somethingAlex

Exactly this.

I was at a "company retreat" back when I was in IT consulting. It was mostly the top salespeople for that year so I, being on the delivery side of things, found myself in a strange world. After some drinking one of the salespeople started talking about a project that went 2 months past the deadline. I asked "...and they just paid for that extra time?"

Everyone got all awkward as I realized that not only did they pay for that, they would continue to pay us to go over deadline. I was the naive one. It's just one of those topics you don't talk about.

It seems that ctags requires you to run a command which builds the index used to jump to the definitions. Does this update automatically so the function I just wrote gets indexed?

Yeah I ran across that in my research. I suppose I'm looking for someone with personal experience to vouch for something. Have you used this yourself?

What are you really asking here? Why is there no Wordpress but instead of PHP, you'd use node? If you're wondering why there's no "Flask or Django for node" I would say there most certainly is.

In regards to that four-quadrant infographic - how could Technical Reference fall into the category of "theoretical knowledge?"

I'd think tutorials would fall under theoretical because if it gets too practical, are you not effectively writing a how to guide?

I'm not really sure the four categories fit into that quadrant scheme as well as they'd like.

With regards to his claim that he described a GAN before Ian Goodfellow did, it certainly seems like that is the case.

The problem is that, in his original Artificial Curiosity paper he talks so specifically about world-simulating reinforcement learning. He even uses the terms "World Model" and "Controller" instead of the more general "Discriminator" and "Generator." In this paper, he says the GAN is a more specific application of his Artificial Curiosity paper, but I see it the other way around.

Goodfellow should have cited the Artificial Curiosity paper though, as training two networks at odds with each other in parallel is explicitly mentioned by Schmindhuber in his original paper.

It also doesn't hurt that Bengio and others came out with research when you could actually run the damn thing. Schmindhuber's Artificial Curiosity paper doesn't list a single experimental result.

Schmindhuber perhaps should have shared in the Turing award, but at some point you have to cut it off. We stand on the shoulders of giants, and so do those giants. At some point, a giant is getting left out of the award.

[1] Schmindhuber's original Artificial Curiosity paper: https://people.idsia.ch/~juergen/FKI-126-90ocr.pdf [2] Ian Goodfellow's original GAN paper: https://arxiv.org/abs/1406.2661

I'm not super deep into the world to NLP, but from what I have kept up with, it seems that the state-of-the-art is almost constantly open-sourced. I understand certain companies have personal relationships already built which may be what MS is after more so than the IP, but isn't there a "canonical" way of building a, for example, speech to text system for your phone?

Is there really a lot of NLP IP hidden behind corporate walls at this point? I just assumed Alexa, Siri, Google Assistant, etc were all using the same model architectures. Genuine question, can anyone shed some light?

What do you mean by "more efficient and lighter methods of delivery?"

I'm not speaking strictly, I'm simply alluding to the fact that it's very easy to organize React projects such that its obvious which HTML the JS and CSS is scoped to. Pragmatically, I have not found that to be the case in projects which use jQuery.

While the content of the article is extremely shallow, I'll comment on why software engineers don't see CEO as a career choice.

Any time I've tried to dive into the world of business / finance I find that it is just a completely different culture in terms of knowledge sharing. The technology culture is one of sharing; knowledge is extremely accessible. If you look hard enough, you can find the contents of a complete CS curriculum on Google. You can then find poignant on-the-job lessons just as easily.

Any time I dive into finance I'm just stuck reading old books and having no seemingly active community to discuss what I've learned with.

I find it interesting that half of the comments indicate that people don't really understand why this would be needed and the other half are surprised that is hasn't been offered before.

Personally, I'm in the first camp. I'm used to taking instances for granted on the rare occasion that a low-level issue arises and just promoting a replica or trashing it if it's stateless.

I'm assuming the use case where you care about fixing the type of issues this feature helps debug is fairly esoteric?

Typically the goal is to architect the system such that you don't really care. If it's stateful, there's some other replica. Promote that and then spin up a new replica from a backup and roll it forward. If it's stateless then just kill it and spin up another.

Compared to the seemingly arbitrarily split js files filled with jQuery at other large projects I've worked on, I'd take a declaratively described component per-file React app any day.

I remember trying to find the class for the element I'm manipulating imperatively in jQuery in an equally arbitrary CSS file only to realize almost none of those attributes are applied cause some other more specific selector in another file took precedence. That's obviously due to poor organization, but at least React is readable with HTML, JS, and CSS all right next to each other with very clear intent due to the declarative style.

Image search by content, improved language translation, Siri/Alex/Google Assistant, automatic content moderation to name a few.

With that said, I generally feel like my life has not been impacted greatly. Siri can barely tell me anything other than the first paragraph of a wikipedia page or today's weather. I don't search my photos often. I don't translate languages often, and I'm not in a back office in charge of moderation.