Along with other more direct ways of achieving those outcomes.

For deception, the situation is much more complicated. It seems impossible to define how one agent can communicate to another agent (especially one as biased as a human), and increase the accuracy of the second agent, without defining the second agent properly. More confusingly, this doesn't even stop deception; sometimes lying to a bounded agent can increase their accuracy about the world.

There may be some ways to define deception or truth behaviourally, such as using a human as a crucial node in an autoencoder between two AIs. But those definitions are dangerous, because the AI is incentivised to make the human behave in a certain way, rather than having them believe certain things. Manipulating the human or replacing them en...
Corrigibility thoughts II: the robot operator
by Stuart_Armstrong — 2017-01-18T15:52:22.485Z

This is the first of three articles about limitations and challenges in the concept of corrigibility (see articles 1 and 3).

The desiderata for corrigibility are:

A corrigible agent tolerates, and preferably assists, its operators in their attempts to alter or shut down the agent.

A corrigible agent does not attempt to manipulate or deceive its operators.

A corrigible agent has incentives to repair safety measures (such as shutdown buttons, tripwires, or containment tools) if they break, or at least notify its operators in the event of a breakage.

A corrigible agent preserves its corrigibility, even as it creates new sub-systems or sub-agents, even if it undergoes significant self-modification.

In this post, I'll be looking at one aspect of points 1 and 3. I feel that they're actually pretty much the same point -- the AI has to act while preserving the corrigibility infrastructure.

A summary of the result will be:

Without a formal definition of what an operator is, corrigibility is vulnerable to the AI replacing the operator with a fake version.

The warning in this post should always be born in mind, of course; it's possible that we me might find a semi-formal version of operator that does the trick.

Defining the operator or defining the event

Typically the process of correction of values is defined physically, as being the outcome of specific event (such as a button press, or a specific stochastic event triggered by or triggering a button press). This could also be defined by a digital event (receiving a certain encoded signal, or similar).

In all cases, the idea is the same: there is a very narrow and specific definition of the value correction event, and the physical or digital infrastructure surrounding it plays the role of ensuring the value corrections happen just as we intended. For instance, the physical event could be the button press (or a charge in a wire triggered by the button press) and the button itself could be in a room with an operator, with the operator keeping an eye on the AI and motivated to press the button under the right circumstances.

Such a setup is naturally vulnerable if the AI is motivated to manipulate the operator, or one of its subagents is. But even if we can solve points 2. and 4., there remains another vulnerability: the AI can choose to replace the operator with an artificial one (maybe a robot, maybe a second wire).

Wh...
Corrigibility thoughts I: caring about multiple things
by Stuart_Armstrong — 2017-01-18T15:39:04.833Z

This is the first of three articles about limitations and challenges in the concept of corrigibility (see articles 2 and 3).

The desiderata for corrigibility are:

A corrigible agent tolerates, and preferably assists, its operators in their attempts to alter or shut down the agent.

A corrigible agent does not attempt to manipulate or deceive its operators.

A corrigible agent has incentives to repair safety measures (such as shutdown buttons, tripwires, or containment tools) if they break, or at least notify its operators in the event of a breakage.

A corrigible agent preserves its corrigibility, even as it creates new sub-systems or sub-agents, even if it undergoes significant self-modification.

In this post, I'll be looking more at point 4. A summary of the result will be:

Unless giving the AI extra options can reduce expected utility, the AI must care about every possible utility at least a bit.

Some of the results are formal, but the boundaries of the model are very unclear, so the warning in this post should always be born in mind.

Note that the indifference agents fail to be fully corrigible (they don't create corrigible subagents) and they also don't care about the other possible utilities before being changed (as this is a point of indifference).

Agents versus non-agents

First I'll present a cleaner version of an old argument. Basically, it seems that defining what a sub-agent or sub-system is, is tricky or impossible.

Consider for instance a paperclip maximiser, that may get corriged into a staple maximiser at a later date. The AI has some income, and may buy a large proportion of shares in either General Paperclips (GP) or Staples United (SU). Assume the best way of promoting the use of one of the tools, it to take over the company that makes them.

There are two scenarios; in each scenario, the AI has one of two choices.

In scenario 1, the AI has choice A: it buys stocks in GP, but cannot make further trades (in particular, it can't sell its stocks and buy others). It also has choice B: it retains flexibility, and can sell its GP stocks at some later date to buy stocks in SU.

In scenario 2, the AI delegates its investment decisions to a subagent. Under choice A, the subagent is a pure paperclip maximiser. Under choice B, the subagent is corrigible, and can be changed into a staple maximiser.

Clearly 1A and 2A are equivalent, while 1B and 2B are ...
A measure-theoretic generalization of logical induction
by Vanessa Kosoy — 2017-01-18T13:56:20.000Z

% operators that are separated from the operand by a space

% operators that require brackets

% operators that require parentheses

% Paper specific

Logical induction is defined in terms of logical sentences and theories, but its principles are applicable in much greater generality and abstraction. Indeed, one such generalization was studied under the name "universal induction." We proposed a slightly different generalization in order to model reasoning with incomplete models. Here, we describe a formalism that includes all these cases and many more, using the language of measure theory. This provides the following advantages:

The formalism is applicable to event spaces substantially different from truth assignments or bit sequences, e.g. we can consider sequences of real numbers.

The formalism treats probabilities and expectations on the same footing, rather than constructing expectations as in section 4.8 of original paper. We consider this more convenient.

In our opinion, this language is more mathematically natural than the original formalism, at least for applications unrelated to formal logic.

On the other hand, we ignore all computational considerations. Obviously these are often important, but in the study of purely information-theoretic questions the use of numerical approximations only serves to obscure.

All proofs are in the Appendix.

##Results

Fix X a compact Polish space. For example, X might be Oω or the space of propositionally consistent truth assignments in some language or [0,1]ω. The role of "pricings" is served by P(X): the space of probability measures on X. A market is thus a sequence {μn∈P(X)}n∈N. The "deductive process" is replaced by a sequence of closed sets X=X0⊇X1⊇X2⊇… A trading strategy is a continuous function τ:P(X)→C(X), were P(X) is equipped with the weak* topology (as before) and C(X) is the space of continuous functions from X to R equipped with the uniform convergence topology. Here, we should think of τ(μ) as the share portfolio acquired by the strategy given pricing μ, where the cost of the acquisition is understood to be Eμ[τ(μ)] while the ultimate value of the portfolio is τ(μ)(x) (in the following we will use the less clumsy notation τ(μ,x); in fact, we can equivalently define T(X) as the set of continuous functions from P(X)×X to R) for some x∈⋂nXn. We denote the set of trading stra

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Meetup : LW Sao Paulo - Meetup de janeiro
by leohmarruda — 2017-01-18T00:12:35.618Z

Discussion article for the meetup : LW Sao Paulo - Meetup de janeiro

WHEN:
28 January 2017 02:00:00PM (-0200)

WHERE:
Rua Antônio Carlos, 452. São Paulo, Brazil

Encontro mensal de racionalistas, novatos são bem vindos! Teremos algumas lições de racionalidade prática, discussões diversas e jogos de tabuleiro no final.

Vanilla café - 2o andar

Discussion article for the meetup : LW Sao Paulo - Meetup de janeiro
The substance of money
by MrMind — 2017-01-17T14:51:02.884Z

Not much epistemic effort here: this is just an intuition that I have to model a vast and possibly charged field. I'm calling upon the powers of crowd-finding to clarify my views.

Tl;dr: is the debate in economics over the nature of money really about the definition or just over politics?

I'm currently reading "Money - an unauthorised biography" by Felix Martin. It presents what is to me a beautifully simple and elegant definition of what money is: transferable value over a liquid landscape (these precise words are mine). It also presents many cases where this simple view is contrasted by another view: money as a commodity. This opposition is not merely one of academics definitions, but has important consequences. Policy makers have adopted different points of view and have becuase of that varied very much their interventions.

I've never been much interested in the field, but this book sparked my curiosity, so I'm starting to look around and I'm surprised to discover that this debate is still alive and well in the 21st century.

Basically what I've glanced is that there is this Keynesian school of thougth that posits that yes, money is transferable debts, and since money is merely a technology that expresses an agreement, you should intervente in the matter of economics, especially by printing money when this is needed.

Then there's an opposite view (does it have a name?) that says that no, money is a commodity and for this reason it must be treated as such: it's creation is to be carefully controlled by the market and it's value tied only to the value of an underlying tradeable asset.

I think my uncertainty shows how little I know about this field, so apply Crocker's rule at will. Is this a not completely inaccurate model of the debate?

If it is so, my second question: is this a debate over substance or over politics?
If I think that money is transferable debts, surely I can recognize the merit of intervention but also understand that a liberal use of said tool might breed a disaster.
If I think that money is a standard commodity, can I manipulate which commodity it is exactly tied to to increase the availability of money in times of need?
Am I talking nonsense for some technical reason? Am I missing something big? Is economics the mind-killer too?

Elucidate me!
A note on misunderstanding the boundaries of models
by Stuart_Armstrong — 2017-01-17T12:29:30.000Z

This is a reminder for me as much as for anyone. I'm often posting a bunch of ideas and impossibility results, but it's important to remember that those results only apply within their models. And the models likely don't match up exactly with either reality or what we want/need.

For instance, we have the various no-free-lunch theorems (any two optimization algorithms are equivalent when their performance is averaged across all possible problems), or Rice's theorem (you can't figure out non-trivial semantic properties of every program).

These fail when we note that we have strong evidence that we live in a very specific environment and have to solve only a small class of problems; and that most programs we meet are (or can be) designed to be clear and understandable, rather than being selected at random.

Conversely, we can have results on how to use Oracles safely. However, these results fall apart when you consider issues of acausal trade. The initial results still hold -- the design seems safe if multiple iterations of the AI don't care about each other's reward. However, the acausal trade example demonstrates that that assumption can fail in ways we didn't expect.

Both of these cases stem from misunderstanding the boundaries of the model. For the no-free-lunch theorem, we might be informally thinking "of course we want the AI to be good at things in general", without realising that this doesn't perfectly match up to "good performance across every environment". Because "every environment" includes weird, pathological, and highly random environments. Similarly, "don't have the AIs care about each other's reward" seems like something that could be achieved with simple programming and boxing approaches, but what we achieve is "AIs that don't care about each other's rewards in the conventional human understanding of these terms".

The boundary of the model did not lie where we thought it did.
Welcome to Less Wrong! (11th thread, January 2017) (Thread B)
by Richard Korzekwa  — 2017-01-16T22:25:56.482Z

(Thread A for January 2017 is here, this was created as a duplicate but it's too late to fix it now.)

Hi, do you read the LessWrong website, but haven't commented yet (or not very much)? Are you a bit scared of the harsh community, or do you feel that questions which are new and interesting for you could be old and boring for the older members?

This is the place for the new members to become courageous and ask what they wanted to ask. Or just to say hi.

The older members are strongly encouraged to be gentle and patient (or just skip the entire discussion if they can't).

Newbies, welcome!

The long version:

If you've recently joined the Less Wrong community, please leave a comment here and introduce yourself. We'd love to know who you are, what you're doing, what you value, how you came to identify as an aspiring rationalist or how you found us. You can skip right to that if you like; the rest of this post consists of a few things you might find helpful. More can be found at the FAQ.

A few notes about the site mechanics

To post your first comment, you must have carried out the e-mail confirmation: When you signed up to create your account, an e-mail was sent to the address you provided with a link that you need to follow to confirm your e-mail address. You must do this before you can post!

Less Wrong comments are threaded for easy following of multiple conversations. To respond to any comment, click the "Reply" link at the bottom of that comment's box. Within the comment box, links and formatting are achieved via Markdown syntax (you can click the "Help" link below the text box to bring up a primer).

You may have noticed that all the posts and comments on this site have buttons to vote them up or down, and all the users have "karma" scores which come from the sum of all their comments and posts. This immediate easy feedback mechanism helps keep arguments from turning into flamewars and helps make the best posts more visible; it's part of what makes discussions on Less Wrong look different from those anywhere else on the Internet.

However, it can feel really irritating to get downvoted, especially if one doesn't know why. It happens to all of us sometimes, and it's perfectly acceptable to ask for an explanation. (Sometimes it's the unwritten LW etiquette; we have different norms tha

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Do we Share a Definition for the word "ideal"?
by Flinter — 2017-01-16T20:45:33.220Z

Related: A Proposal for a Simpler Solution To All These Difficult Observations and Problems

Perhaps this will be seen as spam and that I have broken a rule of propriety, but my previous discussion has already served its purpose and I think will continue to do so. I needed it as an open dialogue to get a general idea of what the implications of a stable metric for value would be (ie it would solve many otherwise difficult to solve problems).
In relation to such a unit of value, I would like to ask what is your definition of the word "ideal". On the surface this question might seem empty, but I often observe with people that we don't all share the same meaning for the word, and that the discrepancy is significant.

Do we have a shared meaning for this word?