Apple's A7 is Made By Samsung - <AUTHOR> http://createsend.ifixit.com/t/ViewEmail/r/70708E377A9A6CF92540EF23F30FEDED/EBF1F70991A04FD10F8C96E86323F7F9 <comment><USER2> Interesting for those curious about the new processor. The fact that it's Samsung, a company supposedly at odds with Apple, isn't much of a story. It's not even news: (from July 1!)

[http://online.wsj.com/article/SB1000142412788732468220457851...](http://online.wsj.com/article/SB10001424127887324682204578513882349940500.html)

They've provided parts to Apple for years, and their bottom line and stock price have been rewarded. (represents about 15-20% of their components sales)

The mobile division and the components divisions in the company operate fairly separately. That's why the oft commented, "They should just stop shipping parts to Apple to punish them for the lawsuit!" is kinda ridiculous.

When it was RUMORED that Samsung lost a significant contract for Apple chips to a competitor, they lost $10B in market cap: [http://www.reuters.com/article/2012/05/16/us-samsung- chips-i...](http://www.reuters.com/article/2012/05/16/us-samsung-chips- idUSBRE84F0BT20120516)

<comment><USER3> What is Samsung's primary stock ticker symbol?

<comment><USER4> It appears to be SSNLF, or 005930.KS? [http://finance.yahoo.com/echarts?s=005930.KS](http://finance.yahoo.com/echarts?s=005930.KS)

<comment><USER5> Some more info: this is on the Korea Exchange[0], and it's Samsung Electronics only.

0: [http://en.wikipedia.org/wiki/Korea_Exchange](http://en.wikipedia.org/wiki/Korea_Exchange)

</comment></comment></comment></comment><comment><USER6> Apple CEO has a glass of wine with Samsung CEO, laughing while reading fanboys of both companies fight about which company is best. _wink_

<comment><USER7> Samsung has fanboys ?

<comment><USER8> I work with a fair number of (awesome!) Korean engineers, and I can say that yes, Samsung has a lot of fanboys. It's sort of like the Apple of Korea. It's also been gaining a lot of admiration from A/V enthusiasts in the States, having easily supplanted Sony as the high-end consumer electronics maker of choice for TVs and such.

Samsung is a very impressive company. It doesn't use the Nike-esque marketing playbook Apple does, and as such, it doesn't inspire the hero worship or the fanaticism in the States. But it's been quietly building up a massive empire.

[Full disclosure: I say this as an Apple fanboy, and as the resident iOS 7 apologist at my office (everyone here's firmly on Team Android). I own plenty of Apple and Samsung devices, and I don't feel the need to declare absolute brand loyalty to any one provider.]

<comment><USER9> It always amuses me to see how much vitriol comes from anti-apple folks, when, if they were living in Korea and held the exact same standards, they would likely have equal hate for Samsung. Their vertical and horizontal control on their home turf dwarfs Apple's. But no, it's just a foreign company that has managed to do wonders _somehow_. As a chaebol, Samsung got where they are now thanks to very favorable government assistance, that, if occurred here, would be considered pretty controversial.

<comment><USER8> While I agree with a lot of your points, I'm a little puzzled by this one:

 _" It always amuses me to see how much vitriol comes from anti-apple folks..."_

Was I the intended recipient of that description? I'd hardly call myself "anti-Apple," nor would I say I've engaged in "vitriol" directed at Apple. Almost the complete opposite, in fact. :)

Apologies if I'm misreading you.

<comment><USER10> In my reading that wasn't directed at you, but the general "wake up sheeple" type comments you often find directed at people making pro-apple comments.

You did a great job, summed up my thoughts. If I wasn't an Apple kind of guy, I'm sure I'd have an S4.

</comment></comment></comment></comment></comment></comment><comment> jack-r-abbit My favorite part about this is that Gruber has been repeating the speculation that the chip was not manufactured by Samsung. He turned out to be wrong. When others are wrong with their "Claim Chowder" (as he likes to call it) he shits all over then. But this is all you get from him when _he_ is wrong: [http://daringfireball.net/linked/2013/09/20/a7](http://daringfireball.net/linked/2013/09/20/a7)

<comment><USER11> If you look at his other posts for the day they're all short statements. Maybe he's just in a particularly non-verbose mood.

<comment><USER12> He almost always does short statements often of a slightly ambiguous nature. This helps him avoid having to say he's wrong in the future.

His sterling analysis (i.e. complete failure to see it'd be a success) of the Galaxy Note monster phones is a fine example of this.

</comment></comment></comment><comment><USER13> Let me figure this out: Samsung manufactures Touchscreen, CPUs and perhaps other chips for Apple, yet Apple sue them from time to time over software patents and design ideas.

Can someone help me understand this?

<comment><USER14> Yes, when you are working with a company and sharing trade secrets only to have them turn around and take those secrets to make their own devices that compete directly with yours and then they make commercials making fun of said products and your customers, that doesn't really help the business relationship.

<comment><USER15> I keep hearing this. What are those 'secrets' that you are talking about (Any link which gives specific information?) Samsung has been making phones much before Apple has been. Are those secrets "Rounded phone corners", "icons in a grid", "Slide to Unlock", or "jumping scrollbar"?

<comment><USER16> If you really think Apple has contributed nothing more to the modern smartphone than 'rounded corners', this is not the appropriate place to ask this question because you risk being mistaken for someone trying to start a flamewar. I recommend quora instead.

<comment><USER17> Recommend Quora? Remind me to ignore your recommendations in the future.

As for Apple contributions, you should probably check out the lawsuits vs Samsung.

<comment><USER16> I know where the assertion about the round corners comes from.

Are you seriously claiming that this is all that Apple has contributed to the design of modern smartphones?

</comment></comment></comment></comment></comment></comment><comment><USER18> Somehow, companies like Apple (and nVidia) get to call themselves "completely vertically integrated" even though they don't own any fabs. I've never understood this.

<comment><USER19> Samsung is considered completely vertically integrated by many, even though they don't mine their own raw materials or operate their own power plants to power their factories. I've never understood that either.

<comment><USER20> I wouldn't be too sure! [http://en.wikipedia.org/wiki/Samsung_Engineering](http://en.wikipedia.org/wiki/Samsung_Engineering)

</comment></comment></comment><comment><USER21> They also make their retina displays and many components.

[http://www.wired.com/gadgetlab/2012/04/samsung-tablet- ipad-r...](http://www.wired.com/gadgetlab/2012/04/samsung-tablet-ipad-retina- display/)

</comment><comment><USER22> If anyone is interested as to the feats of this processor, this is an interesting review:

[http://www.anandtech.com/show/7335/the- iphone-5s-review/2](http://www.anandtech.com/show/7335/the-iphone-5s-review/2)

</comment><comment><USER23> Correction. Apple's A7 is fabricated by Samsung. It is wholly designed by Apple. They're just using Samsung's foundry.

<comment><USER24> It's actually mostly designed by ARM holdings. Apple simply designs the SoC layout and chooses the components.

<comment><USER25> The GPU is a design from Imagination Technologies, the CPU is an Apple design. What part of the SoC is "mostly designed by ARM"?

<comment><USER24> The CPU. Apple licenses ARM's reference designs, then creates an SoC around that.

<comment><USER25> The last SoC where this has been the case is the A5 (using the the ARM Cortex-A9 MP). The Apple A6 and A7 don't use ARM cores and neither does Qualcomm in their Snapdragon SoCs.

</comment></comment></comment></comment></comment><comment><USER26> I'm not sure how this is significant. Many of Apple's core technologies and hardware are produced by Samsung or with Samsung's help, e.g. Retina display for MacBook Pro, A4 and previous chips, and SSDs in the MacBook Pro and MacBook Aero.

It's quite disappointing to see this on the frontpage, in all honesty.

<comment><USER27> I agree. I feel like I see this posted all the time (in comments on various threads) and there's always a large group that is shocked and/or don't understand.

It's silly.

</comment></comment><comment><USER28> Not sure if OP realises that the unsubscribe and edit your subscription links work for his account.

</comment><comment><USER29> How isn't Apple afraid that Samsung could steal their chip designs? Do they pick Samsung because they simply have no other option?

<comment><USER30> Most prior Apple designs have been close to stock ARM core implementations coupled with a PowerVR GPU. There hasn't been much if anything to steal, given that Samsung also licenses PowerVR GPUs and is also an ARM licensee.

The A7 is interesting in that it's the first mainstream 64-bit part, but I see Samsung being far more interested in the A53/A57 (designs that Samsung gets straight from ARM) than the A7. The A7, if rumors are true, is a sort of hybrid approach to use one of the next generation, 64-bit ARM cores early, similar to what Qualcomm does.

EDIT: ARM nomenclature is such a mess. The A7 runs ARMv7 or ARMv8 using a sort-of A57 dual-core architecture, but should not be confused with the ARMv7 Cortex-A7.

<comment><USER31> The A7 is not a hybrid approach. A7 is an ARMv8 chip (it implements the ARMv8 ISA), but not an ARM design (that's the difference between the processor license (can use ARM-designed cores) and the architecture license (can design own core implementing ARM ISA). Apple and Qualcomm have both licenses. There's nothing "hybrid" about using your architecture license.

ARMv8 provides two architectures: AArch64 and AArch32. Implementors can implement either or both. AArch64 is the brand new architecture while AArch32 is backwards-compatible with ARMv7-A. If both are implemented, it's possible to switch between AArch32 and AArch64 on the fly at specific change points, giving the ability to run AArch32 (= ARMv7) applications seamlessly on an AArch64 kernel (or an AArch32 guest os in an AArch64 hypervisor).

The A7 implements both, so do the A53 and A57.

<comment><USER30> _The A7 is not a hybrid approach._

Unless you work at Apple in silicon design, you don't know that, and to be fair neither do I. But history has shown that every A# release gets greeted with incredible fanfare about the completely-custom CPU work at Apple, to later quietly get corrected when it turns out that it is at most a marginally derived ARM core.

I'm just going with history. Given that the A57 finished design last year, and started taping out early this year, it seems unlikely -- if not strategically risky -- that Apple just went their own way. From a pure performance perspective, ARM is hyping a clock-for-clock tripling of performance with the A57 over a Cortex-A15, or a quintupling of performance at a given power usage level.

<comment><USER32> >> _Unless you work at Apple in silicon design, you don 't know that, and to be fair neither do I. But history has shown that every A# release gets greeted with incredible fanfare about the completely-custom CPU work at Apple, to later quietly get corrected when it turns out that it is at most a marginally derived ARM core._

His hasn't been true since the A6, which (amongst others) chipworks confirmed is a full custom design, manually laid out even, which proves it is not a modified reference ARM design. I don't think Apple would invest hundreds of millions acquiring chip design shops, gradually move from increasingly customized reference designs to full custom designs, to throw their brand new Swift core out after one generation and start over with a reference design. So I think it's pretty safe to assume the A7 is a pimped up A6 that implements armv8.

I'm not sure why you're so skeptical about Apple designing their own ARM cores, they've been going further down this path since after acquiring Intrinsity and PA-Semi, and they are not the only ones doing this, Qualcomm, nvidia and previously TI also design full custom ARM cores.

<comment><USER33> I'm curious, what arm cores did TI design? I know Marvell has custom designs, but I always thought TI only did custom DSPs and licensed arm cores.

</comment></comment></comment></comment></comment></comment><comment><USER34> It's also fun that they found the M7, considering that the initial teardown left iFixit with the impression that it was just some marketing buzzword for a part of the A7. As has been mentioned in the thread, it is still an off-the- shelf component, but at least we know the truth now (that the "M7" really still is a separate IC).

</comment><comment><USER35> IMHO Samsung and Apple compete on marketing and not in technology. That's why you can find Samsung chips in Apple devices.

So, the vertical integration Samsung has in the manufacturing process is not great competitive advantage. However, Apple is strongly vertical integrated in the retail side. This makes a difference.

</comment><comment><USER36> _made_ != _manufactured_

Title is a bit confusing.

</comment><comment><USER37> Samsung as a chip fab is not the same Samsung that designs phones. They fall under the same name umbrella, but likely they have individual targets to meet annually, and Apple is one of the biggest chip consumers in town. It's a good fit, I never get why people are surprised by that.</comment>


Mahmoud Ahmadinejad founded the Iranian Tunneling Association in 1998 - <AUTHOR> http://www.nytimes.com/2010/01/06/world/middleeast/06sanctions.html?pagewanted=all <comment><USER2> Never knew the guy was in to Lisp either.

<comment><USER3> Is that a joke, or does any source actually suggest that? If so, links?

<comment><USER2> The standard response on posting non-hn political or entertainment stuff seems to be that X,Y or Z is a hacker of sorts, so I guess the fact that this is posted implies our Iranian head-of-state must be a hacker too.

HN being rather lisp centric I figure that must be the reason ;)

Think of it as my roundabout way of saying that I think this is not hacker news in any way that I can understand.

See this: <http://news.ycombinator.com/item?id=1035952> for a prime example.

</comment></comment></comment><comment><USER4> > Iranian officials say years of veiled bombing threats prompted their country > to exercise its "sovereign right" to protect its nuclear facilities by > hiding them underground.

Sounds far from unreasonable to me.

Man, this article reads like Mossad ordered up some war drum beating.

<comment><USER5> Except Iran isn't a sovereign nation, it's just a patch of soil run, in practice, by a bunch of unelected mullah-thugs. The doctrine of sovereign rights should be restricted to democracies. The mullahs have no more sovereign right to the territory they happen to control than the Crips or Bloods.

<comment><USER2> The CIA disagrees with you:

[https://www.cia.gov/library/publications/the-world- factbook/...](https://www.cia.gov/library/publications/the-world- factbook/geos/ir.html)

You may not like them (I certainly don't) but they're a sovereign nation as much as any other.</comment></comment></comment>


Ask HN: Collecting all HN member "side projects" - <AUTHOR>

If you are like me then you enjoy the side projects that HN members develop and announce on HN. I would like to collect a list of these so I can publish new ones each week on www.hackernewsletter.com. I can grab newly created announcements (but feel free to add them as well), but projects that were announced before this month I don't have.
In addition to adding them to my newsletter, I will also publish the results so that everyone can get access to them.
The form is located here: http://simplerise.wufoo.com/forms/hacker-newsletter-side-projects/ <comment><USER2> Why is it develop _and_ have announced?

I have a (couple) side projects that I haven't "announced" on HN, was going to submit, but then I noticed the required "HN Announcement URL".

Maybe my problem is that I just haven't "announced it on HN" formally, and that's what this is all about? Is that the case? Thanks for clarification.

<comment><AUTHOR> Exactly... just so that everyone can comment on it. I think it is a great way to get feedback and to share with others.

<comment><USER3> If you didn't require this I would have submitted my side project. It's a few years old already and "announcing" it on HN seems odd.</comment></comment></comment>


App Marketing 101 - <AUTHOR> http://appdevelopermagazine.com/743/2013/10/2/App-Marketing-101%3b-Crush-the-Competition-and-Rule-the-App-Store/

<comment><USER2> The article is very painful to read, it uses an annoying pattern that I see often in blog posts and other forms of writing. The author keeps saying it will address a given topic in a short while, repeatedly. When he finally does, the main topic is addressed in only a few lines.

When I write something I think what I want to convey and how to best do it; most of the time this only means laying the various topics in a logical, bottom-up fashion, so that a given topic is brought up just when it needs to, it isn't announced before. If you find yourself writing things like "we'll come to this in a while" or "as I'll show in the next section" you should probably reconsider the structure of the whole thing and start again.

</comment><comment><USER3> As an aside, I've found that marketing an app forces you to articulate what's actually novel about it. That should be obvious from the outset, but often gets lost during development or never considered thoroughly enough.

</comment><comment><USER4> Helpful app marketing quiz with actionable advice that we created. [https://sensortower.com/iphone-app-marketing](https://sensortower.com/iphone- app-marketing)</comment>


Speech2Face: Learning the Face Behind a Voice - <AUTHOR> https://arxiv.org/abs/1905.09773 <comment><USER2> I'm a speech scientist. This paper is a neat idea, and the results are interesting, but not in the way I'd expected. I had hoped it would the domain of how much _person-specific_ information this can deduce from a voice, e.g. lip aperture, overbite, size of the vocal tract, openness of the nares. This is interesting from a speech perception standpoint. Instead, it's interesting more in the domain of how much _social_ information it can deduce from a voice. This appears to be a relatively efficient classifier for gender, race, and age, taking voice as input.

I'm sure this isn't the first time it's been done, but it's pretty neat to see it in action, and it's a worthwhile reminder: If a neural net is this good at inferring social, racial, and gender information from audio, humans are even better. And the idea of speech as a social construct becomes even more relevant.

<comment><USER3> > If a neural net is this good at inferring social, racial, and gender > information from audio, humans are even better.

Why would humans automatically be better than machines at that task?

<comment><USER4> I think that's the scary thing. We don't even know if we know. It's all subconscious.

For example most people can easily picture a gender, race, age, and where a person is from based on accent.

But I never realized that I also picture how fat they are, and can do it pretty well! It wasn't until I saw that this project can do it very reliably that I realize that I do it all the time too.

What else are we subconsciously picking up on? And as a counter defense, how can we better hide it? Do I need to change my vocabulary and topic choices to something more posh so they think I am eating healthier? What other info leaks are there?

<comment><USER5> This is a bit different but also an example that made me realize I unconsciously recognize some things I'm unaware of (the difference between pouring hot and cold water): [https://www.youtube.com/watch?v=Ri_4dDvcZeM](https://www.youtube.com/watch?v=Ri_4dDvcZeM)

</comment></comment></comment></comment><comment><USER6> One of the really cool things from the pdf is the ability to construct a normalized face from a video frame: [https://i.imgur.com/SdRHCJ0.png](https://i.imgur.com/SdRHCJ0.png) Basically they are taking a video frame, making it face forward, cancelling out lighting for pure albedo, and removing accessories like glasses. This is incredible in terms of tracking individuals. It's a digital mugshot.

Also, I think it's interesting that the classifiers can pick out ethnicity with a high degree of accuracy. Seems like an easy way to fool this tech, from a privacy perspective, is to talk stereotypically like a specific ethnicity.

<comment><USER7> [https://www.youtube.com/watch?v=72n34fRkA3I](https://www.youtube.com/watch?v=72n34fRkA3I)

</comment></comment><comment><USER8> Absolutely terrifying.

If you add this to the model that guesses identity based on the sound produced by inputs (keyboard , mouse...) you basically end up with an "ambiant sound fingerprinting" tech , where the sounds emitted nearby a device can be used to accurately determine the individual that's standing close to it...

If you add this to china's facial recognition , it scares me to think how "Gattaguesque" our societies are turning thanks to Machine Learning and Big Data...

<comment><USER9> What do you mean by "Gattaguesque"?

<comment><USER10> Gattaca-esque: [https://en.wikipedia.org/wiki/Gattaca](https://en.wikipedia.org/wiki/Gattaca)

<comment><USER9> Cheers!

</comment></comment></comment></comment><comment><USER11> I don't think that we're prepared at all for how much, and from how little, machine learning might be able to deduce about us. Combined with how our behavior is tracked in high resolution by Facebook, Google, and the rest, we're heading straight for the kind of future depicted in films like Gattaca, but they won't even need to test your DNA for it. Just upload a short video of your friend/employee/kid to _videotherapist.ai_ and find out if they're more likely to be an arsonist or a physicist!

<comment><USER12> Absolutely fascinating/terrifying (why do those always come in pairs with AI research?) but I think it's also interesting to use this to make some assumptions about how human brains work. When I hear a voice, I _kinda_ have an idea what the person would look like. I can't really explain it, but there's definitely some assumptions I can make about age, weight – gender is easy but even within that, a deeper voice easily makes me think of more stereotypically male facial features. The model is also slightly racist (as we all are) so if I hear an Asian language, well, I might picture an Asian person.

So, basically, a lot of this stuff is actually likely happening right now in our brain. A lot of crazy complicated stuff, happening entirely subconsciously and never having been tested before because doing so empirically is somewhere between incredibly tedious and impossible.

<comment><USER13> How is it racist to think of an asian person when you hear something with a typically asian pronunciation?

</comment></comment></comment><comment><USER14> For a quick overview, I found the Github page to be more useful: [https://speech2face.github.io](https://speech2face.github.io)

</comment><comment><USER15> Cool, but this seems like a very fancy age, race and gender approximator.

I wonder if the NSA has an in-house version already.

</comment><comment><USER16> I found this scary from the perspective of it's use by Law Enforcement. There have already been serious misuses of facial recognition software by LE, including using celebrity pictures as a query [1] because a witness described a perpetrator as looking like that celebrity. From the perspective of someone familiar with machine learning, this is a terrible misuse that is likely to lead to wrongful arrests. I can easily imagine LE using Speech2Face software to extract a facial reconstruction from a recording, then feeding this reconstruction into facial recognition software, not understanding how this will WILDLY propagate error. I'm not against industry use of machine learning, but this software has the propensity for extreme misuse.

1\. [https://www.washingtonpost.com/technology/2019/05/16/police-...](https://www.washingtonpost.com/technology/2019/05/16/police- have-used-celebrity-lookalikes-distorted-images-boost-facial-recognition- results-research-finds/)

<comment><USER17> Check this: [https://www.afcea.org/content/mind-blowing-promise-ai- driven...](https://www.afcea.org/content/mind-blowing-promise-ai-driven-voice- profiling)

<comment><USER16> I may have slept better tonight not having know about this.

</comment></comment></comment><comment><USER18> I'm underwhelmed.

It fits age, sex, ethnicity and face shape.

The part it does well is age, sex and ethnicity. It's not really surprising that voice can give those away. Most people can guess those correctly from a voice sample.

Face shape is the interesting part, and in my opinion it doesn't do that very well at all. I wouldn't recognise any of those people from their reconstructed images.

</comment><comment><USER13> I like how having a goatee correlates sufficiently with the way you speak for a neural network to learn it.

<comment><USER7> There is a whole field dedicated to this kind of study, namely sociolinguistics (see [1] for a short summary of the seminal experiment). Sociolinguistics study surface variations in language use that cannot be accounted by dialectology only (i.e. geographical factors) among speakers of the same language. There are clusters among linguistics uses, and it turns out they map to clusters in the space of social practices. For what I have studied of the field (not that much), it seems most of the time the variation is driven by the desire to belong or show you belong to the community of the users of the trait you adopt. It's said to be "inconscious" (pretty much like my masterful ability a handling language), but at the same time the subjects at hand can arrive at the same conclusions with the help of some introspection.

What's uncanny here is that having a goatee doesn't make you belong to any social group you could think of explicitly and enjoy belonging to. I guess the relationship is mostly driven by a mix of physionomical traits (gender + age) and the fact they correlate well to having a goatee (which isn't a tiny class anyway). Or there are indeed "deep" social structures to which we belong and are yet unable to identify.

[1] [http://all-about-linguistics.group.shef.ac.uk/branches-of- li...](http://all-about-linguistics.group.shef.ac.uk/branches-of- linguistics/sociolinguistics/research-in-sociolinguistics/william-labov- marthas-vineyard/)

Edit: there may well be an immense data trove hidden in people's voice. That could be a very useful way to enrich datasets internally a bit like recommendation engines work: if my neighbour speaks like I do, then he must enjoy the same things as I.

<comment><USER19> Or it could be that a goatee physically influences the sound of the voice in a way that is perceptible to the algorithm. For example, damping transmission through the skin and attenuating reflections off the chin and upper pip.

<comment><USER20> It doesn't. I worked as a sound engineer in film for a decade and I'm extremely forensically minded.

<comment><USER21> But are you absolutely sure there aren't tiny, tiny differences? I don't think it's likely, but how can you be so sure?

<comment><USER20> I'm dubious because of the frequency range of audio recordings (especially post compression on Youtube) and the size of cells, even large ones like hair follicles.

</comment></comment></comment></comment></comment></comment><comment><USER22> Wow. It looks like my image and voice were used in this study. I was never contacted.

</comment><comment><USER17> Interestingly, US Coast Guard has been using this technology for a long time via Carnegie Mellon (CMU).

CMU also presented a similar research in World Economic Forum last year: [https://www.afcea.org/content/mind-blowing-promise-ai- driven...](https://www.afcea.org/content/mind-blowing-promise-ai-driven-voice- profiling)

with a recent paper: [https://arxiv.org/pdf/1905.10604.pdf](https://arxiv.org/pdf/1905.10604.pdf)

</comment><comment><USER23> would be useful for game designers if worked in reverse

<comment><USER24> Honestly, still probably pretty useful for game designers if they can do some style mapping on top of the generated image. Being able to generate "believeable-enough" portraits for NPCs based on voice actor data + style mapping would cut down on a sizeable art task of portrait assets.

</comment></comment><comment><USER25> Seems like pure overfitting. Since the model is able to see during training the same coocurrences that are used for evaluation, it is just about memorizing the training set, which is trivial.

</comment><comment><USER26> >> In our experimental section, we mention inferred demographic categories such as "White" and "Asian". These are categories defined and used by a commercial face attribute classifier (Face++), and were only used for evaluation in this paper.

So the software accurately depicts the race of a person according to what some other software has determined their race to be? This is so circular it is laughable. I cannot wait to see how many L's I need to mispronounce for this thing to assume I'm an Asian from the Bronx, or how many stutters are needed before it thinks I'm an octogenarian.

I'm reminded of the similar tool that could identify sexual orientation from a photo. It only worked on those who fit certain stereotypical behaviors, persons who actively self-identified as being in a particular category. When tied to immutable characteristics (skull dimensions) it fell apart.

<comment><USER27> _I 'm reminded of the similar tool that could identify sexual orientation from a photo. It only worked on those who fit certain stereotypical behaviors, persons who actively self-identified as being in a particular category. When tied to immutable characteristics (skull dimensions) it fell apart._

That paper was particularly bad. To a large degree it was a dataset detector (the "gay" and "non-gay" facial datasets came from different sources, based on different geography).

This paper is _much_ more limited in the claims it is making - only that it correlates well on faces that appear white and "Asian", and that it doesn't correlate as well for "Indian" and black. The speculate that this is because of under-representation of those classes.

 _So the software accurately depicts the race of a person according to what some other software has determined their race to be?_

Are you arguing that Face++ is inaccurate? It would surprise me if a machine learning model isn't pretty much as good as humans at this. I don't see any numbers quoted by Face++, but an paper claims 93% accuracy[1]

 _I cannot wait to see how many L 's I need to mispronounce for this thing to assume I'm an Asian from the Bronx_

So if you make yourself talk like an "Asian from the Bronx" and it detects that then... it is working, no?

[1] [http://www.bernardjjansen.com/uploads/2/4/1/8/24188166/janse...](http://www.bernardjjansen.com/uploads/2/4/1/8/24188166/jansen_facial_icwsm2018.pdf)

</comment></comment><comment><USER25> Seems like pure overfitting. Since the model is able to see during training the same coocurrence, it is just about memorizing the training set, which is trivial.

</comment><comment><USER10> The results look so good it makes me suspicious it's overfitting! Would be nice if the author said whether the examples were from a held-out set of not...

<comment><USER28> Is it really much better than it would be if it only knew age, race and sex though? Those seem fairly easy to determine from speech, especially when you consider language - and it made a mistake in a case where the race didn't match the language.

</comment></comment><comment><USER9> This is absolutely absurd and really scary. I'm not a hugely scientific person, so I might have misunderstood, but it seems like we are getting close to being able to figure out the whole of a person just from a snapshot of one part of them, like their voice.

I really worry for people born 100 years from now. We need to be really careful with technology like this. This could lead to a dystopia greater than Orwell could have ever predicted, and I don't want to sit idly by while it happens

<comment><USER29> There are some recent developments that are potentially scary, but I don't see how this is one of them. Humans are really good at guessing approximate age, sex, and ethnicity from a voice. I'm not surprised in the least that some machine learning can do it too. I think the "whole of a person" is a lot more than age, sex, and ethnicity, so I don't agree that we are "getting close."

</comment></comment><comment><USER20> Good thing I rarely use phones any more. Maybe I should start making portable vocoders to help maintain privacy against ill-conceived projects like this one.

</comment><comment><USER30> What exactly is the endgoal here?

<comment><USER31> Science.

<comment><USER30> That's not really an answer. You can research everything in the name of science, but one should hope that there is some rationale behind the effort.</comment></comment></comment>


A novel approach to neural machine translation - <AUTHOR> https://code.facebook.com/posts/1978007565818999/a-novel-approach-to-neural-machine-translation/ <comment><USER2> I'm relatively novice to machine learning but here's my best attempt to summarize what's going on in layman's terms. Please correct me if I'm wrong.

\- Encode the words in the source (aka embedding, section 3.1)

\- Feed every run of k words into a convolutional layer producing an output, repeat this process 6 layers deep (section 3.2).

\- Decide on which input word is most important for the "current" output word (aka attention, section 3.3).

\- The most important word is decoded into the target language (section 3.1 again).

You repeat this process with every word as the "current" word. The critical insight of using this mechanism over an RNN is that you can do this repetition in parallel because each "current" word does not depend on any of the previous ones.

Am I on the right track?

<comment><USER3> Yes, that's pretty accurate. Step 3 (attention) is repeated multiple times, i.e. for each layer in the decoder. With each additional layer, you incorporate more of the previously translated text as well as information about which parts of the source sentence representation were used to generate it. The independence of the current word from the previous words applies to the training phrase as a complete reference translation is provided and the model is trained to predict single next words only. This kind of computation would be very inefficient with an RNN: it would have to run over each word in every layer sequentially which prohibits efficient batching.

When generating a translation for a new sentence, the model uses classic beam search where the decoder is evaluated on a word-by-word basis. It's still pretty fast since the source-side network is highly parallelizable and running the decoder for a single word is relatively cheap.

</comment></comment><comment><USER4> I really like that Facebook open sources both code and model along with the paper. Most companies don't: e.g. Google, deepmind, Baidu.

<comment><USER5> Google has released some framework for translation: [https://github.com/google/seq2seq/](https://github.com/google/seq2seq/)

DeepMind has also released some framework which also has all the building blocks for translation: [https://github.com/deepmind/sonnet](https://github.com/deepmind/sonnet)

<comment><USER6> Sure but these don't address parent's statement: they don't release code with research. These both came years after the original seq2seq paper.

<comment><USER7> Google's Seq2Seq came 3-4 months after the NMT paper.

</comment></comment></comment></comment><comment><USER8> > Facebook's mission of making the world more open

That's a rather strong statement, for a company that has become one of the world's most complained-about black boxes.

But yes, they have done a lot of good in the computer science space.

<comment><USER9> > Facebook's mission of making the world more open

Like many big companies, they want to commoditize their products' complements.

"Smart companies try to commoditize their products' complements." [https://www.joelonsoftware.com/2002/06/12/strategy- letter-v/](https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/)

<comment><USER10> And they innovated in this space with the "facebook patent grant" \-- they give away free stuff, with a patent grant that disappears as soon as you sue them.

And they're better at marketing than many - heard of the amazing new zlib replacement, Zstd? It's better in every way except one - unlike zlib (unconditionally patent free), it is only patent free as long as you don't sue Facebook. But almost no one is aware of that.

</comment></comment></comment><comment><AUTHOR> paper: [https://s3.amazonaws.com/fairseq/papers/convolutional- sequen...](https://s3.amazonaws.com/fairseq/papers/convolutional-sequence-to- sequence-learning.pdf)

code: [https://github.com/facebookresearch/fairseq](https://github.com/facebookresearch/fairseq)

pre-trained models: [https://github.com/facebookresearch/fairseq#evaluating- pre-t...](https://github.com/facebookresearch/fairseq#evaluating-pre-trained- models)

<comment><USER11> One logical continuation of adding more attention steps is to make decision of how many attention steps to take determined by the network ala "Adaptive Computation Time for Recurrent Neural Networks", are you planning to go in that direction?

<comment><USER12> One of my students tried something along these lines for Natural Language Inference (NLI) last year. [1] The results where not conclusive, but perhaps Machine Translation is a better target? My reason for believing this is that the specific dataset for NLI most likely does not require multiple steps of inference for most cases (you can get away with simple token overlap), while the decoder in MT does so since it is constrained to output a single token at each step.

[1]: [https://arxiv.org/abs/1610.07647](https://arxiv.org/abs/1610.07647)

</comment></comment></comment><comment><USER13> As far I understood it, Facebook put lots of research into optimizing a certain type of neural network (CNN), while everyone else is using another type called RNN. Up until now, CNN was faster but less accurate. However FB has progressed CNN to the point where it can compete in accuracy, particularly in speech recognition. And most importantly, they are releasing the source code and papers. Does that sound right?

Can anyone else give us an ELI5?

<comment><USER14> I'll give it a shot.

Traditional Neural Networks worked like this: You have k inputs to a layer, and j outputs, so you have O(k * j) parameters, effectively multiplying the inputs by the parameter to get the outputs. And if you have lots of inputs to each layer, and lots of layers, you have a _lot_ of parameters. Too many parameters = overfitting to your training data pretty quickly. But you _want_ big networks, ideally, to get super accuracy. So the question is how to reduce the number of parameters while still having the same 'power' in the network.

CNNs (Convolutional Neural Networks) solve this problem by tying weights together. Instead of multiplying every input by every output, you build a small set of functions at each layer with a small number of parameters in each, and multiple nearby groups of inputs together. Images are the best way to describe this: a function will take as inputs small (3x3 or 5x5) groups of pixels in the image, and output a single result. But they apply the same function all over the image. Picture a little 5x5 box moving around the image, and running a function at each stop.

This has given some pretty incredible results in the image-recognition problem space, and they're super simple to train.

Another approach, Recurrent Neural networks (RNNs) turns the model around in a different way. Instead of having a long list of inputs that all come at once, it takes each input one at a time (or maybe a group at a time, same idea) and runs the neural-network machinery to build up to a single answer. So you might feed it one word at a time of input in English, and after a few words, it starts outputting one word at a time in French until the inputs run out and the output says its the end of the sentence.

What Facebook is doing is applying CNNs to text-sequence and translation problems. It seems to me that what they have here is kind of a RNN-CNN hybrid.

Caveats: I'm an idiot! I just read a lot and play around with ML, but I'm not an expert. Please correct me if I'm wrong, smarter people, by replying.

<comment><USER15> > Please correct me if I'm wrong, smarter people, by replying.

You are not an idiot, maybe not an expert but definitely not an idiot. Your description is quite easy to understand for someone without knowledge in the field. I would add only that RNN are called recurrent because their have recurrent connection with other neurons, and that is why they are hard to parallelize. You need the output the one neuron to compute the output of other neuron in the same layer, so you cannot parallelize that layer. This doesn't happen in CNN.

</comment></comment></comment><comment><USER16> As far as I understand, only the use of the attention mechanism with ConvNets is novel, right? Convolutional encoders have been done before.

<comment><USER3> Yes, there have been a couple of attempts to use CNNs for translation already, but none of them outperformed big and well-tuned LSTM systems. We propose an architecture that is fast to run, easy to optimize and can scale to big networks, and could thus be used as a base architecture for future research.

There are a couple of contributions in the paper ([https://arxiv.org/abs/1705.03122](https://arxiv.org/abs/1705.03122)) apart from demonstrating the feasibility of CNNs for translation, e.g. the multi-hop attention in combination with a CNN language model, the wiring of the CNN encoder[1], or an initialization scheme for GLUs that, when combined with appropriate scaling for residual connections, enables the training of very deep networks without batch normalization.

[1] In previous work ([https://arxiv.org/abs/1611.02344](https://arxiv.org/abs/1611.02344)), we required two CNNs in the encoder: one for the keys (dot products) and one for the values (decoder input).

<comment><USER17> > there have been a couple of attempts to use CNNs for translation already, > but none of them outperformed big and well-tuned LSTM systems

It is true that QRNN had results on mostly small-scale benchmarks, but it seemed that Bytenet especially the second version had SOTA results both for language models with characters and for machine translation with characters on the same large-scale En-De WMT task that is used in this paper.

MT with characters, with regards to ordering, structure, etc, is potentially much harder than with words or word-pieces, since the encoded sequences are 5 or 6 times longer on average, and the meanings of words need to be built up from individual characters.

<comment><USER3> Yes, ByteNet v2 outperforms LSTMs on characters but not on word pieces. It would be interesting to see how our model performs on characters, especially when scaled up to the size of ByteNet (30+30 layers) and also how ByteNet performs on BPE codes. I think that character-level NMT is definitely interesting and worth investigating, but from a practical point of view it makes sense to choose a representation that achieves the maximum translation accuracy and speed.

</comment></comment></comment></comment><comment><USER18> In this work Convolution Neural Nets (spatial models that have a weakly ordered context, as opposed to Recurrent Neural Nets which are sequential models that have a strongly ordered context) are demonstrated here to achieve State of the Art results in Machine Translation.

It seems the combination of gated linear units / residual connections / attention was the key to bringing this architecture to State of the Art.

It's worth noting that previously the QRNN and ByteNet architectures have used Convolutional Neural Nets for machine translation also. IIRC, those models performed well on small tasks but were not able to best SotA performance on larger benchmark tasks.

I believe it is almost always more desirable to encode a sequence using a CNN if possible as many operations are embarrassingly parallel!

The bleu scores in this work were the following:

Task (previous baseline): new baseline

WMT'16 English-Romanian (28.1): 29.88 WMT'14 English-German (24.61): 25.16 WMT'14 English-French (39.92): 40.46

</comment><comment><USER19> This smells of "we built custom silicon to do fast image processing using CNNs and fully connected networks, and now we want to use that same silicon for translations. "

<comment><USER20> I was reading about SyntaxNet (I believe an RNN) developed by Google yesterday. One interesting problem they've run into is getting the system to properly interpret ambiguities. They use the example sentence "Alice drove down the street in her car":

"The first [possible interpretation] corresponds to the (correct) interpretation where Alice is driving in her car; the second [possible interpretation] corresponds to the (absurd, but possible) interpretation where the street is located in her car. The ambiguity arises because the preposition in can either modify drove or street; this example is an instance of what is called prepositional phrase attachment ambiguity."[1]

One thing I believe helps humans interpret these ambiguities is the ability to form visuals from language. A NN that could potentially interpret/manipulate images and decode language seems like it could help solve the above problem and also be applied to a great deal of other things. I imagine (I know embarrassingly little about NNs) this would also introduce a massive amount of complexity.

[1] [https://research.googleblog.com/2016/05/announcing- syntaxnet...](https://research.googleblog.com/2016/05/announcing-syntaxnet- worlds-most.html)

</comment></comment><comment><USER21> I wonder if they can combine this with bytenet (dilated convolutons in place of vanilla convs) - gives you a larger FOV and add in attention and then you probably have a new SOTA.

</comment><comment><USER22> This is a very cool development. Has anyone written a pytorch or Keras version of the architecture?

</comment><comment><USER23> Does this mean that we're close to being able to use CNNs for text-to-speech?

<comment><USER24> Yes - [https://arxiv.org/pdf/1609.03499.pdf](https://arxiv.org/pdf/1609.03499.pdf)

</comment></comment><comment><USER25> no demo?

<comment><USER26> Yeah I was searching for one as well. I hope someone can link the demo page if possible. Want to see the comparison between systran, Google and FB

<comment><USER3> There is no online demo but you can run the pre-trained models on your local machine: [https://github.com/facebookresearch/fairseq#quick- start](https://github.com/facebookresearch/fairseq#quick-start). CPU-only versions of the models are available as well.

For a comparison with other translation services, keep in mind that our models have been trained on publicly available news data exclusively, e.g. this corpus for English-French: [http://statmt.org/wmt14/translation- task.html#Download](http://statmt.org/wmt14/translation-task.html#Download) .

</comment></comment></comment><comment><USER27> TLDR: Cutting edge accuracy, nine times faster than previous state of the art, published models and source code.

But go read the article- nice animated diagrams in there.

<comment><USER28> Haha, this really was TLDR-length. Kudos!</comment></comment>


You're not a hustler. You're a bull-shitter - <AUTHOR> https://firstname.lastname@example.com/youre-not-a-hustler-you-re-a-bull-shitter-727d149967a1 <comment><USER2> The story he cites makes no sense to me. The pheasant achieved its goal with the help of bullshit, it wasn't the bullshit that caused its downfall. The goal was the problem, not the bullshit.

<comment><USER3> > it wasn't the bullshit that caused its downfall

this wasn't stated, so i don't understand your complaint. the post even says " _Bullshit might get you to the top, but it won't keep you there._ "

<comment><USER4> Many tops don't require staying there. In the age of golden parachutes for executes, why would anyone want to stay at the top ?

Besides, staying at the top of a company or even country requires things very different from what you'd want to. The game changes, but it doesn't change from being a good bullshitter to being good at the job ... it requires changing from being good at self-promotion to being good at the opposite: not letting others successfully self-promote.

Besides, all of western industry runs on favors and counter-favors. Take the career of Stephen Elop, and explain to me how this person gets any job more important that toilet cleaner ...

His career:

1) Lotus Development Corporation -> victim of hostile takeover by IBM

2) CIO for Boston Chicken -> chapter 11 bankruptcy

3) Macromedia -> Dissolved after takeover, his resignation was demanded by by Adobe a few months later

4) COO of Juniper Networks -> he was legendary at this company for his incompetence

5) Head of Microsoft office -> where he "successfully" "held off" competitors to office like Google Docs (well done !)

6) CEO of Nokia -> google "burning platform". Half the company was sold off because it was the only way to avoid bankruptcy

7) Rehired at Microsoft as Executive Vice President (along with the devices unit of Nokia, which has since been fully disbanded). His job ended ... when Microsoft terminated

      a) Stephen Elop

      b) 7800 people, mostly those working for Stephen Elop

      c) another 1600 people (later)

Note: Microsoft paid 7.6 BILLION dollars for the privilege of not having Stephen Elop in the company, and even that absurd number doesn't include several extra charges he caused: 1 billion the year after he left for eliminating the last of his department, the 7.3 billion for the Nokia acquisition he talked them into and the $300 million that Stephen Elop his Nokia position in the first place.

8) Telstra -> he is "Group Executive Technology, Innovation and Strategy", stock price has dropped by a little over 33% since his joining

And let me tell you one thing is for sure. There has been ZERO Technology innovation or strategy in Telstra. Zero. In fact Telstra is systematically losing marketshare because of enormous gaps exactly there. (Not that that's particularly strange for a telco incumbent, but still).

In case you wonder, yes, I have encountered "guidance" from Mr. Elop twice in my career, and I have a central policy to my current and future employment: if my employer so much as makes a deal with Mr. Elop, I immediately look for employment elsewhere.

Every single one of his jobs turned into a complete disaster for anyone working for him, and only his first and third positions did not turn into disasters for shareholders of the companies he worked for. Whether all of it is his fault ? Of course not. But at his pay grade, he needs to be able to turn things around and ... to put things VERY mildly, he's not up to that (or even up for that. His behavior has always been to systematically eliminate any political threat to his position, including of course anyone showing even slight traces of rational thought. Ironically, this has often helped him avoid blame. For instance, he claims that his decisions at Nokia were supported by the entire management staff under him, and they were ... after he replaced most of them. There was collective action by company directors, who went to the board of Nokia directly to point out just how bad his decision was. But Elop had cemented his position in the board, not by rational thought, but by money)

And allow me to add, as a commentary on this guy, just listen to his own words:

[https://www.youtube.com/watch?v=owvtKGlYFVA](https://www.youtube.com/watch?v=owvtKGlYFVA)

[https://www.youtube.com/watch?v=kOxCtZSi_Jw](https://www.youtube.com/watch?v=kOxCtZSi_Jw) (he is explaining, yes really, "how to create longevity for your company")

So I think this conclusively illustrates that you can be incredibly successful while being, not just incompetent, but literally be a shit version of king Midas. Everything this guy has ever touched turned to shit in record short time. Everything.

He is known for immediately surrounding himself with yes-men, for constantly delivering ridiculously bad motivational speeches (and I mean ridiculously bad, as in 1960 B horror movie bad, as in people laughing when they know it can get them fired), for immediately biting off the head of anyone doing the slightest bit to criticize him (including firing people 5 levels below him for this).

As an executive to one of the most important companies providing vital services to 30+ million people, he cannot get a room for 30 people to fill up ... What is there to say about just how incompetent this guy is ?

</comment></comment></comment><comment><USER5> I really love this rant. When I was younger I had lots of run-ins with "hustler" bullshitter types in business. Some of them were extremely negative. Eventually I learned how to identify and categorically dismiss not only these types of people but this entire philosophy of business. It's all bullshit.

<comment><USER6> The funny thing is that most business schools drill "customer focus" into their students' heads. But I suppose it's one thing to gain knowledge second- hand, and another to gain it through life experience :(

<comment><USER5> Customer focus is pretty easy to misinterpret as "snow over the customer to sell them in the short term so you can flip it to dumb investors."

It's also sometimes a bit more honest in the sense that a lot of these biz- school graduates have no idea about anything other than business. Being customer focused means gathering what the customer wants and then promising to deliver it _and then_ attempting to do so... and finding out it violates the laws of physics or costs 1000X what they estimated.

<comment><USER6> Yep, people just hear what they want to hear.

As an aside, I think a more telling example of "customer focus" would be how a business handles things _after_ they "find out it violates the laws of physics" etc. I get the feeling a lot of the "hustle" crowd would deal with that by bending the truth. Customer focus would be: keeping the clients informed early as possible, and some sort of gesture to make things up to them. Basically, "do unto others"

<comment><USER4> To be fair, customers are as guilty of that as those business types are.

There's plenty of customers who'll hear what they want to hear ... no matter what the supplier's engineers say (if they're even consulted at all).

</comment></comment></comment></comment></comment><comment><USER7> Probably not going to be a popular article here given the number of people building businesses to flip in a few years time for early-retirement money...

<comment><USER8> Au contraire, this kind of rage-fluff is so popular that we have to penalize it on HN. It's not that it's wrong; it's too right. Nobody's going to disagree, and if they do the rage will turn on them.

It's sort of interesting how a supermajority view can feel like a minority view, but that's not the sort of thing a discussion can get going around.

</comment></comment><comment><USER9> I think your interpreting the wrong definition.

Just like hacker doesn't always mean breaking into computer systems.

So, yes your right to not work for people that break into systems and hustle("con") people for money, but if the definition is:

[http://www.thesaurus.com/browse/hustle](http://www.thesaurus.com/browse/hustle)

verb. work hard make effort

devote apply knuckle down try

dig turn

direct address peg away

give be diligent persevere

give all one's got be industrious plug

give best shot bear down pour it on

give old college try bend pull out all stops

grind buckle down scratch

hammer away commit study

hit the ball concentrate sweat

hustle dedicate throw

<comment><USER10> It seemed right to me. How many industries do you see people on social media, usually start-ups, talking big game about their daily grind and how much involved they are in the work. Many times these individuals are just starting and while they're hustling now they're probably not going to last 'hustling' at their current rate. If you're busy talking about the hustle and not actually hustling you might just be hyping your shit up to people to make your new business work.

A hustler doesn't necessarily aim to con people but they con themselves into thinking what they're doing is more groundbreaking than it is.

</comment></comment><comment><USER11> This article would be much stronger if it referenced a concrete example.

The author argues bs is endemic to "quick-flip startups," but then clarifies that many of these startups are actually good-- not bs.

but no startups in either category are named, and the distinction is unclear in the abstract.

Anyway, I don't get it.

</comment><comment><USER12> I don't think hustle necessarily translate to BS. Having worked with a team of hustlers and a team that were not, it was clear to me hustlers get the job done better with more enthusiasm and energy. The team that were not hustlers seemed to have dreaded their jobs and complained way too much. You can guess which team members got the promotions.

<comment><USER13> Don't confuse hustle with enthusiasm!

Enthusiasms is good - it drives people there is no misrepresentation involved.

Hustling is when you cross the line and start bs'ing people for your personal gains.

</comment></comment><comment><USER13> Well most start ups are bullshit and as a result most do fail.

</comment><comment><USER14> "Hustle" is a word used often on a sports field to basically mean "work hard." In business environments, this is what it almost always means - someone who goes out and works hard to get the job done.

<comment><USER15> Dont confuse 'Hustle' i.e. to run or work energetically with 'Hustler' i.e. slang term for one who swindles (oversimplified, but this is the definition used in the article)

with regards to [0] You're using definition 1. the writer is referring to definition 4

although it does seem like the writer is ascribing uses of definition 3 instead to definition 4.

[0] [http://www.dictionary.com/browse/hustle](http://www.dictionary.com/browse/hustle)

</comment></comment><comment><USER13> Typical start up operator is a bullshitter. They bullshit investors for money. It's completely normal and legal.

If you don't bullshit - you don't get funded. Simple.

Theranos for example was a typical bullshit company on steroids.

<comment><USER13> Fake it until you make it is widely accepted norm.

</comment></comment><comment><USER16> It's all about appearance of intent and managing expectations. If you cannot deliver, don't make a promise. Also, individuals tend to have their own range of fudge factor, think "claimed divided by actual."

George Carlin on BS:

[http://zenpencils.com/comic/25-george-carlin-on-bullshit- exp...](http://zenpencils.com/comic/25-george-carlin-on-bullshit-explicit/)

</comment><comment><USER17> I can't imagine this article will produce an interesting discussion. The article is entirely vague, and addresses the issues in only the broadest of platitudes, without even a single concrete example. The whole article could be more concisely written as "Some founders act in ways I morally questionable, and I don't like it." Not exactly fertile ground for debate.

</comment><comment><USER18> I'm from South Dakota, where pheasants are plentiful. I have seen zillions of them in cornfields, not a single one in a tree.

Then again, we didn't have many trees....

</comment><comment><USER19> this article is bullshit, but i can see the hustle, no?

</comment><comment><USER12> OP should post this on the /r/politics</comment>


Companies from the YC Summer 2018 Batch - <AUTHOR> https://blog.ycombinator.com/s18-16-companies/ <comment><USER3> Cool batch, particularly LemonBox which provides personalized vitamin packs to buyers in China. There's a huge user base right there, and I imagine the U.S. market is largely saturated / competitive already.

I must respectfully express my concern over: "Grabb-it Inc. turns rideshare cars into digital billboards." There's probably a market for it as online advertisers face an uncertain future with possible regulation, and at least scrutiny, of social media companies, but is this really the future we want to build? Where the only goal of some of these companies is to unrelentingly cover every square inch of the world with ads? Surely there are lawsuits waiting to happen when drivers, distracted from these eye-catching ads on all the cars around them, kill people.

I am worried that our public spaces are turning increasingly hostile to our citizens. Won't this trend continue to make the public space more unpalatable?

<comment><USER4> All we need is a YC-backed AR startup that removes ads from your personal visual space. Although it might be difficult to get that tech safe enough to be used while driving.

<comment><USER5> hahahahahahah.... classic mafia extortion techniques. Create a problem, then demand people pay you to remove it.

</comment></comment></comment><comment><USER6> Optic [0] caught my eye. They seem to have made possible a new kind of abstraction in code. Something that's different from both functions and macros. It's a kind of abstraction that can be arbitrarily customized at each call site, yet retain its identity across all of them.

Example:

      foo = do_a()
      bar = do_b(foo)

Example 2:

      foo = do_a()
      bar = do_z(foo)
      baz = do_b(bar)

Example 3:

      foo = do_a()
      bar = foo + 10
      baz = do_b(bar)

The three examples have strong similarity in their first and last statements. There is a pattern there, but that pattern cannot be abstracted into a single macro or a function. So this pattern does exist, and is recognizable to the human eye, but the language does not allow one to express it.

What Optic seems to do is to recognize the pattern, create a single model out of it, and allow you to re-use that pattern elsewhere, or even transform it into new ones and re-use those new patterns elsewhere.

Again, this would be a new kind of abstraction. One whose leaks are easier to fix. You can have your cake and eat it too!

[0] [https://useoptic.com](https://useoptic.com)

<comment><USER7> Hey -- thanks for checking us out!

You're definitely understanding the premise of our parser. It evaluates a regex-like set of rules on an AST tree to match different forms of code. These rules can be recursive as you can see in the first example of our home page [0]. In that example Optic matches an express js route and the headers, parameters and responses inside it. The result is a nice json object with shape {method, url, parameters: [], headers: [], responses" []}. OOTB the JS interpreter couldn't do this because it doesn't encounter the calls to req.query.param_name until that code runs.

PG wrote about building the language/abstraction to fit your problem [1]. In an ideal world we all would do this, but in practice there's always a gap between our program and the abstraction we describe it in. Today that gap is only bridged by a human understanding the code. Until now...Optic is allowing us to programmatically deal with these implicit abstractions.

We believe most of the dev tools created over the next decade will be built on top of your code in a way that allows them to collaborate with real developers. To realize this world we need a programatic interface to read, generate and mutate code so we created Optic.

[0] [https://useoptic.com](https://useoptic.com) [1] [http://www.paulgraham.com/progbot.html](http://www.paulgraham.com/progbot.html)

<comment><USER8> I was really fascinated by this as well. I think it's a great idea. Where does the 15 hours/per developer/per week come from. Couldn't find more information about that anywhere on your website.

<comment><USER7> Before I started Optic I watched 20 developers code for 1 day each. I kept logs of what they were working on and asked people to rate how they felt throughout the process. The 15/hrs/wk is a rough estimate of what we could automate. Our most avid users over the last month self report a little less but I think once we add better support for a few more things we'll catch up.

I never thought to publish those results. Sounds like something people would be interested in?

<comment><USER9> I'm sure neither the technical-minded audience here wouldn't nor the prospective clients wouldn't mind having a look at them at all.

</comment></comment></comment></comment></comment><comment><USER10> I think companies like Grabb-It Labs only make the world more hellish. I really don't understand why you would start a company like this. On my commute each day they've started video advertising on my local bus and also in the subway. It just really SUCKS to be bombarded with more shitty ads for banks and big box stores.

I'm not trying to scold the founders. It just confounds me that people would start companies like this when there's clearly no net value to social capital.

<comment><USER11> It isn't that someone starts this sort of company, it's that respected people still fund them. Do we actually need more advertising companies?

</comment></comment><comment><USER12> The CSPA thing is interesting to me because the founder/CEO was formerly the head of engineering at Crunchyroll, and interviewing at Crunchyroll was one of the worst interviewing experiences I've ever had.

Let's just say I'm skeptical. I hope it works out, but...

MacD looks interesting... but I'm kinda surprised YC would invest. A Mac n Cheese company? Very strange.

I'm very excited about the biotech space, so looking forward to seeing how that shakes out.

<comment><USER13> Sorry to hear it was that bad! :( I'd love to hear your experience, especially if I was directly involved -- you can reply or email me anonymously at email@example.com. If you know anything about Crunchyroll's early culture, I always made a point to listen to feedback and improve ourselves. It'll be the same at CSPA.

Crunchyroll's interview process definitely was not consistent, which is why I spent my final two years there trying to improve our processes. I learned a lot from it, and that's why we're doing CSPA :)

\- James Lin (firstname.lastname@example.com)

<comment><USER12> I have no idea if you were directly involved, unfortunately, which was part of the problem. I described my experience in another comment: [https://news.ycombinator.com/item?id=17795308](https://news.ycombinator.com/item?id=17795308)

</comment></comment></comment><comment><USER14> CSPA [1] sounded interesting, and is something similar to what I've been wanting to do, but after looking at the sample exam, I was pretty disappointed.

I thought it would be about computer science and engineering fundamentals. It has some of that (e.g., representation of integers, memory access speeds, etc.) but I also noticed it's full of completely inessential things, like escaping XHTML, identifying valid JSON payloads in HTTP, and alignment rules in CSS. There was heaps of JavaScript, HTML, CSS, SQL, and drawn out shell exercises.

To be clear, I'm not claiming that's all useless knowledge. It's not! But, as a hiring manager, I'd rather look for strong critical thinking skills and strong foundational knowledge. Remembering that keys in JSON must be quoted strings is not at all a demonstration of that.

The exam seems to be more of a "well rounded full-stack engineer" rather than what they suggest.

[1] [https://cspa.io/](https://cspa.io/)

<comment><USER15> I would hope that the scoring differentiates between core CS knowledge and skills vs. details and trivia of web development, as well as any other categories of questions. That way, the exam is useful for multiple hiring philosophies, and the hiring manager can decide how much they care about specific types of questions. After all, different roles at different companies have different needs, and I'd hate to have a biased scoring algorithm take those decisions away from a hiring process.

<comment><USER13> Excellent points -- this is something we discuss extensively. After talking with so many employers, we found that each of them look for something different.

Adaptive scoring algorithms and ways to let employers create custom projections of our multidimensional data is something we're thinking about. If you have ideas on how to build this, I'd love to hear them! email@example.com

<comment><USER16> Hey James.

The issue you're having is that you're trying to test over 7 dimensions, with the majority of those dimensions being irrelevant to an individual taking the test to signal mastery of 2-3 of them.

The standard method in testing to deal with issues of this nature is to provide people with the opportunity to test themselves on modules, rather than taking the omnibus test so that they can spend the majority of it signalling that they aren't good at things they don't care about. If they're good at everything, they can sit for all of the modules. If they aren't, they aren't wasting their time.

Additionally, this frees candidate time up to let you test people in more depth in the areas they're actually professing skill in. From that point, you're open to do interesting things; evaluate question difficulty/time to completion and adjust the questions provided as the test unfolds to match the expected skill of the applicant for instance.

As it is now, you're getting buy-in from corps that little have no skin in the game, but I don't see how the sample test you have provides more signalling value than an applicant having a CS degree or a list of github projects they can discuss.

<comment><USER13> Re: custom modules. One (arguably) interesting thing we do is to only take the MAXIMUM subscore of all 7 topics and use that component in the composite score, ignoring the other 6 topics. This allows candidates to focus on only certain topics if they want. Employers want someone who is strong in one area, rather than a jack-of-all trades and master of none.

Adaptive testing ([https://en.wikipedia.org/wiki/Computerized_adaptive_testing](https://en.wikipedia.org/wiki/Computerized_adaptive_testing)) is something we're looking into, and would be extremely interesting (and challenging!) to build.

<comment><USER16> Hi James,

The algo to take Core + the best other section, right? As it stands, the test difficulty makes it pretty easy to hit 2 perfect scores, so you'll be competing against those pretty consistently.

How does the CSPA help my interviewing process if I'm a network guru and knock all those questions out of the park, but apply for a ML job where my knowledge is paltry? How does my CSPA testing help me differentiate from the above guy as a network guru when I also aced the ML section? This seems like an obvious area the test can outperform other testing metrics.

As far as I can tell, the only value for the current scoring system is to weed out people who are just plain abysmal at everything. Eventually people are going to game your exam and pop up very close analogues onto google. At that point even the floor function is dead with the added deadweight cost of the exam still being an application requirement at BigCo.

Maybe I'm just blinded because I think having accurate skill radar charts that test takers and employers could use for self improvement and prospect evaluation, respectively, could be an absurdly large value add.

<comment><USER13> Yep I agree! In fact, about 50% of the people who take the CSPA, do it more for self-evaluation than for employment purposes.

You're right, so far most of the test takers are entry-level or changing careers. To accurately assess specialities like ops/IT, we'll need separate, dedicated subject tests (or adaptive tests).

That said, no one has yet gotten a perfect 1600 :) . The highest is something like 1380.

<comment><USER16> Cool! Thanks for the clarifications. I'm looking forward to whatever comes next!

</comment></comment></comment></comment></comment></comment></comment></comment><comment><USER17> I found CSPA to be an interesting proposal. I'm currently studying CS and Biology and found that the comparison of the exam to the "SAT" of computer science is a bit of a misnomer. The SAT requires minimal prior knowledge to take (albeit if you want to score well, you end up needing to study for it through test prep books etc). I think perhaps it may be more likened to an MCAT equivalent (or any professional school exam equivalent) because of the need to know about certain subject fields.

Someone also brought up a point regarding the validity of testing for skills in web development if test takers won't end up in those fields anyway. However, when looking at similar exams (e.g. MCAT), you can make the argument that the majority of doctors won't use Organic Chemistry or won't need to know about the minutiae of Psychology & Sociology to be successful. The MCAT becomes a successful discriminator of good vs bad test takers because of the amount of work and critical thinking skills that is needed to cover the litany of subjects tested. Similarly, the CSPA could follow a similar pattern as the MCAT by testing a range of subjects as a means of measuring one's ability to learn a large sample of topics.

The only thing I can really think of that CSPA can improve upon is providing more preparatory materials. Additionally, it'd be useful to see how people end up performing on the exam and if there is an actual "Bell Curve" like distribution of scores that results.

<comment><USER13> Great feedback! We do have a distribution of scores for our latest exam session here: [https://medium.com/@cspa_exam/july-2018-cspa- results-509ace9...](https://medium.com/@cspa_exam/july-2018-cspa- results-509ace9dd9bb)

</comment></comment><comment><USER18> I have to admit that I'm missing the take-over-the-world/crazy-moonshot kind of company among the entries. I appreciate that Y-Combinator is a commercial institution with commercial goals and that personalized daily vitamin packs, better SaaS conversion and AI-powered programming are all sensible iterations on existing themes - but wouldn't there have been space for at least one next- gen Googe/Space X/Hyperloop sort of venture? Surely someone applied that'd fit that bill.

<comment><USER19> They don't announce all companies from the batch, only the ones that are ready to go "public".

<comment><USER20> Looking at previous, recent batches, you'd be hard pressed to find startups that look seemingly undoable/like a joke (which are usually the ones with huge potential). For me, only Boom and a couple biotech companies make the list.

</comment></comment></comment><comment><USER21> Personalize testimonials to the person reading them? Did I read that correctly? Fake reviews taylored to the reader?

<comment><USER14> I take it as: "See the reviews which will skew your perception positively." Doesn't sound enticing as a consumer. I just want the facts.

<comment><USER22> Devil's advocate. A ML service offering being viewed by two users -- an engineer and an executive.

Even with 100% truthful reviews, they're interested in different things and need very different language.

</comment></comment></comment><comment><USER23> Saddens me that there are not many mechanical hardware companies from any YC year/season and this year is no exception.

<comment><USER24> I interviewed, I've talked to several other robotics companies that interviewed for S18. Absolutely dumbfounded that none of them made it, some of these in the batch seem like they could never be worth $10b.

<comment><USER23> Do you know of any open opportunities for Mechanical Engineers on the East Coast at these robotics company?

<comment><USER24> Sorry no, I'd have to dig up my notes from the interview and I'm slammed working on my startup. You don't wanna move to the west coast? You like bicycles and robots perchance?

</comment></comment></comment></comment><comment><USER25> Cutting edge biotech and a mac n cheese company in the same list. Wow.

<comment><USER26> If only they combined their tech... they could become more powerful than anyone could possibly imagine.

<comment><USER9> I'm sure you're only half joking... The potential synergy might be a meat idea for a eco-friendly lab-grown patty.

</comment></comment></comment><comment><USER27> Nice batch. I think the concept federacy is tackling has huge potential. We lose a lot of sleep thinking about security and I like the idea of being able to tap a hive mind to help us plug security gaps our team is unaware of (and we would pay a lot for it).

<comment><USER28> Appreciate the kind words! Reaching out now -- would love to get you into our beta and work from any of your feedback. Finsight seems like a perfect use case and we're excited to dig in.

</comment></comment><comment><USER29> MAC'D sounds like an interesting idea. If some of the options were healthy (perhaps you could pair mac'n'cheese with tomato soup) I could definitely see myself eating here.

Open up in Chicago please!

</comment><comment><USER30> Congrats! These all look really great ;)

Biocontainment solutions (Synvivia) that prevent environmental contamination of genetically modified organisms looks to be a very high demand market.

<comment><USER31> Say what??

</comment></comment><comment><USER32> > Synvivia develops biomolecular ON-OFF switches to make synthetic biology > safe for use outside of the laboratory.

Yep...then the on-off switch jumps species and the only way to survive is to consume a particular (patent pending) brand of parsnip which can't be grown from seed.

Pretty much the only place I could reasonably be accused of being a luddite is in releasing GM organisms into the wild.

<comment><USER33> I definitely agree about the wild. But what about designing gut bacteria that target parasites in your poop? Amongst many other possibilities.

You can use oxygen, temperature, and light-sensitive gene promoters and toxin- antitoxin plasmids to make ON-OFF switches that would hobble bacterial replication in the wild.

It's a tough problem, and my guess is you'd want to use several of these switches together. There's no guarantee that stressed bacteria will 'run' your programs. And you're right that the gene could move horizontally, so you'd have to be very clever with your kill switches, and keep up with evolution.

Apart from the last resort of antibiotics, you can also selectively deplete particular bacteria from your poop using adhesion inhibitors such as mannosides and galactosides ([https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5654549/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5654549/) \- research from my lab).

</comment></comment><comment><USER34> I like the idea of Grabb-It but it also does seem like it would very distracting to have those ads playing all around me on the road.

</comment><comment><USER9> The downside to Web MD might be that any charlatan may pretend to be a doctor.

When you've got the answers for common diseases from the doctor who gets them from an app, the doctor himself is a repleacable middleman and can be anybody with enough confidence, even if faked.

</comment><comment><USER35> "Customers pick a cheese sauce, a pasta base, add unlimited toppings like roasted broccoli and mushrooms, and top it off with anything from truffle oil to Hot Cheetos." \- So basically Noodles and Co.

</comment><comment><USER35> The concept of HappiLabs calls to me, and while spendy for a new lab, is definitely less than a qualified lab manager would be.

Sadly, really only useful for wet labs.

<comment><USER36> > Sadly, really only useful for wet labs.

I'm not an insider on the terminology, but it seems a "wet lab" is what a lay person, like myself, thinks of when we hear "a lab" (in the physical sciences context).

What's the "dry" (or is the term of art different?) lab situation you wish it would be useful for?

There could be a different startup opportunity, if it's scalably computer- heavy [1]. I'm aware of computer-controlled instruments for wet labs being a very underserved market (and presumably with high margins for the computer portion), but that seems like a consulting/services/labor-intensive business.

[1] Around HN, that usually means software, but my background is Ops, so I actually consider commodity hardware quite scalable, too, despite the hardware-is-a-nightmare-we-have-to-do-cloud mythology.

<comment><USER35> Yeah, "wet lab" is what most people thing of as a "lab". Some fields (lots of the biology and biology-adjacent ones) use "lab" as a logical unit of organization. So Professor So-and-So and their graduate students, postdocs, etc. is "The So-and-So Lab".

"Wet lab" is useful to distinguish people with actual labs vs. my lab, which is just a bunch of people with laptops.

The situation I'm dreaming of, is, for example, it not being _my_ problem when a grad student is having trouble getting software installed on the server. That _would_ be something I'd love a lab manager or dedicated programmer to deal with, but for a small lab, I don't have nearly that level of funding.

<comment><USER36> > my lab, which is just a bunch of people with laptops.

OK, so it's something akin to Wikipedia's description of "dry lab".

> The situation I'm dreaming of, is, for example, it not being my problem when > a grad student is having trouble getting software installed on the server. > That would be something I'd love a lab manager or dedicated programmer to > deal with

This sounds very much like IT/Helpdesk support, of the kind a programmer or a lab manager typically wouldn't _want_ (and might not be qualified) to deal with, either.

Is there something unique to your lab environment that you couldn't use some kind of shared/general IT helpdesk service, perhaps focused on scientific software users?

If not, there may well be an unmet need that a startup could fill, especially if everyone in each lab needs identical support and they can combine it with some kind of cloud-based service with basics pre-installed. However, if you're looking for unlimited, individualized user support for a limited, flat fee, that's not sustainable.

> I don't have nearly that level of funding

For a service provider, the question then becomes, do you have enough funding for the amount of service you'd require (plus the provider's overhead/profit), even if it's not nearly enough for a full-time person (which can be remarkably expensive in popular tech hubs)?

Reasonable IT support to user ratios are somewhere in the 1:20-1:100 range, which is the same range (for a 3-person lab) as HappiLab's pricing.

<comment><USER35> "This sounds very much like IT/Helpdesk support, of the kind a programmer or a lab manager typically wouldn't want (and might not be qualified) to deal with, either." \- Not if they were hired with that qualification in mind.

"Is there something unique to your lab environment that you couldn't use some kind of shared/general IT helpdesk service, perhaps focused on scientific software users?" \- It's more there are parts of HappiLab that I could see using (managing grant budgets, which was...an unpleasant part of my week last week), etc. but my pain points are slightly different from those of a wet lab, but still within the realm of "I'd prefer someone familiar with academia and research rather than a general purpose group."

<comment><USER36> > Not if they were hired with that qualification in mind.

That's a big "if". That's why I said "typically". I think you'd find it difficult, if not impossible, to hire someone like that, even though they exist.

This is a conceit I often see in job postings, of listing skills that belong to two (or more!) different specialties. It seems more common in cases where the goal seems to be to have two specialists for the price of one [1].

> "I'd prefer someone familiar with academia and research rather than a > general purpose group."

I'm pretty sure "prefer" rather than "urgently need" isn't compelling enough to give a startup a enough of a competitive advantage. There's also not much (if any) synergy with any of the rest of HappiLab's core competencies to make sense as an addon for them.

IT services could be just another vendor HappiLab handles.

[1] Uncharitably, two full-time experts for one salary, but, charitably, merely two half-time experts in one person, which seems likely for startups and other cash-strapped groups.

<comment><USER35> "I'm pretty sure "prefer" rather than "urgently need" isn't compelling enough to give a startup a enough of a competitive advantage. There's also not much (if any) synergy with any of the rest of HappiLab's core competencies to make sense as an addon for them.

IT services could be just another vendor HappiLab handles."

Potentially. I think one of the things that will become more important - <USER2> will synergize - is the number of wet/dry labs. There are an _awful_ lot of new computational servers being bought by non-tech savvy PIs.

<comment><USER36> > bought by non-tech savvy PIs.

I'd hope that's not just viewed as an opportunity exploit information assymetry by tech/IT services firms.

I do believe, however, that scientific computing needs are similar enough to general computing needs that this won't be widespread (or at least not for long). It would also mean that it would make sense for a lab services provider to offer generic IT except as a pass-through for convenience (subject to cost- saving disintermediation).

IOW, I doubt you're that special, but that's a good thing!

</comment></comment></comment></comment></comment></comment></comment></comment><comment><USER37> _For example, Mutiny can dynamically change a website's customer testimonials to match the visitor's industry._

Clever idea!

</comment><comment><USER38> Has anyone used Optic yet? I'm not a Javascript developer but I'm interested in the concept.

<comment><USER39> It's seems very interesting, but to be honest after watching the demo video, most of the thing "automated" (I don't see any AI here) are related to poor practices... Some other features seems interesting, let's see how the product evolve.

<comment><USER7> Hey, founder here, thanks for checking us out. The demo video is a couple months old and since then we've gotten much closer to figuring out the best use cases after talking to users. Do you have any suggestions on the use cases you'd like to see the 'interesting features' put towards?

This is definitely the kind of product that takes several iterations to get right so thank you for following our journey.

<comment><USER40> Not the person you are replying to, but I have a use case that I think optic would be good for.

I'd like to have a two-way sync between business logic code and a flow chart for documentation.

Something like the slack push notifications decision: [https://m.imgur.com/gallery/0p5bV](https://m.imgur.com/gallery/0p5bV)

</comment></comment></comment></comment><comment><USER41> Good luck to all. But for those who want to join Rocket Ship companies any unicorns here?</comment>


Responsive Web Design is bad for performance - <AUTHOR> http://www.guypo.com/technical/responsive-web-design-is-bad-for-performance-there-i-said-it/ <comment><USER2> Personally (probably dreaming here), I'd just like to have a standard http header like "Screen-Width: 958", that I could then parse on a per request basis to decide what to send in my http body content. This would make RWD a whole lot easier...</comment>


NIH study finds calorie restriction does not affect survival - <AUTHOR> http://www.nia.nih.gov/newsroom/2012/08/nih-study-finds-calorie-restriction-does-not-affect-survival

<comment><USER2> Related post: <http://news.ycombinator.com/item?id=4450308></comment>


Ask HN: Have any companies on here used Triplebyte? - <AUTHOR> I wanted to learn more about what it is like to hire from Triplebyte. <comment><USER2> They only recently started working with non YC companies. => Talk to YC grads.</comment>


Could Unix be the next MS-DOS? (1985 video) - <AUTHOR>

<comment><USER2> Meaning this Copy of Computer Chronicles - UNIX, 1985? (28 minutes) [https://www.youtube.com/watch?v=L8G1qg99Kl4](https://www.youtube.com/watch?v=L8G1qg99Kl4)

<comment><AUTHOR> Yea... It lost the link. Weird.</comment></comment>


What happens to programmers when they get old? - <AUTHOR> http://jaxenter.com/the-deal-with-older-programmers-114615.html

<comment><USER2> Over 50 programmer here. What I bring to the table is not only programming skills, but life experience, people skills, and a desire to mentor others since retirement is on the horizon. Keeping up-to-date is important, but just being around as one of the old men in the office is also important. Not the CTO, rather the one who knows, or who knows who else knows the answer to a question.

<comment><USER3> Does it get easier, or are ( different )algorithms still as hard to code as they were in the first years of programming?

( Not a troll. )

<comment><USER2> That was probably a troll, but I'll bite. See, that's what I mean. Anyone can code a quick sort or a sobel filter. But when to avoid the sort in the first place, or how to build an image processing pipeline for CPU,GPU,FPGA, asic and when to use each requires years of experience, its not just the algorithm, its the complication and maintainability and security of the resulting system as installed on the hardware

</comment></comment></comment><comment><USER4> They go into the Home for Burnt-out COBOL programmers.</comment>