The post is here: http://blogs.discovermagazine.com/gnxp/2011/07/reader-survey-questions

So like many LWers, I'm primarily interested in what Robin Hanson calls "foragers", as opposed to "farmers". By LW standards, even most Nobel laureates are "farmers".

However, there are some exceptions (and some farmers can be interesting, too).

Anyways, which ones are your favorites? I'm starting to *really* like all the biographies of Watson and Crick (especially Olby's biography of Crick), since they were both iconoclastic foragers (Crick, in fact, seems uncannily similar to Feynman). Crick also has an autobiography but it isn't that insightful about his life.

And then there are the Feynman and Einstein biographies - Olby's biography of Feynman being the prototypical example. I'm checking out an Einstein biography right now - I'll see how that goes.

Among people who are especially underrated - I think Herbert Simon's autobiography (http://www.amazon.com/Models-My-Life-Herbert-Simon/dp/026269185X/ref=sr_1_3?ie=UTF8&qid=1310586938&sr=8-3 ) might be particularly interesting. I just checked it out from the library.

Then there's Elizier Yudowsky's autobiography.

Venkatesh Rao (who is amazing because he literally constantly challenges our definitions of everything):

http://www.quora.com/Venkatesh-Rao/Quora-Portfolio-Year-1

http://www.quora.com/Venkatesh-Rao

http://www.ribbonfarm.com/be-slightly-evil/

http://www.ribbonfarm.com

Gregory Rader:

http://www.quora.com/Gregory-Rader

http://onthespiral.com/

Daniel Lemire:

http://www.quora.com/Daniel-Lemire and http://lemire.me/blog/ are also super-super-super interesting

==

I tried doing google searches of site:lesswrong.com + their names (or websites), but ended up with little. I'd like to see what LessWrong thinks of Rao in particular. I have NEVER seen posts that were as amazingly insightful as his. It's worth it just to sacrifice a day just to see all his amazing posts. The others were also amazing and really make you think about everything.

==

E.g. with Rao, you have

My favorites: 

http://www.quora.com/What-are-the-benefits-to-obtaining-a-PhD-in-the-field-of-Education/answer/Venkatesh-Rao?srid=0WH

http://www.ribbonfarm.com/2010/05/19/intellectual-gluttony/

http://www.ribbonfarm.com/2011/04/07/extroverts-introverts-aspies-and-codies

http://www.quora.com/What-careers-or-industries-are-the-most-meritocratic/answer/Venkatesh-Rao?srid=0WH

http://www.ribbonfarm.com/2008/09/11/how-to-measure-information-work

http://www.quora.com/What-are-some-tips-for-advanced-writers/answer/Venkatesh-Rao

http://www.quora.com/Children/Why-do-some-humans-not-want-children/answer/Venkatesh-Rao

http://www.quora.com/What-is-the-best-way-to-motivate-oneself-to-finish-a-PhD/answer/Venkatesh-Rao

http://www.quora.com/History/What-is-the-most-important-human-decision-ever-made/answer/Venkatesh-Rao

http://www.quora.com/Is-the-following-follower-model-the-new-standard-of-social-connection/answer/Venkatesh-Rao

http://www.quora.com/What-discipline-specific-principles-help-reframe-activities-so-as-to-provide-useful-insights-and-or-improvement

1. "Premature optimization is the root of all evil" -- Knuth, computer science
2. Never design a law with the worst case in mind -- law/legislation

Rader:

http://onthespiral.com/stop-wasting-time-and-effort-developing-fragile-capabilities

http://onthespiral.com/unifying-value-universe

http://onthespiral.com/principles-disruptive-learning-environments

Lemire:

http://lemire.me/blog/archives/2010/08/16/working-long-hours-is-stupid

http://www.daniel-lemire.com/blog/archives/2010/02/08/trading-latency-for-quality-in-research

http://www.daniel-lemire.com/blog/archives/2009/09/14/how-things-change-cheaters-are-innovators

http://www.daniel-lemire.com/blog/archives/2008/08/19/the-secret-to-intellectual-productivity/

http://www.daniel-lemire.com/blog/archives/2010/01/13/the-fundamental-properties-of-computing/

http://www.daniel-lemire.com/blog/archives/2007/12/05/formal-definitions-are-less-useful-than-you-think/

http://www.daniel-lemire.com/blog/archives/2007/11/19/directed-research-is-useless/

http://lemire.me/blog/archives/2006/08/09/big-schools-are-not-longer-giving-researchers-an-edge/

http://lemire.me/blog/archives/2011/06/23/probabilities-are-unnecessary-mathematical-artifacts

http://lemire.me/blog/archives/2011/06/14/the-language-interpreters-are-the-new-machines

http://www.daniel-lemire.com/blog/archives/2009/08/01/why-i-hardly-ever-blog-about-my-ongoing-research/

http://lemire.me/blog/archives/2011/06/08/is-wikipedia-anti-intellectual

http://www.daniel-lemire.com/blog/archives/2008/06/05/why-pure-theory-is-wasteful

http://lemire.me/blog/archives/2011/06/06/why-i-still-program

Autodidactism

Individual academic subjects: http://www.quora.com/Alex-K-Chen/Useful-Science-Learning-Resource-Collections

Data Science already covered by Alex Kamil: http://www.quora.com/Educational-Resources/How-do-I-become-a-data-scientist

Can anyone please give me some feedback? Thanks!

http://cacm.acm.org/magazines/2011/6/108655-qa-a-lifelong-learner/fulltext

Wow, this is quite interesting. What are your thoughts?

You made a number of important contributions to parallel computing in the 1980s. Can you tell me about your more recent work in that arena?

The root problem with parallel machines has not been that any one is inherently bad, but more that it is difficult to make sense of them as a totality. The most striking fact about sequential machines is that they are all the same—they emulate the von Neumann abstraction. This sameness has been vital to the growth of computing since it has enabled us to write portable software for this abstraction and make machines to emulate it. Thus the von Neumann model has served as the vital "bridging model" between the software and hardware.

I have been long interested in the question of whether a similarly effective bridging model exists for parallel machines and what that might be. Parallelism has now arrived with multiple cores in our laptops. My view is that the irresistible driving forces that have brought this about have been the imperatives of physics. It follows, I think, that we have to look to the driving physical limitations to see what is most fundamental about parallel computing. Computation, communication, synchronization, and memory all need various resources, which in turn are limited by the physical arrangements of the devices. The sought-after bridging model has to account for these costs. We have been spoiled over the last 60-odd years by having an abstraction as simple as the von Neumann model suffice. Life will become a little more complicated now. The absence of an accepted bridging model is evident even at the theoretical level of algorithms.

My most recent work addresses these issues via a particular proposed bridging model, called the Multi-BSP, which tries to capture the physical limitations of machines as simply as possible. It uses barrier synchronization in a hierarchical manner and idealizes a machine as a set of numerical parameters that specify a point in the space of possible machines. Mention of the many architectural details of current machines that are not inevitably suggested by physics is deliberately omitted.

Let's talk about your most recent work in computational neuroscience. Can you explain the "neuroidal model" you developed in your book Circuits of the Mind?

The neuroidal model is designed to describe how basic computational tasks are carried out in the brain at the level of neurons. We do not know what changes the brain undergoes when it memorizes a new word or makes a new association. We need some language for describing the alternative algorithms that a network of neurons may be implementing. Memorizing a new word is easy enough for a computer, but it is still mysterious how the brain does it. Devising algorithms that do not obviously contradict biology is not easy, because biology appears very constrained. Each neuron is connected to only a small fraction of the others, and its influence on each of those is believed to be weak on the average.

You also developed a formal system called robust logics that seeks to reconcile what you've described as "the apparent logical nature of reasoning and the statistical nature of learning."

Robust logic is aimed at tasks which we do not understand how to do on any computational device, let alone the brain. The tasks in question are those of reasoning using information that is learned from experience and therefore uncertain. Humans manage to make effective decisions even in an uncertain world and even with only partial knowledge. This skill is close to the heart of intelligence, I believe, and understanding it better raises some fundamental scientific questions.

This is one of the cases where better information (which we can easily get as rationalists) can really pay off. I got paid over $500 to test Concerta, which is basically the extended-release version of Ritalin (and it was one of the last stages too, even after it got FDA-approved). And Ritalin's effects are already very well-known.

Many people are systematically prone to overestimating the risks from drugs (due to an intrinsic resistance to anything "unnatural"), especially since they don't do any research into them. Now, certainly, there is *some* chance that Phase I drugs could end up as disasters - http://en.wikipedia.org/wiki/TGN1412 foremost among them. And there's also a chance that you could end up with http://en.wikipedia.org/wiki/Rosiglitazone (but then, if you're only taking it for a few weeks, there probably won't be any permanent damage).

But these are still the exceptions rather than the norm, and you're still probably more likely to die from a car accident. Furthermore, these types of things aren't going to happen to extended release forms (and the FDA still somehow requires stringent testing for new extended release forms of drugs). Some of us rationalists excel at doing research, and can easily estimate the risks of most drugs (and go straight into the journal papers to see relative ratios and LD50s [and the dose required to cause severe side effects in any human, which is usually much higher than the normal dose]). Furthermore, in the new era of genomic sequencing, we may even be able to better predict how our body will respond to these drugs (for example, we can see which receptors we're over-sensitive too, and which ones we're not so oversensitive to, as well as our expression of Cytochrome p450 enzymes). So maybe this is a potential windfall for some of us? 

Especially in the future, since we're in the age where we are rapidly learning about biological networks?

==

Even then, it doesn't have to be Phase I.

With that all being said, it's probably wise to avoid getting too many blood draws from the same vein

So... Has anyone considered that mentoring could possibly be an alternative to having children? In terms of overall psychological well-being? The studies do show that having children is inversely related to happiness, but many people choose to have children anyways since they get a different sort of satisfaction from having children.

But with mentors, they can get many of the benefits and few of the costs (plus, they'll know unambiguously that they've helped someone - with parenting, it's different). And the people they mentor are most likely more compatible with them than the children that they'll most likely have (it's selection bias for compatibility, basically)

That being said, maybe there is something psychological missing out from all this. I was never brought up in a loving or close family (asian parents), so I don't really see any benefits to having a family (incidentally, this might be one huge reason why the fertility rate in places like Taiwan/Japan/South Korea are now among the lowest rates in the world - see http://en.wikipedia.org/wiki/List_of_countries_and_territories_by_fertility_rate#The_CIA_TFR_Ranking ).

I generally agree with Bryan Caplan that news is generally useless:

However, there are some analytical sources of news that are exceptions.

Personally, what I find most stimulating is http://www.mckinseyquarterly.com/home.aspx . http://www.cato-unbound.org/ is also quite stimulating, although it isn't contemporary news. Then on the more non-news-y side, IEEE Spectrum and Communications of the ACM can occasionally be great. And while Scientific American has dumbed down in recent years, the featured articles in Science News (http://www.sciencenews.org/view/feed/type/article) are *amazingly* insightful. Two examples are below:

http://blog.sciencenews.org/view/feature/id/330593/title/Inside_Job http://www.comoogl.sciencenews.org/view/feature/id/74456/title/Healthy_Aging_in_a_Pill

You usually don't even get analyses like these in Science or Nature.

And http://www.newgeography.com/ is surprisingly insightful.

What do people here think of The Atlantic? It's better than other news sources, and I was impressed with it when I was in 8th grade. And some columnists, particularly Reihan Salam, are great (Reihan isn't in the position of supporting *any* politician or party, so that puts him in a position where I don't suspect him of twisting facts [even a little bit] to support his position - something that I do often suspect for people like Ezra Klein and other left-wing pundits). But even The Atlantic does seem to be too much in line with mainstream opinion.

Other than that, I get most of my analysis from Gene Expression and Information Processing. I'm not too much of an economist yet, so I don't really read the economics blogs. FuturePundit/ParaPundit is right on many things, but its blogposts don't contain too much depth in them. 

http://www.reddit.com/r/INTP/comments/i27i1/does_anyone_agree_that_this_is_the_biggest/

What are your thoughts on this thread?