Bertrand Russell also paints an extremely negative view of Nietzsche in that book. The whole work is extremely biased, it's a dangerous book to read on philosophy if you dont already know the field well, theres too much bias and its too difficult to tell what is bias.
I wouldn't call it dangerous. It's pretty obvious from the literary style that it's partly a diatribe against particular intellectual factions Russel didn't like. It's an enjoyable diatribe! But... the lack of objectivity should be pretty obvious to any modern reader. For example the part about Virgin Mary being direct retcon of Artemis is not really defendable. Although the larger idea of a continuity of worship of a mother goddess from pagan times is correct in the larger context.
I say dangerous because of the way I came about the book. I read it in high school before I knew any philosophy and it formed the basis of my perception about which philosophers are important. A decade later, after much self study, I reread the book and was horrified at the hand waving and blatant bias.
It would be easier to list a success. Seriously. The failures jump off the page. Maybe it's not PC enough for you because it looks partisan, but frankly, they've proven rank incompetence over and over again.
The best compliment I could give Tillerson is he probably WANTS the State Department to collapse which is why he's utterly under-managing and under-staffing it. By any other metric, of course, he's an abject failure.
Parent already provided you an example. I feel like your question is begging for a political argument, and there's multiple places on the internet (that's not Hacker News) where you can have those.
People here are not obliged to do research for you and rephrase it in language you can understand. There is a lot of clearly written source material out there if you look for it.
Unless you've been hiding under a rock, you know how the current administration has been running things. You are free to have a positive of negative opinions of their actions, but you are clearly aware of what other people might characterize as failings.
You might think you're being the calm and reasonable one here, but you're not asking this question in good faith. It's clear you just want to pick a political fight on hacker news. While we are not above politics here, there is a time and a place for that. And it's not here or now.
When I started in this game, just being able to put up a website made you hot stuff. HTML and CGI were cutting edge. But for a long time now, those skills are totally commoditized. That’s what I mean.
That's not a rebuttal. It's absolutely non-technical gatekeepers looking for certificates.
Graduates get mad because they realize they've wasted a bunch of money on something they can learn for free, so they reject the notion and make others go through the same abusive system.
Do you have any proof that non technical managers are setting the hiring bar and writing the requirements for data science positions at most companies, which is what it seriously sounds like you’re implying. The guy above you was joking
I actually think that machine learning can be much harder than you are claiming here, and that your inexperience is showing when you make such claims. Sure a toy problem from some dataset in a book or pulled off the internet is easy. So is implementing a k-means algorithm. But go to a large corporation with 100 datasets, all with 100 fields each, each having significant seasonal bias (among other biases), and build something that lasts, works, is clean, and is better than someone else can build. You need to convince them to trust you, maybe you can write an algorithm that works, but the business people cant understand that algorithm, and so they have to trust your word.
Often this comes down to the ability to communicate, work with people, and understanding real world business. In addition, often the more simple a learning method used the better. I actually find most businesses needs can be solved by straightforward analytical methods such as traditional statistics. Very few situations require actual machine learning and it is often misapplied.
In fact, having a PhD does not tell you a whole lot about these skills.
More advanced modern techniques such as deep neural networks, reinforcement learning, etc.. are all extremely proficient at certain niche problems but these do not come up nearly as often in a business context.
This is why I don't advertise myself as a machine learning engineer. Rather, I am a business consultant that knows when, and when not to utilize machine learning methods.
So true. Missing from university textbooks is the fact that real world data is often dirty as hell, inconsistent, distorted, biased, incomplete, and/or just plain invalid (measured the wrong source or too imprecisely to be useful).
Unless your DS group is big enough to warrant hiring data engineers / cleaners, as a data scientist it'll be your job not only to eventually choose the algorithm, but foremost, to confirm that the data is sufficient to serve the intended purpose of mining it, ideally before you waste a lot of time curating it or paying for a raw data dump you can't use.
