skysight-inc/HackerNews huggingface dataset:
I don't know, Amazon seems pretty bloated at times. The page may look similar to old Amazon pages but there is just so much happening I'm overwhelmed. "Related Items" " People who bought this also looked at", multiple sections of "Sponsored Items", "Customers Also Bought", "Frequently Bought together". Sigh. Just show me this product and the information about it (specs, reviews, questions [and don't show me any questions which were just answered with "I don't know"])
This is a problem inherit to all malloc() implementations, since it never gets any type information it can't ever make any adjustments about alignment of the final type that's involved. I'm not actually sure what the fully correct way to ensure that would be.
"Currency creation" is not value-creation. A good currency is a medium of exchange and a unit of account.
AI learns from the training data it's given and copies any biases this data exhibits

I hate to sound like "that pedantic guy", but I'd argue that the quote above is only partially true. It's the case that some subset of AI techniques "learn from the training data it's given and copies any biases this data exhibits". There are AI techniques that aren't based on supervised learning from a pre-existing training set. That doesn't mean that those techniques can't wind up adopting the biases of their human overlords, but I believe some aspects of AI are less susceptible to this kind of bias, than others.
GCC added an extension years back that allows VLAs as struct members and function arguments. Clang had the good sense not to, thankfully.
> While this is a step in the right direction, MS still seem to be "all in" on telemetry.

In the latest release of VSCode getting to see the exact telemetry data sent got even easier and they even pointed it out in the release notes. (It is also easy to turn it off (see below). I haven't done it since the data I see are OK with me and I want to help the devs improve VSCode for me.)

> Until the telemetry and data sent by MS software can realistically be turned off completely, they shouldn't get any kind of positive treatment.

Another instance of this "all or nothing" attitude that we have here on HN (I guess it is legacy from Slashdot and Usenet).

Steps in the right direction should be praised. This holds true for pets, kids and grown ups and I think companies too.

You might not get a chance to reward the results you want if you cannot reward the steps to get there.

Edited to add the first quote and comment.

Edit 2: Turns out it is easy to turn off telemetry.
We're talking about Amazon, one of the biggest powerhouse ML employers. I don't buy that the model was poorly designed or ineffective. They also didn't just scrap the model without understanding how or why it failed to meet its objectives.

And whatever the cause was, it was not the poor quality of the training data. They tried to stop the model from downranking women based on obvious keywords, only to find it learning to downrank them based on more subtle language cues:

> Amazon edited the programs to make them neutral to these particular terms. But that was no guarantee that the machines would not devise other ways of sorting candidates that could prove discriminatory, the people said.

So the answer is 3 or 4.

If the answer was 4 then they would have probably mentioned the cause of the bias somewhere in that otherwise detailed article. But they didn't, possibly because the cause is controversial - probably option 3 but possibly still option 4.

And then there's the subtle cop-out:

> Gender bias was not the only issue. Problems with the data that underpinned the models’ judgments meant that unqualified candidates were often recommended for all manner of jobs, the people said. With the technology returning results almost at random, Amazon shut down the project, they said.

If the model was actually useless and returning random noise, then there wouldn't be any bias, and the article wouldn't need to talk about discrimination. This paragraph reads to me like they decided to mention long-tail results (that you'd find in any ML model) as supportive 'evidence' that the model was somehow broken rather than producing valid but controversial results.
>Never said to do away with the fundamentals. Will say that most symbolic differentiation and integration (which is a big chunk of the coursework) is not fundamental as much as it is fruitless busywork.

It's no more busywork than being able to multiply two single digit numbers in your head. Whether it's useful to your job really depends on the job. I had a job once in the engineering industry. When we were in meetings discussing projects, if you could not do those types of analyses (e.g. asymptotic behavior of certain Calc II type integrals) in your head, you would not know what's going on. Sure, everyone could explicitly show all the steps for your benefit, but you'd be slowing everyone down.
Because that's an inconvenient truth that doesn't fit the oppressor vs oppressed narrative.
When I was in college, my tech-heavy school was about 50% Republican.

No more.

The GOP's descent into anti-intellectualism started with a war on science and has since escalated to a war on engineering.

Unfortunately the GOP seems bound to win that war.
Stupid question probably but why not have two models.
I don't think you can detect "bias" by running "a bunch of tests". "Bias" is a very slippery concept and is probably essentially subjective. When people say an algorithm is "biased" what they seem to mean is that when the judgements of the algorithm are compared with the judgements of a committee of fair-minded and diligent humans then the number of positive outcomes for members of some fashionable minority that we care about is less that what it was with the human judges. It's hard to automate that. And in any case, if you manipulate the algorithm until it "passes" a test like that then you might not really have improved it: when you turn a measure into a target it ceases to be a good measure.
I wonder why he thinks so. What are the calculations? Why can't we use green energy to separate CO2 in atmosphere from clean air and do that faster than we as civilization add CO2 to air, thus buying some time to apply non-technical solutions?
I've been using Rust in production for a little over half a year, and my team and I have run into very few issues. We're also calling through the FFI to an SGX SDK and a large C++ codebase, and that's been delightfully painless.

We're all extremely glad we chucked C++ and Go and switched to Rust. Rust isn't just a safer C++, it's a language that stands on it's own as extremely productive, safe, and fun to write. We combat the learning curve by giving people the O'Reilly book and the main Rust book, and we find new team members becoming productive in about a week. The Rust community is also amazing, and Cargo + RLS are the best ecosystem tools I've seen in any language.

I highly recommend Rust. If you have any questions, feel free to ask here. We're extremely bullish on Rust continuing to accelerate.
I'm doing a bunch of ML on a very different data set -- looking at what people eat (survey data). What's interesting to me is that if you do principal component analysis, for instance, there are some differences between the boys & girls in the sample, but they're not very distinct. If you do clustering or random forests on the dietary intakes of the whole cohort, you get mushy and unclear signals. If you split the survey respondents and bin by age and gender, and run different models for each, suddenly signals jump out of clustering incredibly clearly! What's weirdest is that you get some of the same dietary clusters for the different demographic groups -- but those clusters were not evident when you did clustering across the cohort.

It's surprising to me that Amazon didn't (apparently) try different models for different populations. Sure, it might open you up to criticism, but there are some good data-driven reasons to do so. Women's colleges won't show up with regularity on men's resumes, for instance. Similarly, there are fraternities and sororities around engineering and STEM that may provide different signals, but won't appear equally distributed on men's & women's resumes. Language use on resumes does differ by gender, and using "Captured value of $100 million by..." rather than "Created value of $100 million by..." may describe the same project. (I gotta say, using verbs at all seems silly, since it really is about how well you market, rather than what you did.)

So, curious about the model. Different models for different subsets of the training data can lead to big wins.
> is just a Cold War 2.0 rhetoric

During Cold War Russia hadn't annexed parts of European countries or left the radioactive trails all across town after assassinating their 'enemy of the state'.
Hey. I did read the article. It doesn’t support the conclusion OP is drawing. The aim of the AI is to “mechanize the search for talent”. It doesn’t care to, nor have any means to, make decisions “like the recruiters did”. Obviously machines don’t make decisions like humans do. They’re trying to reverse engineer an alternate decisions making process from the previous outcomes.
This was the Goal!

Thank you.
I don't understand what you're disagreeing with.

> streaming platforms can then compete

It seems fragmentation is part of your premise rather than something you're disagreeing about.

The post your replied to explained why there's fragmentation: one winner takes it all if you let it.
Damn markdown :). Looks like it's far too late for me to fix it now, but I'll have to keep that in mind in the future.
> Phone goes on airplane mode overnight, and comes out of airplane mode once I've completed my morning ritual, which takes about an hour and a half

Yeah, no. How are people going to contact in case of an emergency?
This sentence seems to have been inserted by the ZDNet editor. The GlobeNewsWire article (to which this post has been redirected), and the original OIN PR (https://www.openinventionnetwork.com/pressrelease_details/?i...), don't have anything like it.

It's not in the ZDNet article body either, just in the small lede under the header. The body instead says,

> You see, Microsoft, with the major legacy exception of its Windows desktop and desktop application code, is an open-source company.

It seems like an editing mistake or a badly worded statement.
Indeed. I do not see a contradiction between that and what I have written.

(Having seen many Excel spreadsheets, 80% of the work is done by rudimentary means: pivot tables are Advanced Magic in this regard)
I'm very confident my tests would be very simple under this system.
I don't interpret that as the request. I interpret it as wanting virtual memory "allocated" but not counted against the processes' virtual memory allocation, and then getting a secondary mechanism to allocate the sorta-but-not-quite-allocated memory. I also don't see the value in that.

(I don't see any request for different physical memory allocation, so I assume that would still be handled by page faulting in the kernel.)
Probably best to just flag it or send an email to HN. It's easy to spot people doing it, but we should have proof if we are calling someone out by name.
If, and only if, those are the only differences between candidate resumes. I don't think that's a reasonable assumption to make. Work history differences, sentence structure, word choices - all of these can quietly reflect gender differences.
It doesn't matter - just another person that jumped on the MS bash bandwagon because it's fun and it lets you score quick brownie points. Most people have crap articulations of why MS is evil or why it sucks besides "patents", and it's the same basic drivel I see repeated about any company. Never ceases to amaze me how critical thinking skills can cease so thoroughly on a subject.
> The lower certainty would in turn lead to lower rankings for women even without any bias in the data.

I don't think that's true. "No bias" means that gender is irrelevant (i.e. its correlation with outcome is 0%). Therefore the system shouldn't even take it into account - it would evaluate both men and women just by other criteria (experience, technical skills, etc), and it would have equal amounts of data for both (because it wouldn't even see them as different).

You need bias to even separate the dataset into distinct categories.
this sounds like a bad ML tool to begin with because the input data sucks. it’s probay judging who can write a “good” resume. plenty of bad candidates write good resumes. and good hires write bad ones
You know what they used to call 'junk science'?

'Science'.

Apart from the particular injustice of this and other cases, it would behoove everyone to be a little more circumspect and humble about what we believe we know.
Even DNA analysis can be flawed.
Appreciate your time and opinion, although, it seems as though you may be in the minority on this argument.

Thank you.
Managing and Monitoring Your Existing MySQL NDB Clusters
From the article:

> Gender bias was not the only issue. Problems with the data that underpinned the models’ judgments meant that unqualified candidates were often recommended for all manner of jobs, the people said. With the technology returning results almost at random, Amazon shut down the project, they said.

It looks like the bias wasn't the only flaw.
>so, it's a more compact HoloLens, with better content/games?

No wireless. Less space than a nomad. Lame.
A 100% Perl project in 2018 is a rare sight.

(Implementing a SQL formatter in pure SQL is not impossible, but would be quite a tour de force. Just a thought.)
Help shape the future  of transit in New York
I'm going to make a supposition here but one of the first things I think they did (especially when trying to fix the AI) was to balance and normalize the data so that there would be no skew between men and women number of records in the data set.

If my supposition is correct then the other parameters are at fault here from which gender and language used stick out.

Another supposition I'm going to make is that they even removed the gender from the data set so that AI didn't know it, but cross-referencing still showed "faulty" results due to hidden bias that the AI can pick up, like language used.
Is it still Capitalism when there is regulatory capture?
Not sure. The attached readings and class notes are great resources though.
Lambdas as Lambdas: The magic of simple serverless Functions
I don't think I'm that greedy, I definitely ask for and have more than I actually need. I just built the assumption from the common pattern in why socialism has failed in the past ("All of us are equal, but some of us are more equal than the others")

The top of the pyramid in socialism always gets more therefore humans are greedy.

Also, it would make sense in my mind, from an evolutionary standpoint of why being greedy is actually a good trait. I'm not advocating that unchecked greed is great. I am advocating that understanding our brains is better, then we can check ourselves from that.
I feel like many comments here are taking this story on face value, but the cynic in me reads this as a planned leak to skapegoat a (hitherto unknown) AI system for their existing hiring biases. Public perception of AI is more aware of model biases nowadays, and we seem all to willing to accept this explanation over the simpler explanation that tech hiring at Amazon is broken in the same way it is everywhere.
Please read the article. This article is about automated reasoning that discards resumes that are strongly correlated to resumes of women.
I can't help but think why did this story get posted now when it started in 2012 / 2013 and has been known for a long time, another PR pieces for the on going trade war? Hong Kong got rid of the 14 days return policy because of the exact same problem mentioned, at the time no body cared to write about it because it wasn't "news worthy" enough.

Why do they not include all the Genius Bar appointment slot has been pre booked and on-sale in Taobao?( At least some point in the past ) Getting Apple to look at their phone in a timely manner would have some help so people don't go to third party repair shop.
The methods should at least be peer reviewed.
Some people are happy with that (they might consider it satisficing, essentially). It's not the best, but not the worst option either.
As someone who was involved in the GDPR work for an organisation that holds some fairly critical information about people and needs to share it with other organisations both as Data Controller and Data Processor, it really wasn’t too bad, mainly because we had already thought quite carefully about privacy and data security.

As a committee member on a local swimming club, it took about 2 hours.
I download albums not available on Spotify on 7digital, which is legal and DRM-free (and expensive). Although it's only available in Europe.
What are my options if I don't want my ISP (and thus the government, which in my case can easily get that information) to easily know which sites I'm visiting and thwart their attempts to block sites using trivial methods — assuming I don't use my ISPs DNS service and use something else? I know that my ISP would have the IP addresses I'm connecting to and also the encrypted traffic (for TLS) to the addresses along with timestamps, my IP address/location, etc., which would/may allow for various inferences to be made.

I'm not looking for perfect security or perfect anonymity. Is there nothing better that's available today than the status quo of plain old DNS?

P.S.: I read the related FAQ on sockpuppet.org.
Any idea why they picked XDR rather than something that is more widely used in the present day, like Thrift? That is efficient on the wire, and pretty widely supported:

https://thrift.apache.org/docs/Languages
Yes, that sounds like ADHD. I was only very recently diagnosed. After my diagnosis, I read the book "You Mean I'm Not Lazy, Stupid or Crazy?" and it explains how ADHD is a problem both of over- and under-stimulation (recommend picking that book up, btw. It made me cry a few times as I recognized myself in it).

You're addicted to stress because it gives you enough stimulation to focus.

You keep yourself constantly busy to keep yourself stimulated enough for your brain to function.

You are distracted by things because your brain lacks the filter to determine which stimulus is "important" so instead of focusing on the actual important thing, everything feels like it's of equal importance, and your thoughts race from one thing to the next. I can't hear people correctly if there's a lot of other conversations going on at once, or too much visual noise in the background. It's overwhelming.

I saw a psychologist who specializes in ADHD. He did not interview my family (I was afraid of that too, my mom insists I can't possibly be ADHD because "I did so well in school."). He interviewed me about my history and symptoms going back to childhood, and I took a psychological profile test that involved ~400 true/false questions to rule out other problems. It was two visits to get my diagnosis, and now medication is in the hands of my regular physician. If Kaiser won't cover it, it might be worth paying out of pocket to get your diagnosis. Not very familiar with Kaiser, but hopefully once you were diagnosed, they would still cover your meds?

I think https://add.org/ has a page somewhere where you can search for professionals in your area. Or just call a psych, any psych, and ask for their recommendations on someone who specializes in ADD.
No, it costs $2,300. They have developed the tech to do multiple stacked waveguides (12 I think?) but this device only does 2, probably for price reasons. Anyway the company is valued at $4.5 billion which has nothing to do with how much it will cost, only the amount of profit they think they can make from devices and patent licensing.
Not only hasn't the GUI changed, the reposts and comments are the same, too (sorry couldn't resist).
Music and movies are not substitute goods. Ignoring piracy, if you have an exclusive on some content, the fans of that content will come to you instead of choosing some other work to consume.
You can visit specific subreddits.
Oooh! I was wondering how you do a volumetric autoencoder.
China and Russia have major disinformation campaigns running in the United States and around the world. These campaigns have been heavily documented by news organizations and American defense agencies.

A major branch of Russia and China's attack is the ongoing negative PR campaign against tech companies like Facebook, Google, and Amazon. Can these companies improve? Sure. But their infractions are incredibly minor compared to the dictatorships rising in Russia, China, Turkey, etc that are murdering foreign citizens as well as large numbers of their own. They are stealing billions of dollars from their own citizens and taking the world down a terrifying path.

Please reconsider the easy targets of tech companies and have the courage to face true evil in these dictatorships.
Dutch court rules that government must help stop climate change
Alice: Our AI says that graduates from that Boston college are poor job candidates.

Bob: Seems reasonable, that college's teaching quality is bad.

--

Alice: Our AI says that graduates from that all-women college are poor job candidates.

Bob: That can't be right, we all know that women are great job candidates whichever college they graduated.
> Google may be hurt the least, but I doubt they're a "beneficiary" in economic terms.

Quite the opposite, if you pass a 20% tax on every company, except for Google, who gets a 10% tax, it is a beneficiary.

Everything is relative.
Want to know more about Azure Security Centre?  Read the blog  :-D
Turing completeness has a lot of costs which are not acknowledged in this post. For example they are more complicated, more can go wrong, more vulnerable to hacking.
Referencing D. Schmitts article referenced in the BBC article, he's quoted as saying

>"that using someone's sex to work out what you think their personality will be like is "like surgically operating with an axe"."

Being phrased by the article as a dismissal of Damore, along with G. Rippon's statements However in the article Schmitt is quoted from, he writes that

>"Culturally universal sex differences in personal values and certain cognitive abilities are a bit larger in size (see here), and sex differences in occupational interests are quite large. It seems likely these culturally universal and biologically-linked sex differences play some role in the gendered hiring patterns of Google employees. For instance, in 2013, 18% of bachelor's degrees in computing were earned by women, and about 20% of Google technological jobs are currently held by women."

He goes on to write that Pyschological sex differences might lead to less than 50% of technology employees being women.

This seems to disagree with Professor Rippon's opinion that

>"but even if you accepted the idea that there are some biological differences, all researchers would assert that they're so tiny that there's no way that they can explain the kind of gender gap that's apparent at Google."

I think there's reason to consider both the societal reasons women might be pressured and excluded from STEM-ey fields, as well as potential inherent differences in interest, and that they can both coexist as considerations, and agree that a biased AI is unhelpful, and many women lack a fair shot of success, however disagree that there is nothing useful in Damore's perspective.

Additionally if such inherent differences are distributed on a bell curve, it would make sense that at cases further along the trail that small differences in populations and their medians are more pronounced.
It's downvoted because it's missing the entire point of this article?
I don't think that Trump style Republicans will be in power long, so in addition to worrying about the short term consequences of gutting the EPA, I that the blowback when they aren't in power will be so extreme that we'll go to far in the other direction.