I took part in the second signal data science cohort earlier this year, and since I found out about Signal through a slatestarcodex post a few months back (it was also covered here on less wrong), I thought it would be good to return the favor and write a review of the program. 

The tl;dr version:

Going to Signal was a really good decision. I had been doing teaching work and some web development consulting previous to the program to make ends meet, and now I have a job offer as a senior machine learning researcher¹. The time I spent at signal was definitely necessary for me to get this job offer, and another very attractive data science job offer that is my "second choice" job. I haven't paid anything to signal, but I will have to pay them a fraction of my salary for the next year, capped at 10% and a maximum payment of $25k. 

The longer version:

Obviously a ~12 week curriculum is not going to be a magic pill that turns a nontechnical, averagely intelligent person into a super-genius with job offers from Google and Facebook. In order to benefit from Signal, you should already be somewhat above average in terms of intelligence and intellectual curiosity. If you have never programmed and/or never studied mathematics beyond high school² , you will probably not benefit from Signal in my opinion. Also, if you don't already understand statistics and probability to a good degree, they will not have time to teach you. What they will do is teach you how to be really good with R, make you do some practical machine learning and learn some SQL, all of which are hugely important for passing data science job interviews. As a bonus, you may be lucky enough (as I was) to explore more advanced machine learning techniques with other program participants or alumni and build some experience for yourself as a machine learning hacker. 

As stated above, you don't pay anything up front, and cheap accommodation is available. If you are in a situation similar to mine, not paying up front is a huge bonus. The salary fraction is comparatively small, too, and it only lasts for one year. I almost feel like I am underpaying them. 

This critical comment by fluttershy almost put me off, and I'm glad it didn't. The program is not exactly "self-directed" - there is a daily schedule and a clear path to work through, though they are flexible about it. Admittedly there isn't a constant feed of staff time for your every whim - ideally there would be 10-20 Jonahs, one per student; there's no way to offer that kind of service at a reasonable price. Communication between staff and students seemed to be very good, and key aspects of the program were well organised. So don't let perfect be the enemy of good: what you're getting is an excellent focused training program to learn R and some basic machine learning, and that's what you need to progress to the next stage of your career.

Our TA for the cohort, Andrew Ho, worked tirelessly to make sure our needs were met, both academically and in terms of running the house. Jonah was extremely helpful when you needed to debug something or clarify a misunderstanding. His lectures on selected topics were excellent. Robert's Saturday sessions on interview technique were good, though I felt that over time they became less valuable as some people got more out of interview practice than others. 

I am still in touch with some people I met on my cohort, even though I had to leave the country, I consider them pals and we keep in touch about how our job searches are going. People have offered to recommend me to companies as a result of Signal. As a networking push, going to Signal is certainly a good move. 

Highly recommended for smart people who need a helping hand to launch a technical career in data science.

 

---

 

1: I haven't signed the contract yet as my new boss is on holiday, but I fully intend to follow up when that process completes (or not). Watch this space. 

2: or equivalent - if you can do mathematics such as matrix algebra, know what the normal distribution is, understand basic probability theory such as how to calculate the expected value of a dice roll, etc, you are probably fine. 

A half-baked literature review: Skills training for dating anxiety

In order to infer whether sociosexual skills training is a useful adjunct to standard treatment of anxiety, the first page of Google scholar was systematically reviewed for unique interventional studies that include with any measure of anxiety as an outcome, studies with comment on methodological issues or otherwise theorising with implications for the interpretation of the empirical evidence were discovered using the search terms: (1) social skills training for anxiety and (2) heterosexual social skills and (3) dating anxiety. And (4) behavioural replication training and (5) sensitivity training 10 studies were found, each very dated. The search space was expanded from (1) to searches (2) till (5) due to the keywords found in potentially relevant studies.

Studies that did not contextualise in terms of sexual motivations (e.g. dating) were excluded (namely: the study - Social skills training augments the effectiveness of cognitive behavioral group therapy for social anxiety disorder : www.sciencedirect.com/science/article/pii/S0005789405800619)

The studies found were (strike out: excluded):

 

• Social skills training and systematic desensitization in reducing dating anxiety: www.sciencedirect.com/science/article/pii/0005796775900546
• Treatment strategies for dating anxiety in college men based on real-life practice.: psycnet.apa.org/psycinfo/1979-31475-001
• Evaluation of three dating-specific treatment approaches for heterosexual dating anxiety.: psycnet.apa.org/journals/ccp/43/2/259/
• A comparison between behavioral replication training and sensitivity training approaches to heterosexual dating anxiety.: psycnet.apa.org/journals/cou/23/3/190/
• Social skills training and systematic desensitization in reducing dating anxiety: www.sciencedirect.com/science/article/pii/0005796775900546
• Social skills training augments the effectiveness of cognitive behavioral group therapy for social anxiety disorder : www.sciencedirect.com/science/article/pii/S0005789405800619
• Skills training as an approach to the treatment of heterosexual-social anxiety: A review.: psycnet.apa.org/journals/bul/84/1/140/
• Self-ratings and judges' ratings of heterosexual social anxiety and skill: A generalizability study.: psycnet.apa.org/journals/ccp/47/1/164/
• Heterosexual social skills in a population of rapists and child molesters.: psycnet.apa.org/journals/ccp/53/1/55/
• The importance of behavioral and cognitive factors in heterosexual-social anxiety1: onlinelibrary.wiley.com/doi/10.1111/j.1467-6494.1980.tb00834.x/abstract

 

The search is halted prematurely due to the discovery of a systematic review (see: Skills training as an approach to the treatment of heterosexual-social anxiety: A review.: psycnet.apa.org/journals/bul/84/1/140/) However, other studies emerged after the review anyway. In any case, the review’s conclusions are likely to hold true and they do suggest that there is promise to sociosexual skills training, but methodological issues will hold back good empirical research. Therefore, it is not expected to be productive to continue this review.

It is hypothesised that the evidence is so dated due to changes in terminology. The literature approximates exposure treatments for social phobia or social anxiety. However, searches of the first page of Google Scholar (exposure therapy and social anxiety; exposure therapy and social phobia) yield no results except where pharmacotherapies are in adjunct to the therapy) which are inappropriate for our purposes.

 

Tl;dr. See: Skills training as an approach to the treatment of heterosexual-social anxiety: A review.: psycnet.apa.org/journals/bul/84/1/140/

 

Research translation idea

 

I have an idea for teaching certain vulnerable young people the skills needed to achieve social skills without intoxication. I was wondering if you have any feedback for my proposal so that I can revise it. Many students report they drink or get high for the disinhibiting effects that help them socialise with the other sex. It is hypothesised that this is because of latent anxieties and inproper self-medication. Due to the irresponsiveness of the target population at universities to respond to demand reduction programs and health promotion, the inflexibility of the university’s institutions to delivering supply reduction campaigns,  and the relative resource intensity of harm minimisation programs, alternative, innovative interventions are sought. One innovative strategy is to treat the underlying anxiety that motivates substance use in young people. The purpose of this social skills training program is to train groups of young people to socialise romantically and sexually with the opposite sex to replace substance-assisted romantic and sexual initiatory behaviour. Initial steps will be surveying the evidence-base, followed by the design, implementation and evaluation of a pilot program. This will be disseminated for critique by the broader scientific and clinical community before scaling if and as appropriate. The success of the program will be evaluated by structured interview eliciting psychological distress.

Background reading

 

Gender differences in social anxiety disorder: results from the national epidemiologic sample on alcohol and related conditions. - www.ncbi.nlm.nih.gov/pubmed/21903358

 

Examining Sex and Gender Differences in Anxiety Disorders - www.intechopen.com/books/a-fresh-look-at-anxiety-disorders/examining-sex-and-gender-differences-in-anxiety-disorders

 

not academic but interesting: https://www.youtube.com/watch?v=YSZky8dk7OE

For our solstice event I tried to put together a list of this year’s biggest scientific achievements.  They can likely all be looked up with a bit of searching and each one is worthy of a celebration in their own right.  But mostly I want to say; we have come a long way this year.  And we have a long way to go.

I tried to include science and technology in this list, but really anything world-scale (non-politics or natural disaster) is worthy of celebrating.

• Rosetta mission lands on a comet

 

• using young blood to fight old age (rats)

 

• kinghorn human sequencing machines (Sydney relevant)

• 100,000 genomes project

 

• the world's oldest cave art @ 40,000 years old

 

• tesla battery//released their patents on their electric engines for use by anyone.

 

• Virtual reality (cardboard) 

 

• Astronauts growing their own food 

 

• Self driving cars

 

• cubesats

 

• Lab grown kidneys successfully implanted into animals 

 

• synthetic DNA

• Chicken with a reptile face 

 

• nearly an altzeimers cure (ultrasound techniques)

 

• DAWN orbits Ceres

 

• Deepdreaming machine learning (and twitch-deepdream)

• Prosthetic limbs that transmit feeling back to the user 

• Autonomous rocket landing pointy end up 

• Lightsail project 

• Ion space travel engine 

• Anti - aging virus injected into the patient 0

• Super black substance made 

• Q-carbon 

• High temperature superconductor (-70c)

• 23&me were allowed to open back up

• Enchroma colourblindness adjusting glasses

---

from https://en.wikipedia.org/wiki/2015

April 29 – The World Health Organization (WHO) declares that rubella has been eradicated from the Americas.

July 14 - NASA's New Horizons spacecraft performs a close flyby of Pluto, becoming the first spacecraft in history to visit the distant world.

September 10 – Scientists announce the discovery of Homo naledi, a previously unknown species of early human in South Africa.

September 28 – NASA announces that liquid water has been found on Mars.

---

Recommendations from the slack:

china makes a genetically modified micropig and sells it: http://www.theguardian.com/world/2015/oct/03/micropig-animal-rights-genetics-china-pets-outrage

psyc studies can’t be reproduced: http://www.theverge.com/2015/8/27/9216565/psychology-studies-reproducability-issues

zoom contact lenses

http://mic.com/articles/118670/this-painless-eye-implant-could-give-you-superhuman-vision#.4S5ihAKNE

room temperature synthetic diamonds

http://phys.org/news/2015-11-phase-carbon-diamond-room-temperature.html 

---

Notable deaths

terry pratchett passed away

malcolm fraser

John Forbes Nash Jr

Oliver Sacks

Christopher lee

---

Nobel medals this year

Chemistry – Paul L. Modrich; Aziz Sancar and Tomas Lindahl ("for mechanistic studies of DNA repair")

Economics – Angus Deaton ("for his analysis of consumption, poverty, and welfare")

Literature – Svetlana Alexievich ("for her polyphonic writings, a monument to suffering and courage in our time" )

Peace – Tunisian National Dialogue Quartet ("for its decisive contribution to the building of a pluralistic democracy in Tunisia in the wake of the Jasmine Revolution of 2011")

Physics – Takaaki Kajita and Arthur B. McDonald ("for the discovery of neutrino oscillations, which shows that neutrinos have mass")

Physiology or Medicine – William C Campbell, Satoshi Ōmura ("for their discoveries concerning a novel therapy against infections caused by roundworm parasites") and Tu Youyou ("for her discoveries concerning a novel therapy against Malaria"[116])

 

Other:

The dress 

Ebola outbreak

Polio came back 

(also this year) - upcoming spaceX return flight on the 19th dec

runner up: vat meat is almost ready.

runner up: soylent got a lot better this year

runner up: quantum computing having progressive developments but nothing specific

 

Things that happened 100 years ago (from wikipedia):

• March 19 – Pluto is photographed for the first time
• September 11 – The Pennsylvania Railroad begins electrified commuter rail service between Paoli and Philadelphia, using overhead AC trolley wires for power. This type of system is later used in long-distance passenger trains between New York City, Washington, D.C., and Harrisburg, Pennsylvania.
• November 25 – Einstein's theory of general relativity is formulated.
• Alfred Wegener publishes his theory of Pangaea.

• Thomas Huckle Weller, American virologist, recipient of the Nobel Prize in Physiology or Medicine (d. 2008)
• Charles Townes, American physicist, Nobel Prize laureate (d. 2015)
• August 27 – Norman F. Ramsey, American physicist, Nobel Prize laureate (d. 2011)
• Clifford Shull, American physicist, Nobel Prize laureate (d. 2001)
• November 19 – Earl Wilbur Sutherland Jr., American physiologist, Nobel Prize laureate (d. 1974)
• Henry Taube, Canadian-born chemist, Nobel Prize laureate (d. 2005)

• Paul Ehrlich, German scientist, recipient of the Nobel Prize in Physiology or Medicine (b. 1854)
• December 19 – Alois Alzheimer, German psychiatrist and neuropathologist (b. 1864)

---

Meta - This list was compiled for Sydney’s Solstice event; I figured I would share this because it’s pretty neat.

Time to compose: 3-4hrs

With comments from the IRC and slack

To see more of my posts visit my Table of contents

As usual; any suggestions welcome below.

The article is here.

The book is by William MacAskill, founder of 80000 Hours and Giving What We Can. Excerpt:

Effective altruism takes up the spirit of Singer’s argument but shields us from the full blast of its conclusion; moral indictment is transformed into an empowering investment opportunity...

Either effective altruism, like utilitarianism, demands that we do the most good possible, or it asks merely that we try to make things better. The first thought is genuinely radical, requiring us to overhaul our daily lives in ways unimaginable to most...The second thought – that we try to make things better – is shared by every plausible moral system and every decent person. If effective altruism is simply in the business of getting us to be more effective when we try to help others, then it’s hard to object to it. But in that case it’s also hard to see what it’s offering in the way of fresh moral insight, still less how it could be the last social movement we’ll ever need.

Following in the path of So8res and others, I’ve decided to work my way through the textbooks on the MIRI Research Guide. I’ve been working my way through the guide since last October, but this is my first review. I plan on following up this review with reviews of Enderton’s A Mathematical Introduction to Logic and Sipser’s Introduction to the Theory of Computation. Hopefully these reviews will be of some use to you.

Discrete Mathematics and Its Applications

Discrete Mathematics and Its Applications is wonderful, gentle introduction to the math needed to understand most of the other books on the MIRI course list. It successfully pulls off a colloquial tone of voice. It spends a lot of time motivating concepts; it also contains a lot of interesting trivia and short biographies of famous mathematicians and computer scientists (which the textbook calls “links”). Additionally, the book provides a lot of examples for each of its theorems and topics. It also fleshes out the key subjects (counting, proofs, graphs, etc.) while also providing a high level overview of their applications. These combine to make it an excellent first textbook for learning discrete mathematics.

However, for much the same reasons, I would not recommend it nearly as much if you’ve taken a discrete math course. People who’ve participated in math competitions at the high school level probably won’t get much out of the textbooks either. Even though I went in with only the discrete math I did in high school, I still got quite frustrated at times because of how long the book would take to get to the point. Discrete Mathematics is intended to be quite introductory and it succeeds in this goal, but it probably won’t be very suitable as anything other than review for readers beyond the introductory level. The sole exception is the last chapter (on models of computation), but I recommend picking up a more comprehensive overview from Sipser’s Theory of Computation instead.

I still highly recommend it for those not familiar with the topics covered in the book. I’ve summarized the contents of the textbook below:

Contents:

1.     The Foundations: Logic and Proofs

2.   Basic Structures: Sets, Functions, Sequences, Sums, and Matrices

3.     Algorithms

4.     Number Theory and Cryptography

5.     Induction and Recursion

6.     Counting

7.     Discrete Probability

8.     Advanced Counting Techniques

9.     Relations

10.  Graphs

11.  Trees

12.  Boolean Algebra

13.  Modeling Computation

The Foundations: Logic and Proofs

This chapter introduces propositional (sentential) logic, predicate logic, and proof theory at a very introductory level. It starts by introducing the propositions of propositional logic (!), then goes on to introduce applications of propositional logic, such as logic puzzles and logic circuits. It then goes on to introduce the idea of logical equivalence between sentences of propositional logic, before introducing quantifiers and predicate logic and its rules of inference. It then ends by talking about the different kinds of proofs one is likely to encounter – direct proofs via repeated modus ponens, proofs by contradiction, proof by cases, and constructive and non-constructive existence proofs.

This chapter illustrates exactly why this book is excellent as an introductory text. It doesn’t just introduce the terms, theorems, and definitions; it motivates them by giving applications. For example, it explains the need for predicate logic by pointing out that there are inferences that can’t be drawn using only propositional logic. Additionally, it also explains the common pitfalls for the different proof methods that it introduces.

Basic Structures: Sets, Functions, Sequences, Sums, and Matrices

This chapter introduces the different objects one is likely to encounter in discrete mathematics. Most of it seemed pretty standard, with the following exceptions: functions are introduced without reference to relations; the “cardinality of sets” section provides a high level overview of a lot of set theory; and the matrices section introduces zero-one matrices, which are used in the chapters on relations and graphs.

Algorithms

This chapter presents … surprise, surprise… algorithms! It starts by introducing the notion of algorithms, and gives a few examples of simple algorithms. It then spends a page introducing the halting problem and showing its undecidability. (!) Afterwards, it introduces big-o, big-omega, and big-theta notation and then gives a (very informal) treatment of a portion of computation complexity theory. It's quite unusual to see algorithms being dealt with so early into a discrete math course, but it's quite important because the author starts providing examples of algorithms in almost every chapter after this one.

Number Theory and Cryptography

This section goes from simple modular arithmetic (3 divides 12!) to RSA, which I found extremely impressive. (Admittedly, I’ve only ever read one other discrete math textbook.) After introducing the notion of divisibility, the textbook takes the reader on a rapid tour through base-n notation, the fundamental theorem of arithmetic, the infinitude of primes, the Euclidean GCD algorithm, Bezout’s theorem, the Chinese remainder theorem, Fermat’s little theorem, and other key results of number theory. It then gives several applications of number theory: hash functions, pseudorandom numbers, check digits, and cryptography. The last of these gets its own section, and the book spends a large amount of it introducing RSA and its applications.

Induction and Recursion

This chapter introduces mathematical induction and recursion, two extremely important concepts in computer science. Proofs by mathematical induction, basically, are proofs that show that a property is true of the first natural number (positive integer in this book), and if it is true of an integer k it is true of k+1. With these two results, we can conclude that the property is true of all natural numbers (positive integers). The book then goes on to introduce strong induction and recursively defined functions and sets. From this, the book then goes on to introduce the concept of structural induction, which is a generalization of induction to work on recursively-defined sets. Then, the book introduces recursive algorithms, most notably the merge sort, before giving a high level overview of program verification techniques.

Counting

The book now changes subjects to talk about basic counting techniques, such as the product rule and the sum rule, before (interestingly) moving on to the pigeonhole principle. It then moves on to permutations and combinations, while introducing the notion of combinatorial proof, which is when we show that two sides of the identity count the same things but in different ways, or that there exists a bijection between the sets being counted on either side. The textbook then introduces binomial coefficients, Pascal’s triangle, and permutations/combinations with repetition. Finally, it gives algorithms that generate all the permutations and combinations of a set of n objects.

Compared to other sections, I feel that a higher proportion of readers would be familiar with the results of this chapter and the one on discrete probability that follows it. Other than the last section, which I found quite interesting but not particularly useful, I felt like I barely got anything from the chapter.

Discrete Probability

In this section the book covers probability, a topic that most of LessWrong should be quite familiar with. Like most introductory textbooks, it begins by introducing the notion of sample spaces and events as sets, before defining probability of an event E as the ratio of the cardinality of E to the cardinality of S. We are then introduced to other key concepts in probability theory: conditional probabilities, independence, and random variables, for example. The textbook takes care to flesh out this section with a discussion about the Birthday Problem and Monte Carlo algorithms. Afterwards, we are treated to a section on Bayes theorem, with the canonical example of disease testing for rare diseases and a less-canonical-but-still-used-quite-a-lot example of Naïve Bayes spam filters. The chapter concludes by introducing the expected value and variances of random variables, as well as a lot of key results (linearity of expectations and Chebyshev’s Inequality, to list two). Again, aside from the applications, most of this stuff is quite basic.

Advanced Counting Techniques

This chapter, though titled “advanced counting techniques”, is really just about recurrences and the principle of inclusion-exclusion. As you can tell by the length of this section, I found this chapter quite helpful nevertheless.  

We begin by giving three applications of recurrences: Fibonacci’s “rabbit problem”, the Tower of Hanoi, and dynamic programming. We’re then shown how to solve linear homogenous relations, which are relations of the form

a_n = c₁ a_n-1 + c₂ a_n-2 + … + c_k a_n-k+ F(n)

Where c1, c2, …, ck are constants, ck =/= 0, and F(n) is a function of n. The solutions are quite beautiful, and if you’re not familiar with them I recommend looking them up. Afterwards, we’re introduced to divide-and-conquer algorithms, which are recursive algorithms that solve smaller and smaller instances of the problem, as well as the master method for solving the recurrences associated with them, which tend to be of the form

f(n) = a f(n/b) + cn^d

After these algorithms, we’re introduced to generating functions, which are yet another way of solving recurrences.

Finally, after a long trip through various recurrence-solving methods, the textbook introduces the principle of inclusion-exclusion, which lets us figure out how many elements are in the union of a finite number of finite sets.

Relations

Finally, 7 chapters after the textbook talks about functions, it finally gets to relations. Relations are defined as sets of n-tuples, but the book also gives alternative ways of representing relations, such as matrices and directed graphs for binary relations. We’re then introduced to transitive closures and Warshall’s algorithm for computing the transitive closure of a relation. We conclude with two special types of relations: equivalence relations, which are reflexive, symmetric, and transitive; and partial orderings, which are reflexive, anti-symmetric, and transitive.

Graphs

After being first introduced to directed graphs as a way of representing relations in the previous chapter, we’re given a much more fleshed out treatment in this chapter. A graph is defined as a set of vertices and a set of edges connecting them. Edges can be directed or undirected, and graphs can be simple graphs (with no two edges connecting the same pair of vertices) or multigraphs, which contain multiple edges connecting the same pair of vertices. We’re then given a ton of terminology related to graphs, and a lot of theorems related to these terms. The treatment of graphs is quite advanced for an introductory textbook – it covers Dijkstra’s algorithm for shortest paths, for example, and ends with four coloring. I found this chapter to be a useful review of a lot of graph theory.

Trees

After dealing with graphs, we move on to trees, or connected graphs that don’t have cycles. The textbook gives a lot of examples of applications of trees, such as binary search trees, decision trees, and Huffman coding. We’re then presented with the three ways of traversing a tree – in-order, pre-order, and post-order. Afterwards, we get to the topic of spanning trees of graphs, which are trees that contain every vertex in the graph. Two algorithms are presented for finding spanning trees – depth first search and breadth first search. The chapter ends with a section on minimum spanning trees, which are spanning trees with the least weight. Once again we’re presented with two algorithms for finding minimum spanning trees: Prim’s Algorithm and Kruskal’s algorithm. Having never seen either of these algorithms before, I found this section to be quite interesting, though they are given a more comprehensive treatment in most introductory algorithms textbooks.  

Boolean Algebra

This section introduces Boolean algebra, which is basically a set of rules for manipulating elements of the set {0,1}. Why is this useful? Because, as it turns out, Boolean algebra is directly related to circuit design! The textbook first introduces the terminology and rules of Boolean algebra, and then moves on to circuits of logic gates and their relationship with Boolean functions. We conclude with two ways to minimize the complexity of Boolean functions (and thus circuits) – Karnaugh Maps and the Quine-McCluskey Method, which are both quite interesting. 

Modeling Computation

Who should read this?

If you’re not familiar with discrete mathematics, this is a great book that will get you up to speed on the key concepts, at least to the level where you’ll be able to understand the other textbooks on MIRI’s course list. Of the three textbooks I’m familiar with that cover discrete mathematics, I think that Rosen is hands down the best. I also think it’s quite a “fun” textbook to skim through, even if you’re familiar with some of the topics already.

However, I think that people familiar with the topics probably should look for other books, especially if they are looking for textbooks that are more concise. It might also not be suitable if you’re already really motivated to learn the subject, and just want to jump right in. There are a few topics not normally covered in other discrete math textbooks, but I feel that it’s better to pick up those topics in other textbooks.

What should I read?

In general, the rule for the textbook is: read the sections you’re not familiar with, and skim the sections you are familiar with, just to keep an eye out for cool examples or theorems.

In terms of chapter-by-chapter, chapters 1 and 2 seem like they’ll help if you’re new to mathematics or proofs, but probably can be skipped otherwise. Chapter 3 is pretty good to know in general, though I suspect most people here would find it too easy. Chapters 4 through 12 are what most courses on discrete mathematics seem to cover, and form the bulk of the book – I would recommend skimming them once just to make sure you know them, as they’re also quite important for understanding any serious CS textbook. Chapter 13, on the other hand, seems kind of tacked on, and probably should be picked up in other textbooks.

Final Notes

Of all the books on the MIRI research guide, this is probably the most accessible, but it is by no means a bad book. I’d highly recommend it to anyone who hasn’t had any exposure to discrete mathematics, and I think it’s an important prerequisite for the rest of the books on the MIRI research guide.

Review of our fifth LessWrong Meetup - Report from Berlin

Summary

We had visitors fank1 and just_existing from the Bielefeld/Paderborn Meetup. The meetup was great. It was a continuously lively discussion with everybody contributing personal and/or insightful and/or relevant pieces.

After ashort introduction of each other (because of the guests) we plunged immediately into interesting discussions mostly revolving around LeeWrong topics.

In between I retold my very positive experience from the Berlin LW community event. After a short summary about the effects of meditation we had a Mnemonics session inspired by the Berlin workshop.

One on-going topic was "Extrovert in Training" - techniques for and experience with getting in touch with people. How to start a conversation. What I still don't get is how to steer a conversation from small-talk phase to more personal topics - esp. in a group setting. Though this was not a problem during the meetup.

We also discussed selection pressure on humans. We agreed that there is almost none on mutations affecting health in general due to medicine. But we agreed that there is tremendous pressure on contraception. We identified four ways evolution works around contraception (see appendix for a short summary). We discussed what effects this could on the future of society. The movie Idiocracy was mentioned. This could be a long term (a few generations) existential risk.

There were other topcis which I recollect less clearly. Maybe the participants can comment on them below.

There will definitely be more LW Hamburg meetups. The next step is a joint Skype meetup with the Bielefeld group. I also relayed the Jonas Vollmers advice to get in contact with the Giordano-Bruno-Stiftung. 

The meetup ended with a photo and positive impression feedback round (peak-end rule). Afterwards out guests from Bielefeld stayed overnight in my (Gunnars) place.

Appendix

Four ways evolution works around contraception:

 

1. Biological factors. Examples are hormones compensating the contraception effects of the pill or allergies against condoms. These are easily recognized, measured and countered by the much faster operating pharma industry. There are also little ethical issues with this.
2. Subconscious mental factors. Factors mostly leading to non- or mis-use of contraception. Examples are carelessness, impulsiveness, fear, and insufficient understanding of the contraceptives usage. These are what some fear leads to collective stultification.
3. Conscious mental factors. Factors leading to explicit family planning e.g. children/family as terminal goals. The lead to a conscious use of contraception. The effect is less pronounced but likely leads to healthy and better educated children.
4. Group selection factors. These are factors favoring groups which collectively have more children. The genetic effects are likely weak here but the memetic effects are strong. A culture with social norms against contraception or for large families are likely to out-birth other groups.

 

Other LW Hamburg Meetup reviews

• Fourth Meetup (no notes)
• Third Meetup Notes: Small Steps Forward 
• Second Meetup Notes: In need of Structure
• First Meetup Notes: Starting small

This is a review of The Everyday Parenting Toolkit: The Kazdin Method for Easy, Step-by-step, Lasting Change for You and Your Child by Alan E, Kazdin (all phrases in quotes below are from this book if not otherwise indicated). I was pointed to this book by tadamsmars comment on Ignorance in Parenting. 

This is a post in the sequence about parenting. I also see some cross relations to learning and cognitive sciences in general. Kazdins advice also is not only applicable to children but to adults as well if you read the book with a mind open to the backing research (Kazdin actually gives some such examples to illustrate the methods).

Summary TD;DR

Define the positive behavior you do want. Communicate this clearly and provide events that make it likely to occur. Praise any occurrence of the positive behavior effusively. Think about and communicate consequences beforehand. Use mild and short punishments (if at all). Provide a healthy environment.

I'm Nate. I was recently introduced to the ideas of existential risk and unfriendly AI. I've decided to read the books suggested in the MIRI course list. I'll review the books as I read them. Repeating the knowledge is expected to help solidify it. Public accountability is expected to help keep me on track. Hopefully my notes will also be useful to others. This is the first such review.

Gödel, Escher, Bach

I'll be reviewing this book from memory. I started it in 2010 at the recommendation of a friend. I got frustrated early on when Hofstadter introduced topics that I already knew well (such as recursion). This turned out to be a mistake; the book picks up shortly thereafter. I finished the rest of it maybe four months ago, around the time that I finished the sequences.

Overview

Gödel, Escher, Bach is an incredibly well-written foray into the intersection of art, mathematics, philosophy, and biology. It explores the border between syntax and semantics. It's not a textbook, but you'll learn more about logic than most introductory courses will teach you.

The book is composed of alternating dialogs and chapters. The dialogs are witty narratives which take on the structure of concepts discussed in the following chapter. It's hard to describe how this works, but it's very effective. The dialogs are brilliantly designed and are by far the most entertaining part of the book.

http://kajsotala.fi/2013/07/book-review-mindmelding-consciousness-neuroscience-and-the-minds-privacy/

I review William Hirstein's book Mindmelding: Consciousness, Neuroscience, and the Mind’s Privacy, which he proposes a way of connecting the brains of two different people together so that when person A has a conscious experience, person B may also have the same experience. In particular, I compare it to my and Harri Valpola's earlier paper Coalescing Minds, in which we argued that it would be possible to join the brains of two people together in such a way that they'd become a single mind.

Fortunately, it turns out that the book and the paper are actually rather nicely complementary. To briefly summarize the main differences, we intentionally skimmed over many neuroscientific details in order to establish mindmelding as a possible future trend, while Hirstein extensively covers the neuroscience but is mostly interested in mindmelding as a thought experiment. We seek to predict a possible future trend, while Hirstein seeks to argue a philosophical position: Hirstein focuses on philosophical implications while we focus on societal implications. Hirstein talks extensively about the possibility of one person perceiving another’s mental states while both remaining distinct individuals, while we mainly discuss the possibility of two distinct individuals coalescing together into one.

I expect that LW readers might be particularly interested in some of the possible implications of Hirstein's argument, which he himself didn't discuss in the book, but which I speculated on in the review:

Most obviously, if another person’s conscious states could be recorded and replayed, it would open the doors for using this as entertainment. Were it the case that you couldn’t just record and replay anyone’s conscious experience, but learning to correctly interpret the data from another brain would require time and practice, then individual method actors capable of immersing themselves in a wide variety of emotional states might become the new movie stars. Once your brain learned to interpret their conscious states, you could follow them in a wide variety of movie-equivalents, with new actors being hampered by the fact that learning to interpret the conscious states of someone who had only appeared in one or two productions wouldn’t be worth the effort. If mind uploading was available, this might give considerable power to a copy clan consisting of copies of the same actor, each participating in different productions but each having a similar enough brain that learning to interpret one’s conscious states would be enough to give access to the conscious states of all the others.

The ability to perceive various drug- or meditation-induced states of altered consciousness while still having one’s executive processes unhindered and functional would probably be fascinating for consciousness researchers and the general public alike. At the same time, the ability for anyone to experience happiness or pleasure by just replaying another person’s experience of it might finally bring wireheading within easy reach, with all the dangers associated with that.

A Hirstein-style mind meld might possibly also be used as an uploading technique. Some upload proposals suggest compiling a rich database of information about a specific person, and then later using that information to construct a virtual mind whose behavior would be consistent with the information about that person. While creating such a mind based on just behavioral data makes questionable the extent to which the new person would really be a copy of the original, the skeptical argument loses some of its force if we can also include in the data a recording of all the original’s conscious states during various points in their life. If we are able to use the data to construct a mind that would react to the same sensory inputs with the same conscious states as the original did, whose executive processes would manipulate those states in the same ways as the original, and who would take the same actions as the original did, would that mind then not essentially be the same mind as the original mind?

Hirstein’s argumentation is also relevant for our speculations concerning the evolution of mind coalescences. We spoke abstractly about the ”preferences” of a mind, suggesting that it might be possible for one mind to extract the knowledge from another mind without inherting its preferences, and noting that conflicting preferences would be one reason for two minds to avoid coalescing together. However, we did not say much about where in the brain preferences are produced, and what would be actually required for e.g. one mind to extract another’s knowledge without also acquiring its preferences. As the above discussion hopefully shows, some of our preferences are implicit in our automatic habits (the things that we show we value with our daily routines), some in the preprocessing of sensory data that our brains carry out (the things and ideas that are ”painted with” positive associations or feelings), and some in the configuration of our executive processes (the actions we actually end up doing in response to novel or conflicting situations). (See also.) This kind of a breakdown seems like very promising material for some neuroscience-aware philosopher to tackle in an attempt to figure out just what exactly preferences are; maybe someone has already done so.

This chapter by chapter review was inspired by Vaniver's recent chapter by chapter review of Causality. Like with that review, the intention is not so much to summarize but to help readers determine whether or not they should read the book. Reading the review is in no way a substitute for reading the book.

I first read Introduction to Connectionist Modelling of Cognitive Processes (ICMCP) as part of an undergraduate course on cognitive modelling. We were assigned one half of the book to read: I ended up reading every page. Recently I felt like I should read it again, so I bought a used copy off Amazon. That was money well spent: the book was just as good as I remembered.

By their nature, artificial neural networks (referred to as connectionist networks in the book) are a very mathy topic, and it would be easy to write a textbook that was nothing but formulas and very hard to understand. And while ICMCP also spends a lot of time talking about the math behind the various kinds of neural nets, it does its best to explain things as intuitively as possible, sticking to elementary mathematics and elaborating on the reasons of why the equations are what they are. At this, it succeeds – it can be easily understood by someone knowing only high school math. I haven't personally studied ANNs at a more advanced level, but I would imagine that anybody who intended to do so would greatly benefit from the strong conceptual and historical understanding ICMCP provided.

The book also comes with a floppy disk containing a tlearn simulator which can be used to run various exercises given in the book. I haven't tried using this program, so I won't comment on it, nor on the exercises.

The book has 15 chapters, and it is divided into two sections: principles and applications.

Principles

1: ”The basics of connectionist information processing” provides a general overview of how ANNs work. The chapter begins by providing a verbal summary of five assumptions of connectionist modelling: that 1) neurons integrate information, 2) neurons pass information about the level of their input, 3) brain structure is layered, 4) the influence of one neuron on another depends on the strength of the connection between them, and 5) learning is achieved by changing the strengths of connections between neurons. After this verbal introduction, the basic symbols and equations relating to ANNs are introduced simultaneously with an explanation of how the ”neurons” in an ANN model work.

A new book from Cambridge University Press describes the impetus of the forthcoming Rationality Group in its title: Judgment and Decision Making as a Skill. It begins:

Our scientific understanding of human judgment and decision making (JDM) has grown considerably over the past 60 years in terms of the normative benchmarks... by which we assess performance, the descriptive models we use to describe JDM, and the prescriptive models we offer to improve JDM...

...[But] how do we learn to make good decisions? How can we improve or aid our decision making? Fortunately, there is an emerging body of work that is interested in long-term and short-term changes in JDM skills... There is research on the acquisition of expertise in JDM, and training and aiding of JDM. Researchers more interested in short-term changes have begun to study learning of JDM tasks...

[We] introduce a new conception of JDM, seeing it as a dynamic skill rather than a static capacity...

Chapters 1 and 2 survey the evolution and neurobiology of JDM, while the chapters 3-5 discuss JDM in young children, adolescents, and the aged. Chapters 6-10 were the most interesting to me, because they concern the learning and improving of JDM skills.

In particular, chapter 7 discusses the use of causal Bayes nets to model JDM processes and thereby make better-informed choices among possible debiasing interventions, and chapter 8 discusses JDM in the context of skill-learning (from feedback). Chapter 9 reviews the ways in which JDM can be improved simply by communicating and representing information in particular ways. Chapter 10 reviews "procedures or devices that are intended to improve the quality of people's decisions."

Chapter 11 contains personal reflections on JDM as a skill from nine past presidents of the Society for Judgment and Decision Making.

Overall, the book is a handy collection of review articles on JDM (what LW calls epistemic and instrumental rationality) written from a useful perspective. But it is not as useful as Stanovich's Rationality and the Reflective Mind, and I anticipate it being less useful than the forthcoming Oxford Handbook of Thinking and Reasoning.

Today I had the pleasure of wading through Oxford's new Handbook of Philosophy of Cognitive Science. The book consists of review chapters on the following topics:

• Cognition (computationalism vs. embodied cognition, mental representation)
• Consciousness
• The nature of thought (concepts, language and thought)
• Specific mental phenomena (perception, attention, emotions)
• Meta-theoretic issues (the assumptions of cognitive science, relationships between disciplines)
• Conceptual issues (the relation between concepts used by psychology, neuroscience, and philosophy)
• First-order empirical issues (theory of mind, language, culture and cognition)
• Traditional philosophical issues (rationality, metaphilosophy)

Below, I'll summarize a few of the chapters I found most useful and interesting.

In "Consciousness and Cognition," Robert van Gulick begins by listing 10 different things that is often meant by the term "consciousness." He then explains that some regard consciousness as more basic than cognition, while others see consciousness as dependent on cognition. (I take the latter view.) Van Gulick then surveys three main categories of theories of consciousness: philosophical theories (including Dennett's "multiple drafts" theory), cognitive theories (including Baars' "global workspace" theory and Tononi's information integration theory), and neurobiological theories (including Dehaene & Naccache's neuronal version of global workspace theory and Lamme's "local recurrence" model). Van Gulick concludes by surveying some of the methods used in consciousness studies.

In "Embodied Cognition," Lawrence Shaprio attempts to explain the difference between computational and embodied theories of cognition. Computational theorists treat the mind as a computational system, and seek to infer the algorithms by which the mind transforms inputs into outputs. (I'll add that at some level of organization this must be true, for physics appears to be computational and the mind runs on physics.) And what do embodied theories claim?

After reading the chapter, I remain confused as to how embodied approaches are supposed to be non-computational. For example, Shapiro quotes Ester Thelen explaining embodied cognition this way: "...to say that cognition is embodied means that it arises from bodily interactions with the world [and] depends on the kinds of experiences that come from having a body with particular perceptual and motor capabilities that are inseparably linked and that together form the matrix within which reasoning, memory, emotion, language, and all other aspects of mental life are meshed." But I don't see anything non-computational about that!

As far as I can tell, embodied theories are motivated by a rejection of early theories which naively proposed that the human mind is entirely a symbol-manipulating computation system ala the General Problem Solver and that the agent's body had little importance for how the the agent's cognitive algorithms manipulated those symbols. Embodied theories are correct to reject these theses, but this makes embodied theories incompatible only with the most naive forms of computationalism. The way I would put it is that the mind is computational, and we must be careful to remember that human cognition is embodied, situational, and dynamical. But this is not how Shapiro prefers to describe things.

In "Computationalism," Gualtiero Piccinini outlines the three research traditions of computationalism: classicism, connectionism, and computational neuroscience. He goes on to describe different kinds of computation:

On page 237, Piccinini says something similar to my thoughts above on Shapiro's chapter:

While it is safe to say that cognition involves computation in the generic sense, and that nervous systems perform computations in the generic sense, it is much harder to establish that cognition involves a more specific kind of computation.

The rest of the chapter offers a preliminary look at how the evidence might weigh for and against different kinds of computationalism about the mind.

In "Representationalism," Frances Egan describes a specific kind of computationalism. Representationalism is "the view that the human mind is an information-using system, and that human cognitive capacities are to be understood as representational capacities." The chapter discusses several kinds of representationalism, and the arguments given for and against them.

In "Artificial Intelligence," Diane Proudfoot and Jack Copeland focus on the quest for human-level AI. Their opening paragraph quotes Turing on the obvious achievability of AI, the whole brain emulation route ("One way [of making AI] would be to take a man as a whole and to try to replace all the parts of him by machinery"), and the danger of "runaway AI." Section 1 discusses the Turing Test. Section 2 discusses and rejects the Chinese Room argument against Strong AI, and section 3 discusses an "a priori" argument for Strong AI:

Given that the mind is scientifically explicable rather than a mystery, the mind is a mechanism, an information-processing machine; since the set of possible operations that can be carried out by information-processing machines is identical to the set of operations that can be carried out by the universal Turing machine... the mind must ultimately be explicable in terms of the computational properties of the UTM.

Section 4 discusses Moore's law and "the furistists" (Moravec, Joy, Kurzweil, Bostrom, the Singularity Institute). They quote SI as saying that "...at the very least it should be physically possible to achieve a million-to-one speedup in thinking." Proudfoot & Copeland reply:

But... the appeal to Moore's (or other similar) projections is fallacious. These provide no reason to think that relative increases in computer speed will be matched by increases is speed of thought.

And of course I agree. Faster computer speed only results in faster thought under certain ways of building a thought-machine. Faster computer speed only establishes the possibility for faster thought. I think faster thought is highly likely, but the argument for this conclusion is not given in the quoted article.

Section 5 concerns Singularitarianism, and unfortunately associates the word only with Ray Kurzweil, and is thus rather dismissive.

I recently received an advance review copy of historian and philosopher Richard Carrier's new book, Proving History: Bayes' Theorem and the Quest for the Historical Jesus.

The book belongs to a two-volume work on the Historical Jesus that argues for two major claims:

1. Correct historical method is Bayesian. (The first book.)
2. The application of this method to our data concerning the Historical Jesus strongly suggests that Jesus never existed. (The second book.)

Claim #1 might provoke a yawning "Yes, of course..." from many scientists and philosophers, but both claims are currently heretical in the field of Jesus Studies, which shows many signs of being an unsound research program in general. The book is written for a mass audience, but is also aimed at historians in general. It is, as far as I know, the first book to lay out the detailed case for why historians should be using Bayesian methods. (For an overview of the other methods historians typically use, see Justifying Historical Descriptions.)

Though the Bayesian revolution of the sciences has already slammed into archaeology and a few other fields of historical inquiry, it has not yet overwhelmed mainstream historical inquiry. Carrier's book may be seen as the first salvo in that attack, but this makes me wish his case had not been presented in the context of such a parochial and disreputable sub-field of history as Jesus Studies. No chapter in the book discusses the evidence concerning the historicity of Jesus in much detail, and it clearly isn't necessary to make Carrier's points, so why poison the presentation of such a clear and powerful case (in favor of Bayesian historical methods) by marinating it in such a disreputable field (Jesus Studies), and with anticipation of a startling conclusion almost everyone disagrees with (Jesus myth theory)? (For the record, I take Jesus myth theory pretty seriously, but most people don't.)

Chapter 3 is a tutorial on Bayes' Theorem, similar to Carrier's Skepticon IV talk. Chapter 4 provides an analysis of non-Bayesian methods of historical analysis, showing that they are wrong in exactly the degree to which they depart from the Bayesian method. Chapter 5 provides a similar analysis of typical "Historicity Criteria" used in Jesus Studies, e.g. "multiple attestation." The final chapter tackles some more detailed issues with the application of Bayes' Theorem, for example the interaction between frequentism and Bayesianism.

At first, the contents of Proving History seemed too obvious and underwhelming for me to strongly recommend it. Then I remembered that no other book I've read on historical methodology or the Historical Jesus had correctly used probability theory to justify its judgments. Which means that Proving History may actually be the best book yet written in either field.

 

Recently I began to write a review of Hofstadter's Godel, Escher, Bach, until I realized that the book defied summary more than all the other books I had previously said "defied summary." Thus, I gave up on reviewing the book after not too long. I present my discarded review below just in case it motivates someone else to pick up this masterful tome and let it enrich their life.

The jacket text for Keith Stanovich's The Robot's Rebellion sums up the book well:

The idea that we might be robots is no longer the stuff of science fiction; decades of research in evolutionary biology and cognitive science have led many esteemed scientists to the conclusion that... humans are merely the hosts for two replicators (genes and memes) that have no interest in us except as conduits for replication...

Accepting [this] disturbing idea, Keith Stanovich here provides the tools for the "robot's rebellion," a program of cognitive reform necessary to advance human interests over the limited interest of the replicators and define our own autonomous goals as individual human beings. Drawing on the latest research... The Robot's Rebellion describes how short-term and reflexive thinking processes dominate the higher-order thinking necessary for achieving autonomy from our biological programming. These higher-order evaluative activities of the brain... hold the potential to fulfill our need to ascribe significance to human life.

We may well be robots, but we are the only robots who have discovered that fact. [This] is the first step in constructing a radical new concept of self based on what is truly singular about humans: that they gain control of their lives in a way unique among life forms on Earth — through rational self-determination.

The book is an excellent introduction to the first stage of Yudkowskian philosophy: We are robots in a mechanistic universe running on a swiss army knife of cognitive modules. But at least we finally noticed we're robots, and we can use the skills of rationality to hop off our habit treadmills and pursue our values instead. These values are complex and often arbitrary, but we can use our reflective capacities to extrapolate our values based on "higher-order" desires, a desire for preference consistency, and other considerations. All this is argued for at length in Stanovich's book. The only thing missing is a discussion of what to do about all this when AI arrives.

Thinking, Fast and Slow is Kahneman's first book for a general audience, and a summary of his far-reaching and important work. Over the course of about 400 pages (this does not include the appendices, notes, or index), Kahneman explains his current views on: System 1 vs. System 2 thinking, heuristics and biases, overconfidence, decision making under uncertainty, the differences between the experiencing self and remembering self, and the implications of combining all this knowledge.

In short: If you care about improving your thinking and decision making, and thus you care about the cognitive science of rationality, then you are likely to enjoy — and benefit from — this book. And if you know people who won't read the Core Sequences, getting them to read Thinking, Fast and Slow will take them 30% of the way.

Kahneman leaps deftly between demonstration ("try this word problem, notice what your brain does"), theory, and research stories. He covers dozens of issues likely to familiar to veteran LWers, and perhaps a dozen more that have never been discussed on Less Wrong: availability cascades, causal stereotyping, illusion of validity, the stuff on expert intuition from chapter 22, duration neglect, the peak-end effect, affective forecasting and "miswanting," 

Each chapter ends with snippets of fictional dialogue, showing what it would like to use the concepts introduced in that chapter in everyday speech. What is remarkable is how much these snippets sound like things I hear in daily conversations at Singularity Institute. For example:

• "What came quickly to my mind was an intuition from System 1. I’ll have to start over and search my memory deliberately."
• "She knows nothing about this person’s management skills. All she is going by is the halo effect from a good presentation."
• "Do we still remember the question we are trying to answer? Or have we substituted an easier one?"
• "This start-up looks as if it could not fail, but the base rate of success in the industry is extremely low. How do we know this case is different?"
• "Let's reframe the problem by changing the reference point. Imagine we did not own it; how much would we think it is worth?"

Other dialogue snippets from Kahneman's book are considered so obvious within Singularity Institute that sentences similar to Kahneman's snippets are often half-spoken before somebody interrupts and moves on because everyone in the room already knows the rest of the sentence, and everybody knows that everybody else knows the rest of the sentence:

• "They were primed to find flaws, and this is exactly what they found."
• "He underestimates the risks of indoor pollution because there are few media stories on them. That’s an availability effect. He should look at the statistics."
• "The mistake appears obvious, but it is just hindsight. You could not have known in advance."
• "He's taking an inside view. He should forget about his own case and look for what happened in other cases."
• "He weighs losses about twice as much as gains, which is normal."

Other dialogue snippets from the book are even more obvious within Singularity Institute, and they can be communicated merely by raising an eyebrow at what someone has said:

• "This is your System 1 talking. Slow down and let your System 2 take control."
• "The sample of observations is too small to make any inferences. Let’s not follow the law of small numbers."

In the final chapter, Kahneman reflects on the good news that his and his colleagues' work is having an effect at the policy level. As a result of a book he wrote with Richard Thaler, Nudge: Improving Decisions about Health, Wealth, and Happiness, Cass Sunstein was invited by President Obama to be the administrator of the Office of Information and Regulatory Affairs. From that post Sunstein has successfully implemented many new policies that treat humans as humans instead of as members of Homo economicus:

...applications that have been implemented [by Sunstein] include automatic enrollment in health insurance, a new version of the dietary guidelines that replaces the incomprehensible Food Pyramid with the powerful image of a Food Plate loaded with a balanced diet, and a rule formulated by the USDA that permits the inclusion of messages such as “90% fat-free” on the label of meat products, provided that the statement “10% fat” is also displayed “contiguous to, in lettering of the same color, size, and type as, and on the same color background as, the statement of lean percentage.”

The British government has also responded by forming a special unit dedicated to applying decision science to successful policy-making. Officially it is called the Behavioural Insight Team, but internally people just call it the Nudge Unit.

Purpose of Review

Owen’s recent post brought up the topic of optimizing education. One particular approach, Direct Instruction (Misha’s better explanation), claims to have essentially solved the problem. In particular, Direct Instruction (DI) does allegedly not only work for basic reading skills, but any teaching task. Owen brought up the Michel Thomas language courses as a good application. Language learning is one of my main interests, so I gave the French Foundation course a try.

The main point of the review is to summarize what Michel Thomas actually does, how it differs from other common paradigms and how effective it seems to me.

Summary: Nice for beginners and people with bad learning experiences; limited use afterwards. The audio-only aspect is very convenient. It complements other strategies well and I see it as a good proof-of-concept of DI-like methods for language learning.

Overview

Let’s start with a disclaimer. Michel Thomas (MT) is not officially a DI course and as far as I could google, Thomas propbably wasn’t aware of DI at all. However, according to Solity's The Language Revolution and Owen, the reason MT works so well is that it applies (an approximation of) DI techniques. It is right now the best realistic example beyond the grade school level, so it’ll have to do.

I had some French in high school and thanks to fluency in German and English, I can read some French, but I have no active skill at all, nor have I ever used French in a serious way.

I have now completed the first half of the French Foundation course and skimmed many other courses. You can listen to the first 20 minutes here; they are very representative. Furthermore you can read the booklets to get an idea of the material covered in each course. The whole course is audio-only, consists of 8 CDs (and 2 review CDs) and is intended to be listened to only once.¹ There are several advanced courses which merely cover more grammar points and vocabulary. Structurally, they are all the same.

Method

MT teaches the course to two new students². You’re supposed to take the role of a third student, pausing the recording whenever MT asks a question so that you can say your own answer. One of the two students also answers and you can compare your reply and listen to MT’s advice and error correction. Both students are beginners, so most of your mistakes will be covered that way.

MT introduces one language component at a time and makes you use it in a given sentence. He provides a short explanation first and then lets the students answer a couple of examples by giving them an English sentence and asking them to translate it into French. Each component is thus reinforced through many examples.

MT also tries to combine the translation tasks over time by re-using partial sentences. This way, sentence quickly look complex, but always stay easy. (“What impression do you have of the political and economical situation in France at the present time?” is used about one hour in!)

Vocabulary is only introduced as necessary and relies heavily on cognates. The primary focus is on teaching structure. MT strongly emphasizes not to guess or try to remember anything, but instead to rely on induction (“Do not guess, but think it out!”). This works because the examples are carefully chosen to be as obvious as possible. All translation tasks have only one correct answer. All production is tightly controlled. MT relies on the constant tests to see that the students are successfully keeping up. He is never unsure if some concept has been understood or not.

Complex rules that might thematically belong together (like verb conjugations) are broken apart so that each individual new form or word is learned on its own. Similar rules that might be confused are deliberately spread out.

MT stresses that you aren’t supposed to try to remember anything. If you don’t know something, then he has not succeeded as a teacher yet and he will take care of it, not you. He does this by doing manual spaced repetition, i.e. he repeats previous questions (or similar ones) over time and tests the students constantly. If they have trouble answering, then he quickly goes back to the relevant lesson. This is of course how most language textbooks are supposed to be used, but they rely entirely on the student doing the testing themselves. Instead, MT provides the complete lesson including all necessary repetitions so the student doesn’t have to do anything at all except answer MT’s constant stream of translation tasks. (As a programmer, I’m strongly reminded of loop unrolling.)

What I stood out for me was the reward structure. Students rarely make big mistakes and actual correction is mostly needed for pronunciation issues. The major way students do fail is by simply not remembering something, which MT easily fixes by reminding them again. The students have good confidence in their answers and don’t have to guess. The lessons are fast-paced and consist mostly of tests. MT is constantly positively reinforcing the students, rarely correcting them. The whole lesson looks a lot more like an Anki session than a class room or a traditional textbook.

Comparison to other methods

The course is basically a (minimally edited) live class MT teaches. The result is a very natural pacing. This has the major advantage that it never goes too fast. Most other courses edit out mistakes or necessary repetitions out of fear they might be too boring, but by doing so, no student can actually keep up. This can’t happen with MT’s untrained live students. (Unfortunately, MT’s courses are also unscripted, so he does make a few organizational mistakes and the later courses don’t exactly fit together. Fortunately this is not a big issue due to MT’s large experience.)

A major difference to most other approaches is that MT actively implements what Krashen calls “i+1”, where i is the current level of a learner, meaning that concepts are taught in the order of minimal effort. Each new step contains exactly one new rule. Most language courses group rules according to some underlying pattern, like tenses, and expect you to learn a whole group at once.

MT focuses entirely on production, both by using only translation tasks and by teaching only useful components, i.e. parts of the language you need for a wide variety of contexts. No lesson has only one narrow use. This creates a very active learning experience. I fully agree with this early focus on grammar (but not grammar theory!). Once you’re done with that, you can go more-or-less monolingual and immerse yourself in the target language, relying on spaced repetition software to rapidly build your vocabulary.

Furthermore, MT’s course is very engaging. There is little downtime where you merely listen. It consists almost exclusively of quick tests. Thanks to i+1, you never have to juggle more than one new rule at a time. The subject matter does not get repetitive and MT is a very enthusiastic teacher. This can be a major problem with other language courses.

My main criticism, especially as an autodidact, would be that MT never makes his methods explicit. You entirely rely on him. He may have an awesome lesson plan, but you’re never taught how he arrived at it or how to continue beyond that.³ Hopefully that’s not a general problem with DI. In particular, any language course should teach you how to use spaced repetition. It’s the only sane way to handle vocabulary and prevent unnecessary review sessions.⁴

For contrast, look at the (excellent) Remembering The Kanji, which similarly teaches Japanese characters through decomposition, logical ordering and the use of mnemonics. However, much of the book focuses on teaching the method and the logic behind it, so that you can use it for any amount of characters you want. It is very simple to move beyond the scope of the book. I wish every textbook worked like this.

Outlook

I’m quite impressed by the course design. It’s really effective at building a solid speaking foundation. It won’t get you anywhere near fluency and, being audio-only, totally ignores literacy, but by the end of the course you should have enough skill to actively engage the language.

After finishing MT, you should have a good grasp of the grammar. A good follow-up course might be something like Assimil (video overview), which would take care of literacy and fill in any remaining grammar gaps. After that, the only thing missing is vocabulary and general practice. This is the point where traditional language teaching ends, but graded readers, parallel texts and so on, combined with spaced repetition, solve this problem nicely. Or, you know, start talking, maybe on lang-8.

Personally, I plan to work through the full French, Spanish and Italian courses, and would recommend checking them out. Again, try listening to the preview to see if this approach appeals to you.

Footnotes

A nice article just appeared in Reviews of Modern Physics. It offers a brief coverage of the fundamentals of Bayesian probability theory, the practical numerical techniques, a diverse collection of real-world examples of applications of Bayesian methods to data analysis, and even a section on Bayesian experimental design. The PDF is available here.

The abstract:

Rev. Mod. Phys. 83, 943–999 (2011)

Bayesian inference in physics

Udo von Toussaint^* 
Max-Planck-Institute for Plasmaphysics, Boltzmannstrasse 2, 85748 Garching, Germany

Received 8 December 2009; published 19 September 2011

Bayesian inference provides a consistent method for the extraction of information from physics experiments even in ill-conditioned circumstances. The approach provides a unified rationale for data analysis, which both justifies many of the commonly used analysis procedures and reveals some of the implicit underlying assumptions. This review summarizes the general ideas of the Bayesian probability theory with emphasis on the application to the evaluation of experimental data. As case studies for Bayesian parameter estimation techniques examples ranging from extra-solar planet detection to the deconvolution of the apparatus functions for improving the energy resolution and change point estimation in time series are discussed. Special attention is paid to the numerical techniques suited for Bayesian analysis, with a focus on recent developments of Markov chain Monte Carlo algorithms for high-dimensional integration problems. Bayesian model comparison, the quantitative ranking of models for the explanation of a given data set, is illustrated with examples collected from cosmology, mass spectroscopy, and surface physics, covering problems such as background subtraction and automated outlier detection. Additionally the Bayesian inference techniques for the design and optimization of future experiments are introduced. Experiments, instead of being merely passive recording devices, can now be designed to adapt to measured data and to change the measurement strategy on the fly to maximize the information of an experiment. The applied key concepts and necessary numerical tools which provide the means of designing such inference chains and the crucial aspects of data fusion are summarized and some of the expected implications are highlighted.

© 2011 American Physical Society

The Moral Psychology Handbook (2010), edited by John Doris, is probably the best way to become familiar with the exciting interdisciplinary field of moral psychology. The chapters are written by philosophers, psychologists, and neuroscientists. A few of them are all three, and the university department to which they are assigned is largely arbitrary.

I should also note that the chapter authors happen to comprise a large chunk of my own 'moral philosophers who don't totally suck' list. The book is also exciting because it undermines or outright falsifies a long list of popular philosophical theories with - gasp! - empirical evidence.

 

Chapter 1: Evolution of Morality (Machery & Mallon)

The authors examine three interpretations of the claim that morality evolved. The claims "Some components of moral psychology evolved" and "Normative cognition is a product of evolution" are empirically well-supported but philosophically uninteresting. The stronger claim that "Moral cognition (a kind of normative cognition) evolved" is more philosophically interesting, but at present not strongly supported by the evidence (according to the authors).

The chapter serves as a compact survey of recent models for the evolution of morality in humans (Joyce, Hauser, de Waal, etc.), and attempts to draw philosophical conclusions about morality from these descriptive models (e.g. Joyce, Street).

 

Chapter 2: Multi-system Moral Psychology (Cushman, Young, & Greene)

The authors survey the psychological and neuroscientific evidence showing that moral judgments are both intuitive/affective/unconscious and rational/cognitive/conscious, and propose a dual-process theory of moral judgment. Scientific data is used to verify or falsify philosophical theories proposed as, for example, explanations for trolley-problem cases.

Consequentialist moral judgments are more associated with rational thought than deontological judgment, but both deontological and consequentialist moral judgments have their sources in emotion. Deontological judgments are associated with 'alarm bell' emotions that circumvent reasoning and provide absolute demands on behavior. Alarm bell emotions are rooted in (for example) the amygdala. Consequentialist judgments are associated with 'currency' emotions provide negotiable motivations that weigh for and against particular behaviors, and are rooted in meso-limbic regions that track a stimulus' reward magnitude, reward probability, and expected value.

This chapter might be the best one in the book.

 

Chapter 3: Moral Motivation (Schroeder, Roskies, & Nichols)

The authors categorize philosophical theories of moral motivation into four groups:

• Instrumentalists think people are motivated when they form beliefs about how to satisfy pre-existing desires.
• Cognitivists think people are motivated merely by the belief that something is right or wrong.
• Sentimentalists think people are morally motivated only by emotions.
• Personalists think people are motivated by their character: their knowledge of good and bad, their wanting for good or bad, their emotions about good or bad, and their habits of responding to these three.

The authors then argue that the neuroscience of motivation fits best with the instrumentalist and personalist pictures of moral motivation, poses some problems for sentimentalists, and presents grave problems for cognitivists. The main weakness of the chapter is that its picture of the neuroscience of motivation is mostly drawn from a decade-old neuroscience textbook. As such, the chapter misses many new developments, especially the important discoveries occurring in neuroeconomics. Still, I can personally attest that the latest neuroscience still comes down most strongly in favor of instrumentalists and personalists, but there are recent details that could have been included in this chapter.

 

Chapter 4: Moral Emotions (Prinz & Nichols)

The authors survey studies that illuminate the role of emotions in moral cognition, and discuss several models that have been proposed, concluding that the evidence currently respects each of them. They then focus on a more detailed discussion of two emotions that are particularly causal in the moral judgments of Western society: anger and guilt.

The chapter is strong in example experiments, but a higher-level discussion of the role of emotions in moral judgment is provided by chapter 2.

 

Chapter 5: Altruism (Stich, Doris, & Roedder)

The authors distinguish four kinds of desires: (1) desires for pleasure and avoiding pain, (2) self-interested desires, (3) desires that are not self-interested and no for the well-being of others, and (4) desires for the well-being of others. Psychological hedonism maintains that all (terminal, as opposed to instrumental) desires are of type 1. Psychological egoism says that all desires are of type 2 (which includes type 1). Altruism claims that some desires fall into category 4. And if there are desires of tyep 3 but none of type 4, then both egoism and altruism are false.

The authors survey evolutionary arguments for and against altruism, but are not yet convinced by any of them.

Psychology, however, does support the existence of altruism, which seems to be "the product of an emotional response to another's distress." The authors survey the experimental evidence, especially the work of Batson. They conclude there is significant support for the existence of genuine human altruism. We are not motivated by selfishness alone.

 

Chapter 6: Moral Reasoning (Harman, Mason, & Sinnott-Armstrong)

The authors clarify the roles of conscious and unconscious moral reasoning, and reject one popular theory of moral reasoning: the deductive model. One of many reasons for their rejection of the deductive model is that it assumes we come to explicit moral conclusions by applying logic, probability theory, and decision theory to pre-existing moral principles, but in the deductive model these principles are understood in terms of psychological theories of concepts that are probably false. The authors survey the 'classical view of concepts' (concepts as defined in terms of necessary and sufficient conditions) and conclude that it is less likely to be true than alternate theories of mental concepts that are less friendly to the deductive model of moral reasoning.

The authors propose an alternate model of moral reasoning whereby one makes mutual adjustments to one's beliefs and plans and values in pursuit of what Rawls called 'reflective equilibrium.'

 

Chapter 7: Moral Intuitions (Sinnott-Armstrong, Young, & Cushman)

The authors refer to moral intuitions as "strong, stable, immediate moral beliefs." The 'immediate' part means that these moral beliefs do not arise through conscious reasoning; the subject is conscious only of the resulting moral belief.

Their project is this:

...moral intuitions are unreliable to the extent that morally irrelevant factors affect moral intuitions. When they are distorted by irrelevant factors, moral intuitions can be likened to mirages or seeing pink elephants while one is on LSD. Only when beliefs arise in more reputable ways do they have a fighting chance of being justified. Hence we need to know about the processes that produce moral intuitions before we can determine whether moral intuitions are justified.

Thus the chapter engages in something like Less Wrong-style 'dissolution to algorithm.'

A major weakness of this article is that it focuses on the understanding of intuitions as attribute substitution heuristics, but ignores the other two major sources of intuitive judgments: evolutionary psychology and unconscious associative learning.

 

Chapter 8: Linguistics and Moral Theory (Roedder & Harman)

This chapter examines the 'linguistic analogy' in moral psychology - the analogy between Chomsky's 'universal grammar' and what has been called 'universal moral grammar.' The authors don't have any strong conclusions, but instead suggest that this linguistic analogy may be a helpful framework for pursuing further research. They list five ways in particular the analogy is useful. This chapter can be skipped without missing much.

 

Chapter 9: Rules (Mallon & Nichols)

The authors survey the evidence that moral rules "are mentally represented and play a causal role in the production of judgment and behavior." This may be obvious, but it's nice to have the evidence collected somewhere.

 

Chapter 10: Responsibility (Knobe & Doris)

This chapter surveys the experimental studies that test people's attributions of moral responsibility. In short, people do not make such judgments according to invariant principles, as assumed by most of 20th century moral philosophy. (Moral philosophers have spent most of their time trying to find a set of principles that accounted for people's ordinary moral judgments, and showing that alternate sets of principles failed to capture people's ordinary moral judgments in particular circumstances.)

People adopt different moral criteria for judging different cases, even when they verbally endorse a simple set of abstract principles. This should not be surprising, as the same had already been shown to be true in linguistics and in non-moral judgment. The chapter surveys the variety of ways in which people adopt different moral criteria for different cases.

 

Chapter 11: Character (Merritt, Doris, & Harman)

This chapter surveys the evidence from situationist psychology, which undermines the 'robust character traits' view of human psychology upon which many varieties of virtue ethics depend.

 

Chapter 12: Well-Being (Tiberius & Plakias)

This chapter surveys competing concepts of 'well-being' in psychology, and provides reasons for using the 'life satisfaction' concept of well-being, especially in philosophy. The authors then discuss life satisfaction and normativity; for example the worry about the arbitrariness of factors that lead to human life satisfaction.

 

Chapter 13: Race and Racial Cognition (Kelly, Machery, & Mallon)

I didn't read this chapter.