skysight-inc/HackerNews huggingface dataset:
Developers and other technical people tend to use the latest Chrome or Firefox as their primary browsers. Others (marketing, admin staff, etc.) tend to use the default system browser, e.g., IE 11.
If you have the IP as of now, you may be able to use that as a pressure point. IANAL, but I used this successfully with a deadbeat client. The client had run low on money after I finished an iPhone app for them. They had been distributing the app on the app store, but hadn't made the final payment, which was five figures. They were still paying the Android contractors, who hasn't finished yet. After they stopped returning my calls for several weeks, I called up their main investor, who they needed a follow-on round from, and told him that they didn't own copyright to their app. I got a call back a couple of hours later, they claimed poverty, but that they were getting money real soon. I offered them a one week license for $2000 paid that day. I had to do this a few more times before they eventually paid the balance. They wanted a final copyright assignment letter from me at the end, presumably to show the due diligence people from their investors.
It's still in beta, but https://www.gigalixir.com gives you a Heroku-like experience for stateful Elixir/Phoenix apps complete with node clustering, hot upgrades, remote observer, and high process uptime. You can use websockets and distributed Phoenix channels without Redis. Full disclosure: I'm the founder.
Which teams / projects would you recommend?
No, the training material for something like Python is orders of magnitude easier to get into than things like OCaml, Haskell, and Lisp. Python has doc and many many books taking you from A through Z. Many of these other languages have "A" and then pick back up at "S", but skip over "B"-"R". You basically get a feel for syntax, control flow, and a few other topics, but don't get the meat of why that language is different. Not everyone wants to learn by digging into open source projects.
Thanks for the recommendation. As someone pursuing gamedev who also knows their math background is lacking, I'm always looking for tailored stuff like this.
The structure I need for my particular project (ConceptNet) is:

* The definitions from each Wiktionary entry.

* The links between those definitions, whether they are explicit templated links in a Translation or Etymology section, or vaguer links such as words in double-brackets in the definition. (These links carry a lot of information, and they're why I started my own parser instead of using DBnary.)

* The relations being conveyed by those links. (Synonyms? Hypernyms? Part of the definition of the word?)

* The links should clarify the language of the word they are linking to. (This takes some heuristics and some guessing so far, because Wiktionary pages define every word in any language that uses the same string of letters, and often the link target doesn't specify the language.)

* The languages involved should be identified by BCP 47 language codes, not by their names, because names are ambiguous. (Every Wiktionary but the English one is good at this.)

There are probably analogous relations to be extracted from Wikipedia, but it seems like an even bigger task whose fruit is higher-hanging.

Don't get me wrong: Wiktionary is an amazing, world-changing source of multilingual knowledge. Wiktionary plus Games With A Purpose are most of the reason why ConceptNet works so well and is mopping the floor with word2vec. And that's why I'm so desperate to get at what the knowledge is.
Clean data is basically impossible to get. You won't find very many experts who agree what it would even mean for data about these topics to be clean. There are tons of methodological disagreements, not to mention the fact that even if we started collecting new "clean" data, the historical data could never be used.

The law is a human construct, not a computer program. It is precisely because of issues of bias, fairness, methodology, etc. that the law is complicated and needs to be administered by people who can understand the complexities and use their education and initiative to ensure fair outcomes.

It's not perfect, and it never will be. But the legal system was designed with the assumption that judges and lawyers would be able to use discretion. To apply rigid computer logic on top of a system that was intentionally designed to be subject to human discretion would be a disaster.
It sounds like there are some valid methodological criticisms that can be made of this criminal prediction paper. But I don't like the rush to condemn the paper as "pseudo science" just because it has implications that the OP is uncomfortable with. The OP's main point, which could have been made far more succinctly, is that "criminality" is a human judgment that encodes bias, and therefore the paper authors' machine learning methods were merely trained to learn these biases. I don't think anyone will claim that there's a perfect criminal justice system anywhere. But to dismiss these results off hand on the basis that criminal justice systems are imperfect is extremely hasty. The OP is too close minded by politically correct ideology to even consider that one might actually be able to accurately predict criminality with such a methodology.

There are many behaviors we can predict about a person with a high degree of confidence just by looking at an image of him. Why is predicting criminality not one of these behaviors? If the premise is nonsense then it won't stand up to further scrutiny. OP should do a larger study that addresses the methodological deficiencies in the original paper. Definitively shoot down this paper with hardcore research and gain widespread critical acclaim.
Pulp Fiction was a popular (and extremely violent) movie which depicted exactly what you're describing. John Travolta is comfortable with his heroin guy - they banter, talk about travel, and then discuss the dope in genial terms. When money changes hands, it's practically a formality between two people who are acting out a ritual, like a regular at a coffee shop.

I guess it doesn't end up too well for Travolta, but I think his dealer made it out okay. Come to think of it, that guy had a negative body count by the end of the film; he saved Uma Thurman's life.
"There are two algorithms."

In the case of machine learning techniques that employ a learning algorithm (e.g. SGD) and a separate classification algorithm (e.g. forward prop), the situation is no different. Gradient descent in itself doesn't take "unbiased" data and pollute it with "bias" -- neither does, forward propagation.

These algorithms are merely functions that are _parametrized_ by a potentially biased learned model (which really entirely amounts to having biased data).

I recommend taking a look at Stanford's Intro to Machine Learning CS 229 course notes for a brief overview of these concepts. I can't speak anymore highly of the material in this course; it's by far the best out there, and it's how I learned ML.

"The generated model is then incorporated into an algorithm for making decisions or categorizing objects. If the original data contains biases, the final model will be biased."

Right, the "final model" (which comes directly from the data) will be biased, but not the ALGORITHM (the "math," as you call it).

For example, I can't make an unbiased data set "biased" by using linear regression instead of logistic regression. It would be quite a tremendous discovery if you somehow figured out a way SGD in itself was biased, but the burden of proof on such an extraordinary claim would be on you.

So like I said in my parent post, I can see how the data can be biased, but I'm struggling to find any biases in the algorithms themselves (assuming they are properly implemented and not an Underhanded C Contest [1] entry).

Granted, if we're not actually using ML, and we're using something like a hardcoded decision tree or FSM instead (it was pretty strongly implied that we weren't though because we were talking about data-driven algorithms), then sure, that could very easily be a biased algorithm, but I've maintained that all along.

[1] http://www.underhanded-c.org/
Don't forget your health in your rush to build wealth
> From that, other things follow: embracing change, learning to be a better spouse, etc

In my experience, the harder it is to dissolve a relationship, the worse it is. There's no incentive for good behavior because the other person has to stick with you no matter what. Adult relationships with siblings and parents are often quite toxic for this reason.
That'd make sense. Written in python, for python (at least initially) since the author likes python :-) Looks like a neat start to a new project. The built-in IRC client made me chuckle, though.
Landing on celestial bodies lacking atmosphere seems to be a case by case problem, which we will most likely need to solve for one case only: the moon
Argentina paid, and we will keep on paying. Don't worry.
I've come to realize this. I've always been a reader, but after college and after getting into audio-books, I took to it in earnest. I peaked at reading 75 books in 2012 (and BIG ones too, History of Third Reich, Thinking: Fast and Slow, Ulysses, Godel-Escher-Bach etc...).

And after that I was like 'wew. Okay, I enjoyed that and learned a lot and have, but my life isn't THAT much better because of it.' I've definitely traded investing in relationships or other hobbies for it.

So I've since to slowed down to about 27 a year, and have found that's a good pace for me.
> To put into perspective just how extraordinary a 90% accuracy claim is, consider that a well-controlled 2015 paper by computer vision researchers Gil Levi and Tal Hassner find that a convolutional neural net with the same architecture (AlexNet) is only able to guess the gender [5] of a face in a snapshot with 86.8% accuracy. [6]

This seems like a rather misleading comparison. I would be really surprised if CNNs, which can hit <5% on ImageNet out of 1000 classes, and whose GANs can generate nearly-photorealistic clearly gendered faces, can't even distinguish gender at least that well. And guess what happens when you click through to factcheck this claim that CNNs do worse on a binary gender prediction problem than guessing hundreds of categories? You see that the facial images used by Gil & Hassner are not remotely similar to a clean uniform government ID facial photograph dataset, as they are often extremely low quality, blurry, at many angles or lighting conditions, and I can't even confidently guess the gender on several of the samples at the beginning and end, because as they say:

> These show that many of the mistakes made by our system are due to extremely challenging viewing conditions of some of the Adience benchmark images. Most notable are mistakes caused by blur or low resolution and occlusions (particularly from heavy makeup). Gender estimation mistakes also frequently occur for images of babies or very young children where obvious gender attributes are not yet visible.

It wouldn't surprise me, going off the samples, if human-level performance was closer to 86% than 100%, simply due to the noise in the dataset.

(It's also bizarre to make this claim shortly after presenting ChronoNet! So which is it: are CNNs so powerful learning algorithms that they can detect the subtlest biases and details in images to the extent of easily classifying photographs of random scenes to within years of their manufacture and so none of their results are ever trustworthy, or are they so weak and dumb that they cannot even distinguish male vs female faces and so none of their results are ever trustworthy? You can't have it both ways.)
"I teach online advanced-Python programming workshops every month... In the last 2 months, it's consistently 60%-70% Python 3."

So you're basing your conclusion on a sample of two? :)

I teach advanced Python (and machine learning) courses too, albeit in corporate settings (groups of ~15, rather than 100), and it's still very much <20% for Python 3 there.
They're all valid measurements. The length of a string could be measured in bytes (which doesn't require you to know the encoding), code points (which doesn't require huge Unicode grapheme clustering tables), grapheme clusters (which doesn't require a font) or even pixels (which does require a font).
Rails 5.1 Released
Books can really vary in their excitement/engagement dimension. I'm currently reading a history of Europe since WW2 and it's quite a bit of work, on the other hand there's other non-fiction like John Krakauer that I've found pretty easy to burn through.
I also graduated in 2012 and my school was the only one in the county (that I knew of) that offered any sort of programming class. It was taught in Visual Basic! Previously it had been taught in C++, but the teacher thought that C++ wasn't used any more!

When I got to college, I was quite surprised to learn how common AP CS was. I was especially blown away by a classmate who took 4 years of CS in high school (at Stuyvesant IIRC).
> I guess you're one of the people to whom there is no "real" AI below general purpos, human-level intelligence.

I don't think we need human level intelligence to have a more real intelligence. After all there is not just one tier of human intelligence, even among "normal" human brains. A toddler isn't very intelligent compared to an adult.

For me the difference is between:

A: A piece of software designed to fulfill a narrow task which appears to be intelligent to those who don't know how it works.

B: A piece of software which can actually understand and reason in some capacity about things more generally and especially does not need explicit programming to add new features.

As per my previous comment, I think Siri, Watson, Alexa, etc. are applications which appear intelligent as opposed to actual AI because they do not really understand anything. They appear to understand things, but it's a trick. They understand the command "Give me directions to Starbucks." but only in the same sense that bash understands "egrep -r 'PizzaController' ./bloated-app/classes". bash only knows those are arguments that get passed into the egrep program. The current assistants are just audio command lines.

This is a long way of saying; yes I guess you could say that I think only "general AI" is real AI. That's because anything short of that, at least that we've seen so far, is just really applications that appear intelligent but aren't in any real sense intelligent. Importantly, like I said before, I don't think they've moved us forward toward general AI. Look how slowly they have advanced and the techniques used to create them, other than the voice processing and voice responding, are not techniques that will lead to general AI, because all the features are explicitly individually coded. Which is something you absolutely cannot do for general AI.

EDIT:

I realize it sounds like I'm just shitting all over Alexa, Siri, etc. but I don't mean to. They are useful pieces of software, but I think they are over hyped by calling them AI and especially how the media and certain people who should know better take them and say "strong AI is just around the corner!"

The whole "AI is just around the corner!" thing has happened in the past (the last major investment boom being in the 80s), and I see no reason to be any more optimistic about this one. Our computers are more powerful and our datasets are bigger, but I don't see any techniques that are revolutionary, and the datasets and computer power are still a joke compared to a human brain.
NYC’s New Tech to Track Every Homeless Person in the City
Scheme 9 from Empty Space
“I cannot remember the books I’ve read any more than the meals I have eaten; even so, they have made me.” - Ralph Waldo Emerson
Errors in Rust are just normal values, so there's nothing special you have to do. You just write your retry logic, presumably after inspecting the kind of error that occurred.
These questions are oddly specific. If I might ask, do you have any sort of affiliation, paid or otherwise, with Wikipedia or WMF?

Not saying this to denigrate your line of questioning or to imply nefarious activity, but it's fair to get an answer to this on the record (contrary to the downvotes) given what you've asked.
If you don't want to take the long drive out to the Racetrack on a road that'll probably pop your tires. The same thing happens on a dry lake bed just out side of Death Valley near Bonnie Claire. It's on HWY267 about 5 miles SE of the junction with HWY95. It also happens on the dry lake bed near there that's visible from HWY95, but I wasn't able to find a (legal) way of getting to it.
And even if it was, there were ways to get anonymous execution through the task manager, or the infamous help-dialog login bypass.
Yeah, this urban legend always gets trotted out to criticize neural networks, but after years of looking, I've never been able to confirm it, and even when Minsky tells it, he can't name any names or concrete details about when or where - for example, in Minsky's version, it was how the photographs were developed, but in yours, it's winter/summer, and in other versions, it's night vs day, or it was forest vs grass: http://lesswrong.com/lw/td/magical_categories/4v4a http://lesswrong.com/lw/lvh/examples_of_ais_behaving_badly/c...
> I don't think its any coincidence that TV and movies show the murderous and thieving side of piracy as to condition us to be appalled by them, in general.

Is this really true? There's a long history of sympathetic portrayals of drug culture, from Trainspotting (novel and movie) to A Scanner Darkly (novel and movie) to Jesus' Son (novel and movie) to Pineapple Express (mainstream movie), and a whole lot more.
I'm a bit more optimistic. For example, for all intents and purposes, I would consider a positive DNA test result to be overwhelmingly damning proof of guilt.

Indeed, although unpopular, technological advances in things like data logging and DNA testing are able to establish both guilt and innocence in a much more scientific manner than a judge crippled by such things as their current mood (maybe they had an argument with their spouse before hearing the case and now they're feeling particularly vindictive) and whether they "like" the defendant as a person (maybe the defendant looks like their high school bully).

The Innocence Project has successfully exonerated many convicted "criminals" solely on the basis of DNA evidence, some of whom have been behind bars for many years. If that doesn't point to an extremely biased and flawed existing judicial system, I don't know what does.

When I think of bias in the judicial system, I don't immediately point the finger at machine learning algorithms and say "if we implemented these types of systems, then we'd be biased" -- instead, I look at peer juries and law enforcement officers as the source of bias, things like "Stop and Frisk" or "driving while black." You have very faulty systems in place like plea bargaining which effectively nullify due process and encourage false confessions in order to obtain a guaranteed lighter sentence. You have prosecutors with their own illicit motives like appearing in public to be "tough on crime" resulting in such atrocities as Aaron Swartz -- this very concept of "making an example out of somebody" is a biased one.

Someone brought up the point that "bias can be inserted accidentally, by well-meaning programmers" in an unintentional matter. On the other hand, right now, instead of employing some well-meaning programmer who is trying their very best to make sure things are fair, you have policies in place like racial profiling which are INTENTIONALLY biased. Which is worse?

In my mind, ML has the potential of creating a bias-free judicial system more than we could ever hope for by continuing to rely on fickle, highly error-prone, and even outright malicious humans. People just need to get past the fact that a "cold-hearted," emotionless computer is in the judge's chair because that's EXACTLY what we want.
Darn it, what is PA in this article?
I've heard that ROS is difficult to setup and that YARP can be easily setup in around 2 minutes. I haven't used either myself.
And if there were enough density to cover both rich and poor there would be way less homeless. Many of the homeless even here in Seattle are because they had an apartment but got priced out or lost their jobs.
>Just one more example of Chicago ruining things in Michigan.

Generally speaking, single large population centers are really good at screwing the entire area around them. California has SoCal. NY has NYC, MA has Boston, VA has DC and so on.
> It is exactly the same kind of polymorphism that languages like Java and C# had before they adopted generics.

That's false. Go provides a smattering of blessed polymorphic types (slices, maps, chans, pointers) and functions (len, append, delete, chan send, chan recv) that go a long way. They are horrifying to civilized PL enthusiasts, but they cover a lot ground.

As I said in one of my sibling comments, navel gazing isn't going to get you anywhere. And Go isn't the only statically typed language without generics. C has that designation as well.
Cursor position is based on glyphs though, not grapheme clusters. Grapheme clusters are a well-specific Unicode concept.

>Thus: one grapheme cluster, or two, depending on the font.

One glyph or two depending on the font. Two grapheme clusters, always.

>That's a font, making entirely-arbitrary clusters out of codepoints. As far as I am aware, it's fully within its rights as a font to do so. There's nothing in the Unicode standard saying that the code-points ['f', 'i', 'l', 'e'], put in a row, can't combine to form a single grapheme cluster. They don't have combining behavior themselves, but—unlike, say, things in the Unicode "Separator" class—they don't have any property that says they don't combine with anything.

No, I think those are glyphs. 'file' is always four grapheme clusters.
In your "pseudo real world example" who is to say that allowing tens of thousands of "immigrants" into the country is not the "extremist" position?

Maybe, just maybe, the one who says "that's a bad idea" is actually the "reasonable participant".
You clearly didn't read the article because everything you cited is false or misleading, except, coincidentally, about the overfitting.

But I agree, the authors of the paper are either lying or stupid.
Sinatra 2.0 released
https://indeed.com can be a great way to find stuff like this. For example, search "c++ storage engine" in/around Los Angeles, CA and you'll get a few hits. Tweak keywords as desired.
I don't see why 4 legs wouldn't work.

We see 4 legged animals in forests and on mountains, seeming to donjust fine navigating the terrain (and often doing better than humans).
My interpretation of the data is that women tend to tire more quickly of their husbands and are more likely to end a marriage over it. We have the stock story of the man "trading in" his wife, but as we can see from the statistics, women are much more likely to divorce husbands than vice-versa. (A shocking statistic: 90% of divorces amongst college educated people, presumably the target demographic of the original article, are initiated by the woman.)

I think this behavior has its roots in evolution: women have far fewer fertility cycles (which are correspondingly more valuable) and are much less able to remain in a marriage while pursuing other mates.

It is not a popular interpretation here, at least not one that is spoken out loud, but I believe it to be reasonable and I think it is worth burning some karma to say so.
Actually, it can be VERY hard.