trentmkelly/LessWrong-43k huggingface dataset:
University of Amsterdam (UvA), Master's Artificial Intelligence
by Master Programs ML/AI — 2020-11-14T15:49:02.800Z

This article gives an overview of the master's program in Artificial Intelligence at the University of Amsterdam (UvA). It is meant to be both useful for people who want to decide where to study, as well as for UvA students who want to get information about how to make the most of their experience – including which courses are good and which research opportunities exist.

This article is part of a series of articles on different European master's programs related to artificial intelligence and machine learning.

Earlier versions of the post received feedback from Henning Bartsch, Remmelt Ellen, Micha de Groot, Benjamin Kolb, Evangelos Kanoulas, Dmitrii Krasheninnikov, Linda Petrini, Attila Szabó, and Oskar van der Wal. All mistakes and opinions are mine.

General note: The master’s program changed significantly in the last 3 years. I’m putting in the information of the newest edition, but cannot guarantee that the information is still correct when you apply.

About myself

I have a background in mostly pure mathematics, where I did a master’s degree at the University of Bonn before. After getting interested in the social movement of Effective Altruism, I tried out Software Engineering and Data Analytics and then began studying AI at the University of Amsterdam (not the same as the Vrije Universiteit Amsterdam). That was since I felt I want to go closer to research again, but in a field that’s more relevant to the planet than pure maths. I did my master’s in AI at the UvA from September 2018 to July 2020. In September 2020 I started a PhD in the intersection of Information Theory, Causality, and Deep Learning at the UvA in collaboration with the Computational Science Lab (CSL) and Amsterdam Machine Learning Lab (AMLab). If you have any questions, please contact me by email.

The AI master’s at the UvA – An Overview

The master's program in AI at the

University of Amsterdam

is a research-oriented 2 years master program. “Research-oriented” means that a significant proportion of the degree consists of the students doing research, but usually still less than 50%, with the remainder being coursework. Graduation requires the students to collect 120 Credit Points (CP). Most courses (lectures/projects/seminars) are awarded 6CP upon completion, and a research thesis in the end is rewarded w...
On Arguments for God
by Chris_Leong — 2020-11-14T09:06:57.339Z

This post is about God, but of course, it isn't really about God, but about a particular pattern in general.

We're pretty much all in agreement that God doesn't exist.

This is correct or at least let's say it is for the purposes of this post. However, this also poses a trap.

Suppose there are forty arguments for God. Even if we know definitely for a fact that God doesn't exist, it doesn't mean that all of these forty arguments are wrong.

It would if all of these arguments claimed to definitely prove that he exists, but not if some of these arguments only claim he is more likely to exist than not or that he isn't as unlikely as we might think.

In fact, it'd actually be suspicious if all forty of these arguments came out against God. Surely we should expect the advantage to belong to the deists in at least one or two?

But since we have very good reasons to believe God doesn't exist and someone presents us with those arguments, surely we'll assume that they have to be wrong. And so we'll search very hard, until we find something that is plausibly an error or at least more plausible than God and talk ourselves into believing it.

And once we've introduced that first error, we've opened up the door for more.
What are some good examples of fake beliefs?
by Adam Zerner — 2020-11-14T07:40:19.776Z

I'm reading through Rationality from AI to Zombies again and just finished the section on Fake Beliefs. I understand the ideas and now I'm trying to ask myself whether the ideas are useful.

To me, the biggest reason for them being useful is if I myself have some fake beliefs, it'd be good to identify and get rid of them. I don't think I do though.

Another reason why they might be useful is to identify them in others and better understand what is going on. Actually, this doesn't seem particularly useful, but it at least is somewhat interesting/satisfying. Anyway, I'm having trouble thinking of good examples of fake beliefs in others. The examples in the blog posts seem pretty contrived and perhaps uncharitable to what is actually going on in their heads.

So, to wrap this question up, I am most interested in hearing about examples of fake beliefs that rationalists might be prone to having, but I am also interested in hearing any other examples.
My Confusion about Moral Philosophy 
by Nacruno96 — 2020-11-14T03:49:39.115Z

Something about the academic discussion about moral philosophy always confused me, probably this is a more general point about philosophy as such. Historically people tried to arrive towards truths about objects. One used to ask questions like what is right or wrong. Then one started to ask what the definition of right and wrong could be. One could call that Platonism. There is the idea of truth and the game is won by defining the idea of truth, a chair or a human in a satisfying way. I claim the opposite is true. You can define an object or an idea and the definition of the idea makes the idea to a useful entity which one can develop further. At least this would be the right way to philosophies in my opinion. Something similar is done in mathematics too to my knowledge. Axioms seem to be the beginning. On a few axioms in math all theorems and all sentences seem to be built upon. Change the axioms or subvert them then one would end up with a totally different system of mathematics, with different theorems and sentences most likely. However the main difference in this analogy is that we know of the axioms in mathematics to be true on an intuitive level. That’s the unique difficulty of philosophy. We do not seem to have axioms in philosophy. We could however make a somewhat reasonable assumption that if one of the foundational axioms will prove wrong the system of mathematics might entangle itself in contradictions or at least in some inconsistencies. Historically this did. in fact happen. To give an historical example there was a Fundation crisis of mathematics in the second half of the 19. century and in the early 20. century. Therefore one could argue that the same could happen to philosophy once philosophy is evolved enough. Now I will explain my confusion about moral philosophy.

Moral philosophy seems to me to be a judgement about once own utility function. You can basically choose if you care more about being just to people, maximizing their utility or doing what is regarded as honorable by your peers. You can choose if you want to include animals, plants or just humans in your considerations. There does not seem to be a right answer in the sense that a right answer would have a special pair of attributes. In the usual academic discussion of utilitarianism, deontological ethics or virtue ethics there will always appear something that makes a theory proble...
Sharding the Brigade
by jefftk — 2020-11-14T03:30:06.652Z

The

Secular Solstice is
planning on using the

bucket
brigade singing

app that
some friends and I have been building. While the events we've hosted
so far have been something like 20 people, this might be 500 or more.
I've spent some time over the last couple weeks figuring out how to
scale it, and I think it's in a good place now.

I started, as one always should, with profiling. By far the biggest
amount of time was being spent in Opus
encoding and decoding. This is what lets us send audio over the
internet at decent quality without using an enormous amount of
bandwidth, so it's not optional. We're also already doing it in C (libopus with Python bindings) so this
isn't a case where we can get large speedups by moving to a
lower-level language.

The first question was, if we pulled off encoding and decoding into
other processes, could we keep everything else in the same
process?

The app is built around a shared buffer, that everyone is
constantly reading and writing from at various offsets, and it's a lot
nicer if that can stay in one process. Additionally, there is a bunch
of shared state that represents things like "are we currently in a
song and when did it start?" or "who's leading?" that, while
potentially separable, would be easier to keep together.

I split the code into a "outer" portion that implemented decoding and
encoding, and a "inner" portion that handled everything else. Running
a benchmark
[1], I got the good news that the inner portion was fast enough to
stay all in one process, even with a very large number of clients:

$ python3 unit-stress.py
2.45ms each; est 245 clients
2.44ms each; est 246 clients
2.46ms each; est 243 clients

$ python3 unit-stress.py inner
0.05ms each; est 11005 clients
0.05ms each; est 11283 clients
0.05ms each; est 11257 clients

Since encoding and decoding are stateful, I decided that those should
run in long-lived processes. Each user can always talk to just one of
these processes, and it will always have the appropriate state. This
means we don't have to do any locking, or any moving the state between
CPUs. I don't know of a great way to manage many sharded processes like
this, but since we only need about eight of them we can do it
manually:

location /echo/api/01 {
include uwsgi_params;
uwsgi_pass 127.0.0.1:7101;
}
location /echo/api/02 {
include uwsgi_params;
uwsgi_

...
Notes on Wisdom
by David Gross — 2020-11-14T02:37:16.027Z

This post examines the virtue of wisdom. It mostly explores what other people have learned about this virtue, rather than highlighting my own opinions about it, though I’ve been selective about what I found interesting or credible, according to my own inclinations. I wrote this not as an expert, but as someone who wants to learn more. I hope it will help people who want to know more about this virtue and how to nurture it.

Singing the praises of wisdom at LessWrong has a bringing coals to Newcastle feel to it. After all, isn’t this community all about working hard and passionately to hack through the jungle of bias, illusion, and ignorance in search of the hidden temple of Athena?

I was tempted to skip wisdom and research some other virtue instead. But I’m hoping that by exploring wisdom as-a-virtue I can illuminate some facets of it that otherwise receive less attention here.

Two varieties of wisdom

Two senses of wisdom are commonly found in virtue traditions:

phrónēsis, or “practical wisdom” (sometimes translated “prudence”), which concerns knowing how the world works, and reasoning well about how to pursue goals effectively (and about which goals are worth pursuing—which sometimes gets separated out into “conative wisdom”[1]). Phrónēsis is about particular cases.
philosophy, which concerns a more comprehensive understanding of “what it’s all about,” whether or not there seems to be a way to make practical use of that understanding. Philosophy is about generalities and commonalities and rules.

They are both important: Phrónēsis without philosophy can make you merely clever; while without phrónēsis, philosophy can leave you with your head in the clouds, unable to bring your wisdom down to earth where you can make it matter.

“The title wise is, for the most part, falsely applied. How can one be a wise man, if he does not know any better how to live than other men?—if he is only more cunning and intellectually subtle?” ―Thoreau[2]

Philosophy is also sometimes considered an important end in itself. Aristotle thought it was the richest and most satisfying activity for people to engage in, and reasoned that it was the pastime of the gods.[3]

The person with the virtue of wisdom habitually and regularly prioritizes thinking and behaving wisely. This raises the question: why wouldn’t you? You might at first think that the only reason you would think or behave unwisely is be...
The Darwin Game - Rounds 3 to 9
by lsusr — 2020-11-14T02:26:51.126Z

Edit: This unofficial version of the game is missing AbstractSpyTreeBot.

Rounds 3-9

MeasureBot maintains its lead. SimplePatternFinderBot takes second place.

Deep dive into SimplePatternFinderBot

SimplePatternFinderBot

Yonge's SimplePatternFinder can speak for itself.

if pattern != None:
if pattern.IsPatternFairOrGoodForUs():
# Try and stick to it as it looks good
ret = pattern.OurNext()
else:
# We have a problem. If it is a smart opponent we
# don't want to encourage it to stick with it, on
# the other hand if it is a dumb bot that will stick
# with it regardless then we are better getting
# something rather than nothing. It's also possible
# we might not have been able to establish
# co-operation yet

if pattern.OurNext() >= 3:
# The pattern is godd for us for at least this
# move, so stick to it for now.
ret = pattern.OurNext()
elif (self.theirScore + pattern.GetNext())/self.turn\
>= 2.25:
# Under no circumstances allow it to get too many
# points from playing an unfavourable pattern
ret = 3
elif self.theirMoves[-1] + self.ourMoves[-1] == 5:
# If we managed to co-operate last round,
# hope we can break the pattern and co-operate
# this round.
return self.theirMoves[-1]
elif not self.hasTotalOfFiveBeenPlayed:
# If the combined scores have never been 5
# before try to arrange this to see if it will
# break the deadlock.
ret = pattern.OurNext()
elif self.round < 4 and pattern.OurNext() >= 1:
# It looks like we are probably dealing with
# a nasty bot. Tolerate this within limits in
# the early game where it is more likely to be
# a dum bot than a sophisticated bot that is

...
The Wisdom of the Crowd social learning group
by Jason Geringer — 2020-11-13T21:33:25.405Z
Link: https://forms.gle/fGeEq9NLCXZ4SZ57A

The wisdom of the crowd is a unique "social learning" group, with "group projects" designed as experiments for social good. While only loosely modeled on A theoretical group able to take advantage of the crowd wisdom phenomena, the groups main focus in on a unique way to crowdsource, and evaluate it's own lesson library in a process similar to a machine learning algorithm called a G.A.N. or a Generative Adversarial Network. A G.A.N. only has 2 parts: a Generator and a Discriminator. Using opinion polls, every member acts as discriminators, and after completing testing becomes a Generator able to crowdsource new or improved lessons and information for the group to evaluate. with the better more popular lessons replacing previous lessons, the group will evaluate and generate more and better and better data in the form of the most preferred and informative lessons.

It is not focused on any one subject, but virtually all subjects to give the group a wide and eclectic knowledge base, which will hopefully give members a more profound perception on reality or "wisdom"..

The group uses another new feature that facebook only made available in early 2019.It is What's called a "Social Learning" group. The key feature of this new social learning group is the addition of a "Unit's" tab, which can theoretically hold within it the equivalent of 100 news feeds from other pages, that can now be organized, in the order you want and saved there indefinitely without the issue of getting buried by new incoming posts. The addition of The "Units" tab seems to have gone largely unnoticed and unused by other facebook groups to my observations, but i'm very sure in time it will become very popular, because of the huge actual difference it makes to the functionality and what can be done with a FB group page. It's a big difference. Like the difference between having 1 facebook page with one newsfeed that you can't edit like everyone has, into having 100 newsfeeds that you can organize and save and edit. So like a 1-100 difference in my opinion.

There's 10 levels, the first five levels are only with opinion polls. Levels 6-10 contains the same exact lesson as levels 1-5, but has a 100 point quiz, and then another opinion Poll.

Another new thing is we will have 3 "Types" of units called: Library, Discussion and Archive. The Discussion unit is for new information, The library unit is for vote...
A Self-Embedded Probabilistic Model
by johnswentworth — 2020-11-13T20:36:24.407Z

One possibly-confusing point from the

Embedded Agents

sequence: it’s actually not difficult to write down a self-embedded world model. Just as lazy data structures can represent infinite sequences in finite space, a lazily-evaluated probabilistic model can represent a world which is larger than the data structure representing the model - including worlds in which that data structure is itself embedded. The catch is that queries on that model may not always be computable/well-defined, and even those which are computable/well-defined may take a long time to compute - e.g. more time than an agent has to make a decision.

In this post, we’ll see what this looks like with a probabilistic self-modelling Turing machine. This is not the most elegant way to picture a self-embedded probabilistic model, nor the most elegant way to think about self-modelling Turing machines, but it does make the connection from probabilistic models to quining and diagonalization explicit.

The Model

Let’s write out a Turing machine as a probabilistic model.

Pieces:

Tape
Head
k-bit-per-timestep input channel
k-bit-per-timestep output channel
k-bit-per-timestep random bit channel

I’m including an input channel so we can have data come in at every timestep, rather than just putting all the input on one tape initially (which would let the machine perform arbitrary computation between each data point arriving). Similarly with the output channel: it will have a bit in it every timestep, so the machine can’t perform arbitrary computation between output bits. This is a significant difference between this model and the usual model, and it makes this model a lot more similar to real-world computational systems, like CPUs or brains or .... It is, effectively, a bounded computation model. This will play only a minor role for current purposes, but if you’re thinking about how decision-theoretic queries work, it becomes much more relevant: the machine will only have finite time to answer a query before a decision must be made.

The relationships:

The tape-state at time t and position position, Tape(t)[position], is a function of Tape(t−1)[position] and Head(t) (if position=Head(t−1).position).
The head-state at time t, Head(t), is a function of Head(t−1), tape-state at the head’s previous position Tape(t−1)[Head(t−1).position], and the input bits

...
How to get the benefits of moving without moving (babble)
by mingyuan — 2020-11-13T20:17:59.278Z

If you've been following along with the location discussion (you probably haven't, that's okay), you'll know that I've become convinced that trying to get the community to leave Berkeley en masse is probably not a good idea. However, that leaves us in a bit of a cheeky conundrum (sorry, been watching lots of British comedy) – there are in fact real reasons why some people are excited about moving, and we shouldn't just throw all that in the garbage, even if we decide not to move.

So in this post, I want to figure out how we can get the things that we want out of moving, without moving (thanks to Aray for the general idea). The point of this is to stop thinking of move/don't-move as a binary, and instead focus on ways of achieving whatever goals are hidden at the root of our desire to move.

I'm choosing to focus on what I've come to believe are three of the main cruxes:

Opportunity to stop stagnating / be a new person
Political stability
Nicer surroundings

I've taken inspiration from jacobjacob and generated 50 dumb ways to get each of the things (in spoiler tags, in case you want to generate your own!). In inviting you to do this babble challenge, I also invite you – if you so choose – to babble not on these topics, but on cruxes of your own.

Stop stagnating

We've been in Berkeley for a long time, and some people just want to move because they want to be anywhere other than the place they already are. Your physical location definitely shapes the thoughts you have and the actions you take, so if you feel stuck in a rut, shaking up your whole life by moving can sound pretty appealing. How else can we shake up our lives?

Babble:

Move to a different room in your house
Start living with different housemates
Move to a different physical house in the same neighborhood
Move to a new neighborhood
Rearrange your furniture
Redecorate the house
Paint your walls
Spend a lot of time in VR
Meditate a lot to become more attentive to your experience
Start using a different room as the default common space
Go to more conferences
Go on retreats for much of the year
Commute to an office instead of working from home, for context change
Go on a walk / bike ride / drive in a different place each day
Rotating offices - instead of having people from the same org working in the same place all the time, we reorganize once every one to three months

Maybe we have one big office building and people work on diffe

...
Interest survey: Forming an MIT Mystery Hunt team (Jan. 15-18, 2021)
by jchan — 2020-11-13T18:33:13.745Z

The upcoming MIT Mystery Hunt will be all-remote, which presents an opportunity to form a widely distributed team for the LW/SSC community. The Mystery Hunt can be a lot of fun and is a great exercise in solving challenging problems. With enough people on the team we might even have a chance of winning!

If you think you might be interested please fill out the survey.
Where can I find good explanations of the central limit theorems for people with a Bayesian background?
by Maxwell Peterson — 2020-11-13T16:36:01.611Z

My knowledge of probability theory is based mostly on reading E.T. Jaynes’ Probability Theory book, Andrew Gelman’s blog, and various LessWrong posts. I now want to get a strong grasp of the central limit theorem(s), but YouTube videos and googled pages speak so much in the language of sampling from a population, and random variables, that it’s hard to be sure what they’re saying, given that my background doesn’t really include those ideas. I’m especially interested in the different kinds of CLTs, like the Lyapunov condition, the Berry-Esseen theorem, and so on. I often have a tough time with diving right into algebra - something like http://personal.psu.edu/drh20/asymp/fall2002/lectures/ln04.pdf gives me terrible trouble. Given all these constraints, does anyone know of good resources from which I can gain a strong grasp of the CLTs?

Some things I am confused about after googling so far:

Do distributions converge to gaussians, or do means converge to the mean of a gaussian? Is the former a more difficult convergence to achieve, or are they actually the very same condition?

Is the CLT even about means? Does it say anything about the resulting variance or skewness of the resulting distribution?

Is it actually necessary to be sampling from a population, or does the CLT apply to taking the means of arbitrary distributions, regardless of where they were obtained?

Any form of media is OK, for recommendations - no preference. Please feel free to suggest things even if you’re not sure it’s what I’m looking for - you are probably better than google!