The exposure of the general public to the concept of AI risk probably increased exponentially a few days ago, when Stephen Colbert mentioned Musk's warnings and satirized them. (Unrelatedly but also of potential interest to some LWers, Terry Tao was the guest of the evening).

Politics/religion:

• "How Interest Rates Were Set, 2500 BC – 1000 AD"
• "How Successful Was Christianity?"
• "How is the biomarker ID aid plan going in India?"
• "The Chaos Company: Wherever governments can’t—or won’t—maintain order, from oil fields in Africa to airports in Britain and nuclear facilities in America, the London-based “global security” behemoth G4S has been filling the void"
• "Book Review: A Poisonous Affair: America, Iraq, and the Gassing of Halabja, by Joost R. Hiltermann"

Statistics:

• "Laplace the Bayesianista and the Mass of Saturn"
• "Why do Phase III Trials of Promising Heart Failure Drugs Often Fail? The Contribution of 'Regression to the Truth'", Krum & Tonkin 2003 (excerpts)
• "The Efficacy of Psychological, Educational, and Behavioral Treatment: Confirmation From Meta-Analysis", Lipsey & Wilson 1993 (I love these meta-meta-analyses, since they let you say things like 'Published studies yield mean effects 0.14 SDs larger than unpublished studies.' Isn't that useful to know?)

Literature:

• "Sanderson's First Law" of fantasy

Medicine:

• "Biomarkers and Long-term Labour Market Outcomes: The Case of Creatine"
• Caloric restriction, evidence pyramid, and drug risks
• "Meta-Regression Analyses, Meta-Analyses, and Trial Sequential Analyses of the Effects of Supplementation with Beta-Carotene, Vitamin A, and Vitamin E Singly or in Different Combinations on All-Cause Mortality: Do We Have Evidence for Lack of Harm?", Bjelakovic et al 2013

Short Online Texts Thread

What fiction should I read first?

I have read pretty much nothing but MoR and books I didn't like for school, so I don't really know what my preferences are. I am a mathematician and a Bayesianist with an emphasis on the more theoretical side of rationality. I like smart characters that win. I looked at some recommendations on other topics, but there are too many options. If you suggest more than one, please describe a decision procedure that uses information that I have and you don't to narrow it down.

Got my dissertation proposal approved.

First paragraph: This dissertation continues the tradition of identifying the unintended consequences of the US health insurance system. Its main contribution is to estimate the size of the distortions caused by the employer-based system and regulations intended to fix it, while using methods that are more novel and appropriate than those of previous work.

The most relevant field here is International Monetary Economics.

After TAing a class on the subject, I became convinced that most people (including economists) would be better off ignoring money most of the time, and just following where the goods went. So think of this as a transfer of $1000 worth of goods from the US to Kenya.

You could get the official answer with an IS-LM-BP model and some masochism.

More seriously, this does make me want to look into theoretical work on the macroeconomics of charity. On the empirical side, the best evidence is that even average (poorly targeted and managed) foreign aid has positive effects on country-level growth.

While I agree with almost all of the antifaq (the general point is apparently more plausible to economists than non-economists), this is pretty misleading:

"The future cannot be a cause of the past."

True, but human expectations about the future can be very important in the present. If you expect that 5 years from now FAI will take over, you won't bother to make many long-term investments like building factories and training new workers.

Why are we seeing long term unemployment instead of shorter work weeks now? Is this inevitable or is there some structural or institutional problem causing it?

Shorter work weeks didn't just happen. It took a huge amount of effort from unions, which were a lot more powerful then than they are now.

Most jobs don't let you freely trade off how long you work for how much money you get. There are fixed per-employee costs, so businesses would rather have one person working 40 hours per week rather than two people working 20 hours per week. Especially when 40 is the norm and wanting to work less is "lazy".

The main question is why is automation associated with unemployment today when it wasn't in the past. To answer, you have to consider the kinds of jobs created by and lost to automation and the determinants of workers incomes in the jobs.

Most of the industrial revolution is associated an increasing number of workers in manufacturing and fewer in farming. The industrial work force grew primarily at the expense of the peasants or farmers. Today, automation is causing manufacturing jobs to be replaced by service jobs. Farming jobs were the first to go because our need for foodstuffs is limited. Manufacturing jobs went next because manufacturing is easier to automate than services.

But manufacturing jobs paid better than farming jobs; service-industry jobs pay worse than manufacturing jobs. If the jobs pay better, there are also more of them, because well-paid citizens create greater aggregate demand. So today we have manufacturing jobs declining relative to service-industry jobs with the result that the workforce is poorer, which means fewer workers can be employed.

The explanation lies in whatever causes some jobs to be paid considerably more than others. It could be status. Manufacturing jobs are higher status than farming jobs because the city is high status compared to the sticks. And service industry is low status because of the low status of servitude. Groups of workers with higher status get paid better. It probably makes a greater difference than we realize.

People who think that automation is currently increasing unemployment don't generally just talk about jobs lost during the Great Recession. They see an overall trend of reduction in employment and wages since at least 2000.

You're absolutely right that the recession was caused by a financial shock. The thing is, a normal effect of recessions is for productivity to increase; businesses lay off workers and then try to figure out how to run their operation more efficiently with less workers, that happens in every recession. The difference might be that this time, it is easier then ever in the past for employers to figure out how to do more with less workers (because of the internet, and automation, and computers, ect), and so even when demand starts to come back up as the GDP grows again, they apparently still don't need to hire many workers.

The economists making the automation argument aren't saying that automation caused the great recession or the loss of jobs that happened then; they tend to think that it's a long ongoing trend that's been going for quite a while, that it was partly hidden for a few years by the housing bubble, but that the great recession has accelerated that trend by increasing the need for employers to find ways to be more cost-effective.

Edit: the main assumption EY is making in this article seems to be here:

Since it should take advanced general AI to automate away most or all humanly possible labor

and I don't think that's true. I think that a majority of labor done today, either physical or intellectual, is basically a series of routine or repeatable tasks, and I think that a big chunk of it could be done by either narrow AI software or robotics or internet-based logistics.

Anyway, you wouldn't really have to automate most or all of human labor to create an unemployment crises; if we hit long-term unemployment levels of 20%-30% that would probably not be sustainable without some fairly significant social and economic changes.