I just came back from a medical conference. Everyone knew each other. In addition to questions about their practice and research, lots of questions such as "how are the kids doing?" were flying around. If that wasn't a "community", I don't know what was.
Not to say the existence of a community is a good thing... but one does exist.
Surviving in a saturated market requires standing out in some way. If you succeed at it enough, you'll be surprised at how many recruiters will come to you (assuming you have a web presence, of course... but if you're in the IT field, you should). Now, I'm only half your age and consequently at a much earlier stage of my career, but I don't see why this wouldn't generalize.
But do what you feel is best. If you think truck driving is a good career, great!
Also, healthcare is not only direct clinical practice. There's a lot of work that goes into it, much of it IT-related.
I think this is the best advice posted on this thread so far. A complex system is not a better system, it's just a more difficult system to understand.
I think people worry a bit too much about what people read, when in reality, it's the very act of reading that we should be encouraging. There are plenty of reasons to read almost anything: vocabulary building, knowledge of action and consequence, ethical drama (often intertwined with action and consequence: how do "ethical" outcomes compare with "unethical" ones?), observing character progression, and, perhaps most importantly, because establishing reading as a fun activity ensures that a person will read in the future (not just books, too - reading in general).
How do we do it in primary school? We push a collection of "classics" that children would really not care to read upon them, then we enforce this reading by giving tests on the material. Years later, we wonder why so many people hate reading.
I am a voracious reader. I've filled two very large bookcases and I'm working on a third. I've probably covered all of the major literary categories by now. It wasn't some collection of classics that I cut my teeth on, however; it was Nintendo Power magazine. The classics came afterwards, and of my own volition.
Basically. There's no advantage to observation over learning with a focused objective, but I think the key point is that learning is an unconscious process that is primarily carried out intuitively. You can direct your attention towards a subject and think a great deal, but you can't direct your intuition - all you can do is foster an appropriate environment. I've thought of it as a sort of receptiveness for new ideas (which I think are exogenous but are learned only after personal interpretation).
I would question whether this is how all of the gifted learn, however. I know a lot of gifted people who nevertheless think they can somehow coerce themselves to learn things through sheer conscious effort, without intuition ever taking over - people who make their primary goal thinking about something vs. understanding it, if that makes any sense. If they keep drilling enough, they eventually get whatever it is they were trying to learn, but it tends to take a long time, usually leaves them exhausted, and is swiftly forgotten. Unsurprisingly, these people have all ended up building small, narrowly focused knowledge bases.
That's not to say that learning difficult material is easy. It's a struggle irrespective of intelligence, and if learning a particular topic comes easily, there's always something harder. Until you intuitively understand something, conscious thought may be your only way of comprehending it - and if you don't intuitively understand a concept, thinking about it is hard work.
It seems like they really botched this, from not knowing when the date would be until last week to starting the day at 1 PM without getting the word out and now to their site going down in the middle of it.
I know someone who had far worse numerical statistics than I did (as in a whole point lower GPA and around 150 points lower on the GRE) and somehow got into a better Ph. D. program (we're both going for PhDs in CS). He said that his professors told him a good deal of getting into a good Ph. D. program (perhaps this doesn't apply to just getting into one at all) was name-recognition of his recommenders, and he went to a large university with lots of recognizable faculty, while I went to a small one with only a few. Apparently, it reflects well on you if you worked with someone famous, even if all you did was sweep the floors of the lab.
To this day, that system still sickens me, but I probably would have been spared much grief if I had known that when I applied. Perhaps it would be useful information to you. You may also want to try contacting faculty directly with your research topics and seeing if you could work with them if you get in prior to submitting your application. The faculty make up the committee that reviews your application, so having someone on it that really wants to work with you is probably a good idea.
A Ph. D is a lot of hard work and involves a lot of nonsense, however, and you can honestly gain about the same degree of skill as an average Ph. D. program will impart with about three months of research. Consider whether you really need to subject yourself to one.
If a "Powers of 10" poster exists, it would make an excellent reminder of the growth rate of exponentials - something every computer scientist should keep in mind.
I think (and so do several leading experts) that too much skepticism, especially when applied too early, is detrimental to creative thought - and lack of creativity WILL hurt you as a scientist or engineer, perhaps even more than a lack of critical thought. I wouldn't be too hasty to run off and start doubting everything.
Hopefully I won't butcher this explanation, but here goes:)
Artificial neural networks (ANN), usually just called "neural nets", are basically models of the biological neural networks that occur in the brain - neurons connecting to others and exchanging information. Theoretically, a large enough neural network can behave just like the brain (in the same sense that throwing a bunch of bricks in the air will self-assemble into a house; that is, it would be highly unlikely to happen spontaneously, but possible if we understood precisely how to build it). Right now, neural networks are used primarily to create "learning" models and answer questions such as: "If it was hot and windy two days ago and it rained, it was cool and calm yesterday and it didn't, and it's hot and calm today and it did, will it rain tomorrow if it's cool and calm?" (That particular sort of question is known as "classification")
The idea of a neural net has been around since the 1950s, I believe, but neural networks became rather unpopular for a long time when a proof by Marvin Minsky was published demonstrating that a perceptron, a certain type of neural network, could not learn a certain class of problems known as "XOR problems". People misunderstood the proof as saying that no neural networks could learn those problems at all, and a lot of research on them stopped.
Neural nets experienced a resurgence in the mid-1980s with the advent of something known as the backpropagation algorithm (I believe it was by Hinton, but I could be off on that). To skip the details, it significantly reduced the computational cost of training a neural network by calculating prediction errors by going backwards through the 'net. This led people to reevaluate neural networks, and they've been a common technique since, although different (I won't say "more advanced") techniques such as support vector machines and Gaussian processes have since replaced them at the cutting edge.
There are more things in life than money, and more than one way to make that, besides. I personally find making less and working 40 hours a week a much better proposition than making more and working 80. And part of what I do in my downtime is develop contingency strategies in case I desire some other way of finding income in the future. Starting a business, doing consulting, and becoming a professor are all options that can provide good income with less time investment than traditional industrial jobs (not in all cases, of course, but sometimes). I'm not going to call them better options, but they are different options should one type of lifestyle seem unsatisfactory.
And then there's always just finding a higher paying job. That's the advantage of having an advanced degree and being able to do a lot of different things - you can potentially fill a great number of valuable niches.
We are nowhere near understanding how the mind works in sufficient detail to design an AI. A lot of the 80s optimism was probably a result of the growth of neural nets around that time, in no small part due to the advent of the backpropagation algorithm.
Slashdot Mirror
I just came back from a medical conference. Everyone knew each other. In addition to questions about their practice and research, lots of questions such as "how are the kids doing?" were flying around. If that wasn't a "community", I don't know what was.
Not to say the existence of a community is a good thing... but one does exist.
Not just one government, mind you, but all of them - I doubt aliens care about our political boundaries.
It's like Wikipedia but without the open collaboration which made Wikipedia successful.
You're all going to hate me for posting this, but this sounds like what VB was made for.
Surviving in a saturated market requires standing out in some way. If you succeed at it enough, you'll be surprised at how many recruiters will come to you (assuming you have a web presence, of course... but if you're in the IT field, you should). Now, I'm only half your age and consequently at a much earlier stage of my career, but I don't see why this wouldn't generalize.
But do what you feel is best. If you think truck driving is a good career, great!
Also, healthcare is not only direct clinical practice. There's a lot of work that goes into it, much of it IT-related.
I think this is the best advice posted on this thread so far. A complex system is not a better system, it's just a more difficult system to understand.
Title IX wasn't the rule from the start in science either.
Ideally, we wouldn't even need weapons for people to avoid killing each other, but I suppose that's unfortunately impractical for a number of reasons.
This brings up a good point:
Why are people who don't know the first thing about programming allowed to make hiring decisions about people who do?
HR people should be required to possess at least basic knowledge in the skills they're looking for. Otherwise they'll necessarily make bad decisions.
Ultimately? Read Adams' or Jefferson's biography and you can see that they started undoing the country from day one!
I think people worry a bit too much about what people read, when in reality, it's the very act of reading that we should be encouraging. There are plenty of reasons to read almost anything: vocabulary building, knowledge of action and consequence, ethical drama (often intertwined with action and consequence: how do "ethical" outcomes compare with "unethical" ones?), observing character progression, and, perhaps most importantly, because establishing reading as a fun activity ensures that a person will read in the future (not just books, too - reading in general).
How do we do it in primary school? We push a collection of "classics" that children would really not care to read upon them, then we enforce this reading by giving tests on the material. Years later, we wonder why so many people hate reading.
I am a voracious reader. I've filled two very large bookcases and I'm working on a third. I've probably covered all of the major literary categories by now. It wasn't some collection of classics that I cut my teeth on, however; it was Nintendo Power magazine. The classics came afterwards, and of my own volition.
Basically. There's no advantage to observation over learning with a focused objective, but I think the key point is that learning is an unconscious process that is primarily carried out intuitively. You can direct your attention towards a subject and think a great deal, but you can't direct your intuition - all you can do is foster an appropriate environment. I've thought of it as a sort of receptiveness for new ideas (which I think are exogenous but are learned only after personal interpretation).
I would question whether this is how all of the gifted learn, however. I know a lot of gifted people who nevertheless think they can somehow coerce themselves to learn things through sheer conscious effort, without intuition ever taking over - people who make their primary goal thinking about something vs. understanding it, if that makes any sense. If they keep drilling enough, they eventually get whatever it is they were trying to learn, but it tends to take a long time, usually leaves them exhausted, and is swiftly forgotten. Unsurprisingly, these people have all ended up building small, narrowly focused knowledge bases.
That's not to say that learning difficult material is easy. It's a struggle irrespective of intelligence, and if learning a particular topic comes easily, there's always something harder. Until you intuitively understand something, conscious thought may be your only way of comprehending it - and if you don't intuitively understand a concept, thinking about it is hard work.
But that's just my own experience.
Obligatory xkcd.
No, but at least it might help Google fix its search engine.
You bring up a good point, but I still think vi is better :)
He would have to be, for if someone who wasn't already famous tried to do that, they would be ridiculed rather than taken seriously.
Because anyone who voted against this bill can be accused of supporting terrorism or some such nonsense.
It's worked pretty well for me, and until there's a school or employer that will teach you everything, it's your only option.
It seems like they really botched this, from not knowing when the date would be until last week to starting the day at 1 PM without getting the word out and now to their site going down in the middle of it.
I know someone who had far worse numerical statistics than I did (as in a whole point lower GPA and around 150 points lower on the GRE) and somehow got into a better Ph. D. program (we're both going for PhDs in CS). He said that his professors told him a good deal of getting into a good Ph. D. program (perhaps this doesn't apply to just getting into one at all) was name-recognition of his recommenders, and he went to a large university with lots of recognizable faculty, while I went to a small one with only a few. Apparently, it reflects well on you if you worked with someone famous, even if all you did was sweep the floors of the lab.
To this day, that system still sickens me, but I probably would have been spared much grief if I had known that when I applied. Perhaps it would be useful information to you. You may also want to try contacting faculty directly with your research topics and seeing if you could work with them if you get in prior to submitting your application. The faculty make up the committee that reviews your application, so having someone on it that really wants to work with you is probably a good idea.
A Ph. D is a lot of hard work and involves a lot of nonsense, however, and you can honestly gain about the same degree of skill as an average Ph. D. program will impart with about three months of research. Consider whether you really need to subject yourself to one.
If a "Powers of 10" poster exists, it would make an excellent reminder of the growth rate of exponentials - something every computer scientist should keep in mind.
I think (and so do several leading experts) that too much skepticism, especially when applied too early, is detrimental to creative thought - and lack of creativity WILL hurt you as a scientist or engineer, perhaps even more than a lack of critical thought. I wouldn't be too hasty to run off and start doubting everything.
Hopefully I won't butcher this explanation, but here goes :)
Artificial neural networks (ANN), usually just called "neural nets", are basically models of the biological neural networks that occur in the brain - neurons connecting to others and exchanging information. Theoretically, a large enough neural network can behave just like the brain (in the same sense that throwing a bunch of bricks in the air will self-assemble into a house; that is, it would be highly unlikely to happen spontaneously, but possible if we understood precisely how to build it). Right now, neural networks are used primarily to create "learning" models and answer questions such as: "If it was hot and windy two days ago and it rained, it was cool and calm yesterday and it didn't, and it's hot and calm today and it did, will it rain tomorrow if it's cool and calm?" (That particular sort of question is known as "classification")
The idea of a neural net has been around since the 1950s, I believe, but neural networks became rather unpopular for a long time when a proof by Marvin Minsky was published demonstrating that a perceptron, a certain type of neural network, could not learn a certain class of problems known as "XOR problems". People misunderstood the proof as saying that no neural networks could learn those problems at all, and a lot of research on them stopped.
Neural nets experienced a resurgence in the mid-1980s with the advent of something known as the backpropagation algorithm (I believe it was by Hinton, but I could be off on that). To skip the details, it significantly reduced the computational cost of training a neural network by calculating prediction errors by going backwards through the 'net. This led people to reevaluate neural networks, and they've been a common technique since, although different (I won't say "more advanced") techniques such as support vector machines and Gaussian processes have since replaced them at the cutting edge.
There are more things in life than money, and more than one way to make that, besides. I personally find making less and working 40 hours a week a much better proposition than making more and working 80. And part of what I do in my downtime is develop contingency strategies in case I desire some other way of finding income in the future. Starting a business, doing consulting, and becoming a professor are all options that can provide good income with less time investment than traditional industrial jobs (not in all cases, of course, but sometimes). I'm not going to call them better options, but they are different options should one type of lifestyle seem unsatisfactory.
And then there's always just finding a higher paying job. That's the advantage of having an advanced degree and being able to do a lot of different things - you can potentially fill a great number of valuable niches.
We are nowhere near understanding how the mind works in sufficient detail to design an AI. A lot of the 80s optimism was probably a result of the growth of neural nets around that time, in no small part due to the advent of the backpropagation algorithm.