273 karma · joined April 7, 2016
The poster is saying that global climate change is probably even worse than people think. Often, the reporting around climate change focuses on the best case, the average case, or what could be achieved with an immediate strong response. But the actual paths we are on seems to track some of the worse, more pessimistic cases.
Consider the input “ba”
This solution will return “ab”, which is not a strong that can be generated by deleting characters from the input.
This is not an easy problem.
Author would surely have been well served by leaving science sooner. As another commenter noted, his output wasn't impressive, and it should have been obvious he would never be hired as an independent researcher. But that can be psychologically difficult for a 40-year-old whose project of becoming an independent scientist started when he was literally a teenager.
On the plus side I do still know a tremendous amount of trivia about topics that I was interested in as a child, I'm not particularly interested in following rules, and I'm not afraid to be alone or to learn things from a book without anyone to help. These qualities have served me in good stead.
On the balance, I'm not sure I would change anything, but you should be aware of what your kids might be in for.
1. http://theconversation.com/anti-vaccination-beliefs-dont-fol...
So "new" antibiotics aren't actually new, and resistance to them probably already exists somewhere -- bacteria have been fighting this war for as long as there have been bacteria.
I tend to appear standoffish and several friends have told me that they initially believed that I disliked them. I have to tell people explicitly that I am interested in spending time with them to overcome this.
Working people spend their money.
I don't think this would work because pre-publication work is usually not a secret, it gets promoted at conferences and seminars well before it's formally published.
Based on my personal experiences, a pool of anecdotes from friends, and the generally accepted advice of the medical community: sustained weight loss of more than 1 lb per week is incredibly difficult and not realistic for most dieters.
Since, by definition, most "severely obese" people are more than 52lb from a "healthy" weight, statistics like this seem inevitable even if the person does everything perfectly and achieves results that they're satisfied with.
Example: I've totally transformed my body with diet and exercise. But I've failed by this metric, because it took longer than a year, and I'm significantly heavier now than at my lowest weight (albeit much more muscular)
If I opt out of google, then I miss out on google's services. If I "opt out" of my country's laws, I go to prison.
Google censorship only controls what people say on google. Government censorship can control what their citizens say anywhere.
The key difference is that you're allowed to make your own platform and set whatever rules you like
Maybe there are some jobs and some problem spaces where you can just tweak big black box models and you don't ever need to think about what their output means. But if you're the kind of data scientist who helps make decisions with data -- you better believe statistics and probability is conceptually relevant. As soon as models meet the real world, you've got to understand probability in order to know what to expect.
Trying to do data science with zero knowledge of the fundamentals of probability is dangerous. Bayes rule isn't some kind of deep magic, it's covered within the first few lectures of an undergraduate probability course and it's absolutely necessary to understand the output of any machine learning model.
In the examples, some of the plots (such as the classifier factory ROC curve) generate many curves, but this is not explained -- is it one curve per cross-validation split?