trentmkelly/LessWrong-43k huggingface dataset:
Signalling & Simulacra
by abramdemski — 2020-11-14T19:24:50.191Z

"We lie all the time, but if everyone knows that we're lying, is a lie really a lie?"

-- Moral Mazes

A common Bayesian account of communication analyzes signalling games: games in which there is hidden information, and some actions can serve to communicate that information between players. The meaning of a signal is precisely the probabilistic information one can infer from it.

I'll call this the signalling analysis of meaning. (Apparently, it's also been called Gricean communication.)

In Maybe Lying Can't Exist, Zack Davis points out that the signalling analysis has some counterintuitive features. In particular, it's not clear how to define "lying"!

Either agents have sufficiently aligned interests, in which case the agents find a signalling system (an equilibrium of the game in which symbols bear a useful relationship with hidden states, so that information is communicated) or interests are misaligned, in which case no such equilibrium can develop.

We can have partially aligned interests, in which case a partial signalling system develops (symbols carry some information, but not as much as you might want). Zack gives the example of predatory fireflies who imitate a mating signal. The mating signal still carries some information, but it now signals danger as well as a mating opportunity, making the world more difficult to navigate.

But the signalling analysis can't call the predator a liar, because the "meaning" of the signal includes the possibility of danger.

Zach concludes: Deception is an ontologically parasitic concept. It requires a pre-existing notion of truthfulness. One possibility is given by Skyrms and Barrett: we consider only the subgame where sender and receiver have common goals. This gives us our standard of truth by which to judge lies.

I conclude: The suggested solution seems OK to me, but maybe we want to throw out the signalling analysis of meaning altogether. Maybe words don't just mean what they probabilistically imply. Intuitively, there is a distinction between connotation and denotation. Prefacing something with "literally" is more than just an intensifier.

The signalling analysis of meaning seems to match up rather nicely with simulacrum level 3, where the idea that words have meaning has been lost, and everyone is vibing.

Level 3 and Signalling

Out of the several Simulacra definitions, my understanding mainly comes from Simulacra Levels...
[Event] Ajeya's Timeline Report Reading Group #1 (Nov. 17, 6:30PM - 8:00PM PT)
by habryka — 2020-11-14T19:14:43.550Z

Ever since Ajeya's timeline report came out, I've been wanting to discuss it with other people, and see what they think about it. This is the relevant LessWrong post: https://www.lesswrong.com/.../draft-report-on-ai-timelines

The report is split into four files, and this meeting will be about the first one. If you want to attend, please make sure that you have given the first file at the very least a very thorough skim.

If this time doesn't work well for people, I am also happy to do multiple sessions on the first report, or to reschedule it.

Structure will be mostly open discussion, with a Google Doc to help us decide on what to explore open in parallel.

The event will be happening at this link. After you spawn, walk upwards until you see the Tardis, then teleport to the library. If you get lost, just send me a message via the menu on the right and I can get you:

http://garden.lesswrong.com?code=9A40&event=ajeya-s-ai-timelines-report-reading-group
Stuart Russell at the SlateStarCodex Online Meetup
by JoshuaFox — 2020-11-14T18:42:37.227Z

Professor Stuart Russell will speak briefly on his book "Human Compatible", and then will take questions. The event begins Dec. 6, 2020 at 20:30 Israel Standard Time, 10:30 Pacific Standard time, 18:30 UTC.

Please register here and we will send you an invitation.

Stuart Russell is a Professor of Computer Science at the University of California at Berkeley, holder of the Smith-Zadeh Chair in Engineering, and Director of the Center for Human-Compatible AI. His book "Artificial Intelligence: A Modern Approach" (with Peter Norvig) is the standard text in AI, used in 1500 universities in 135 countries. His research covers a wide range of topics in artificial intelligence, with an emphasis on the long-term future of artificial intelligence and its relation to humanity.
Specialized Labor and Counterfactual Compensation
by philh — 2020-11-14T18:13:43.044Z

I have three purposes in this post. The first is to review the formal game theory found in Robert Ellickson's Order Without Law. It's not a large part of the book, but it's the part that I'm most qualified to judge. Not that I'm a formal game theorist myself, but I'm closer to being one of them than to being any kind of social scientist, historian or lawyer. If his formal game theory is nonsense, that would suggest that I ought to discount his writing on other fields, too. (Perhaps not discount it completely, especially because formal game theory is outside his main area of study. Then again, lots of the book is outside his main area of study.)

Spoiler alert: I think he holds up reasonably well. I want to ding him a few points, but nothing too serious, and he possibly even contributes a minor original result.

My second purpose, which is valuable for the first but also valuable of itself, is to try to extend it further than Ellickson did. I don't succeed at that.

My third is simply to be able to cut it from my in-progress review of the rest of the book.

Ellickson discusses two games. One is the classic Prisoner's Dilemma, in which you either Cooperate (for personal cost but social benefit) or Defect (for personal benefit but social cost).1 The other he calls Specialized Labor, in which two people must choose whether to Work on some common project or Shirk their share of it. It differs from the Prisoner's Dilemma in two ways. First, it's asymmetrical; one player is a less effective worker than the other, and gets less payoff from Working while the other Shirks than does the other player. The other is that in this game, the socially optimal outcome is Work/Shirk, not Work/Work.

(Many authors consider that the second change isn't really a change, and that a Prisoner's Dilemma can perfectly well have Cooperate/Defect be socially optimal. So they'd say Specialized Labor is simply an asymmetrical version of the Prisoner's Dilemma. In my taxonomy I define the Prisoner's Dilemma more narrowly than that; see also this comment. Ellickson uses the same narrow definition as me. I'd instead say Specialized Labor is an asymmetrical version of Too Many Cooks.)

Note that payoffs aren't measured in utility. They're measured in something Ellickson calls "welfare". He doesn't really explore the formal consequences of this. But what it gives us is that, since welfare is supposed to be...
European Master's Programs in Machine Learning, Artificial Intelligence, and related fields
by Master Programs ML/AI — 2020-11-14T15:51:30.131Z

While there is no shortage of detailed information on master’s degrees, we think that there is a lack of perspectives from students that have actually completed the program and experienced the university.

Therefore we decided to write articles on multiple European master's programs on Machine Learning, Artificial Intelligence, and related fields. The texts are supposed to give prospective students an honest evaluation of the teaching, research, industry opportunities, and city life of a specific program. Since many of the authors are Effective Altruists and interested in AI safety a respective section is often included as well.

It may not always be obvious, but there are many English-language degrees across Europe. Compared to America, these can be more affordable, offer more favorable visa terms, and a higher quality of life. We hope that you will consider bringing your talents to Europe. These are the articles that have already been written:

University of Amsterdam (Master's Artificial Intelligence)
University of Edinburgh (Master's Artificial Intelligence)
ETH Zürich (ML related M.Sc. programs)
EPF Lausanne (ML related M.Sc. programs)
University of Oxford (Master's Statistical Science)
University of Tübingen (Master's Machine Learning)
University of Darmstadt (Master's Computer Science with ML focus)

This selection of Master’s programs is not an ultimate list of “good” master's programs – we just happened to know the authors of the articles. If you want to add an article about any ML-related program anywhere in the world don’t hesitate to contact us and we will add it to the list. We also don't claim that this is a complete overview of the respective programs and want to emphasize that this does not necessarily reflect the perception of all students within that program.

Authors: Marius (lead organizer), Leon (lead organizer), Marco, Xander, David, Lauro, Jérémy, Ago, Richard, James, Javier, Charlie, Magdalena, Kyle.
EPF Lausanne, ML related MSc programs
by Master Programs ML/AI — 2020-11-14T15:51:17.502Z

By Kyle Matoba

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

Let me preface this writeup by saying that I did not do a master’s degree at EPFL and unfortunately, I have no special insights on the minutiae of a study plan. To avoid wrongly conveying factual information, I will not be discussing specific details of academic programs, and I will focus more on machine learning research and the broader environment around EPFL with an emphasis on aspects of being a student that cannot easily be learned from an internet search.

Masters degrees at EPFL

EPFL is completely a technical university, all academic departments are STEM-y. The degrees are academic and research-oriented (like most degrees in Europe, but different to some shorter and more vocationally-oriented masters degrees that can be found in the US). As I understand it, a master's degree at EPFL is generally composed of 120 ECTS, with something around 40% of the credits coming from research projects and a master’s thesis, and the remainder coming from coursework, of which about one third will be required courses.

More details on the requirements for master's degrees can be seen in the program overview.

Machine learning at EPFL is fairly dispersed across departments - many engineering and science departments offering their own variants. There is also a complete list of programs.

My impression is that the bulk of the teaching and research is done in the electrical engineering and computer science departments, but realistically I think that a determined student could be a solid ML education in many of these programs. For example, I recently TAed for a large “Deep Learning” course offered by the EE department, and we had students from about 10 different engineering and science disciplines, math, and computer science. About one-third of the students hadn’t done their bachelor’s degrees at EPFL.

Academics: Research and Coursework

EPFL is generally a well-regarded university. Along with ETH, it benefits from Switzerland’s strong economic situation and high quality of life that tend to attract good faculty and students. Switzerland emphasizes science and technology, and the perception is that although EPFL and ETH are public institutions they have excellent funding and support. My impression is that within the broad

...
University of Oxford, Master's Statistical Science
by Master Programs ML/AI — 2020-11-14T15:51:02.393Z

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

Summary

This programme runs for 1 year, composed of 2 terms of 8 weeks, followed by a 3-month dissertation over the summer. You typically take 4 courses per term and are able to choose 1 course of the 4. There are 3 week-long practicals throughout the year, and two exams at the end of May. The courses are pretty theoretical, sometimes dry, but mostly good. The course as a whole is pretty good preparation for a PhD in stat-heavy ML, particularly if supplemented with seminars, keeping up-to-date on ML research trends, as well as extra-curricular coding.

The biggest downside of the course is the coding--it’s in R :( However, you don’t have to do much of this and can choose whatever language you want for your dissertation. The course itself doesn’t take too much time, so you should be able to spend a fair bit of time doing the supplementary activities I mention above, as well as maybe doing some research throughout the year with a prof, who are mostly keen to take students.

The biggest upside of the course is the people--academic and otherwise. Being in an Effective Altruism (EA) and AI-safety (AIS) hub has strong benefits, as does being able to attend lots of seminars and talks across departments.

Getting In

The acceptance rate is around 1/15. You’ll need strong grades from a good university. A decent majority of the cohort had research experience before they came. A minority had published. As a UK master’s, I expect that they expect less research experience than similar-quality non-UK programmes.

The Course

Relevance of courses

As you might expect, a statistics master’s will approach areas from a theoretical angle. While you gain a deep understanding of the underpinnings of ML, a fair bit of the material is dry (maybe ⅓?), and you won’t be learning the hottest ML--i.e. little discussion of the latest breakthroughs in DL or RL.
That being said, doing the master’s will make it easy to read any new ML papers, and the coverage of important probabilistic inference topics is good.

Quality of courses

¾ of the courses are of very high quality--lectures and lecture notes and problem sheets are typically very good and inspire insight.

Difficulty of courses

⅓ are easy, ⅓ are moderately difficult, ⅓ are pretty difficult.
There isn’t actually

...
University of Tübingen, Master's Machine Learning
by Master Programs ML/AI — 2020-11-14T15:50:41.984Z

The purpose of this post is to give an overview of the Machine Learning (ML) Master’s program at the University of Tübingen. It should function as guidance for people who are interested in studying ML and weigh the pros and cons.

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

General Overview

The ML program in Tübingen has 120 credits, 30 of which are assigned for the thesis. The program has three mandatory courses, Deep Learning, Statistical Machine Learning, and Probabilistic Machine Learning. All other courses can be chosen more or less freely with some small restrictions, e.g. they have to be in the broad area of ML. The full range of lectures can be found in the

module handbook

, though not all of them exist yet. Since 2019 was the first year of the Master, I expect these gaps to be closed in the next two years. In the winter semester 20/21 there are already many new courses.

Regarding prerequisites: There are some specifications on the website but they can be a bit vague. According to the creator of the program, who also oversees admissions, absolutely necessary criteria are having a sufficient understanding of proof-based math (e.g. through a math or Computer Science (CS) Bachelor’s degree) and a basic understanding of algorithms and other CS concepts. To give you a prior probability for a successful application we can only look at the first iteration of the degree where aone-thirdround 150 people from all around the world applied, 60 were deemed to be sufficiently qualified and around 40-60 actually started the Master. The program probably could have handled more students but the creator decided that applicants need to pass a certain level of skill. This acceptance rate of one third does not seem very low but I expect it to get more competitive in the future. Last time the program was only announced three months before the application deadline and already 150 people applied. Since then the University of Tübingen/the MPI has had more exposure within the media and the

official ML YouTube-channel

hit 800 subscribers in its first week I would expect the program to become more competetive. An alternative in case of rejection is to apply for the CS Master’s and transfer to the ML Master’s later. However, if too many p...
TU Darmstadt, Computer Science Master's with a focus on Machine Learning
by Master Programs ML/AI — 2020-11-14T15:50:24.472Z

by Magdalena Wache

This post is a compilation of information about the TU Darmstadt's Computer Science program with a focus on machine learning. I hope it will be useful for people who are interested in doing a machine learning master's and are deciding which university to apply for. It is part of a series of articles on different European master's programs related to artificial intelligence and machine learning.

Caveat: While writing this post, I found out that the degree program requires a DSH-2 certificate in German (level C1 equivalent), or the completion of a German bachelor’s degree. This surprised me, because the AI-related courses are almost entirely in English, and it is possible to choose courses in English only. I wrote to the university to ask if I had misunderstood something, but they confirmed the certificate is necessary. Therefore, the TU Darmstadt's CS master's program is only an option for you if you speak German or intend to learn enough German to pass a C1 exam.

Overview

In the TU Darmstadt Computer Science (CS) master's degree you can choose the courses in a way that basically makes it a machine learning master's. I am currently enrolled and will start my master's thesis soon.

The degree is a two-year program with 120 ECTS, which consist of:

66 ECTS of elective courses that you can choose relatively freely from a very large number of CS subjects. There are some restrictions that I will explain in further detail below. As there are a lot more than 66 ECTS of interesting ML/AI-related courses, this part can be almost entirely about machine learning.

24 ECTS of elective courses in a minor (In my case: Mathematical Logic)

30 ECTS for the master's thesis

This means there are no mandatory courses and you have a lot of choices.

Darmstadt is not as well-known as Amsterdam, Oxford, Cambridge, etc., but its academic output seems quite presentable. If you filter for publications in AI and ML, the TU Darmstadt ranks 9th in Europe on CSRankings. However, CSRankings considers only the publication count — not the citations, etc. — so I'm not sure how significant this rank is.

Getting in

In order to get into the TU Darmstadt CS master's degree program, you will need to have completed a bachelor's degree that is "similar enough" to the CS bachelor's degree at the TU Darmstadt. This means your studies addressed the core topics taught at the TU Darmstadt.
I...
University of Edinburgh, Master's Artificial Intelligence
by Master Programs ML/AI — 2020-11-14T15:49:50.088Z

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

Basic data on the degree

Duration: 1 year (or 2 years part-time)
Cost: ~15.000€ p.a. for EU students (before Brexit)
90 ECTS

Purpose of this article

The main focus of this text is to help you decide, whether studying an MSc AI degree at the University of Edinburgh (AI@Ed) is an option for you. It is not supposed to give detailed technical information like links to admission pages or tips for how to find a room in Edinburgh. You will need this kind of information once you decide to apply to UoE. I hope that I can guide your decision-making process with this article.

This is also not an advertisement, I have no affiliation with the University of Edinburgh. It's just an honest and objective opinion on the degree, the university, and the city.

Text is by: Marco Kinkel, feel free to message me on

hi@marco-kinkel.de

The degree

Courses and Areas

Being able to choose from a variety of topics was important to me. AI research is manifold and includes grasping a notion of intelligence from different viewpoints. I was happy to choose from interdisciplinary courses on Cognitive Science, Neuroscience, Robotics, Bioinformatics and of course many core ML courses. The university is especially known for its research and lectures on Bayesian ML, Computer Vision, Natural Language Processing and Biomedical Sciences. Here is a list of most important courses (which obviously changes over the years). The bold courses can be considered the standard of the degree, you will find the majority of your friends in these. However, depending on your interests, it can be worth thinking outside the box and taking a less popular course. It can expand your horizon and you will get better teaching because they are less crowded (e.g. 10 instead of 200 students).

ML

Introductory Applied Machine Learning
Machine Learning and Pattern Recognition
Machine Learning Practical
Reinforcement Learning

Vision

Image and Vision Computing

Language

Accelerated Natural Language Processing
Automatic Speech Recognition
Natural Language Understanding, Generation, and Machine Translation

Data Science

Text Technologies for Data Science
Data Mining and Exploration

Design and HCI

Case Studies in Design Informatics
Human-Computer-Interaction
The Human Factor: Working with Users

Bio and Neuroscience

...
ETH Zurich, ML related MSc programs
by Master Programs ML/AI — 2020-11-14T15:49:31.460Z

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

The purpose of this post is to give an overview of Machine Learning related Master’s programs at ETH Zurich. Machine Learning is a topic that is researched and taught in different departments and institutes at ETH, which means that there exist many resources that can be hard to navigate. Our goal is to give you an inter-departmental overview of the Machine Learning landscape at ETH, and point you to the right resources. We also want to put an emphasis on opportunities that ETH provides for people interested in technical AI safety. This post is structured in two parts. The first part covers general information about ETH, its Master programs and its research possibilities in ML. The second part describes a few relevant Master’s programs in more detail.

General Information

Overview

ETH Zurich is consistently ranked as a top university: for example 14th overall and 3rd in Computer Science in the

2021 Times Higher Education Rankings

, 6th in the

QS World University Ranking

, and currently

29th on csrankings.org when considering publications in the area of AI & ML

.

Interestingly there are many different kinds of Machine Learning related Master programs that ETH offers. Most of them are 4 semester (120 ECTS) programs, with 30 ECTS assigned to the thesis. The Data Science Master program is most specifically tailored to an ML curriculum. However, admissions for this program are very competitive. Note that most courses of the Data Science Master can also be taken in other programs, such as the Computer Science Master, which allows students to adapt big parts of the curriculum to their interests.

There are numerous groups at ETH doing ML research. As ML is becoming more accessible for research in other fields, there also are many new groups using ML for their domain-specific applications. Consequently, there are countless courses on ML and related topics, offered by various groups. To give you a better idea of what kinds of subjects there exist, here are a few courses to give you an overall impression (note that this is by far an incomplete list):

Introduction to ML
Advanced ML
Deep Learning
Statistical Learning Theory
Machine Perception
Natural Language Processing
Reliable and Interpretable Artificial Intelligence
Probabil

...