skysight-inc/HackerNews huggingface dataset:
This is an interesting article. It made me think of the socio-political consequences of the move towards neural networks.

Many of us, children of the personal computer revolution, were attracted to computers for its empowering and democratizing effects. Just with a computer you could build anything! You are as powerful as any of "them"! The proprietary software model was developed to monetize end consumers by enchaining them, but the free software model and the fertile communities of the early Internet showed that the model of personal computation could not simply be displaced by legalese.

But if we take the author's position on the impact of deep networks, the model of computation is changing again.

If teaching deep networks is the new way to write useful programs, our brains and personal computers are not enough... they are obsolete. We now need clusters and, most importantly, massive access to data! This gives extraordinary leverage to the hoarders of data and servers of the Internet, the googles and the facebooks.

I'd like to think that maybe we are just at the beginning, the early "mainframe neural networks" era. That we just have to wait until there is enough technology and new markets discovered to build the "personal neural network". That consensual, open and distributed ways of sharing data will emerge and that new massively parallel computers will become affordable by the masses. That the models of neural networks will become well divulged and simplified and kids will be able to program them with their "Neural Basic"...

But at the moment the prospects don't look good. The Internet is becoming more and more centralized, personal computers harder and harder to program and there is a general "war on general computation" [1]. Even universities seem to bedisplaced by the Internet Lords in driving neural network development...

Maybe it is already a good time to start thinking about what "Libre Neural Networks" look like. And how can we get there.

[1] https://www.youtube.com/watch?v=HUEvRyemKSg
While I have nothing against black-box models and believe that interpretability is over-rated, deep learning is less interpretable than other models. But compare something like a simple linear model with a deep model and the linear model is much easier to interpret, especially for someone without a mathematical background. You don't have to cite papers to give examples of how to interpret a linear model. A single decision tree is also much easier to understand and explain than a deep model. Where deep learning and tree ensemble methods excel is in accuracy and ease of use.
That comment id great, but where is the source? I can't find any technical details following links in the article. Moreover, Embedi team who found the vulnerability says[0] on their site: "Intel representatives have asked Embedi to hold off on disclosing any technical details regarding this issue until further notice."

Or is the commenter just guessing?

0. https://www.embedi.com/news/mythbusters-cve-2017-5689
OT but does this problem of sites broken at higher resolutions happen to you often?

t. Exclusively 13" screens user
Birds Sing for International Dawn Chorus Day
Not that anyone has suggested this but it doesn't seem so far fetched given that dogs can supposedly smell cancer. Also, if bees really can smell fear...
I expect that your issue is more with the fact that there is a mega state object that contains almost everything - not that it is somehow global, which it isn't. You can't access it unless it is passed to you.
I'm looking forward to the discussion on this. I'm normally quite liberal in privacy concerns and think that a lot of people tend to overreact when it comes to what Facebook et al does. But this just puts it all into perspective how powerful and nefarious closed organizations can become.
There is also a NYT article focused on the researchers: https://mobile.nytimes.com/2016/07/25/business/vw-wvu-diesel...
All machine learning models are capable of overfitting. Deep learning does not have a special relationship to overfitting, and in fact, there is some evidence that the structural priors in complex deep networks actually inhibit overfitting and improve generalization [1].

Citing certain examples of misuse of deep learning by folks who "tweaked it till it worked" doesn't say anything about deep learning at large. The same team would do the same with a much less powerful model.

Of course, being able to explain is a very valuable trait. But there's just no evidence that deep learning methods are any less amenable to interpretation than SVMs or decision trees. The latter models' "interpretation" is mostly stuff like "feature 543 and 632 were on" , while deep learning methods can not only do that, but can also synthesize examples of characteristics that the model looked for [2].

[1]: https://arxiv.org/abs/1611.03530
[2]: https://arxiv.org/abs/1605.09304
Opera is the only non-web client that I know of that has labels. But how does it actually represent them on IMAP level? And what happens when you view the same mailbox using another client?
AFAIK, most advanced RISC-V design includes superscalar and OoOE (e.g. comparable to a MIPS R10000 (1995) or an Intel Pentium Pro (1995)), while the RISC-V for the IoT is comparable to a typical single instructions per clock RISC CPU, e.g. MIPS R3000 (1988).

The success in the IoT will depend not only in a cheaper price because of no royalties, but also in the "ecosystem": peripherals, buses, etc. Running Linux is a huge start, so I have no doubt it can be a success in this field.

Regarding the use in mobile and desktop, it will have to wait until SIMD extensions are introduced, and software being adapted (e.g. ffmpeg/libav including RISC-V assembly SIMD implementation for the codecs).

Anyway, realistically, for the RISC-V getting enough traction, some big player should bet on that, which is currently highly improbable, unless some Apple/Samsung/Huawei/Google gets crazy enough for doing it.
> Yes, examples of minimal ELF files are good starting points for getting the back end part up and running.

Mmm, one of several components.

> I'm not sure that reading real-life code will get you very far, because it often deals with lots of edge cases and attempts to be efficient and generate clever code.

Very good point. It takes a bit more time and effort to build a proper mental map, but there's really no worthwhile alternative.

> However, there are compilers that have been made as educational tools or with simplicity as a major design goal. I would stick to those in the beginning.

I agree! These help a lot, and make study almost (if not very) fun :)

> See my home page (http://t3x.org) for lots of examples!

Will do, thanks!

> Then, books. I'll make a list some day, I promise! ;) From the top of my head: Wirth's "Compiler Construction", Richard's "BCPL, the Language and its Compiler", and - shameless plug - "Practical Compiler Construction" by myself. I have also read the Dragon Book back then - it's good, but too heavy on theory for a beginner.

Interesting. I actually stumbled on a copy of the Dragon Book while at an op-shop some time ago. I didn't actually know what I was grabbing at the time, just that it looked like a really good idea to prioritize getting it. I'm very happy to have it, and hope to be able to make sense of it at some point :)

Looks like Wirth's Compiler Construction can be found at http://www.ethoberon.ethz.ch/WirthPubl/CBEAll.pdf

BCPL, the Language and its Compiler and Practical Compiler Construction don't seem to have free options; that's fine, I've added these (and your other books!) to my (burgeoning) wishlist :P
So where all this authentication and web UI code resides? Is it in the BIOS? Is there somewhere a packed JQuery or something?
Yep, I absolutely don't care. Just give me a nice car at a good price.
Killing it in a boys club
Here in Europe all or most of the major carmakers had been infringing the "Euro N" emission protocols set by the EU. The now current Euro 6 is however quite restrictive and is in effect since 2014/2015. Despite Euro 6 being twice as permissive as EPA-standards, diesel cars emissions are actually an order of magnitude higher than most current standards, so if they remain within Euro 6 boundaries that would be great news. Apparently that is the case now for new cars in the market since Sep/2015.

NOx wasn't taken too seriously in the EU by member states until recently and many countries were not fining or prosecuting carmakers [1]. This is probably a result of hard-fought car plant concessions each member state has to make to get these valuable blue-collar jobs from going somewhere else. The mindset is changing now due to raised awareness by VW-gate but also the continuous struggle some large cities such as Paris, Madrid, London, Athens and Rome are having to keep smog levels under control [2]. So, in general, we can say the EU, public and cities are putting pressure, but states are still very lenient and reluctant and are seeking long-term exemptions [3].

I believe the EU is too dependent on heavy NOx-emitting diesel cars to take drastic regulatory measures, so diesel is being pushed out by the cities with upcoming taxation and city-limits restrictions. If the trend follows, taxation, and not regulation, will be the NOx deterrent in the coming years [4].

[1] http://europa.eu/rapid/press-release_IP-17-238_en.htm

[2] http://www.independent.co.uk/environment/paris-cuts-harmful-...

[3] http://www.economist.com/news/leaders/21714991-europe-should...

[4] http://www.telegraph.co.uk/motoring/news/11018725/This-diese...
Not sure why it use this title… click bait?
I take issue with precisely the notion that deep learning models are "black-box". They're pretty transparent, and just because people haven't gained adeptness in it yet says more about its cutting-edginess than about its interpretability.

The average programmer at a tech company won't be able to tell us how a particular complex piece of code works, but that doesn't stop us from building complex software.

Deep learning methods are also not off-the-shelf type algorithms. Using them does require knowledge of the domain. This doesn't fit with the "black-box" narrative.

In fact, SVMs and DTs are black-boxes due to their off-the-shelf nature. (jk lol)
Case studies in copy protection
Oh TIL, thanks. Nice name :)
How do you find the highly qualified? How are they called? How do you find the fitting one, without having tons of money to through out like a big manager would have (but probably enough, specially if the result is promising)

Thanks!
Porsche Carrera S – Dangerous Ride on a Serpentine Road
Being an advertiser has its privileges.
Yes, but when it can be automated and the engineering resources could be used for building something even more useful, I think that's a clear decision for the business?
The older OpenRISC had already found a few niches. For example, the management core in some of Allwinner's newer ARM SoCs is apparently OpenRISC, presumably because they didn't want to license another lower-end ARM core or something. Similarly, some Samsung TVs apparently use it.
You can do the same with deep learning, your model will fail when the train data and test data are systematically different. This isn't about understanding the model, it is about understanding the data. Understanding the data is much more important than interpreting the model. Systematic uncertainty is a problem for all models.
> Sadly protocols are made by those who show up and put the effort in.

Yes. And like everybody, I have my own priorities, and will not apologise for JMAP not being one of them. Somebody like yourself who is actually invested in making JMAP a success should welcome people pointing out real life use cases where the protocol you're trying to improve upon actually works better than the one you've come up with. I would hope you're spending significant effort trying to get people to look over your spec and point out flaws.
Most important thing is JavaScript from browser can use only HTTP, so if you want to build web-based mail client, you won't have to build proxy HTTP service, you can just implement protocol on client. Another important thing is proxy, many organizations don't allow anything but HTTP, so it's easier to just use it.

HTTP is not that bad, anyway. You don't have to use every quirk.
Elon Musk – The future we're building and boring [video]
Psyops.

I occasionally delve into the CIA CREST database and one day stumbled on old articles with dollar values [0] Russian Intelligence services dumped on psyops against their adversaries at the height of the cold war. In 1980 for instance this was reported:

"According to the CIA's reckoning, the Soviets in 1979
poured at least 200 million dollars (I assume US) into
a variety of campaigns - using propaganda and covert
operations - to isolate the US from it's friends." [1]

Nothing has changed.

" Palantir is practically now a trigger word. The data-mining firm has contracts with governments all over the world – including GCHQ and the NSA. It’s owned by ^Peter Thiel^, "

Let that sink in. How long till this article is flagged?

Reference:

[0] Values in CIA reports are often redacted because they are an indication of resources.

[1] CREST (Psyops Russia 1980): DEPARTMENT OF DIRTY TRICKS, SOVIET STYLE
Document Release Date: January 13, 2011

Summary: https://www.cia.gov/library/readingroom/document/cia-rdp90-0...

Document: https://www.cia.gov/library/readingroom/docs/CIA-RDP90-00552...
No, a web based UI which basically does the work of providing an interface to the DB (think of phpMyAdmin), but also adds meaning to the data based on the entities and teams that need the data.

For example, the tool processes the DB schema, builds a UI. Learns which field is username, and then shows in the front end the way user management softwares work. For the sales guy, its just all the information from Salesforce, different forms, the value of their converted leads. For the finance team, its the payments, fraud disputes etc.,

All this data is in the DB. And engineering efforts go in to build these tools for the non-engineers and keep re-iterating. I am trying to solve that problem of re-inventing the wheel. Any functions specific to the business can always be added using JS or Python (just like Zapier).
I'm using gitlab exactly because it's cheaper (free private repos).
Having too little is certainly very stressful and having a lot can enable some early retirement. Having enough, but a full-time job means having little time.

I guess we just don't hear from those who retire after their first 4 millions.
>> This push towards elegance is unscientific

I think you are right on that one. The Standard Model for example is absoluteley not elegant, it is a huge convoluted mess of parameters and constants (just google for "Standard Model Lagrangian"). Yet, it is the best we came up with, explaining the dynamics of fields and particles[0], that surround us. Correct answers don't have to be elegant per se, its nice when they are, but it's not a prerequisite.

But, consider this: You can train a Neural Network to predict the distance of an object, thrown with velocity v_0, an angle of α, under the influcence of gravitational acceleration g. After a few hunderd rounds of training, a suited NN can reasonably predict the outcome of said experiment, with the input: v_0, α and g. And, as you pointed out, a researcher can explain to you why this is the case based on activation, feedback-loops, learning algorithm and other parameters. But neither the NN nor the researcher will be able to give you a rule like: "F=(G m_1 m_2)/(r^2)" to explain the underlying reasons for the object-trajectory dynamics.

A Neural Network can give you answers and predictions, yes, but you are not able to incorporate them into a wider theory, since the output is always numerical in nature. It is also always tied to one specific fact you are interested in and cannot give you a generalization.

[0]: In the model of QFT particles are also fields of a different kind.
The World’s 8 Richest Men Are Now as Wealthy as Half the World’s Population
Thanks, I'll make sure the "params" object gets documented also. Currently, there is not so much you can set per HTTP request. You can basically add HTTP headers and you can also add tags that lets you single out specific results based on those tags (e.g. when using thresholds or when plotting graphs of results).

So - HTTP request timeouts can not be changed at the moment, but this is something I think would be very simple to implement. If you open an issue about it I'll see what we can do to get it implemented quickly.
Ah, shit. Yes. :(

Should I be giving up?
Evolution of Batman Games in 9 Minutes (2017)
If scrutiny improves the project it's ultimately a good thing.
There have been a lot of speculation and finger pointing at social media platforms for influencing high-level decisions. What I'd like to ask the elders here on HN is:
Wasn't this always the case? Were elections in the 70s 80s all fair and fact-based? Were not political parties using information to their advantage and for pushing their own agenda?

Because to me it seems that only the tools have changed, rest is pretty much played by the same book.
Do you have a list of favorite blogs? Need just to mention the top ones (great would be google+ blogs, so I can register them there, or how do you manage them, if more than a couple?)
What's really scary about these recent developments is that we're losing the ability to critically analyze the ideas and arguments that are being used to influence people.

Back in the days of network TV, you might not agree with everything that was being said, but you could at least see the arguments that were being used to convince others.

Even the early so-called behaviour change campaigns, such as 'Five a Day' (a marketing campaign that most didn't realize was a marketing campaign that has long since been accepted as a piece of general health advice) were quite benign.

Here it seems that an influence / behaviour change campaign could be waged relatively cheaply against a relatively small number of people, exist completely outside the normal rules for fact checking or veracity, and the majority of the population would have no idea about it.

I'm not sure I agree with the thesis of the piece - that Cambridge Analytica played even a primary role in Brexit happening (I think it was always pretty likely, due to UK tabloids having campaigned for around 20 years on the issue, lack of organization in the Remain campaign, Corbyn failure to engage working class voters etc, hell even a latent feeling that the EU maybe isn't that great after all), but the techniques for doing this will only become more sophisticated and I wouldn't be surprised to see them become a common feature of our democracy.
There is a no free lunch theorem in learning, which says that without limiting your hypothesis space, you cannot learn. That means, you need to have some assumptions.

No doubt that DNNs are subject to the same theorem, the only question is, how does their hypothesis space looks like? Does anyone have idea about this? I suspect we don't really know what the DNNs assumptions are.
" The facts speak loudly for themselves; in most cases, deep learning-based solutions lack mathematical elegance and offer very little interpretability of the found solution or understanding of the underlying phenomena."

I don't agree with this statement, a simple look at cs231n lecture series will show you how much math is involved. A lot of articles/people claim its a black box, but while writing a small network architecture you realise it's not. Methods like stride, padding, activations, learning rate, optimisations, drop outs etc. give you "aha" moment which is followed by a mathematical explanation. One should study the topic thoroughly before criticising it.
At a much smaller scale I have experienced similar situations in the past when I was very briefly a phb. I learned to ask questions like the following and answer them with hard data. Sometimes changed minds.

How many users in total are you supporting?

What number of incidents do you see in a week/month?

How many extra help desk people have you had to employ/redeploy?

Whose budget do the extra support costs come from?
Here is a compiled list of resources for erlang: https://www.tutorack.com/search?subject=erlang and for elixir: https://www.tutorack.com/search?subject=elixir .

Disclaimer - I work at the above service
Then requiring algorithms to explain their reasoning should give neural networks an edge over other methods, if they are really easier to interpret.
It sounds a bit arrogant. US systems are in a much worse shape than Chinese now (lack of innovation, constant bipartisan struggle, inability to make critical decisions and move forward fast, ridiculous expences) and a lot of that can be attributed to the current political system that supports individualism and freedom to express radically opposing opinions (creating turbulence and distracting from solvig the real problems). To me, after spending some time in both US and China, it seems that the optimal system is authoritarian meritocracy - you need a strong monolythic leadership, the question is how to avoid thyrany. China thus far balances it pretty well. Also, intelligent thinking doesn't really correlate with freedom of speach, btw.
This is a textbook example on how to create a demonizing narrative with little to no facts at all.

Guilt by association, throwing in the Russians and Holocaust deniers, some evil billionaires - it has it all. Funneling foreign money into national elections is always happening - through foundations, local branches, tax location, and so on.

Absent from all of this are of cause any facts on what Cambridge Analytica is doing different than being just another programmatic ad buying firm.
Can you recommend some good books/papers that explain how exactly the hypothesis space of DNNs looks like? By no free lunch theorem, there has to be one.
I just realized I built something similar to https://developers.google.com/appmaker/

Irony, I wrote it for AppEngine, didn't host it though.
I find it amazing how people can stay in denial about the Brexit vote: that it happened, and that people in Britain actually disliked EU enough to want to get out of it. That what people voted for is not their will.

I'm in the remaining EU, if I'd had a vote in the British referendum, I'd have voted for Remain. But the increasingly paranoid conspiracy theories about shadowy conspiracies makes me more and more disgusted with the EU status quo.

New emperor-wannabes come up with 50 billion or 100 billion or 200 billion that Britain should pay to the union. Looks like that the more absurd numbers you can come up with to punish Britain for insulting the honor of EU institutions, the better.

Get real: such payments are not going to happen. You're coming up with a "favoured trade partner" or whatever status with Uganda; you'll be able to do that with Britain as well. The British isles aren't floating away to the Atlantic, and the countries will need to continue trading.

EU is not working very well. The leader of the Commission is a drunkard. Institutions are grabbing power that I and very many others want to belong to national governments. The union wants to expand to new countries and deepen to a federal state at the same time. Very many people want free trade, but otherwise want to retain national sovereignty. Accept that, stop blaming Trump and shadowy conspiracies.
Seems like yet another conspiracy theory to grab attention while automation continues to eat jobs.
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