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I've been searching LessWrong for prior discussions on Anxiety and I'm not getting very many hits. This surprised me. Obviously there have been well developed discussions on arkrasia, and ugh fields, yet little regarding their evil siblings Anxiety, Panic, and Mania.
I'd be interested to hear what people have to say about these topics from a rationalist's perspective. I wonder if anyone has developed any tricks, to calm the storm, and search for a third alternative.
Of course, first, and foremost, in such situations one should seek medical advice.
EDIT: Some very slightly related discussions: Don't Fear Failure, Hoping to start a discussion about overcoming insecurity.
Your math department example reminds me of a few experiences. From time to time, I'd be present when a small group of 3-4 professors were quietly discussing roadblocks in their research. Problems would be introduced, mentioning a number of unexpectedly connected fields, Symplectic This-Thats, and the Cohomology of Riff-Raffs. Eventually as the speaker relaxed and their anxiety settled, it would turn out that they were having trouble with an inequality and lost a constant along the way. So, the group would get to work, perhaps they would be able to fix the issue, then the next speaker in the circle would start to announce his problem.
What was surprising to me, was that they were not strangers. Most had been friends for over a decade. I wonder if the others were even still listening to the name-dropping. The context it provided wasn't at all helpful for finding a typo, that's for sure. I suppose it may be nice for "Keeping up with the Joneses", so to speak.
Great, thank you very much for the references. I am now reading your FAQ before moving onto the texts, I'll post any comments I have there.
Could anyone provide me with some rigorous mathematical references on Statistical Hypotheses Testing, and Bayesian Decision Theory? I am not an expert in this area, and am not aware of the standard texts. So far I have found
- Statistical Decision Theory and Bayesian Analysis - Berger
- Bayesian and Frequentist Regression Methods - Wakefield
Currently, I am leaning towards purchasing Berger's book. I am looking for texts similar in style and content to those of Springer's GTM series. It looks like the Springer Series in Statistics may be sufficient.
Started an additional job where I wrote some voice recognition software to automate video lecture transcription with a fairly high success rate (82.13%). I also streamlined the training process for non-technical people.
Hopefully, if there's a thread next month, I'll be posting about the GRE subject test in Mathematics.
Sure, will you take some python code as an example? I had to replace spaces with periods, the verbatim formatting doesn't seem to take into account python indented by 4 spaces.
Without taking into negative training data into account:
possible_properties.=.[]
for.p.in.Object.properties():
....for.x.in.training_set:
........if.not.x.has_property(p):
............break
....possible_properties.append(p)
Taking negative training data into account, here we have a 'positive set', and a 'negative set':
irrelevant_properties.=.[]
for.x.in.negative_set:
....for.property.in.x.properties():
........irrelevant_properties.append(property)
relevant_properties.=.[]
for.p.in.Object.properties():
....for.x.in.positive_set:
.......if.not.x.has_property(p).or.p.in.irrelevant_properties:
............break
....relevant_properties.append(p)
See the difference? In the second case, 'potential properties' is smaller. Note that this is not an optimal solution, since it looks up all possible properties in order to find the common properties of a training set, I wrote it because it's a little more succinct than intersections.
I write paragraphs beneath headings, to prevent rambling.
Indeed, and people would do well to remember that there may be situations wherein you are in fact the relatively "low-level NPC".
Recognizing some common characteristics of objects to be placed in the not 'odd' bin would also lower the upper bound on the complexity.
Just did this last night, actually. I've been noticing that my major goals, i.e. both professional and research goals have been playing host to a number of other side projects, like: learn this new language, write this cool script, start learning the latest trendy math field.
What I ended up doing is allocating 6 hours to my side projects on the weekends, promising to myself that I will use time-tracking, and once 6 hours is filled, spread over whichever projects, then I will not spend any more time on them.
Regarding annoyances, I simply wrote down everything that was annoying me, came up with solutions, wrote the solutions (and deadlines) in my planner, and then I was able to cross them off the list. Once I really got going, my days seemed to become a lot simpler, just because I could see has been bothering and distracting me.
Long term goals were still good. I had effectively been suffering feature creep for a while, and for now I can work much more efficiently.
I'm kind of confused. Did we really mean odds or primes? If we told the robot that this statement was true for the N integers, shouldn't we have said it correctly? If we did mean primes, then could at least have been honest, and said '2, 3, 5, 7'.
The combination of verified pointwise causal isomorphism of repeatable small parts, combined with surface behavioral equivalence on mundane levels of abstraction, is sufficient for me to relegate the alternative hypothesis to the world of 'not bothering to think about it any more'.
The kind of model which postulates that "a conscious em-algorithm must not only act like its corresponding human, under the hood it must also be structured like that human" would not likely stop at "... at least be structured like that human for, like, 9 orders of magnitude down from a human's size, to the level that you a human can see through an electron microscope, that's enough after that it doesn't matter (much / at all)". Wouldn't that be kind of arbitrary and make for an ugly model?
Given that an isomorphism requires checking that the relationship is one-to-one in both directions i.e. human -> em, and em -> human, I see little reason to worry about recursing to the absolute bottom.
Suppose that it turns out that in some sense, ems are little endian, whilst humans are big endian, yet, all other differences are negligible. Does that throw the isomorphism out the window? Of course not.
In Australia, a Medicare funded physician makes anywhere between 100k to 150k [1], whereas the avg. finance position pays 88k [2]. So you're right.
[1] http://www.health.qld.gov.au/hrpolicies/wage_rates/documents/hpeb2-wage-rates.pdf
[2] http://content.mycareer.com.au/salary-centre/financial-services
Sorry that these are Australian wages. I don't care about U.S. wages.
@D_Malik Quantitative finance is bursting at the seams, take a look at the latest trends in MFE programs and Wilmott's CQF. Although it is fun :-)
The engineering programs you listed, coupled with an MBA, will equal bigger bucks than simply engineering on it's own, in my opinion. If you're lucky (rather unlucky, from other people's perspective, hah), you'll be able to join the ranks of the superhuman species of "all pay, no work" Suits.
Also, suppose you do a PhD. In your case, given your interest in altruism, don't simply "do a PhD". Use the opportunity for your own purposes. There have been many theses that have positively affected humanity, see "Tate's Thesis", or Shannon's thesis on Boolean Algebra. I know these examples are old, however, they came to mind simply because of my field. Look up some Systems Biology theses for more recent examples.
Also, think beyond right now. For example, what are the reasons behind wanting to improve leadership skills? Do you want to use your potential leadership skills to influence others to adopt your pov w/ respect to altruism? How are you going to get into such a position (i.e. most people with successful and useful TED talks are not simply good speakers, but they have something concrete happening as well.)
IMO most of these things will be minutely beneficial, however, you'll also likely burn out in the process. Find the most important prerequisites to your success, they'll probably take up most of your time.
What exactly is the successor of a set?
I feel that this position could be equally argued if the scopes were switched, given the following motivation.
...if we mentally anthropomorphised certain risks, then we'd be more likely to give them the attention they deserved. -- OP
For example, a harmless :-) play on your comment. All the while, keeping the above maximization criteria in mind.
I would suggest that this is a useful thing to do on a group level (because it's mind-killing; take Yellow Peril for example) but a terrible thing to do on an individual level (to adjust for scope insensitivity and so forth).
Vassar's essay may benefit from a thorough rewrite, in my opinion. Certain sentences seem to make desperate attempts at describing the intension of his personal views. For example, the following lines required several rereads.
Some of those programs allocate attention to things that can be understood fairly rigorously, like a cart, a plow, or a sword. Other programs allocate attention to more complicated things, such as the long-term alliances and reproductive opportunities within a tribe. The former programs might involve situational awareness and detailed planning, while the latter programs might operate via subtle and tacit pattern detection and automatic obedience to crude heuristics.
Although, it is easy to see how one develops such a style of exposition, spending most waking hours trawling through research.
However, more to the point, the conclusion that I came to was that Vassar was advocating educational reform, moving towards something similar to the Montessori approach, and for what it's worth, I wholeheartedly agree.
Would agree about my reading of his short essay?
Mildly. The essay seems suggestive of a 10th point, which I described above. However, the truth lies with the original author, not me.
How solid do you think his argument is?
8/10. The most striking segment of his argument, in my opinion, is the following line.
However, with their attention placed on esteem, their concrete reasoning underdeveloped and their school curriculum poorly absorbed, such leaders aren't well positioned to create value.
Currently it is just a repository of links sorted by a SR algorithm. However, I'll consider pinging tldr.io for summaries, thanks for the reference.
I'm wary of implementing the flashcard behavior, as it allows users to cherry pick information, and possibly exclude more important information on a page, thereby by-passing the utility of learning the material.
Personally, flashcard usage seems to reinforce some sort of reflexive response to queries, rather than encouraging one to turn fields of knowledge into well-trodden gardens, as a neuroprosthetic should. I'm not sure whether this happens to the majority of users or not, more research needed.
Since I've often found myself in similar situations, I decided to start developing a spaced repetition web application, called memoread, for importing information and links straight from the browser.
Ideally there'll also be Chrome and Firefox extensions, plus an Android interface of some sort. Currently, you can either add links directly to memoread, or through a bookmarklet.
You can check it out at http://damp-wave-1655.herokuapp.com/ . I'm planning on releasing the source on GitHub soon, once I create a separate repo for deployment specifics.
Keep in mind, the app should be considered PRE-alpha, with no guarantees of any functionality whatsoever, hence it being located on some obscure heroku subdomain, not a domain of it's own.
EDIT: Also, although in most spaced repetition software you can select a difficulty level of 1-5, this is not yet available as I have not had the time to implement the changes on the UI side.
On a lighter side, this study reinforces (by a small quantity, due to all the caveat outlined in the comments) my idea that women are as promiscuous as men, but they are culturally forced to lie about that: not really big news.
Keep in mind that this study only reflects upon individuals born between 1978-1985. Based on the recent increase in entertainment promoting promiscuous behavior (ie. the American Pie series, EuroTrip, ), I expect that current attitudes (of 18-25 y/o's) would differ, even from those in 2003.
Is the study group still going ahead?