HN user
Philosophical about data and business.
I built this game to simulate how different decisions can impact popularity, the economy, and re-election chances for the oval office.
Please do not interpret this as any kind of political commentary! Most of the relationships / mechanics were AI generated.
WTF is often wrong, and actually, I don't think your answer in the 2 year old thread is correct
This. No one is getting released from prison because of this decision. It has been worded in such a way that makes it sound like it will have far more impact, which is of course intentional and political.
To be clear, 6,500 is the number of individuals receiving a pardon. The percentage of these 6,500 who will be released from prison? Zero - none of these individuals are currently in prison solely for possession of marijuana: https://www.washingtonpost.com/national-security/2022/10/07/...
Interesting... given that this is actually in use for the Australian Stock Exchange and the Hong Kong Exchanges, this smart contract language could become a major player and continue to get corporate adoption, especially now that it's open sourced.
I don't think it's necessarily a good idea to use SQL for everything. If you are constantly inserting records and don't need relational structures, DynamoDB might be a better choice. DynamoDB scales quickly and cheaply, so it's potentially the preferable option if it works in your architecture.
Another technique for unsupervised feature selection is Principal Feature Analysis (PFA): http://venom.cs.utsa.edu/dmz/techrep/2007/CS-TR-2007-011.pdf
I use goodreads to keep track of the books I want to read, books I am reading, and books I have read.
Unless you do a masters degree in Data Science, AI, CS, Statistics, or a related field, a masters degree only serves as proof that you are intelligent and work hard. Someone without a masters degree can still have those attributes, but they would just have to prove it some other way.
For example, if you have a bachelors degree from a top engineering school (MIT, Cal Tech, Stanford, Berkeley, etc.) you have proven that you are intelligent and can work hard.
People without a masters degree, but more business experience, bring a different perspective, and are often more business results focused, and potentially work more collaboratively than an individual who just graduated from a masters program.
Source: I am a Data Science hiring manager, and have interviewed 100+ candidates at several companies
I am not a lawyer, but I would not. There is a very small chance that you get sued here.
Keep in mind, we have a company called "swissinfo" declaring that Switzerland has the most competitive economy.
Bias much?
As I said last time, I have one major qualm about this.
There seems to be a confirmatory bias among the academic community that a measure of intellectual success can be found in the percentage of individuals who earn a PhD. Sure, it's true that you need to be reasonably smart to earn a PhD, but I think that someone can have a lot of intellectual success (potentially just as much or maybe more) if they don't earn a PhD.
I think you could even argue that, depending on the field of study, a PhD is the "easy" route for someone who is intellectually gifted - it's a simply a continuation of what you have been doing. I would be more impressed by the intellectual who not only realizes that they can conduct their own independent research, but also has the creativity to come up with a use case that can improve and contribute to the world (and presumably, make a living doing so).
My point here is that, given two gifted cohorts, one which has a 45% PhD graduation rate and one which has a 50% PhD graduation rate, I don't know that you can conclusively say that one is more gifted than the other without looking at other metrics associated with intellectual accomplishment."
They tracked everyone, and measured success by the percentage that earned a PhD. So, while Mark and Lady GaGa were tracked, I don't think I saw any statistic that would have associated these two individuals with a positive outcome (except for the anecdotal line in the paper). I would argue that both Mark and Lady Gaga have probably had more influence than 99.9% of the tracked individuals in the study, and yet they are lowering the presented metric for success (percent that earned a PhD).
Interesting read, but I have one major qualm about this.
There seems to be a confirmatory bias among the academic community that a measure of intellectual success can be found in the percentage of individuals who earn a PhD. Sure, it's true that you need to be reasonably smart to earn a PhD, but I think that someone can have a lot of intellectual success (potentially just as much or maybe more) if they don't earn a PhD.
I think you could even argue that, depending on the field of study, a PhD is the "easy" route for someone who is intellectually gifted - it's a simply a continuation of what you have been doing. I would be more impressed by the intellectual who not only realizes that they can conduct their own independent research, but also has the creativity to come up with a use case that can improve and contribute to the world (and presumably, make a living doing so).
My point here is that, given two gifted cohorts, one which has a 45% PhD graduation rate and one which has a 50% PhD graduation rate, I don't know that you can conclusively say that one is more gifted than the other without looking at other metrics associated with intellectual accomplishment.
Another issue that contributes to anger on Twitter is that comments on tweets are displayed based on the time which they appeared. So angry and unhelpful comments often appear at the top, even if no one likes them, because they were posted first.
Good fresh home made turkish delight is actually awesome. If you ever come across it, try it.
The pre-packaged stuff is mostly too sweet and not that good.
Enjoy Garry! I was just in the south of France, it was a very different world and one of the best trips I've taken. Let me know if you're looking for any recommendations.
Thought this was going to be about Adaboost on Apache Spark, but it is not!
Dimensionality reduciton is correct - the cosine similarity on a character/word level really just provides a metric for measuring the "anagramness" of two words. But "Mary" and "Army" would have a perfect similarity.
For these reasons I have been using ear muffs (similar to the ones you would use for lawn mowing). Currently I am using Howard Leight Sync. It's basically an "over the ear" ear plug when it's not on. You can also use them as poor quality headphones.
It gets hot, but it seems to be one of the best solutions I have found.
On a tangent, why is this problem soooo pervasive? I would say the large majority of people I work with fall into the over-confidence bucket.
Perhaps it's that people in tech are smart, and usually right when they find themselves in an argument. Perhaps it's that the hiring process at many companies favors those who display egregious confidence.
Agreed. I would say this answer closely resembles the Socratic method, which is the frame of mind I try to maintain when dealing with the over confident souls out there.
Was there a training set made available and distributed? I got the impression that there was not.
In a contest like this, I would expect (and I could be wrong) that there is a final validation set not accessible until "game day". Thus, overfitting to a training data set of any kind would be detrimental and degrade model performance.
If they are building an algorithm which overfits to training data, their algorithm will lose performance on in the actual validation test.
It is NOT like looking at leaked answers. It's like re-reading the textbook more than everyone else. Definitely an unfair advantage however.
This is clearly cheating.
HOWEVER, the goal of these contests should be to promote the most accurate and powerful image recognition algorithms that will transform the world as we know it. Limiting access to training data makes it more difficult to test changes to an algorithm. These rules do not make sense to me, and I would advocate against them.
() is not a tuple. (a,b) is a tuple or (a,) would also be a tuple.
then again, your username is Rainymood :-)
I believe this algorithm must (partially) base the sentiment decision on the first name of the target. A couple of my friends with an unusual first name did not get a result, even though I know they are on LinkedIn. Also, very common names (Dan, Matt, etc.) come up with the same generic message.