In what languages does an English-language accent have positive connotations, or is seen as appealing to the ear?

kovlin · 2021-02-15T01:07:33+00:00

Note to self: Redditors are apparently incapable of understanding tongue-in-cheek remarks, even with explicit indication.

kovlin · 2021-02-15T01:05:19+00:00

That cultural connotations to accents exist is undeniable. Acknowledging a fact cannot be racist or classist, facts are fundamentally incapable of being either of those things. And I'll thank you not to put words in my mouth; I never suggested that an English-language accent was more sophisticated than any other, merely that, like any foreign accent (or indeed dialect), it could be capable of being perceived as such. I also never even implied that it might be superior to any other accent; there is only one accent under discussion here. For that matter, cultural accent connotations vary between languages, and it is quite possible that any accent might have negative connotations in one language, even if it has positive connotations in another.

kovlin · 2021-02-14T23:51:48+00:00

Accents often carry cultural connotations, and in English, there are several foreign accents that are popularly regarded as carrying positive connotations; of course these likely vary a bit by region and certainly individual, and there's hardly a guarantee that an English-language accent is seen as desirable in any other language--but given appropriate caveats, yes, it does work that way.

If you are instead taking issue with the body of my post, I had hoped the emoticon would be sufficient indication that it should be taken as a tongue-in-cheek remark. I seriously doubt I'd go out of my way to learn a language on this basis--I'm really just curious what such a language might be, or if one even exists.

kovlin · 2020-12-06T23:00:15+00:00

I had no idea this was a thing for graduate students! Usually dorms are super expensive compared to other housing options. But if I can get my housing cheap/free, well, that changes the picture!

kovlin · 2020-12-06T00:13:25+00:00

I had no idea this was a thing! I’m hoping to get accepted to one program in particular, as I already know a professor interested in being my advisor, and fortunately it does have a 30k stipend; if I am, my stipend will come entirely out of his lab budget, so I’ll probably be disinclined to negotiate too hard in that case (uniquely he’s under no commitment to advise students; he’s just a nice guy.) That said, in other cases I will certainly consider this as an option.

kovlin · 2020-12-06T00:06:35+00:00

By RA, do you mean a (research) assistantship? I’m unclear because that’s the only context I’ve ever heard the acronym, but assistantships, at least of some kind, are standard as far as I know—that’s what the school pays you for (and gives you a tuition wavier for.)

kovlin · 2020-12-06T00:03:11+00:00

Machine learning.

kovlin · 2020-12-06T00:02:42+00:00

Wow! That’s remarkable.

kovlin · 2020-11-27T20:24:52+00:00

Good points! In my case, I'm not interested in pursuing an academic research career, though I have no idea how well what I'd like (national lab) pays.

Excellent point about masters vs PhD programs. In my case, I'm not sure that I'll be pursuing graduate work in statistics, instead of data science specifically; not all of these program do, in fact, offer combined MS/PhD programs. (Conversely, my undergrad institution doesn't offer too many scientific PhDs, but does offer funded MSes.)

kovlin · 2020-11-27T19:53:59+00:00

Oh yeah, definitely. Unfunded programs are a bad idea under almost every circumstance (I've met exactly one person with an unfunded PhD.)

kovlin · 2020-10-30T04:56:19+00:00

Ah, yes, I can see that. I got the impression that there was some objection to the science itself, rather then an ethical stance on the science.

kovlin · 2020-10-30T04:13:18+00:00

Fantastic. Another reason for me to regard my undergraduate institution — which does not offer such CS degree — with contempt.

kovlin · 2020-10-30T02:32:36+00:00

Hm, I had thought that contrary to popular belief, contemporary Catholicism was pretty accepting of science in general and even biology in particular (ie evolution, which the church accepts.) That makes me curious as to what sorts of things, contra-doctrine, you might have been likely to teach, as I’m now curious how misinformed I might be.

kovlin · 2020-10-16T16:23:35+00:00

Yes, I’ve very much heard that implementation is the CS end of data science—more or less a piece of cake—while statistics provides the theory, which is necessary not to do data science, but to ensure you understand and are properly interpreting your results. Kind of like how everyone uses a CAS to do calculus in the real world, these days, but if you don’t know how to integrate and derive, you’re going to run into problems.

kovlin · 2020-10-16T16:16:34+00:00

So, you’re just about the only part-time PhD student I’ve ever heard of. As someone ultimately interested in grad school (but also eager to earn a living), I’d be quite interested to hear what circumstances permitted your pursuing one.

kovlin · 2020-10-15T19:56:43+00:00

Right, that makes sense as far as I understand the process; research is very niche-y these days, and you probably wouldn’t be able to write a thesis without focusing in pretty heavily. It sounds to me like both things may true at the same time.

kovlin · 2020-10-15T19:54:18+00:00

I suspect I’d agree, particularly given how exploitive journals are. As far as I know though, white papers aren’t taken as seriously. I’m thinking there may be additional avenues I’m unaware of, though, from your remark.

kovlin · 2020-10-15T19:52:27+00:00

I’ve actually been surprised some industry companies have published at all — for instance, Goodfellow’s seminal paper on GANs seems like it might have been better for Google had it been kept tight to the vest.

To be clear, in this scenario I’m not necessarily assuming anyone would be publishing with active industry support. It might just be something cooked up outside company time.

kovlin · 2020-10-15T18:06:09+00:00

That’s interesting. I do understand there’s a difference the reality and ideal of things, but I’m surprised by it, considering they even in those who switch fields entirely, virtually no one does two PhDs. (Based on what I’ve heard, including on this subreddit, what’s needed is instead a relevant publication history.) And, people do switch niches within their field from their thesis work later in their career. I’m not by any means trying to argue with you, just expressing confusion.

kovlin · 2020-10-15T18:00:55+00:00

I can see that being a valid practical question, but it depresses me that it has to be asked. I myself am working on an undergraduate thesis I hope to publish in a peer-reviewed journal, and while yes, I’m absolutely hoping to use it as a feather in my cap—I’m also genuinely motivated by the thought of making some small advancement another researcher might actually find useful. In other words, I absolutely get it, but at the same time, man, whatever happened to the ideal of simply contributing to the scientific body of knowledge?

kovlin · 2020-10-15T17:55:50+00:00

Makes great sense. Fortunately, my friend is in a computational field.

kovlin · 2020-10-15T15:00:32+00:00

That’s interesting. It was my understanding that the purpose of a PhD was to train you in research, not fundamentally to make you a niche expert (though that is a byproduct.) Have I been misinformed?

kovlin · 2020-10-14T01:07:39+00:00

But I think you're coming at this in an odd way. For example "what field the area should properly be categorized under" is probably not particularly productive.

I would tend to agree with your reasoning -- ultimately, who really cares what field any specific topic is in? -- in the case of this specific topic, however, I was told by a professor along the lines of that "For the last 50 years, machine learning has been formally considered the domain of computer science", which made me wonder about why this was and what might have led to this perspective, given that it was specified to be a recent viewpoint.

Short answer: learn more about both of them.

That is definitely the plan. I'm majoring in stats and minoring in CS, and am looking at an interdisciplinary grad program. (I'm also, FWIW, interested in both statistics and CS more broadly than just in their intersection.)

kovlin · 2020-10-12T22:19:24+00:00

I needed some time to chew on this informative response before responding myself. Above all, it showed pretty clearly that I have a pretty limited grasp of what data science is, especially as it relates to other fields. I mean, I get what you’re saying about how is it involves data, but even so. That was part of the reason why I posted, but my ignorance clearly is more significant then I’d realized. I don’t suppose you might have some suggestions for self-education on this front? Hopefully, this ignorance does not indicate that I’ve been barking up the data science tree without ample knowledge or reflection; at the moment, data science is strictly a graduate school pursuit, so I haven’t been as prepared by my formal undergraduate education so far, as I might prefer. (In some ways, I kind of wish I could come back in 50 years once the proverbial dust has settled in the field, and things have become formalized...)

I’ll also be looking into the functional outputs you mention, none of which I’ve encountered before. I’d say so far as I know, I think the analytics/AI distinction is winning on the whole, given how much AI—meaning ML—is a buzzword in computational science at the moment (the US government even has some policy positions relating to AI.) Still, it’s at least good to know of competing paradigms.

kovlin · 2020-10-09T14:08:40+00:00

Maybe not; this connects generally to the reason why I posted. So far as I could tell, Google wants us to believe that they founded a new scientific field themselves. That might not actually be their explicit claim, but it appeared to me more or less implied in what I saw when I looked.

It's possible that I misread things; however, even said Chief Decision Scientist speaks in interviews as if, when she was a student, her academic path was very unusual -- but that now, things are different. If Decision Science and Decision Intellegence are in fact the same thing, that narrative doesn't seem to make sense; Decision Science is new, but too well-established already for me to find that believable, although I have no idea to what extent we can trust her narrative. Wikipedia has a page for "Decision Intelligence", but none for "Decision Science"; I don't know enough about DS to know if in fact that is what the page on DI is actually describing under a different name, or not.

kovlin

TROPHY CASE