Speech Recognition Is Not Solved - <AUTHOR> https://awni.github.io/speech-recognition/ <comment><USER2> It is one thing to hear and correctly identify the words. It is another to understand the meaning.

I've been thinking about this because my son has Auditory Processing Disorder, APD. He can hear great, even a whisper across the house. The trouble is the words don't always make sense. He can tell me the words he heard and they are correct, but assigning a meaning to them doesn't work like it does for most people.

After playing him a bunch of audio with missing words, the lady who tested him was blown away by how smart he is - explaining that he is quickly filling in almost every missing word based by guessing from all thing possible things it could be, narrowing it down on context and getting it right. I guess that is a normal, all day long task for him.

Since then I've thought about voice recognition differently. The AI to understand the context or fill in the blanks is what will make or break it.

<comment><USER3> I'd never heard of APD.

Interesting, all my life I've struggled to follow a conversation in a crowded environment, so much so that I actively avoid background noise with words in, I work with silicon ear plugs in or headphones and music with no lyrics.

Looking at NHS symptoms they describe me as a child, didn't learn to read until I was 8.

I nearly ended up in the remedial track but for a single awesome teacher who spotted that it wasn't that I was incapable but that I was struggling to understand in a noisy classroom, she gave up her lunch time for 6mths and taught me one on one by the end of that year I could read much better.

<comment><USER4> Wow, I wish there was an easy way to pay back star teachers or mentors.

<comment><USER5> Is it really that they were a star? Or just that they cared.

<comment><USER6> What's the difference?

<comment><USER7> I don't mean to denigrate the wonderful contribution that the teacher in question made, as it's truly a selfless commitment with life changing impact. However -

One distinction may be that the star teacher scales better. What if there were two students with this problem? Should the teacher stay late? Should teachers not take lunch? What if someone needs help in math, etc.

A star teacher may be able to reproduce this effect for a large number of students. Perhaps using technology, recorded lectures, practice apps, etc. Perhaps organizing study groups for kids to teach and learn from each other.

A caring teacher may sacrifice her lunch. A star teacher may not need to.

<comment><USER8> The thing about teaching is that you can be star just by having one success story. If you are a vital part of the success of 2 people you've already clearly contributed more than your share.

But one is enough; some of the most famous scientists only got where they were because they had a specific advisor. Neither the teacher nor student would ever have accomplished half as much working alone.

<comment><USER9> I am famous to maybe six people. So, it's not much. However, I wanted to add an account to this.

I can tell you the exact moment, problem, response, and teacher - that changed my life forever. It was at that single moment that I understood mathematics. I wasn't suddenly good at it. No, I still pretty much sucked at math. But, I understood it was a language and it was descriptive.

Many years later, that same teacher would be there to help me celebrate defending my dissertation. I didn't actually march, I was already to busy by the time spring rolled around. He's an old man, now. I still stop in and we are still fast friends.

That one comment changed my life. There's a not insignificant chance that it has had a small impact on your life, assuming you've been impacted by modern traffic engineering, or in some pedestrian venues, or had some goods delivered. All that from a single comment that is entirely obvious, in retrospect.

</comment></comment></comment></comment></comment></comment></comment></comment><comment><USER10> What I would like to see you would be an open source speech recognition system that is easy to use, and works. There is really no match for the proprietary solutions in the open source world right now, and that is disappointing.

Not to say that there aren't open source speech recognition systems, they just aren't completely usable in the way proprietary solutions are. A lot of research goes into open source speech recognition, where they are lacking is in the datasets and user experience.

Hopefully, Mozilla's Common Voice project [https://voice.mozilla.org/](https://voice.mozilla.org/) will be successful in producing an open dataset that everyone has access to and will spur on innovation.

<comment><USER11> I agree - I don't care how good Google gets it, this is an unsolved problem until I can do it with open source tools operating without an internet connection.

<comment><USER12> Google has basically invented a special processor with a very, very, VERY weird architecture for these sorts of tasks: [https://drive.google.com/file/d/0Bx4hafXDDq2EMzRNcy1vSUxtcEk...](https://drive.google.com/file/d/0Bx4hafXDDq2EMzRNcy1vSUxtcEk/view)

I don't think this level of computational power can be achieved on a modern CPU, or even a GPU! But GPUs are probably the closest analog to Google's absurdly parallel architecture.

To get a GPU working at maximum performance, you either have to go OpenCL2.0 or CUDA. Compared to OpenCL1.2, OpenCL 2.0 has a better atomics model, dynamic parallelism (kernels that can launch kernels), shared memory, and tons of other features.

NVidia of course supports those features in CUDA, but NVidia's OpenCL support is stuck at 1.2. So in effect, CUDA and OpenCL are in competition with each other.

Anyway, that's the current layout of the hardware that's available to consumers. I think its reasonable to expect a graphics card in a modern machine, even Intel's weak integrated-GPUs have a parallel-computing advantage over a CPU.

So for high-parallelism tasks like audio analysis or image analysis, it only makes sense to target GPUs today.

<comment><USER13> The TPU architecture isn't that weird...it's basically a hardware implementation of matrix multiplication. It also isn't a silver bullet for ASR, where neural networks are usually only used for a part of the recognition process.

<comment><USER12> > The TPU architecture isn't that weird

Its "weird" in the ways that matter: there's no commodity hardware in existence that replicates what a TPU does. The only place to get TPUs is through Google's cloud services.

CPUs are basically Von Neumann Architecture. GPUs (NVidia and AMD) are basically SIMD / SIMT systems.

Google's TPU is just something dramatically different, optimized yes for Matrix Multiplication, but its not something you can buy and use offline.

<comment><USER14> > but its not something you can buy and use offline.

But you will. The entire point is to put this in a phone, so you can distribute a trained neural net in a way that people can actually use without a desktop and $500-$4,000 GPU.

<comment><USER12> > But you will. The entire point is to put this in a phone, so you can > distribute a trained neural net in a way that people can actually use > without a desktop and $500-$4,000 GPU.

As far as I can tell, they put a microphone on your phone and then relay your voice to Google's servers for analysis.

Or Amazon's servers, in the case of the Echo.

I don't see any near-term future where Google's TPUs become widely available for consumers: be it on a phone or desktop. And I'm not aware of any product from the major hardware manufacturers that even attempt to replicate Google's TPU architecture.

NVidia and AMD are sorta going the opposite direction: they're making their GPUs more and more flexible (which will be useful in a wider variety of problems), while Google's TPUs specialize further and further into low- precision matrix multiplications.

<comment><USER15> Neural nets take large GPUs (or TPUs) to train. Realtime inference on CPUs has been possible since forever.

Also, I just turned on airplane mode and google assistant recognized my voice.

</comment></comment></comment></comment></comment></comment></comment></comment><comment><USER16> It's always been a puzzle to me that published WER is so low, and yet when I use dictation (which is a lot--I use it for almost all text messages) I always have to make numerous corrections.

This sentence is crucial and suggests a way to understand how WER can be apparently lower than human levels, yet ASR is still obviously imperfect:

> When comparing models to humans, it's important to check the nature of the > mistakes and not just look at the WER as a conclusive number. In my own > experience, human transcribers tend to make fewer and less drastic semantic > errors than speech recognizers.

This suggests that humans make mistakes specifically on semantically unimportant words, while computers make mistakes more uniformly. That is, humans are able to allocate resources to correct word identification for the important words, with less resources going to the less important ones. So maybe the way to improve speech recognition is not to focus on WER, but on WER weighted by word importance, or to train speech recognition systems end-to-end with some end goal language task so that the DNN or whatever learns to recognize the important words for the task.

<comment><USER17> The low WER numbers you've probably seen are for conversational telephone speech with constrained subjects. ASR is much harder when the audio source is farther away from the microphone and when the topic isn't constrained.

<comment><USER18> Very true. Which is why all the home assistants (Google Home, Amazon Echo etc) use array mics and beamforming - they get a much cleaner speech signal from far field audio, and better WER as a result

<comment><USER17> Exactly. This is also why Google sponsors the CHiME challenge, the existence of which is more proof that ASR is pretty far from solved.

[http://spandh.dcs.shef.ac.uk/chime_challenge/](http://spandh.dcs.shef.ac.uk/chime_challenge/)

<comment><USER18> Good stuff. Was looking into cheap array mics with linux drivers a few times in the past but not much is available.

Speech separation - Mitsubishi Research has done some pretty impressive stuff on that - [http://www.merl.com/demos/deep- clustering](http://www.merl.com/demos/deep-clustering). Haven't seen equivalents of that in open source ASR

<comment><USER19> This is the useful info I've seen hardware-wise recently:

[https://medium.com/snips-ai/benchmarking-microphone- arrays-r...](https://medium.com/snips-ai/benchmarking-microphone-arrays- respeaker-conexant-microsemi-acuedge-matrix-creator-minidsp-950de8876fda)

[https://developer.amazon.com/alexa-voice-service/dev- kits/co...](https://developer.amazon.com/alexa-voice-service/dev- kits/conexant-4-mic/)

Amazon's 7-mic hardware has its own OEM program.

</comment></comment></comment></comment></comment></comment><comment><USER20> I think some people overestimate how good humans are at speech recognition. Unfamiliar accents and noisy environments cause havoc with many people. I had a friend who learned English in India when I was in High School, so I was used to that accent; many of my classmates in College could not understand anything our Indian T/A said freshmen year.

Similarly I had friends for whom English was a second language who had lived in the US for years and were definitely fluent, but had to enable subtitles for a movie in which the characters had a strong southern accent; in general non-rhotic accents were very troublesome for them having only spoken english with midwesterners.

The article mentions the Scottish accent, and I would call that the hardest accent of native English speakers for those in the US to understand.

<comment><USER21> My grandfather was on the ferry from Norway to Denmark with my sister. We're all from Norway. He had quite reduced hearing and used a hearing aid.

They were having dinner at the ships restaurant, and the waiter asked my grandfather something. My grandfather just didn't understand the guy and asked the waiter to repeat several times. After the 4th time, my sister told my grandfather: "Grandpa, he's Swedish". My grandpa paused for a second and then immediately recognized what the waiter had said.

Turns out he had assumed the guy was Danish, and thus failed to interpret the limited sounds he could hear, given the hearing loss and background noise.

<comment><USER22> I'm half-deaf with a cochlear implant; context switching is incredibly difficult! I've had similar problems where I assume someone is speaking either Japanese or English and "filter" accordingly.

</comment></comment></comment><comment><USER23> Just the other day, I participated in a discussion about how "language identification" is a solved problem -- in fact, hasn't it been solved for a decade?

As anyone who's had to use langid in practice will testify, it's solved only as long as:

A) you want to identify 1 out of N languages (reality: a text can be in any language, outside your predefined set)

B) you assume the text is in exactly one language (reality: can be 0, can be multiple, as is common with globalized English phrases)

C) you don't need a measure of confidence (most algos give an all-or-nothing confidence score [0])

D) the text isn't too short (twitter), too noisy (repeated sections ala web pages, boilerplate), too regional/dialect, etc.

In other words, not solved at all.

In my experience, the same is true for any other ML task, once you want to use it in practice (as opposed to "write an article about").

The amount of work to get something actually working robustly is still non- trivial. In some respects, things have gotten worse over the past years due to a focus on cranking up the number of model parameters, at the expense of a decent error analysis and model interpretability.

[0] [https://twitter.com/RadimRehurek/status/872280794152054784](https://twitter.com/RadimRehurek/status/872280794152054784)

<comment><USER24> I'm reminded of how often I see Twitter offer to translate English-language tweets containing a proper noun or two from _absurdly_ unconnected languages. And that's with text containing mostly common and distinctive English words.

<comment><USER25> I'm pretty sure Twitter's langid uses character n-grams. You'll see a tweet that's plain English that happens to match n-gram statistics unusually well with some other language, which pushes the likelihood score to just above English. (I checked this by running an example or two through my own langid code.)

It shouldn't be hard to improve on by treating the score on a tweet as bayesian evidence to combine with a prior from preceding tweets.

</comment></comment></comment><comment><USER26> Speech recognition for multi-lingual speakers is another pain point.

I live with a native French speaker, so my conversations naturally include a lot of French proper names, as well sometimes switching languages mid conversation or even mid sentence.

Lots of recognition engines can handle English and French, but treat them as mutually exclusive. It frustrates me to no end when I know that Siri can recognize a French proper name just fine if I switch it modally into French, but will botch it horribly in English.

<comment><USER27> As a multi-lingual speaker I've even had problems with speech _synthesis_. For instance, Google Maps insists on narrating driving directions in the system language, which is set to English; this makes the narrated local non-English place names incomprehensible. If I succumb and change the system language I can understand the narrated place names, but in return I have to put up with the poor speech synthesis of non-English languages.

<comment><USER26> I was just in France last week suffering through the same problem. I'm American, so I want to keep my system set to English both due to familiarity of the interface and because I want data in familiar units (miles, degrees Fahrenheit), but it kills me when every street and place name is absolutely butchered.

<comment><USER26> An interesting observation I made last week was that the poor synthesis of French street names in English mode was _worse_ than American-mispronouncing- French level. I wonder if that exposes some level of mismatch between how the synthesis engine models language and how humans do.

<comment><USER28> In the case of French names it probably depends on if you have any exposure to French pronunciation rules at all. I have a pretty crappy level of high school French but I can usually manage not to butcher pronunciation. I was with a friend in Montreal though and she had absolutely no idea how to pronounce many things.

</comment></comment></comment></comment></comment><comment><USER29> Of course it's not solved. We don't even know how to define the problem.

Speech is[1] a fundamental component of Language, which is a fundamental component of Intelligence[2]. This is addressed somewhat in the conversation around semantic error rate; that there is more to processing raw audio speech than the calculus of mapping signals to tokens; some understanding of semantics and context is required to differentiate between otherwise indistinguishable surface forms.

I find it doubtful that there's a clean interface that separates the 'intelligent' parts of the brain from the 'language' parts of the brain from the 'speech' parts of the brain. This leakiness (or richness, really) means that you can't neatly solve any one part of this chain to the level of competence that the brain solves it. That means to 'solve' speech recognition, you have to 'solve' language, and thus 'solve' general intelligence. And to 'solve' general intelligence, you have to understand it, in a theoretical sense, which we don't. Indeed, it will likely involve solving all the other modalities of sensation as well. It's definitely the case that you need to have a model for prosody to understand speech. It is entirely possible that vision is a large factor as well, in the form of body language, lip reading, eye contact, and so on.

Speech recognition is _quite_ good for what it is. For many practical applications, especially to do with young, white, newspeaker-accented English speakers who sound the most like the people who develop it, and the data sets used to develop it, it is good enough in the 80/20 sense. But it is nowhere near solved by even the least rigorous definition of the word.

\----

[1] according to the philosophy I subscribe to, at any rate

[2] according to the philosophy I subscribe to, at any rate

<comment><USER30> Problem definition really is the issue. Even appealing to the Turing test is less than satisfying. I'm fairly sure I'm a human, but Siri and similar will likely outperform me in certain categories like place names and popular music.

<comment><USER31> Don't forget that being _too_ good can also cause you to fail the Turing test. I would not expect a human to be able to answer some questions that may be trivial to a computer. Things like, "What's the square-root of 137?" Or, "Identify this obscure song within 5 seconds of listening from a random starting point."

<comment><USER30> I suppose that depends on the precise setup of the test. Is the subject (if they're a human) allowed access to a calculator or a computer with an internet connection? Even if they were, timing would be an obvious tell, but an AI could easily be programmed (or could learn) to introduce an appropriate delay.

<comment><USER31> The idea behind the Turing test isn't whether they can arrive at the same answer given the tools. The idea is whether a human can tell the difference. I would expect any human to answer with something like, "I don't know." Or, "Let me find my calculator..." Either answer would be a lie for a computational AI -- it would know the answer and not require a calculator.

This is, I think, one of the failings of the Turing test. It's easy enough for us to make new humans; making a machine that acts exactly like a human seems like a silly endeavor. I want a machine that can _assist_ us and reinforce our failings. Which means that we _can_ necessarily differentiate it from another human. I vastly prefer that over a machine that has learned to lie to us.

<comment><USER30> > The idea is whether a human can tell the difference.

I know, that's why I'm saying the precise setup of the test is important. That's why, for instance, it's usually presented as a text messaging setup, because the goal is to test intelligence via language and conversation skills. A "face to face" setup wouldn't make much sense, unless we wanted to test that aspect of robotics.

</comment></comment></comment></comment></comment></comment><comment><USER32> Mistakes in understanding speech are common even among humans. My wife and I have to repeat and clarify ourselves fairly frequently in our day to day conversations. She even jokes at times that I must check my hearing, because I often mishear what she said, while she thought she was being perfectly clear.

I think where computers fall short is in two areas:

1) The rate of errors hasn't hit the inflection point of being comparable to day-to-day intra-human interaction, and

2) There is no good mechanism for detecting and correcting errors. At least, none that I've seen.

That second one is important. When I hear my wife ask me "Please, hand me a tractor", I realize that I must've misheard, and ask her to clarify "what?" With speech recognition, I either have to manually re-read and modify recognized text, or cancel and repeat the entire request. Both take time, and negate some of the efficiencies of using speech recognition.

<comment><USER33> One needs to know your wife to know that "hand me a tractor" is wrong. Perhaps she makes model farms as a hobby, perhaps you work in plant rental and that means "pass me the keys to one of the tractors", the possibilities are endless. You need vision, memory, profiling, etc., to even begin to properly contextualise day-to-day conversation.

<comment><USER34> Hm, I think I disagree with you and GP. The answer is Bayesian inference to both of you isn't it? The prior is going to give very low weight to your domestic partner asking you to "hand them a tractor", despite the fact that it's not impossible that it's the correct words.

</comment></comment></comment><comment><USER35> Just the other day I was arguing (pleasantly) with someone here on HN saying that AI has solved speech rec (among other tasks).

[https://news.ycombinator.com/item?id=15429287](https://news.ycombinator.com/item?id=15429287)

Wish this article was written a few days earlier.

An analogue of this article exists for most other domains claimed to have been solved.

<comment><USER36> Sorry to bother you, but the translation of your comment is pretty good (English -> Spanish -> French -> English):

      No, it's not learning new classes of objects from a single image or a few images is very difficult. See
      http://www.sciencemag.org/content/350/6266/1332.short
      The machine translation is a joke.
      Put comments on this page by translating Google into another language and go back to English and see what you get.
      I did a little part of you. Just human level for just a simple little prayer.
      > But even if you do not make this assumption, identifying the object involves spitting the distance from the performance of the human level.

source: [https://news.ycombinator.com/item?id=15429862](https://news.ycombinator.com/item?id=15429862)

Not to debunk your claims, just interesting to see how good translation works (even if it's not human-level, I can understand what you say).

<comment><USER35> I am not sure that is a good translation because it completely misses a common idiom: "within spitting distance"

"spitting the distance from the performance of the human level"

If this were spoken out loud, it might get mistaken for "splitting the distance" which seems to have a different meaning (half as good as humans).

But the point is subtle differences matter a lot in human communication.

But, I do agree with your point that in a lot of cases even without human- level performance one can get things done.

<comment><USER36> After reading the translated version again, I'm not sure I can support my statement anymore. I think a big part of my impression was based on the fact the I read the original comment and knew what you were talking about.

> I did a little part of you. Just human level for just a simple little > prayer.

It's just bad. It sounds like it's based on a n-gram model - about something religious, strangely enough.

</comment></comment></comment></comment><comment><USER37> > Latency: With latency, I mean the time from when the user is done speaking > to when the transcription is complete. ... While this may sound extreme, > remember that _producing the transcript is usually the first step in a > series of expensive computations_.

For many applications, making a transcription seems like an unnecessary step and source of errors. Skipping transcription when the user doesn't need it (most cases where I use it) would seem like a way to get some gain, but perhaps at the reduction of debuggability.

> For example in voice search the actual web-scale search has to be done after > the speech recognition.

That's an area where literal exact transcription is usually required. But even then, Siri/Cortana/Alexa might be better off trying to figure out the meaning of what someone's asking rather than figuring out the exact words spoken in order to return the best results.

Most people are quite bad at formulating good internet searches without a lot of trial and error. Let Google listen to a person talk about the problem they have or issue and then formulate the best results for that instead of forcing us to come up with the exact right phrasing to get appropriate results. It would help tremendously with the synonym and homophone issues that are so annoying now.

<comment><USER38> > For many applications, making a transcription seems like an unnecessary step > and source of errors. Skipping transcription when the user doesn't need it > (most cases where I use it) would seem like a way to get some gain, but > perhaps at the reduction of debuggability.

Agree. What's really needed is research into (and development supporting) how to combine the expertise from a speech recognition layer with the next layer in a machine learning process. That higher layer contains the domain specific knowledge needed for the problem at hand, and still leverage a speech layer focused on a broad speech data set and speech-specific learning (from Google, Microsoft, the community, etc.)

Today, how richly can information be shared? I see with Google's speech API you can only share a very finite list of domain-specific expected vocabulary.

Why not have speech tools at least output sets of possible translations with associated probabilities? Do any of the top tools allow this?

Then you could at least train your next level models with the knowledge of where ambiguity most exists, and what a couple of options might have been for certain words or phrases...

</comment></comment><comment><USER39> Speech Recognition, beside transcribing phonemes and match to a NN of possible words, is not solved because speech is highly integrated with the human context: who is speaking, to whom is speaking, where is the speech happening, why is the speech initiated, and so on.

My kids needed 5-6 years of continous daily talking until I could say they understand a conversation almost completly. Every single word or phrase I directed at them was spoken in a certain context and had a certain role in communicating with them when from the context it wasn't clear what my intentions were. Of course, they had their fun, throwing words away and repeating endlesly some funny word or phrase or terribly spelling them. It is interesting that you as an adult need too learn their own prononciation, at some moment in time I even wrote a small dictionary.

Again, speech recognition will simply stay at recognizing phonemes/words only for a very long time, until we have a true AI Assistant that walks with us, sees with us, and hears with us in the same time. Then it can apply some semantic and other context based related NN.

</comment><comment><USER40> As Portuguese native speaker forced to use foreign languages to talk to devices, it is not solved at all.

<comment><USER41> Even English doesn't work the way it should. Remember Apple had to rush out a patch shortly after Siri was released because it couldn't understand Australians.

Siri still doesn't understand the majority of my Minnesota relatives.

</comment></comment><comment><USER42> I would argue that it can't be solved without severe privacy implications. Apple's talk-to-text, for example, should know my son's name by now, but it doesn't. And while I'm mildly frustrated at the fact that I have to go back and edit text messages on a regular basis, I'm pretty glad that Apple doesn't know my son's name. I'd hate for a company like Facebook to have access to all of the proper nouns in my everyday life.

<comment><USER43> You can teach your iPhone to know your son's name:

[http://appleinsider.com/articles/17/10/19/how-to-teach- siri-...](http://appleinsider.com/articles/17/10/19/how-to-teach-siri-to- pronounce-names-correctly-on-your-iphone)

You'd need to create a contact if he doesn't already have one.

</comment></comment><comment><USER44> As a deaf person I am using Ava [0] which uses IBM's speech to text service [1] as its backend AFAIK. I am always impressed by how it picks up on context clues to make corrections in realtime and capitalizing proper nouns (Incredible Pizza for example). However, it does not work with multiple speakers on a single microphone.

[0] [https://www.ava.me/](https://www.ava.me/)

[1] [https://www.ibm.com/watson/services/speech-to- text/](https://www.ibm.com/watson/services/speech-to-text/)

</comment><comment><USER45> I am a native English speaker from California. It is hard to get my Google Personal Assistant to understand the difference between desert and dessert. It's also hard to request music by an artist with name similar to another artist.

"Hey Google, play Mika radio" has a 50/50 chance of starting music by Meiko. The additional "problem" is that I like Meiko, too, but I feel obligated to cancel the Meiko music & re-request Mika so that Google (hopefully) learns to recognize the difference between Mika and Meiko.

Maybe our spoken language will start to transform into distinctly unique sounds so we can verbally interact with computers with relative ease. When I was in the US Army, I was trained to speak in a certain manner to help my communication to be more clear. [1] I don't see a reason humans and computers can't each make reasonable compromises to make verbal communication easier.

[1] [https://en.wikipedia.org/wiki/Voice_procedure](https://en.wikipedia.org/wiki/Voice_procedure)

</comment><comment><USER46> One thing I see missing is "conversational smarts".

People mishear a certain fraction of what they hear and they'll ask you to clarify what you said.

You could have superhuman performance at tasks like Switchboard and still have something embarrassingly bad in the field because the real task (having a conversation) is not trained for in the competitions with canned training sets.

</comment><comment><USER47> I remember using the Merlin Microsoft Agent to create speech-based "apps" in 2001, including text-to-speech as well as speech recognition. I made a SAT preparation that would test me by presenting a word and I had to say the meaning. I also made a Spanish learning app using Merlin, where it would read out the spanish word (in spanish!) and I would have to say the english one. It worked really well. Its been 16 years since then, and I have to say that I had expected this area to have been completely "solved" by now.

</comment><comment><USER48> I'm constantly surprised by the poor contextual quality of speech recognition. I think the basic audio recognition does well, but when there is ambiguity, it seems like systems that are popularly considered high-performing degrade drastically. For instance, I'm using Dragon NaturallySpeaking to dictate this right now, but if I say a certain punctuation mark at the end of a sentence, half the time it's going to say excavation mark!

Ditto with Google's Google Now assistant, or whatever the heck it's called these days. I have a Pixel 2 phone (Dragon heard "pixel to phone" \-- it doesn't have up-to-date context on proper nouns in the news), but when I tried to create a calendar event using "Create calendar event... meet Bruno for pizza", it heard "MIT pronoun for pizza". It has hundreds of samples of my voice, and it already knew I was creating an event! "Meet" has to be one of the most common first words used in events.

It seems to me like there is pretty low hanging fruit, and that we need more focus on flexibility and resourcefulness rather than acting as though we're moving from 99.5% accuracy to 99.6%.

</comment><comment><USER49> I'm part of the stenographer/captioner community as a hobby and you'd be surprised at how many people think that all captions are automatically computer-generated. In reality the practical limitations of autogenerated captions are demonstrated by YouTube's caption system. It's good but even a mistake every other sentence (95%+ accuracy) can completely obfuscate the meaning behind the video.

<comment><USER50> I've seen some pretty terrible human produced captions on television shows as well.

<comment><USER49> Big time. There are various levels of caption quality and part of it is the tools used.

1\. QWERTY transcriptionists working brief, 5-10 minute shifts typing as fast as they can and rotating.

2\. Voice-mask reporters using Dragon with a voice mask (more commonly phonetic typos)

3\. Stenographers using a steno machine, some of them were not trained to do realtime and lack the ability to edit as they go.

It's all up to the individual, unfortunately. Furthermore, the budget for live captions is sometimes smaller than it should be and so the service purchased is the cheapest, not the best.

I pay attention to captions now. I've watched TSN (Canada) and it seems that past 10 p.m. they put in someone who isn't fully trained or graduated from school and they most definitely shouldn't be providing captions as they are near unreadable.

Good captions are priceless for the access that they offer.

</comment></comment></comment><comment><USER51> A big thing for me is the difference between offline and online speech recognition. This really ties in with the last point, advancements need to be efficient in order to be used.

I'm not sure how much work it would take to scale down Apple's voice recognition to run on the device or if it is feasible with their model, but currently it can take 5-10 seconds longer to get an answer from Siri during peak times.

</comment><comment><USER52> I think that some speech recognition systems depend too much on language model priors. This works well for routine tasks where most of the speaker's words are easy to predict. It fails when the speaker's words are specific or unusual.

For example, try speaking the words: "OK Google, the thick round box jumped over the hazy bog."

</comment><comment><USER53> > It's the only way to go from ASR which works for some people, most of the > time to ASR which works for all people, all of the time.

All people all the time doesn't remotely work for people, either. That's why the Air Force, for example, uses the Phonetic Alphabet, and aviation in general uses special jargon that is harder to misinterpret, such as "affirmative" for "yes".

[http://militarytimechart.com/military-phonetic- alphabet/](http://militarytimechart.com/military-phonetic-alphabet/)

Radio DJs and actors tend to enunciate more clearly than regular people do. You'll notice this more if you speed up the sound - the professionals are still clear at higher speeds where non-professionals become unintelligible.

</comment><comment><USER54> While understanding "any" speech is a great long-term goal, I wish they'd allow these systems to be a little dumber when I want them to.

One example: I often give the same commands to Alexa every day, even at similar times. Yet every few days it just _utterly misunderstands_ those commands, picking words I have never even used before. It doesn't even offer to choose similar commands I've used recently. Worse, the misunderstood command might trigger a paragraph of senseless babble about the misunderstood command, forcing me to shout over Alexa to make it hear the command I really wanted.

So please, please, add some "dumb" options. I want to pick some words, have the system learn them, and just _obey commands_ 90% of the time.

</comment><comment><USER55> We recently did a comparative analysis of cloud speech-to-text providers for a project. We looked at:

1\. Google Cloud Speech API

2\. Microsoft Bing Speech API

3\. IBM Watson Speech to Text

The ranking was as listed above but we had real challenges working with call- center audio recordings. The quality was less than idea but still very clear. We saw a huge reduction in accuracy compared to in-browser testing. Additionally Australian English is particularly not solved.

Because Google's API isn't currently doing speaker-detection, we looked at using Watson's speaker-detection as a secondary step but found it too complex and error prone. There is definitely room for a startup in this area and it also needs continued investment from the bigger cloud providers.

<comment><USER56> For Watson Speech to Text - Did you choose the correct model to match your source audio quality? They default to a "Broadband" model intended for high quality audio sources, but you can also select "Narrowband" for things like phone quality. Not guaranteeing a difference, but in my experience, matching the source quality to the correct model makes some difference.

I've not compared them extensively but for streaming realtime, I found that Watson beat the Google api for a specific use-case. Your mileage may vary!

They also provide a handy Mic / File reader interface for browsers: [https://github.com/watson-developer-cloud/speech- javascript-...](https://github.com/watson-developer-cloud/speech-javascript- sdk/)

</comment></comment><comment><USER57> It seems to me that much of this can be explained by one of the fundamental principles of machine learning:

Training and test samples must be iid (ie independently sampled from the same distribution) to get good performance. Otherwise there is bias.

This applies not only to speakers as individuals, but to all the other factors mentioned, like context, bg noise, gender, age, accent, education, mike, emotion, etc etc.

Many of the issues described can be traced down to this principle.

This is why, possibly, the core issue is data.

Although models and *PUs matter too, the theoretical performance boundary dependends on data quality only (with a model to match).

</comment><comment><USER58> Then there is Chinese, which the same "sound" can have many different words. And we constantly make up new short form or words, a lot more so then English means recognizing these are much harder. ( Then there is Cantonese... )

Sometimes I wonder, if Apps could add vocabulary to devices dictionary, so certain terms in Gaming, Fashion, or Tech or other domain, which are rarely used in normal conversation would be recognized in speech to text as well as Auto Correct.

</comment><comment><USER59> Do you guys thing "understanding" needs more that what we have right now in terms of AI, does understanding require intelligence that is self aware? I mean when we talk to each other, we understand in spoken words, little expressions, even when we talk over the phone. I feel like without the ability to process emotion and self-aware AI we wont really achieve "full understanding" only simulated ones.

</comment><comment><USER60> Can this be attributed to lack of good challenging open and large enough dataset that can be used as benchmark?

The switchboard dataset was "challanging" in its time but pointless from current standards. One doesn't have to look beyond trying to do voice dictation in car to see how horrible current speed recognition systems are compared to humans.

<comment><USER61> There are the CHiME challenges ([http://spandh.dcs.shef.ac.uk/chime_challenge/](http://spandh.dcs.shef.ac.uk/chime_challenge/)) that have test data with distant microphone speech and noisy speech data, and IARPA ran its Automatic Speech recognition In Reverberant Environments (ASpIRE) Challenge in 2014/15 ([https://www.iarpa.gov/index.php/working-with- iarpa/prize-cha...](https://www.iarpa.gov/index.php/working-with-iarpa/prize- challenges/306-automatic-speech-in-reverberant-environments-aspire- challenge)).

</comment></comment><comment><USER62> Technically yes. But in the realm of application, it is mature enough that people can use some drop-in solution, without too much tuning, and get passable result.

It is like say Web Application is not solved. Surely, it is not. But the remaining part falls into the arts category, while the hard barrier of the technologies itself is no longer there.

</comment><comment><USER63> Too many people equate speech to text as 'speech recognition' and it clearly isn't. What is not a solved problem is Natural Language Processing. That NLP isn't solved, is a fertile source of papers. What is 'kinda' solved is the voice CLI, that is where speech is converted to text, that text is inserted in a class command interpreter, and if recognized the command is executed, otherwise it isn't.

Nobody expects to be able to type 'find me the files that changed yesterday' into a computer shell and have it do something sensible. But 'find ./ -type -f -mtime yesterday' sure we can expect that to work. Getting from the first one to the second one requires something called an ontology.

I worked on a team at IBM that was building this sort of capability. The plan was that in conjunction with an excellent tool kit for disassembling a sentence into its component parts (verbs, nouns, adjectives etc) and then matching the elements recognized with an onotology associated with that action. 'find' being the verb would select the search action ontology, 'files' as an object of the verb would select the computer files object ontology, and then 'changed' and 'yesterday' would match 'modification' in that ontology and 'yesterday' would match one of the two sub children of 'modification' (time and change-actor). Then you would go back with what was the compiler equivalent of an abstract syntax tree which the 'command generator' would use to emit the necessary command actions and flags.

I have no idea why they cancelled it. It was an inscrutable place to work at in many ways :-).

<comment><USER13> "Speech Recognition" is an accepted term of art for "speech to text" or "transcription." "Speech Understanding" is a related but different problem.

<comment><USER64> I'd challenge you to transcribe a casual conversation without understanding. Too many words sound very similar, if not identical, and the context of the conversation dictates which word is in play.

Even something simple like "They're unhappy, Ness" could be interpreted as "Their unhappiness" unless you know Ness is a person in the converation.

<comment><USER13> That's like saying because "O" and "0" have the same shape that we can't solve OCR without general AI.

A modern statistical speech recognition system has no trouble determining that "they're unhappy, ness" is a dramatically less likely word sequence than "their unhappiness".

edit: I read your example backwards, but still, a statistical system can easily incorporate contextual words without actually understanding what they mean. Names from the speaker's contacts in particular are widely used in ASR systems for this reason.

<comment><USER64> That's because it doesn't care, it just goes for the most statistically probable phrasing in a general conversation, not the one you're actually having.

As for the OCR problem, try writing one for Chinese calligraphy and get back to me on if context is important or not.

<comment><USER13> Context is certainly extremely important, but a model can incorporates context without general understanding.

</comment></comment></comment></comment></comment></comment><comment><USER65> In the caption for figure 2 there is this quote, "Notice the humans are worse at transcribing the non-American accents." But when I look at the bar graph, I see the word error rates of the model is much higher than the word error rates of humans for most of the non-American accents. Am I missing something?

</comment><comment><USER66> It's pretty good right now for English speakers. At least usable.

We've tried [https://www.btwtalk.com](https://www.btwtalk.com) for transcribing our internal calls, and while it does mess up a bit, it's good enough for later reference.

</comment><comment><USER67> Don't forget speech of children and the elderly, which are also ifficult. I had to laugh a little at Indian accent recognition being so low in humans. I like Indian people but I've always hated the accent because it's so difficult to understand.

</comment><comment><USER68> Did you say ... "speed trek ignition involved?"

If yes, say "yes" or press 1, otherwise say "no" or press 2 to speak your request again, star to hear the options, or stay on the line for the next available agent ...

</comment><comment><USER69> [Slightly off-topic] And what about people with minor to major stuttering troubles like I do ? It is so frustrating to tell Alexa, Siri or Google (even Cortana) what I want because the timer delay expiration is so short.

</comment><comment><USER70> Speech Recognition (STT) used to be completely monopolized by Nuance. Slowly the gates are opening (thanks to google and DL), but I guess it is still a minefield of patents.

</comment><comment><USER71> Siri reminds me of that every time I attempt to use it.

<comment><USER72> Baffled every time that Siri has actually been shipped by Apple to be used in production and not as a pre-pre-pre-pre-alpha.

</comment></comment><comment><USER73> I suspect we (human) are very bad at hearing, computers already are doing are much better jobs than us. However, I suspect for most our communication, we do not actually hear the conversation -- we guess the conversation. Only when evidence such as facial response or out-of-context words is caught then we actually try to hear. Even then, we are still trying to second guess.

Computers cannot compete with our guessing ability, not until they are trained with our life experiences.

<comment><USER74> we are exceptionally good at hearing. Our ears are truly exquisitely precise things.Where "hearing" ends and "understanding" begins is, shall we say, a point of contention.

My vote is for the auditory nerve as the inflection point. Anything above the cochlear nucleus on the auditory pathway is black voodoo magic and anyone who claims they understand it needs to reevaluate their kool-aid intake.

</comment></comment><comment><USER75> Seriously, who thought it was solved? Anyone who has used any voice interaction can tell that it's just not there yet.

</comment><comment><USER76> I heard even voice synthesis, ie text to speech, is not solved yet.

</comment><comment><USER77> I would also like to confirm that speech recognition in language other than English is completely not solved.

In Italian, for instance, any device will barely be able to recognize what you're saying, and will almost always do something completely different from what you've asked.

</comment><comment><USER78> Speech recognition is such a gimmick. In practice the applications are quite narrow. The truth is, most people with properly functioning social faculties feel like complete dicks when talking to a machine.

</comment><comment><USER79> Also, water is wet and rocks are hard.

<comment><USER80> Would you please stop posting unsubstantive and/or snarky comments?

[https://news.ycombinator.com/newsguidelines.html](https://news.ycombinator.com/newsguidelines.html)</comment></comment>


YouTube to discontinue video annotations because they never worked on mobile - <AUTHOR> http://www.theverge.com/2017/3/16/14953756/youtube-annotations-feature-discontinue-cards-end-screens <comment><USER2> I've been watching YouTube videos for years and years. I like having something on in the background while working from home, so i have watched a hell of a lot of videos. Never once clicked an annotation. Maybe once or twice by accident, but never on purpose. I was always curious as to whether other people were actually clicking on these, or whether most other people shared my dislike of them.

I could always see why they _could_ be a useful thing, like linking somewhere else at some point during the video, but I always went to YouTube to watch videos, not take part in some interactive shenanigans.

Of course there are the people that obnoxiously spread annotations over an entire video. it was almost natural instinct for me to click on the little settings icon > annotations > off. There are always going to be people that abuse what they have been giving. I'm glad there is soon going to be one less thing for them to abuse. Silly humans.

</comment><comment><USER3> Annotations are a horrible, annoying feature in the first place. Good riddance.</comment>


JavaScript Powered Arduino with Johnny-Five - <AUTHOR> http://blog.safaribooksonline.com/2013/07/16/javascript-powered-arduino-with-johnny-five/

<comment><USER2> Thanks for sharing a new way to programs an arduino. This should allow more web-devs to explore hardware hacking</comment>


A Second Disease Wiped off The Face of the Earth - <AUTHOR> http://www.nytimes.com/2011/06/28/health/28rinderpest.html?_r=1&ref=health

<comment><USER2> and I wonder how many new created.</comment>


Two Strains of H.I.V. Cut Vastly Different Paths - <AUTHOR> http://www.nytimes.com/2015/03/02/science/two-strains-of-hiv-cut-vastly-different-paths.html

<comment><USER2> The startling thought is that SIV has existed for a long, long time - I've read that it's tens of thousands of years old - and yet HIV only emerged in the last century or so.

The only explanation that makes sense to me is that HIV _has_ emerged previously, but only in dead-end strains that have very low infectivity, rather like the HIV-1 groups N and P mentioned in the article. Whilst it makes sense to me, I have a feeling that it _can 't_ be right; seems a bit too easy or convenient to explain.

Are we victims of a million-to-one chance mutation, but a chance that become steadily more likely as time progressed?

Further, is it just a fact of life that we can - and _should_ \- expect another HIV-level-of-infection virus to emerge in the future?

<comment><USER3> > The only explanation that makes sense to me is that HIV has emerged > previously, but only in dead-end strains that have very low infectivity, > rather like the HIV-1 groups N and P mentioned in the article.

Or that modern innovations like transportation and so on made it possible for it to spread before it died out.

<comment><USER4> Exactly. This why Ebola hadn't spread the way it did until now. It was isolated to small remote villages.</comment></comment></comment>


Facebook's Browser is Google's Worst Nightmare - <AUTHOR> http://www.smorepages.com/facebook-browser <comment><USER2> All the user needs that the article lists and that a Facebook browser would answer to (instant notification, always on chat, etc.) could be just as easily be answered by a simple toolbar, on top of another browser.

As for the adoption of a FB browser, I would be curious to see how it would fare. I'm pretty convinced that most early adopters which sustained the initial adoption of Firefox and Chrome would never use a FB browser, because they would be worried about Facebook becoming the sole owner of the internet - and because they're perfectly able to be always on on FB, should they want to.

Could the typical Facebook user become an early adopter? Everything's possible, but it wouldn't be very easy.

<comment><USER3> Why aren't people equally worried about Google becoming the sole owner of the Internet?

Honest question - is it because people are ok with Google being in that role, or because they don't think Google is trying to get into that position?

<comment><USER4> Because Google spins things better.

Google has a nerd-friendly message, and produces things that are building dependence and are genuinely useful (like, say, gMail). So, people are less scared of it, and after some time denial kicks in.

Also, I'm bitter because Google is the one software company that I'm scared of more than of Facebook :-D

<comment><USER5> For Gmail, you can withdraw your emails out via IMAP/POP. You can export your contacts.

<comment><USER4> Of course you can. But then you'd be also abandoning the workflow, and I meant dependance on that. Only relatively recently mail clients supporting a workflow similar to Gmail show up.

</comment></comment></comment></comment></comment><comment><USER6> The title of this submission is quite misleading. This article is _speculating_ that Facebook will make a web browser, not that Facebook is making a web browser.

It is also not clear that a Facebook web browser would be an overnight success, as this article presumes. I'd say the opposite in fact. There has been enough big, old fashioned media interest in Facebook's privacy slip ups, that any Facebook browser would be negatively talked about in the press.

</comment><comment><USER7> _"As Facebook becomes the social platform of the internet, a custom browser is the next logical step."_

You'll need to explain that, because I don't think it's a logical step at all. The remainder of the article presents some benefits, but mentioning those does not make for a clear argument for why facebook should attempt to compete with their own browser.

<comment><USER8> Benefits for Facebook, yes. Benefits for the user? Thin at best.

</comment></comment><comment><USER9> What a bullshit article. Complete with photoshop mockups. Might as well be another rumor about the "next" iPad killer or how it would be cool if Vimeo built refrigerators with built-in video screens. I would certainly not even consider using or downloading a FB browser. Last thing I need is to be constantly Zuckerberged.

</comment><comment><USER10> > Facebook's Browser _would be_ Google's Worst Nightmare _perhaps_

Fixed the headline to be truthful.

</comment><comment><USER11> While the envisioned scenario would be a blow to Google, it would require so many outlandish developments that this is basically impossible. For one, Facebook would have to create a search engine, something that they have shown no interest in (they seem happy to farm that out to Bing for now), much less any capability. More importantly, people would have to use the Facebook browser. The author really overestimating how much people like Facebook. The example of iPhone and iPad apps' popularity aren't proof people love Facebook products (not to mention there is no iPad app), they like accessing Facebook. They like talking to their friends. If you pay attention to most people, they hate new Facebook products. I hear constant bitching about the apps, the interface, etc. The value proposition of a Facebook browser would be basically annoying the user at all times while all other browsers could do the same exact thing in a separate tab. Chrome didn't become popular because of tight Google integration, it became popular because it was faster. I don't see why a browser that just gives me Facebook updates would be any more popular than Rockmelt or Flock.

And, of course, Google isn't stupid. Google+ is a fairly competent attempt to head off even this out there future.

</comment><comment><USER12> Isn't that what RockMelt is trying to be? I know it isn't developed by Facebook, but with integrations like notifications, friends, chat, etc, it basically is the Facebook browser.

<comment><USER13> Yes, and I believe Facebook is an investor in Rockmelt.

<comment><USER9> Rock who?

Exactly. Facebook wasted their investment. That browser will never get above 5% usage. It would be like Camino.

</comment></comment></comment><comment><USER14> " _Imagine that, within a year, a modest 25% of Facebook users use their browser. That would equate to about 200M users; all of which are using their search._ "

I'd be willing to bet that a significant percentage of Facebook's users don't know what a browser is. They fire up Internet Explorer and are blissfully unaware that there are alternatives. This has been my experience with non- techies.

Those who _do_ know what a browser is are likely to have Chrome, Firefox or Opera installed and probably see no personal benefit in downloading yet another browser. They are also unlikely to want to have Facebook invading their privacy even more.

</comment><comment><USER15> I love that 10 years after Microsoft rolled out IE in an attempt to use the browser to disintermediate and control the user's experience, we now have Chrome and Silk and advertisting and everyone is racing to own the browser again.

</comment><comment><USER16> Facebook can make a web browser - or just acquire RockMelt, with which it has worked in the past. Rockmelt is based on Chromium, so FB won't have to take care of the browser basics, similar to how Amazon has forked Android for Kindle Fire. But, a Facebook ad network available off-site will be more interesting. I don't have experience with FB ads but social ads as imagined by OP seem interesting.

</comment><comment><USER17> _Imagine that, within a year, a modest 25% of Facebook users use their browser. That would equate to about 200M users_

The reasoning is backwards. Converting 200 million users to a new browser is not a trivial task. In what way would a facebook browser allow people to better access facebook?

</comment><comment><USER18> It would be everyone's worst nightmare - a nice shot across the bow of the open web.

<comment><USER9> Yeah.. maybe they could team up not only with Bing, but they could revive IE 6. Perhaps even get Active X back in circulation. In just a few short years we can pull the web back into the dark ages. I would likely code all of my pages specifically to display a blue screen if the visitor were using an FB browser. If enough people did that, any Facebook spyware-laced browser would die on the vine.

</comment></comment><comment><USER19> I would imagine that Facebook's phone would be a much bigger nightmare.

</comment><comment><USER20> >> People try Facebook's stuff all the time—just look at the adoption rates for their iPhone and iPad apps

An otherwise interesting article, but there is no iPad app.

<comment><USER6> This slip up does not make me confident that the author of this article has done their homework.

</comment></comment><comment><USER21> I think a Facebook search engine would be Google's worst nightmare. Nothing else directly challenges their top-line revenue.

</comment><comment><USER22> This could work for personal browsing, but I'd imagine few people would want to be seen using the Facebook browser at work.

</comment><comment><USER23> I feel it will have about as much impact as the AOL browser or "Explorer from Yahoo" had.

</comment><comment><USER24> This is pure speculation. They aren't actually making a browser.. "Not yet"

</comment><comment><USER25> Flagged. This is article is entirely fabricated.</comment>


Petition: Revoke Article 50 and remain in the EU - <AUTHOR> https://petition.parliament.uk/petitions/241584 <comment><USER2> Not the most popular view, but if after a referendum politicians can dither about for long enough to make things painful then say "Lets have a do over" whats the point of actually voting?

The whole thing just feels like an assault on democracy. The popular vote was to leave. If you put it to the people, then do what they ask. You can probably ask to join again later.

Was the vote done with imperfect information? Probably, but you can make that case for anything. Its the system we have and if you can do this I makes me feel like what I vote for has no real impact. I feel that this is why we have double jeopardy laws in trials.

NB I am Australian and have no real thoughts either way about brexit. I fully expect this to be down-voted into oblivion because this is such a button for most people.

<comment><USER3> In general, you can't take a strictly advisory referendum with a win margin of a couple % to dictate your international policy for decades.

More specifically, when the winning side has committed campaign violations and the UK's own National Crime Agency has found illegal foreign interference, maybe you should investigate further instead of driving head on. If you ask me, this was the "assault on democracy", not the "do-over" approach as you wrote.

On do-overs - the Prime Minister is pushing to have a third vote on the exact same deal with the EU, after her proposal was rejected twice by the members in the course of a couple of weeks. So it's especially concerning when do-overs in parliament can happen until the government gets the desired result, while at the same time the people aren't given a chance to vote again and the outcome of the referendum is considered final.

<comment><USER4> The win gap was at least 4%. (48 vs 52).

Further, the native population was far higher pro Brexit.

As for misinformation, I don't know any Brexiters who were voting based on Boris Johnson's obviously politically gamed claims from someone who is a Brexiter purely for political reasons (I don't think the population is as naive as you give them credit for, and presuming Brexiters to be stupid is BS fallacy), but on the basis that the EU is not properly democratically accountable and the Brit economic/business attitude and politics is not compatible with European ways (ie, high State control). We want to go back to a low paper-work, low-bureaucracy, liberally-inclined environment. Brexit is a start.

I have lived a significant proportion of my life in Europe, and the UK is not meaningfully European. It should have never have been a member, and has been a problem to the EU ever since. It should have been consulted on joining the EU at the very least, and it was not. Now that it has had 20 years of the EU, a 52% vote against remaining is significant.

<comment><USER5> "The win gap was at least 4%"

That's a narrow gap. Demographics (Brexit voters tend to be older) could easily have eroded that lead by itself in the past 3 years.

"Further, the native population was far higher pro Brexit"

The native population tends to have a lower birth rate also eroding the lead. Plus what is your point here? I cant think of a reasonable reason for even bringing it up.

</comment></comment></comment></comment><comment><USER6> Unless it gets 17.5+ million votes, it'll likely just get referred back to [https://petition.parliament.uk/petitions/239706#debate- thres...](https://petition.parliament.uk/petitions/239706#debate-threshold). It'll be interesting to see the response if it does gets 17.5+ votes even if not an official validated poll. Got my vote anyway, miracles may happen.

Whatever happens, the UK is a divided, broken country and will be for some time. It'll take years to finally come through and fix the damage regardless of the outcome of Brexit.

<comment><USER7> To be fair its heading north of 750,000 as i type. That's not insignificant considering its only been open 24 hours or so.

</comment></comment><comment><USER8> Something like this happened before, with 2.5 million signatures.

[https://theguardian.com/politics/2016/jun/24/petition- second...](https://theguardian.com/politics/2016/jun/24/petition-second-eu- referendum-crashes-house-of-commons-website)

</comment><comment><USER9> The UK map of those who signed the petition: [https://petitionmap.unboxedconsulting.com/?petition=241584](https://petitionmap.unboxedconsulting.com/?petition=241584)

<comment> black-tea Cambridge, London, Bristol. The same people who voted to stay. What a surprise.

<comment><USER5> Poor inner cities voted Labour, posh country areas voted Conservative. What a surprise.

It isn't the hard core that win elections, its the swing voters. So no it isn't a surprise, and it isn't really relevant.

<comment> black-tea I was going for where intelligent people live. I don't think the brexit vote was clearly cut on Labour/Conservative lines.

<comment><USER5> My point remains, you aren't going to see a massive shift in leave/remain areas. If another vote were to happen, the result would hinge on a relatively small percentage of voters.

Ps I live in a probably leave voting area, so please don't tar all people with the same brush. >50% voted against remain, so calling them all less intelligent isn't helpful. We do have to get on again after this after all, and if there is a 2nd referendum, they might respond better to addressing their actual concerns, rather than insults.

</comment></comment></comment></comment></comment><comment><USER10> Relevant reading: [https://johnhiggs.com/newsletter-10/](https://johnhiggs.com/newsletter-10/)

</comment><comment><USER11> The Barnier Robbins document was designed to produce political chaos followed by long extension . Second Referendum and Bingo still in EU. Establishment victory. Unfortunately there will be upheaval politically and economically this year . Just wait . Started today in Holland

</comment><comment><USER12> Regardless of what one thinks of the brexit vote outcome it will be interesting to see if the people of a democratic European country has the power to exit the EU through its own democratic process.

</comment><comment><AUTHOR> 397,618 signatures and gaining about a thousand a minute.

</comment><comment><USER13> A bit late for this, isn't it?

</comment><comment><USER14> And it's just crashed...</comment>


Your worst reply-all mistake was never this bad - <AUTHOR> http://qz.com/816561/a-barrons-editor-inadvertently-revealed-layoff-plans-with-a-reply-all-email-to-the-staff-of-the-wall-street-journal-nws/ <comment><USER2> Clickbait title. The mistake (made by Wall Street Journal editor Gerard Baker) was mentioning unannounced layoffs in a reply-all email.</comment>


UpUp: a tiny script that makes sure your site is always there for your users - <AUTHOR> https://www.talater.com/upup/ <comment><USER2> On paper you should be able to do the same with a HTML5 manifest file.

Don't get me wrong, it's a really neat wrapper, I'm just curious about why I should use this library instead of an already in HTML5 implemented, easy way which does pretty much the same?

<comment><USER3> By HTML5 manifest file, do you mean the Application Cache? Because that is apparently (I've never tried it) anything but an 'easy way' [0]. Whereas this script appears to wrap ServiceWorkers, which is the 'modern', recommended way to do things [1]. Also being a 'neat wrapper' with a simple API is often a good enough reason for something to exist.

[0] [http://alistapart.com/article/application-cache-is-a- doucheb...](http://alistapart.com/article/application-cache-is-a-douchebag)

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

</comment></comment><comment><USER4> Oh wait. The description makes it look like you can server your pages even when YOUR SERVERS are offline.

But in fact, this script just saves stuff somwhere on the client, so the client can see certain things even when THEY ARE offline.

Maybe I was the only one being this mislead.

Just, when I am a client and don't have internet, then I don't have the idea to still browse the internet. So what are the ultimate goals for this "offline first movement"?

<comment><USER5> This will also work when the site's servers are down. It serves offline content from the local cache if the original fetch fails - it can fail either due to your device's connection, the server, or anything in between.

However, since offline content is served _after_ trying the network, it's still going to be slow in flakey network conditions as you wait for the original request to either succeed or give up.</comment></comment>