141 karma · joined August 18, 2013
Both research and business related problems are interesting. You don't have to swing between two extremes every time you have an epiphany.
Just my two cents. Nice article!
This discussion tends to get muddled whenever I bring it up with others because it's seen as an inability to be a team player, or that I'm just trying to daydream instead of "get things done", but my retort is always that you can't know what to get done until you have done the day-dreaming. We need it back.
Currently, statistical/data-driven approaches work best, and that's what you will be expected to use whether you are building your own products, or working for an employer. Most people don't care about the GOFAI approaches anymore, seeing them as outmoded in all respects.
However, if you are curious and want to understand more of the history of approaches we have tried, and learn some really interesting algorithms along the way, I think studying the old school problems and their solutions can be both intellectually stimulating, and potentially increase your depth of understanding. After all, it's only once you've tried to solve a problem and failed miserably that you start to appreciate the depth of its complexity.
That depth of appreciation is sorely lacking in today's new cohorts, who are basically blinded by the incredibly convincing outputs of our cream-of-the-crop LLMs.
1) People nitpicking about the use of mathematical ideas in a loose manner as if every person trying to understand some phenomenon must only open their mouth if they have a watertight theory or shut their mouth otherwise.
2) Getting hung up on the use of the luigi metaphors rather than using it as the basis for a constructive criticism that actually adds to the conversation in an interesting manner.
3) A general snarky attitude towards people exploring ideas on their own. I get it, you might have some expertise that others lack but you're forgetting that you've already made the thousands of mistakes to get to where you are. Do others the courtesy of not judging when they attempt the same.
Any experts wanna chime in?
1) The discrete sequencing is an epiphenomenon. The underlying processes are continuous changes in voltage and current flows. (I'm not sure if Planck scale considerations can throw a wrench in this though. Would love to be educated here.)
2) Our brains do not have ostensibly discrete neural processors. I don't think gradient descent is comparable to how the brain learns, but I think there is some reason to think that it is possible to learn symbolic processes in spite of having a processor that isn't especially built for it.
doTheThing(readFromUser())
What does the compiler do in cases where you have strings coming in like this?Calling a "landlord" a proprietor (example from the article) does nothing to change the actual dynamic between landlord and tenant. You have only changed the symbols you use without affecting any meaningful change in the world. Maybe a devil's advocate would say, "Symbols evoke feelings evoke action and thus we are affecting people's actions". That's the strongest counter-argument I can think of, and it's not very convincing either. Not sure how a landlord would suddenly change how they act because they are now proprietors.
It doesn't always have to be a choice between writing a book and nothing at all because the topic is too complex. I personally have benefited from bird's eye view blog posts and mini-articles more than I can remember. If you have the knowledge then go for it, someone out there will thank you.
One thing I dislike in discussions about floats is this incessant focus on the binary representation. The representation is NOT the essence of the number. Sure it matters if you are a hardware guy, or need to work with serialized floats, or some NaN-boxing trickery, but you can get a perfectly good understanding of binary floats by playing around with the key parameters of a floating point number system:
- precision = how many bits you have available
- exponent range = lower/upper bounds for exponent
- radix = 2 for binary floats
Consider listing out all possible floats given precision=3, exponents from -1 to 1, radix=2. See what happens when you have a real number that needs more than 3 bits of precision. What is the maximum rounding error using different rounding strategies? Then move on to subnormals and see how that adds a can of worms to underflow scenarios that you don't see in digital integer arithmetic. For anyone interested in a short book covering all this, I would recommend "Numerical Computing with IEEE Floating Point Arithmetic" by Overton [1].
It seems intuitively convincing and almost obvious to me that the quality of your output would be inversely proportional to the amount of available "space" for that output, although the exact relationship doesn't seem so clear.
Not really. Not everybody needs to or wants to have a huge circle of friends. And let's face it, it's impossible to have a huge circle of close friends. You wouldn't be able to maintain the friendships AND your studies, hobbies, etc.
> What hobbies do you advise me to adopt that I can do in a group, with camaraderie?
Strength training, hiking, biking, running, dancing, watching anime/drama/movies in a group -- so many to choose from. Just try out whatever interests you. You'll end up meeting lifelong friends just doing that.
And trust me, high school is a not a big deal. It's a very small chunk of your life compared to what's ahead. And what's ahead is much much better.