falkenb0t

I've personally flown a ton of American domestic flights and have never encountered someone who threw a tantrum over reclining seats. In fact, prior to this post, I didn't even realize it was something some people frowned at.

Anecdotal, of course, but then this entire thread is fairly anecdotal.

I had a very similar experience (though not quite as extreme) with a Comcast interview. The interviewer would ask me about my experience and either cut me off before I could answer or practically insult me when I would give an answer to the effect of "I don't have experience with this specific technology but I do have plenty with this equivalent tech." It left a bad taste in my mouth afterwards but honestly, like many others are already saying here, that guy did me a favor by letting me know right up front that I would not like working there.

I was fortunate in that it was only a 30 minute phone interview so I could just grit my teeth and wait it out but it is entirely reasonable to terminate an interview if you are not being given respect.

He very well might be interested. I'll shoot him a message and let you know if he is

Funnily enough, I had a classmate in my grad neural networks class that did exactly this. Results on his end seemed to be pretty similar to yours though I don't think he publicly hosted it anywhere

Regardless, this is an interesting application of NNs

It is essentially the same frequency of links that you'd see on any Wikipedia article. In a field where there is an enormous amount of jargon, it is probably a good thing that they clearly define as much as possible.

was there a title change? the link goes directly to the github page.

wow, that really does hurt to read.

No, you're not wrong. That said, I don't think this is a competition aimed at people with a beginner level knowledge of machine/deep learning. I don't believe there is currently any MOOC that is going to hold your hand from 0 to expert. Given the current state of the field, I believe the only surefire path towards this level of knowledge is either university education or a /lot/ of reading.

Anecdotal but mine have been pretty great. Of course there are songs that I'm not a fan of but for the most part its recommendations have been spot on.

The author cites word2vec as being biased and gives the example of it associating the 'father' with 'doctor' but associating 'mother' with 'nurse'

While this is certainly a bias, I think the insinuation is that this makes a program inherently sexist or racist.

I don't think anyone would really call the statement "in today's society, certain professions are male dominated" particularly sexist.

What I'm getting at is that I think there is a distinction to be made here where bias introduced via training data doesn't necessarily translate to sexism or racism.

Additionally, the problem doesn't as much exist in the programs as they do in the training data. No engineer at Google sat down at their computer and went "yep, men are doctors and women are nurses. Better program that into word2vec". That behaviour was learned from the information available.

That is mind-blowing. 2 billion total users an immense volume in and of itself but 2 billion even semi active users is incredible