trentmkelly/LessWrong-43k huggingface dataset:
Combatting perfectionism
by tomdekan — 2022-10-17T20:58:59.356Z
Link: https://tomdekan.com/combatting-perfectionism

Background

Over the past 2 months, I've experienced two of my startups ending - one in euthanasia and one in sale.

During my startup that we ended, I kept my perfectionism under control by the time pressure of building for our customers. I would ask customers what they wanted; consider what they actually wanted; and then build it quickly.

This customer demand meant that I rejected unnecessary suggestions and controlled perfectionism. I focused on building what I believed the customers needed within the time constraints. Household cats would have congratulated us on our level of agile working.

The problem

Shortly afterwards, I sold my other software business (Redstone HR) to a UK company. Since then, I've been building software to accelerate the future businesses that I build.

Perfectionism has been rampant [1].

My perfectionism has felt like a colony of rabbits alone in a lush woodland. The rabbits spend their days happily increasing in number, without the constraints of hungry predators.

Similarly, the lack of time or financial pressure has allowed my perfectionism to expand. In this early stage of a new business, there are no constraints - such as customer demands - to restrain perfectionism.

As a side note, I think that the word 'perfectionism' is inaccurately self-complimentary.

Perfectionism sounds impressive: a person is so conscientious that they want every aspect of their work to be as good as possible.

Yet, in practice, perfectionism means that you are fixating on insignificant details and failing to do the most important things. Being a perfectionist is bad thing for achieving your goals. As Voltaire said, and as my friend Matty repeats as if a sacred mantra: "Perfect is the enemy of good."

A trial solution (Adding a fox among the rabbits)

-> Add time constraints with real financial consequences.

Set a deadline with specific targets

Send money to a friend immediately

Tell the friend to dispose of the money if you don't meet the deadline[2]

The rationale is that a real financial penalty forces you to focus on the most important things. If you do focus on intricate and trifling details, you increase your chance of real financial loss. So you'll avoid doing so, and instead focus on the actions that maximise achieving your targets. This counters perfectionism. By incentivising yourself to prioritise the most important, you will make greater progress.

On ...
Open Problem in Voting Theory
by Scott Garrabrant — 2022-10-17T20:42:05.130Z

In the previous post, I presented the concept of a maximal lottery-lottery as follows:

We start with a finite set of candidates C and a distribution V∈Δ(C→[0,1]) on utility functions on C. Given lottery-lotteries A,B∈Δ(Δ(C)), we say that A dominates B if PA∼A,B∼B,v∼V(v(A)>v(B))≥PA∼A,B∼B,v∼V(v(B)>v(A)). A maximal lottery-lottery is an M∈Δ(Δ(C)) such that for all other L∈Δ(Δ(C)), M dominates L.

Conjecture: For any C and V, there always exists a maximal lottery-lottery.

We discussed how we can construct a powerful and elegant new voting system, if only we can guarantee that a maximal lottery-lottery exists. However it is an open problem whether or not they exist. In this post, I will discuss a few different angles of attacking this conjecture.

An Infinite Game

First, consider the following partial result:

Theorem: Let D be any nonempty finite subset of Δ(C). Then, there exists an M∈Δ(D) such that for all other L∈Δ(D), M dominates L.

Proof: Consider the following symmetric two-player zero-sum game between Alice and Bob: Alice chooses an A∈D, and Bob chooses a B∈D. Alice gets utility equal to Pv∼V(v(A)>v(B))−Pv∼V(v(B)>v(A)), and Bob gets the negative of this utility. This game must have a Nash equilibrium, and in this Nash equilibrium, Alice must play a mixed strategy M∈Δ(D). Since this is a Nash equilibrium in a symmetric zero-sum game, Alice must get at least 0 utility in expectation, no matter what L∈Δ(D) Bob plays. Alice getting at least 0 utility in expectation is exactly saying that M dominates L. □

This proof is valid (you can check the details), so why doesn't it generalize to infinite subsets D, and in particular D=Δ(C)? The problem is that games with infinitely many options need not have Nash equilibria. For example consider the game where Alice and Bob each choose a natural number, and whoever chooses the larger number wins.

Fortunately, often in cases like this, you can get past finiteness assumptions by instead using compactness assumptions, and Δ(C) is compact. Indeed, games with a compact option space and continuous ut...
Maximal Lottery-Lotteries
by Scott Garrabrant — 2022-10-17T20:39:20.143Z

In the last post we saw how by introducing lotteries, we could resolve one of the major conflicts in voting theory, between Condorcet and consistency (while also satisfying participation and clone independence). Now, we are going to make it better. How? By adding even more lotteries!

As far as I know, unlike the previous posts, the content of this post is a new proposal by me. Everything in this post should be treated as conjecture, rather than proven.

The Proposal

Fix a finite set C of candidates, and an electorate V∈Δ(C→[0,1]).

First, recall the following definition of maximal lotteries. Given lotteries A,B∈Δ(C), we say that A dominates B if Pa∼A,b∼B,v∼V(v(a)>v(b))≥Pa∼A,b∼B,v∼V(v(b)>v(a)). A maximal lottery is an M∈Δ(C) such that for all other L∈Δ(C), M dominates L.

Now for maximal lottery-lotteries. Given lottery-lotteries A,B∈Δ(Δ(C)), we say that A dominates B if PA∼A,B∼B,v∼V(v(A)>v(B))≥PA∼A,B∼B,v∼V(v(B)>v(A)). A maximal lottery-lottery is an M∈Δ(Δ(C)) such that for all other L∈Δ(Δ(C)), M dominates L.

Note that when we say v(A), since A is not a candidate, but a distribution of candidates, we mean the expected value of v(a) when a is sampled from A, and similarly for v(B). Unlike in maximal lotteries, to determine whether v(A)>v(B), we need the full information of v as a utility function (up to affine transformation), not just the preference ordering over candidates.

M is a distribution on distributions on candidates, but we need to output a distribution on candidates. Thus, we imagine sampling a distribution at random from M, and then sampling a candidate at random from that distribution. The output of the maximal lottery-lotteries voting system is the distribution that assigns each candidate the probability that they would be sampled from the above procedure.

Lottery Independence

To argue for maximal lottery-lotteries, I introduce a new criterion: lottery independence.

Given an initial set of candidates, C, we could imagine fixing any distribution over candidates P∈Δ(C), and introducing a new ...
Creating superintelligence without AGI
by Antb — 2022-10-17T19:01:24.358Z

I am a computer scientist with some background in biology and neuroscience that discovered LW and the problem of AI alignment less than a year ago.

Since then I have read many articles on the matter and I have come to understand that alignment is potentially the most severe challenge humanity is facing, and probably ever will. And so I have decided to dedicate my masters and Ph.D. to researching methods of counteracting the negative (lethal) impact a misaligned transformative AI can have, as I have given up on the idea that AGI can be safely aligned in the short timeframe that we have (<8 years, I estimate), especially considering the reckless progress that is being made in the field, and the obscurity of all of it.

As the title says, I plan on researching the possibility to negate an AGI takeover by resorting to transhumanism, that is, resorting to physically augmenting the minds of humans to hopefully have enough cognitive power to not be trivially overpowered by artificial superintelligences. I understand that this sounds ludicrously grandiose and that it will most likely amount to nothing more than yet another failed attempt at stopping AGI, but I surely will not stay twiddling my fingers as the world crashes down in flames, for I believe that anyone capable of using their knowledge to aid humanity in times of need should do so.

It is also a possibility that a human individual or collective with superintelligence could be comparably dangerous to a misaligned AGI, but I believe we stand much better chances at surviving that threat as there would be at least some "humanity" programmed into their brains and, therefore, would not converge into unthinkable horrors by default (although it could easily end up in some sort of dictatorship, but there is a price to everything I guess).

My question is, could you recommend articles, papers, books, research projects and notable figures that are related to this line of work? That is, any modern and relevant research related to Brain-Machine Interfacing for extending the capabilities of the mind with hardware, or Brain-Brain Interfacing for shared cognition.

I am open to any criticism on my ideas and would be willing to redirect my efforts to a more effective field of research if they were to be too absurd or unattainable given modern technology and moral standards. And would also be delighted to join any research group ...
AI Safety Ideas: A collaborative AI safety research platform
by Esben Kran — 2022-10-17T17:01:29.995Z

TLDR; We present the AI safety ideas and research platform

AI Safety Ideas

in open alpha. Add and explore research ideas on the website here:

aisafetyideas.com

.

AI Safety Ideas has been accessible for a while in an alpha state (4 months, on-and-off development) and we now publish it in open alpha to receive feedback and develop it continuously with the community of researchers and students in AI safety. All of the projects are either from public sources (e.g. AlignmentForum posts) or posted on the website itself.

The current website represents the first steps towards an accessible crowdsourced research platform for easier research collaboration and hypothesis testing.

The gap in AI safety

Research prioritization & development

Research prioritization is hard and even more so in a pre-paradigmatic field like AI safety. We can grok the highest-karma post on the AlignmentForum but is there another way?

With AI Safety Ideas, we introduce a collaborative way to prioritize and work on specific agendas together through social features. We hope this can become a scalable research platform for AI safety.

Successful examples of less systematized but similar, collaborative, online, and high quality output projects can be seen in Discord servers such as EleutherAI, CarperAI, Stability AI, and Yannic Kilcher’s Discord, in hackathons, and in competitions such as the inverse scaling competition.

Additionally, we are also

missing an empirically driven impact evaluation

of AI safety projects. With the next steps of development described further down, we hope to make this easier and more available while facilitating more iteration in AI safety research. Systemized hypotheses testing with bounties can help funders directly fund specific results and enables open evaluation of agendas and research projects.

Mid-career & student newcomers

Novice and entrant participation in AI safety research is mostly present in two forms at the moment: 1) Active or passive part-time course participation with a capstone project (AGISF, ML Safety) and 2) flying to London or Berkeley for three months to participate in full-time paid studies and research (MLAB, SERI MATS, PIBBSS, Refine).

Both are highly valuable but a third option seems to be missing: 3) An accessible, scalable, low time commitment, open research opportunity.

Very few people work in AI safety

and allowing dece...
Maximal Lotteries
by Scott Garrabrant — 2022-10-17T08:54:09.001Z

Probabilistic Voting Theory

Recall that in the last post I said that a voting system is a function that takes in a distribution on utility functions on a set of candidates, and produces a distribution on that set of candidates, and that voting theorists tend to make four assumptions:

The set of candidates is finite.
The function only uses the preorders on candidates implied by the utility functions.
The output distribution assigns probability 1 to a single candidate.
The input distribution is the uniform distribution on some finite set.

The fourth assumption doesn't really matter, and isn't really used, so we can just remove it. The third assumption, on the other hand, is important for many of the voting theory conclusions, but we are now going to see what happens if we remove it. We will keep the first two assumptions.

So now, we will be considering lotteries (i.e. distributions) over candidates, since that will be the output of our voting system.

Given a set C of candidates, lotteries A,B∈Δ(C), and an electorate V∈Δ(C→[0,1]), we say that A dominates B if Pa∼A,b∼B,v∼V(v(a)>v(b))≥Pa∼A,b∼B,v∼V(v(b)>v(a)).

Lets unpack this definition. We are given three distributions V, a distribution on voters, and A and B, distributions on candidates. We are imagining independently sampling from these three distributions to get v, a, and b respectively. When we do this, one of three things will happen:

v will prefer a to b. (v(a)>v(b))
v will prefer b to a. (v(b)>v(a))
v will be indifferent. (v(a)=v(b))

We say that A dominates B if the first outcome is at least as likely as the second outcome. Note that we don't need to look at the actual utility functions to determine whether A dominates B, we only need to look at the partial ordering of preferences over candidates, so we are not violating assumption 2. Also note that domination need not be transitive.

Maximal Lotteries

Given a set C of candidates, and an electorate V∈Δ(C→[0,1]), a maximal lottery is an M∈Δ(C) such that for all other L∈Δ(C), M dominates L.

Sounds great, but surely that is too strong a condition, domination isn't even transitive.

Doesn't matter. Maximal ...
Voting Theory Introduction
by Scott Garrabrant — 2022-10-17T08:48:42.781Z

Sequence Introduction

This is the first post in a sequence in which I will propose a new voting system!

In this post, I introduce the framework and notation, and give some background on voting theory.

In the next post, I will show you the best voting system you've probably never heard of, maximal lotteries. (Seriously, it's really good.)

After that, I will make it even better, and propose a new system: maximal lottery-lotteries.

Then comes the bad news: I can't prove that maximal lottery-lotteries exist! (Or alternatively, good news: You can try to solve a cool new open problem in voting theory!)

Thanks to Jessica Taylor for first introducing me to maximal lotteries, and Sam Eisenstat for spending many hours with me trying to prove the existence of maximal lottery-lotteries.

Generalizing Voting Theory

A voting system is a function that takes in a distribution on utility functions on a set of candidates, and produces a distribution on that set of candidates.

This is not what a voting theorist will tell you a voting system is. That is because they like to make a bunch of extra assumptions:

The set of candidates is finite.
The function only uses the preorders on candidates implied by the utility functions.
The output distribution assigns probability 1 to a single candidate.
The input distribution is the uniform distribution on some finite set.

I am going to play along with some of these assumptions, but I want my types and notation to treat them as explicit assumptions, so I will use notation that will make it easy to remove these assumptions as needed.

Technically, I also make one assumption that voting theorists usually don't make. That is that the voting system is homogeneous. Homogeneous voting systems are only a function of what proportion of voters have each preference, not on the absolute number of voters that have each preference. Thus, when I said the input was a distribution on utility functions, rather than a multi-set of utility functions, I threw out the information that would have allowed for non-homogeneous voting systems. I don't know of any seriously proposed voting systems that are non-homogeneous, so this is not a significant extra assumption. Throughout the sequence, I will take for granted that all voting systems are homogeneous.

Assumption 2 is most likely to be violated by voting theorists, as in approval voting and range voting. Next is 3, and the...
Space
by Jarred Filmer — 2022-10-17T06:34:32.836Z

Space begets space

Part III

Like an anaemic finding iron, a week into this process I had a profound sense that I'd gained something in which I'd been chronically deficient. I started calling whatever it was that I'd gained, "Space".

Space is a condition of abundant uninterrupted time, with a free license to follow whatever thoughts arise when there is nothing external to take care of. This means removing anything that could provoke an unexpected context switch, and keeping tools close to hand to persist and peruse whatever comes up.

I had an isolated cabin, an empty calendar, empty inbox, empty to-do list, disabled internet, and weeks to think with no interruptions stretching far ahead. I had my laptop, index cards, blue-tack, pens, A3 sheets of card stock, and a kitchen table on which to lay it all out.

I could start a thought, go to sleep, and then pick up where I left off the next morning. If it was all too much I could leave it all alone for a couple of days. Then when I felt ready, have it right there waiting where I left it. The closet of my half-hour walk to work had expanded into a warehouse big enough to hold as mind-splittingly large a question as: "Who do I want to be?".

A thought that took "4 days to think", was finally a thought that I was capable of having. Now that I had space.

Without space, I lack the combination of time, focus, and tools with which to follow a web of thoughts and feelings from A to B through Z. I can't step back and see how it hangs together in its entirety, nor how it should actually impact my life. I end up with a closet of dreams going stale, to chance upon occasionally and fret about until distracted.

Space allows the hints we encounter on the puzzle of life a chance to actually impact who we are, and what we do with our limited time.

For myself, realising the extent to which I needed this turned out to require a lot of space. This creates a pernicious dynamic, wherein a lack of space eats space.

Whenever I tell someone I took a month away just to stop and think, they respond:
"Wow! Yes! That sounds amazing, I really need to do something like that."
and I say "Well then, why don't you?"
to which they reply... "I'm too busy".

Even if taking a month to think is the most vital thing one could be doing, it never happens if you're too busy to realise it, schedule the time, and then protect that time from all intrusion. I saw I ...
GD’s Implicit Bias on Separable Data
by Xander Davies — 2022-10-17T04:13:26.393Z

TLDR: This post goes through the intuition behind why using gradient descent to fit a linear model to linearly separable data will learn a maximum-margin decision boundary. It’s a simple example of an implicit bias of gradient descent, and also extends (in some form) to more complicated settings (like homogeneous deep networks).

Epistemic Status: I find it useful to write up my thoughts on various topics; I think some of these thoughts might be useful to other people so have decided to post them sometimes, but note that they are quick write ups which may have errors.

Often, we use neural networks to solve optimization problems where there are many different solutions which minimize the training objective. In these cases, the particular minima we learn (or approach) is a consequence of how we go about finding a minimum; this is known as an implicit bias of our optimization algorithm.[1] Since different global minima behave very differently outside of the training set, this implicit bias can have a major effect on how our models generalize. Lots about implicit biases of different optimization algorithms still aren’t well understood; indeed, understanding these biases, and their corollary of consistently finding surprisingly well-generalizing solutions is a fundamental open problem in deep learning theory.[2]

This post will talk about a particular setting where we do understand how this implicit bias is working. The setting is interesting not just as a toy case to develop intuition, but also because it seems like similar versions of these results may hold in much more complicated settings. It’s also useful because it forces us to confront that inductive biases are playing a role in learning; thinking only in terms of loss-minimization doesn’t explain the observed behavior.

Lots of the time I find discussion of implicit bias to be unsatisfying or confused; understanding this example well will, I hope, clear things up. Most of this math is based on “The Implicit Bias of Gradient Descent on Separable Data” by Soudry et al.

The Setting

Consider a classification data set {→xn,yn}Nn=1, with d-dimensional real number inputs →xn∈Rd and binary labels yn∈{−1,1}. We use gradient descent to find a vector →w which minimizes L(→w), defined by:

L(→w)=N∑n=1ℓ(yn(→w⋅→xn))ℓ(u)=e−u

Our loss encourages high values of →w⋅→xn which match the ...
Calling my First Family Dance
by jefftk — 2022-10-17T02:40:01.833Z

I just got home from calling my first family dance, the one I

wrote about preparing
for last week. I think it went well, but it also reminded me why I
had stopped calling. The problem is that calling just makes me very
anxious, because you're only one wrong word from messing up the dance
for a lot of people. I know it's not that serious, and when I do mess
up I can recover and get things back on track fine, but it's just not
something I enjoy very much. Calling a family dance does have less of
this effect on me than calling an evening dance for adults, and I'd
still call another family dance if the alternative was not having one,
but I'd still prefer not to call if there's anyone well-suited
available.

In this case I ended up calling mostly because there aren't that many
people who are really good at both family dances and regular contra
dances, and the people we asked weren't available. Bringing someone
in just to call a 1hr family dance before the regular dance isn't so
practical, so I called the family dance and someone else called the
evening dance.

Here's the program I did, with thoughts:

La
Bastringue, variant where you keep your partner. Very small crowd
at this point, five couples including me dancing. It's hard to get
kids places on time, especially when the dance is basically at dinner
time. I called this without a walkthrough, while dancing,
unamplified, and it went smoothly. I put a few different things in
the B1: swing, dosido and swing, allemande left and right. Keeps it
interesting, and teaches new things.

The Low-Backed
Car. Cuing the line leaders at the A1 and A2 was important, and
initially I led them, skipping. We used the music from Galopede, because the C part fits
the dance's B2 very well.

Favors the Rose (the
Fan Dance. I forgot to bring an interesting object, but Julia
remembered that we had a tambourine in the stroller, which worked
better than anything I'd have intentionally brought from home. This
dance is super simple, enough that it doesn't seem like it would be
that fun, but a lot of family dance callers rave about how well it
goes so I was pretty uncertain going in how much people would like it.
I think it ended up a bit below the other dances in terms of how much
fun it was? I probably wouldn't do this again unless I needed
extremely easy material (drunk wedding?), though I don't think it
bo

...