Blog Post Driven Development - <AUTHOR> http://blog.estimote.com/post/119525082855/user-stories-on-steroids-how-estimote-uses-blog <comment><USER2> This was (still is?) a big part of the development methodology at Amazon; there it was called "starting by writing the press release": [http://www.allthingsdistributed.com/2006/11/working_backward...](http://www.allthingsdistributed.com/2006/11/working_backwards.html)

<comment><AUTHOR> Amazon did a great job with that technique and we were inspired a lot by that. Of course companies/startups do less "press-releases" these days, so we adopted it to blogging.

The content of published articles does help not only with team focus, but also is a natural "deliverable" definition for UI-less products such as SDK. Blog post simply cannot be published unless code is in the Github repo, tutorials and sample apps are there as well as illustrations and narration communicating new features.

<comment><USER3> Tanuj from Estimote business team here. It's also super useful for my efforts. Makes it so much easier to communicate to customers what we're working on, and there's also the intangible benefit of impressing potential customers with how fast and how much we ship -- e.g., [https://twitter.com/stevecheney/status/598905085826043905](https://twitter.com/stevecheney/status/598905085826043905)

</comment></comment></comment><comment><AUTHOR> Hi HN,

this is Jakub, co-founder of Estimote (YC S13).

As a dev-oriented company our SDK team used to ship tons of new code into our Github repos. We realized that our SDK is much harder to productize and define an explicit "deliverable".

Thus past few months we have applied "Blog post driven development" where the aim of the sprint was to build, test, document and communicate SDK changes in a form of a blog post.

This technique is working for us really well, so John who is driving that team as a Project Leader decided to share the details with the HN community. We hope you will enjoy it and feel free to comment.

\--- Here is also a snapshot of our recent blog posts as as a proof shipping code with posts is working really well:

\- 5/15 - OAuth support for easier integration of Estimote Cloud into your services \- 5/06 - Hugely improved Indoor Location SDK (4-meter accuracy) \- 5/04 - Updated Android SDK \- 4/23 - Improved beacon real-world analytics (new SDK + API) \- 4/21 - Estimote Cloud updates (tag beacons with metadata + GPS location) \- 4/15 - Conditional beacon broadcasting (for power saving & easier prototyping) \- 3/24 - Fleet Management API \- 3/18 - New SDK and firmware \- 3/13 - C# and JavaScript support \- 3/04 - Support for Apple Watch \- 2/24 - Infrastructure Sharing (open beacons to 3rd party apps/brands) \- 2/13 - Trigger Engine

<comment><USER4> Is this why I'm still waiting for my estimote stickers? I'm not sure I would ever build anything user facing with your stuff it's just not a useful platform if you can't reliably get the product.

Edit: It's honest feedback, the down vote is unwarranted.

<comment><USER5> Same here, I preordered the sticker September 2014 but still nothing. I lost faith in getting them so I moved forward with other solutions, even if I still would like to have them around my house.

<comment><USER6> Hi BontoJR,

Same as above: you send me your order # to wojtek[at]estimote and I'll update you on the expected delivery. And really sorry for the delay!

Cheers.

</comment></comment></comment></comment><comment><USER7> Readme driven development - [http://tom.preston-werner.com/2010/08/23/readme- driven-devel...](http://tom.preston-werner.com/2010/08/23/readme-driven- development.html)

<comment><USER8> The corresponding HN discussion

[https://news.ycombinator.com/item?id=1627246](https://news.ycombinator.com/item?id=1627246)

</comment></comment><comment><USER9> As a team lead, I have trouble coming up with a unifying theme for a sprint (2 weeks). My team is about 4 devs, 2 qa, a product owner, and myself (tech lead).

If we devote a sprint to finishing a cohesive goal, I find it extremely likely that some team members will be twiddling their thumbs for extensive periods of time. Or that team members will need to re-work stuff to fit with a key piece, which I consider even worse than thumb twiddling.

Do other teams experience this? How do you deal with it?

I try to have 1-2 big goals (stories) for the team in a sprint, and a few smaller, unrestricted ones as well. If we get blocked on the bigger stuff, the smaller stuff is ready to get knocked out.

<comment><USER10> If you're accomplishing cohesive, important goals with your sprint, why do you care about thumb twiddling or some refactoring?

If those doing the thumb twiddling are important team members who are tangential to this sprint, why not offer them a bonus vacation or something else morale boosting that doesn't require their butt to be in a seat when it's not necessary?

If they're not really helping you accomplish _any_ important goals, why are they on your team?

All that said, it sounds like you've already struck a pretty good balance between accomplishing big goals and taking care of small but necessary stuff.

</comment></comment><comment><USER9> I avoided this article for a while because I thought it was going to be about mediocre devs copying-and-pasting code from development blogs into 'enterprise' software and the impact that it had on morale, or culture, or whatever.

What a pleasant surprise!

It did quickly remind me of Amazon's 'press release driven development'. I appreciate the desire to promote, but I wish some popular, public art was credited.

</comment><comment><USER11> I like this approach's writeup for several points it makes. 1) Planning ahead and framing your definitions through a deep engagement helps developers focus through understanding their work and it adds to their motivation. 2) Consistent engagement with users helps with adoption. 3) Code is only part of the picture, and a release produces ongoing opportunities for improvement.

In terms of 1, within Drupal's developer community, I remember an aphorism, "Talk is silver, code is gold", which served to promote valuable productions over endless discussions. Then, someone took that and a general feeling of a need for foresight before hasty implementation and said, "Talk is silver, code is gold, and planning is diamonds."

In terms of 2, for free software and community projects in general, I get a sense that this approach aligns with stakeholder engagement[0] toward empowering active developers with broad community feedback through timely awareness and helps community cohesion, as well as user empowerment.

On a brief theoretical view of this aim to align shared interests, this approach strikes me as an appropriate response to dramaturgical awareness. In terms of impression management[1], I get a sense that contributors generally want users to see them as involved for good cause and effect, and users want to be seen as grateful, willing participants (hence comments sections on release posts with floods of thank yous). This outlet seems like one way to deepen this two-way information flow.

0: [https://en.wikipedia.org/wiki/Stakeholder_engagement](https://en.wikipedia.org/wiki/Stakeholder_engagement) 1: [https://en.wikipedia.org/wiki/Impression_management#Erving_G...](https://en.wikipedia.org/wiki/Impression_management#Erving_Goffman)

</comment><comment><USER12> On a lighter note, Conference Driven Development: [http://devdriven.by/conference/](http://devdriven.by/conference/)

</comment><comment><USER13> At first I laughed but then I got it.

</comment><comment><USER14> Imho the post should be a draft, or private until the tasks are done

<comment><USER15> Wholeheartedly agree! We never publish a blog post before the feature actually is live. We want people to be able to immediately go ahead and play with the it—which is also why we treat these posts as mini-readmes, i.e. we always try not only to paint the story behind the feature, but also do a short intro to how to get started (code snippets etc.)

</comment></comment><comment><USER16> This is very clever.

</comment><comment><USER17> So vaporware is a development methodology now?

</comment><comment><USER18> Isn't there a risk by the time you finish the blog post, someone has posted an even newer JS or Go framework in Grithub, and you have to simply replan the whole tech to use it!

<comment><USER10> I have a feeling you didn't even read the article and instead interpreted the headline to mean "we use whatever technology generates lots of blog posts," which was my interpretation too, prior to reading the article which is describing what's actually a pretty good approach.</comment></comment>


Neural Net Plays Atari "Fishing Derby" - andrew-lucker https://www.youtube.com/watch?v=N0CYtp8bQNI&index=35&list=PLX1tECIw-CXVYcg4k4SJl92rmzU5-hflW <comment><AUTHOR> I assume the 'random' strategy was the one on the left? It didn't ever extend the line - just the pole. Not a fair test?

<comment> andrew-lucker Yes, just a baseline. Trying to arrange a comparison with further strategies X vs Y etc.

Still working on the OpenAI plumbing though, so still a ways before I get to play with all the different research agents available.</comment></comment>


'Funeral' held for aging Web browser - <AUTHOR> http://www.cnn.com/2010/TECH/03/04/ie6.funeral/index.html?eref=igoogle_cnn

<comment><USER2> The title should be 'Funeral held for _sick_ aging Web browser', now who do you suggest should write the obituary?</comment>


AI's Language Problem - <AUTHOR> https://www.technologyreview.com/s/602094/ais-language-problem/?set=602129 <comment><USER2> No one would ever imagine that locking a baby in a featureless room with a giant stack of books would give them general intelligence. I don't understand why AI researchers think it will work for AIs. They need bodies that are biologically connected with the rest of the biosphere, with an intrinsic biological imperative, if they are ever to understand the world. I'm not saying they have to be exactly like us, but they will only be able to understand us to the extent that they have body parts and social experiences that are analogous to ours.

This isn't an engineering problem, it's a philosophical problem: We are blind to most things. We can only see what we can relate to personally. We can use language and other symbol systems to expand our understanding by permuting and recombining our personal experiences, but everything is grounded in our interactive developmental trajectory.

The kitten-in-a-cart experiment demonstrates this clearly: [http://io9.gizmodo.com/the-seriously-creepy-two-kitten- exper...](http://io9.gizmodo.com/the-seriously-creepy-two-kitten- experiment-1442107174) Interaction is crucial for perception. Sensation is not experience.

And here's the rub: Once you give an AI an animal-like personal developmental trajectory to use for grounding the semantics of their symbol systems, you end up with something which is not particularly different or better than a human cyborg.

<comment><USER3> I believe we can get AI from just text. Obviously that won't work for babies, because babies get bored quickly looking at text. AIs can be forced to read billions of words in mere hours!

Look at word2vec. By using simple dimensionality reduction on the frequency that words that occur near to each other in news articles, it can learn really interesting things about the meaning of words. The famous example is the vector "king" minus the vector for "man" plus "woman" equals "queen". It's learning the meaning of words, and the relationships between them.

Recurrent NNs, using similar techniques, but with much more complexity, can learn to predict the next word in a sentence very well. They can learn to write responses that are almost indistinguishable from humans. And it's incredible this works at all, given RNNs have only a few thousand neurons at most, and a few days of training, compared to humans' billions of neurons trained over a lifetime.

All of the information of our world is contained in text. Humans have produced billions of books, papers, articles, and internet comments. Billions of times more information than any human could read in their entire lifetime. Any information you can imagine is contained in text somewhere. I don't think it's necessary for AIs to be able to see, or interact with the world in any way.

If you can predict the word a human would say next, with enough accuracy, then you could also produce answers indistinguishable from theirs. Meaning you could pass the Turing test, and perform any language task they could do just as well. So language prediction alone may be sufficient for AGI.

This is the theory behind the Hutter Prize, which proposes that predicting (compressing) wikipedia's text is a measure of AI progress. The Hutter Prize isn't perfect (it's only a sample of wikipedia, which is very small compared to all the text humans have produced), but the idea is solid.

<comment><USER4> > All of the information of our world is contained in text

Communicate the concept of "green" to me in text.

The sound of a dog barking, a motor turning over, a sonic boom, or the experience of a Doppler shift. Beethoven's symphony.

Sour. Sweet. What does "mint" taste like? Shame. Merit. Learn facial and object recognition via text.

Vertigo.

Tell a boxer how to box by reading?

Hand eye coordination, bodies in 3 dimensional space.

Look, I love text, maybe even more than yourself. But all these things imbibe, structure and influence or text, but are not contained in them.

To make substantial inroads to something that looks like human esque AI, text is not enough. The division of these fields are artificial and based on our current limited tech and the specialisation of our researchers, faculties and limitations.

When we read, we play back memories, visions, sounds, feelings, etc, and inherent ideas gained through experience of ourselves as physical bodies in space.

Strong AI, at least to be vaguely recognised as such, must work with algorithms and machinery that understand these things, but which then works at that next level of abstraction to combine them into proper human type concepts.

Of course, there is the question about why we would want to create a human like AI, it's my contention that human like AI isn't actually what many of us would want, but that's another topic...

<comment><USER3> Are blind or deaf people not intelligent?

But if you must pretend to be sighted and hearing, there are many descriptions of green, of dogs barking, of motors, etc, scattered through the many books written in English (and other languages.)

Are these descriptions perfect? Maybe not. But they are sufficient to mimic or communicate with humans through text. It's sufficient to beat a Turing test, to answer questions intelligently, to write books and novels, and political arguments, etc. If that's not AGI, I don't know what is.

<comment><USER5> Yes they are. However, is a blind, deaf, person with absolutely no motor control, no sense of touch, and no proprioception intelligent? _Unclear._ They certainly have no language faculties.

<comment><USER3> But a blind person can't describe green. A deaf person can't describe the sound of a motorboat. A person without taste can't describe mint flavor. That is the point I was making.

I don't propose that a human could lose _all_ of their senses and still be able to communicate. But I do believe computers could do so, if they are designed to do that. Humans are not designed to work lacking those senses.

<comment><USER5> So a blind person would never be able to understand the different categories of color (other than that they are placeholders for distinct categories of something).

Now we are just speculating. We believe a computer _might_ be able to understand things for which it doesn't have the sense - but that is speculation and totally untested, and certainly can no longer be justified by using human minds as an example.

<comment><USER3> A blind person could pretend to be sighted though. There have been blind authors who wrote about sighted characters, for instance. They need not experience the thing themselves. Just learn from experience how sighted people behave and describe things, and mimic that.

<comment><USER5> Can you provide any examples of blind (from birth) authors giving convincing visual descriptions from the points of view of sighted characters?

That seems hard to believe.

<comment><USER6> You can explain red by saying it's a "warm" color for example. Metaphors work, analogies, senstion from one sense can be explained using sensations from another. Now you need to have at least one sense, which machines clearly don't.

</comment></comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER7> Deep learning has succeeded tremendously with perception in domains that tolerate lots of noise (audio/visual). Will those successes continue with _perception in domains that are not noisy_ (language) and _inference /control_, which the article touches on? I think it really is unclear whether those challenges will require fundamental developments or just more years of incremental improvement. If fundamental developments are needed, then the timeline for progress - which everyone in tech seems to be interested in - becomes much more indeterminate.

If you think about audio/visual data, deep nets make sense: if you tweak a few pixel values in an image, or if you shift every pixel value by some amount, the image will still retain basically the same information. In this context, linearity (weighting values and summing them up) make sense. It's not clear whether this makes sense in language. On the other hand, deep methods are state of the art on most NLP tasks, but their improvement over other methods isn't the huge gap as in computer vision. And while we _know_ there are tight similarities between lower-level visual features in deep nets and the initial layers of the visual cortex, the justification for deep learning in NLP is simpler and less specific: what I see is the fact that networks have huge capacity to fit to data and are deep (rely on a hierarchy of features). My guess is we may need a fundamental breakthrough in a newfangled hierarchical learning system that is better suited for language to "solve" NLP.

I think there are similar limitations with control and inference. When it comes to AlphaGo the deep learning component is responsible for estimating the value of the game state; the planning component is done with older methods. This is much more speculative, but when it comes to the work on Atari games, for example, I _suspect_ that most of what is being learned (and solved) is perception of useful features from the raw game images. I wonder whether the features for deducing game state score are actually complex.

I think what I'm trying to say is that when we look at the success of deep learning, we have to separate out what part of that is due to the fact that deep learning is _the go-to_ blackbox classifier, and what part of this is due to the systems we use actually being a good model for the problem. If the model isn't good, does that model merely need to be tweaked from what we currently use, or does the model have to completely change?

<comment><USER8> It is already succeeding on language tasks, see [https://research.facebook.com/research/babi/](https://research.facebook.com/research/babi/)

It is funny how every AI post on HN turns into a speculative discussion forum full of words "I think", "likely", "I suspect", "My guess" etc, when all the research is available for free and everyone is free to download and read it to get a real understanding of what's going on in the field.

>what I see is the fact that networks have huge capacity to fit to data and are deep (rely on a hierarchy of features).

Actually recurrent neural networks like LSTM are turing-complete, i.e. for every halting algorithm it is trivial to implement an RNN that computes it. It is non-trivial to learn these parameters from algorithm IO data, but for many tasks it is possible too.

>I suspect that most of what is being learned (and solved) is perception of useful features from the raw game images.

It is not this simple, deep enough convnets can represent computations, the consensus is that middle and upper layers of convnets represent some useful computation steps. Also note that human brain can only do so much computation steps to answer questions when in dialogue, due to time and speed limits.

>My guess is we may need a fundamental breakthrough in a newfangled hierarchical learning system that is better suited for language to "solve" NLP.

This is being worked on, see the first link for Memory Networks and Stack RNNs, DeQue RNNs, Tree RNNs. Deep learning is a very generic term, there are dozens of various feedforward and recurrent architectures that are fully differentiable. The full potential of such models has not been nearly reached yet and maybe language understanding will be solved in the coming years (again, the first link shows that it is in process of being solved).

<comment><USER7> I specifically and mindfully added those words because everything is really _an open research question_. Would you rather I dissembled a false sense of confidence? If anything, you're stating your vague case way over-confidently. Turing-completeness is broad and nonspecific. Doing "some computation" is an obvious statement that doesn't add any information. The human brain does not seem to have time limits when it comes to thinking about what to say, and further we don't understand enough about neuroscience to make statements like that. Like I said, these are all active areas of research; the jury is still out on whether any specific approach will be the breakthrough.

EDIT (reply to below): in general these statements are either vague and nonspecific, or perfectly correct and non-informative, comments that don't have much to do with my original point.

<comment><USER8> I agree to your points. Your comment is a quality one, I mostly talked about other ones.

>Turing-completeness is quite broad and nonspecific, like I said.

It is, but feedforward models (and almost every Bayesian/statistical model) don't possess it even in theory, while RNNs do.

>Doing "some computation" is an obvious statement that doesn't add any information.

Let me be more specific: currently researchers think that later stages of CNNs do something that is more interpretable as computation than as mere pattern matching. Our world doesn't require 50-level hierarchy, but resnets with 50+ layers do good, looks like because they learn some non-trivial computation.

>the jury is still out on whether any of those RNN approaches will be the needed breakthrough.

Sure, we'll see. Maybe there won't be need in any breakthrough, just incremental improvement of models. And even current models when scaled up to next-gen hardware (see nervana) can surprise us again with their performance.

</comment></comment></comment></comment><comment><USER9> AI researchers overestimate the role of language in the development of human- like intelligence understood as a common sense heuristic physics that is built empirically through non-linguistic experiences (e.g. if I turn that glass, the wine spills and leaves dirty stain on the couch what has further consequences). The one that is most difficult to implement/reproduce/emulate by machine.

Language is only a communication protocol most efficient in an interactive context (dialog) that allows two agents with shared but not identical set of experiences achieve understanding in some domain and context with the caveat that understanding is unprovable and not absolute. Understanding that is only empirically tested and behavior probed (e.g. long after successful conversation Agent Alice discovers that Agent Bob "did not get it" as she expected).

By analyzing sole language without experiences, the machine, using something like word2vec, may discover semantic dependencies (e.g. man + cassock = pedophile) but not true semantics that has world consequences.

Even with unlimited language corpora the machine does not have the set of axioms that humans have (experiences and observed stories). These axioms are needed to build further more abstract knowledge.

</comment><comment><USER10> It seems to me that a full mastery of language requires a grasp of semantics, that is the ability to understand what a sentence means. I doubt it's possible to do that without having basic common sense along with an overall representation of the world, and that looks very close to strong AI, imho.

So I'm not surprised computers keep on struggling with language applications. Once they succeed strong AI will not be much further away.

<comment><USER11> I think the 'overall representation of the world' requirement is pretty key here. Language in AI is often treated as its own class of problem, with the assumption that there is somehow enough signal in the raw mess of examples provided to any given learning system (usually just plain text, stripped of any prosody, emotion, cultural context, imagery; any of the other modalities of communication available to a demonstrably functioning natural language understander[1]) to build a model that 'understands' general use language. I simply don't see how this is possible[2]. I know the classical philosophies about the complementary nature of language and intelligence are out of fashion right now[3], but I'm not quite convinced they deserve to be.

I'll raise your bet; I'm willing to believe that once we succeed in building a general understanding of language, we'll look back and see that we simultaneously have solved Strong AI. To twist the old saying, I think that language is what the human brain does.

\---

[1] Yes, we can talk about P-zombies if you want. But I mean more in the Turing Test sense here.

[2] Yes, I know the progress has been impressive. The progress in the 60s with GOFAI was impressive at first too. Then it plateaued.

[3] I'm particularly referring to Sapir-Whorfishm and the various communication heuristics proposed by Grice. But I'd throw Chomskian Universal Grammar in there too.

<comment><USER12> Grounding language in other sense modalities (multimodal learning) is a thing. We can even generate captions from images and generate images from captions, albeit, not perfectly.

Another grounding source is related to ontologies. We are already building huge maps of facts about the world like "object1 relation object2".

Another source of "common sense" is word embeddings. In fact it is possible to embed all kinds of things, like, shopping bags, music preferences, networks topologies - as long as we can observe objects in context.

Then there is unsupervised learning from video and images. For example, starting from pictures, cut them in a 3x3 grid, shuffle the tiles and then task the network to recover the original layout. This automatically extract semantic information from images unsupervised. A variant is to take slides from video, shuffle them around, then task the network to recover the original temporal order. Using this process we can cheaply learn about the world and provide this knowledge as "common sense" for NLP tasks.

I am not worried about grounding language. We will get there soon enough, but we're just impatient. Life evolved over billions of years, AI is just emerging now. Imagine how much computing power is in the collected brains of humanity, and how much computer time we give AI to learn. AI is starved of raw computing power and experience yet. Human brains would have done much worse with the same amount of computing.

<comment><USER11> image caption is a separate, albeit related problem to what I'm talking about.

Ontologies are much the same; they are interesting for the problems they solve, but it's not clear how well those problems relate to the more general problem of language.

word embeddings are also quite interesting, but again, are typically based entirely off whatever emergent semantics can be gleaned from the structure of documents. It's not clear to me that this is anymore than superficial understanding. Not that they aren't very cool and powerful. Distributional semantics is a powerful tool for measuring certain characteristics of language. I'm not sure how much more useful it will be in the future.

Uunsupervised learning from video and images is a strictly different problem that seems to me to be much lower down the hierarchy of AI Hardness. More like a fundamental task that is solvable in its own universe, without requiring complete integration of multiple other universes. Whether the information extracted by these existing technologies is actually usefully semantic in nature remains to be seen.

I agree that we'll get there, somewhat inevitably; not trying to argue for any Searlian dualistic separation between what Machines can do and what Biology can do. I'm personally interested in the 'how'. Emergent Strong AI is the most boring scenario I can imagine; I want to understand the mechanisms at play. It may just be that we need to tie together everything you've listed and more, throw enough data at it, and wait for something approximating intelligence to grow out of it. We can also take the more top-down route, and treat this as a problem in developmental psychology. Are there better ways to learn than just throwing trillions of examples at something until it hits that eureka moment?

<comment><USER12> I think the key ingredient is to be reinforcement learning, and more importantly, agents being embedded in the external world.

Regarding the "internal world", we already see the development of AI mechanisms for attention, short term memory (references to concepts recently used), episodic memory (autobiographic) and semantic memory (ontologies).

</comment></comment></comment></comment></comment><comment><USER13> I have to agree to an extent with researchers Tenenbaum and Li. It seems to me that the only way AI is going to learn language is to have some worldly experience to link words to their ultimate semantics.

I don't think AI will be able to fully grasp the intricacies of human language until it has "lived" long enough to form the links between ideas and experiences. Mainly in the physical realm, as obviously a lot of our human development is shaped by our environments. They will need eyes, ears, and maybe noses. We should also consider giving them subconscious or instinctive reactions to certain stimuli. An AI wouldn't immediately know that, for example, rotten meat is bad to humans because it lacks a nose to send a signal of danger and disgust.

We should also consider the idea to communicate with AI in regular face-to- face speech. Talking is not the same as writing, and conveys a lot of information beyond just the words.

</comment><comment><USER14> This is a long metaphor but work with me here...

There are many types of application programmers, but there are 2 types in particular that are interesting. One of them is the purely technology-driven developer. He uses all the new tools, he's read Knuth's books a hundred times, he knows how to build elegant systems. However, he only takes enough interest in the business as is necessary to know what to build. At the end of the day, he'll build the most elegant beautiful system that almost never accomplishes the business goal. He knows how to describe a problem, but he doesn't really know the problem.

The second likes programming, he finds technology fun, but he is really driven by trying to understand the full context of the business. Writing software is a means to see an impact on people. He's driven by seeing a business problem solved. I've only met 2 people in my career who are ACTUALLY like this, they're rare... which is maybe a good thing because they write shit code.

A good engineering team tries to get both of these guys, you have the tech guy making sure your platform is maintainable, and you have the business driven guy who makes sure it's useful. One guy understands the structure of the tool, the other understands the structure of the world the tool is in.

A language is a tool, it can be elegant, it can be beautiful, it can be technically perfect.. and just like poetry, it can have a very little practical utilitarian purpose. When I look at how we're using ANN's to develop language today, this is how it feels to me. We're spending so much time trying to figure out how to get a computer to build the most technically perfect sentence, we're missing the maybe more interesting problem of trying to get a computer to understand the world. My son right now isn't old enough to conjugate a sentence, but he understands what certain things in the world do. He clearly understands that cars move things, he understands you can use the hose to get things wet. He's not that old, but he's developing a mental model of the world. He just doesn't know how to describe it yet.

To me, having a computer look at a crane, and then print the word "crane" is interesting, but even more interesting is if you could give it 3 pictures (a crane, a building, and a pile of rubble) and teach it how 1+1=2.

<comment><USER15> Actually thes two parts do exist in tandem, it's just that those engineers/designers (a role you that can do the same) are usually independently successful and have repeatedly built high revenue products or started a company themselves.

The major flaw i see in manager/corp/team analysis of workers is that it misses out on a portion of the population that is genuinely independently functional and creates and shares the value they create. They don't work for companies because they either don't need to or own their own. These are the ideals worth keeping in mind.

</comment></comment><comment><USER16> If we use animals as a reference, I would say that consciousness is more fundamental than language, so most likely we need that in place before we can get AI to be able to effectively understand language.

<comment><USER17> Do you mean "consciousness" as being aware of one's own existence and relative position in a larger reality, or as having subjective experiences (qualia, feelings)?

<comment><USER16> It's a state of being able to say "I am doing X, I am doing Y. I am thinking Z. (but not necessarily in words of course)" It's also a creation of an executive process that is separate from the analytic processes. The executive process can be aware of the analytic processes (Like we can know about our heart beating, but it's a separate process)

Subjective feelings are not necessarily part of that.

This article on how consciousness evolved does a good job of explaining how it works (finally), and I think it's something we could emulate.

[http://www.theatlantic.com/science/archive/2016/06/how- consc...](http://www.theatlantic.com/science/archive/2016/06/how- consciousness-evolved/485558/)

</comment></comment></comment><comment><USER18> I think about the AI language problem a lot while raising my kids. The article notes the word "forever" and how an AI must distinguish the literal from the figurative meaning of the word in context. My five-year-old still doesn't grasp the literal meaning of this word as "never-ending." To him, "forever" is simply a very very long time. He has the same problem with the concept of "infinity," where the word means both "the biggest number" but also has the characteristic (in his mind) of having an upper quantifiable bound. His young mind has not yet recognized the paradox that "infinity" is the biggest number, so what does it mean when I say, "Infinity plus one"?

Neural networks are going to make huge inroads to the AI language problem simply by exposing the AI to example after example of words in varying contexts. But I wonder if the real problem is getting those neural networks to let go of unnecessary data? Humans rely on excited neurons to recognize patterns, but our neurons let a lot of sensory input pass us by to keep from getting bogged down in the details. Are the image-recognition AI's described in the article capable of selective attention? Will they get bogged down in the morass of information in trying to pattern-match every word to every image and context they learn?

<comment><USER19> Maybe this is good. After all, mathematical abstractions may cause more philosophical problems than they solve. What if there is nothing infinite in this world? Having a firm grasp of reality before venturing into hypotheticals can be good.

<comment><USER20> But what is language if not a tool for manipulating hypotheticals? If your language can only describe what you know to be possible, it can only describe things you've already seen, and the required dataset in your memory to have a conversation or provide useful information is way too large. Abstraction is the very problem of language that AIs are trying to solve.

</comment></comment></comment><comment><USER21> I have often wondered whether the reason why 'AI' has struggled with human language is because most programs are not embodied. If you cannot jump when you hear the word joy, and cannot cry when you hear the word death and feel the other sensations that go with them, you cannot truly understand the word. The lack of additional visual cues and context will continue to severely hamper the ability for machines to correctly understand the semantics of human language. I know the language guys like to work with their strings of characters, but humans can only communicate with those because we have already built our semantic framework by being active agents in the world.

</comment><comment><USER22> My belief is that "Deep Network" systems will fail to produce commercially useful results on language just as symbolic systems failed in the face of visual recognition.

</comment><comment><USER23> In terms of DNA difference, humans are very close to mammals that don't have much language capability but demonstrate some degree of intelligence. This suggests that language is not as fundamental as some researchers claim.

I once told Rod Brooks, back when he was proposing "Cog" (look it up), that he'd done a really good insect robot, and the next step should be a good robot mouse. He said "I don't want to go down in history as the guy who built the world's best robot mouse". "Cog" was a dud, and Brooks went back to insect level AI in the form of robot vacuum cleaners.

We need more machines which successfully operate autonomously in the real world. Then they may need to talk to humans and each other. That might work.

The big problem in AI isn't language, anyway. It's consequences. We don't have common sense for robots. There's little or no understanding of the consequences of planned actions. We need to get this figured out before we can let robots do much.

</comment><comment><USER24> This is also why AI won't overthrow us and become our masters. They don't even know what it means to be a master.

EDIT: This is the most volatile comment I've ever posted. It has been going +2, -2, +2, -2 for the last 35 minutes. People seem to love it or hate.

<comment><USER25> Nor does a meteor know what it means to be an extinction event.

<comment><USER24> True but a meteor doesn't have to. As it has been written of in our sci-fi, the robot uprising specifically requires the machines to understand and, most of all, care, about dominance.

If you're talking about just being victims of machine logic, we've been suffering that since the invention of the traffic light traffic jam.

<comment><USER26> [https://wiki.lesswrong.com/wiki/Paperclip_maximizer](https://wiki.lesswrong.com/wiki/Paperclip_maximizer)

</comment></comment></comment></comment><comment><USER27> We might still have to go much more basic.. Or we might end up going there in future.... Some have mentioned here having consciousness is more fundamental than language.

Even very basic creatures with less intelligence "learn" because they "want to live".

That is the key - You want to stay alive.

You can't be immortal. You just don't live to learn forever. You live to stay alive and feel happy. And that is what drive us to learn.

Could this be true in case of machines?

<comment><USER8> This is not a question of "consciousness", this is a question of Reinforcement Learning.

</comment></comment><comment><USER28> You cannot understand apples by analyzing the word "apple". Language is just the paper trail. There are plenty of examples: You can't understand a bank or money by just analyzing bank statements. You can't understand food or supermarkets by analyzing their receipts. You can't understand the internet by analyzing TCP/IP.

Regardless of the rich context a word may reside, or the infinite sample pool of text upon which we may unleash our learning robots, as long as they are all words, you will never encounter the real apple to which the symbol is linked, nor reach the reality to which all the symbols are linked. The machine will learn something. It just won't be anything like what we know or understand, or what generated the paper trail in the first place. They will be awkward simulations, which is exactly what we have.

My now 20 month old son wasn't born literate, but already spoke. A grunt, a groan, a giggle, and a moan. These are his words. There is nuance, there is rhythm, there is intention, and there is tone. Not that I'm any good at writing baby books, but there is a reason babies enjoy rhyming and puns and silliness. The point is, words offer so much more than their meaning. This is the language they understand. They don't know English, and we aren't teaching them English. But they are slowly but surely articulating themselves, be it that they're hungry, lonely, or just want that grape. In fact, who is teaching who? Parents learn the language of their child first to be any good at parenting. Their expressions of their intelligence precludes the expressions of our own. Maybe all we need is a machine that groans.

I find it no coincidence that the philosophy of Ludwig Wittgenstein evolved with his experiences teaching children. And if I were to make a bold prediction, it appears the field of AI will benefit immensely from all the young and talented AI researches who start having kids of their own. The comments here already seem to attest to this. It's either that or giving up and deciding to teach preschoolers for a while. Either way, we'll soon have our book on AI that will do what Philosophical Investigations did for philosophy. And I can't wait.

</comment><comment><USER29> My intuition is that the language problem will be solved, but that the solution will likely be a hybrid symbolic and deep learning system. BTW, I have been working (some of the time) on both symbolic NLP and neural networks/machine learning since the 1980s: right now is the most exciting time in the field of AI because progress is rapid and accelerating.

</comment><comment><USER19> Somewhere here, Noam Chomsky is still kicking and saying "I told you so"

[http://www.tor.com/2011/06/21/norvig-vs-chomsky-and-the- figh...](http://www.tor.com/2011/06/21/norvig-vs-chomsky-and-the-fight-for- the-future-of-ai/)

</comment><comment><USER30> Solving language for computers seems much like climbing a series of mountains, where each time you surmount one, you realize the next is even higher :) Thanks to deep learning, machines have made rapid gains in speech recognition, as well as improving semantic mapping (a la word2vec and other word embedding approaches).

But once you have a system with human-level speech recognition and semantic mapping, where do we go? The ability to have a meaningful dialogue with a machine seems very difficult to model as a machine learning problem (what constitutes ground truth? What's the reward function look like?), and also has to deal with many unknowns. For example, ask a smart assistant like Alexa or Siri about functionality it wasn't programmed with, and you get a terse "Sorry, can't help you with that." But ask a child, and you prompt a question- answer dialogue (i.e. learning) or perhaps feigned understanding. My toddler son is an expert at giving me the answer he _thinks_ I want to hear, even when he has no idea what I'm talking about :) There are certainly many new problems which we can begin to think about tackling, but certainly no sign IMO that we're running out of applications for deep learning in the field of language.

<comment><USER31> "much like climbing a series of mountains, where each time you surmount one, you realize the next is even higher"

Aren't you describing learning, in general? Physics, math, biology, etc

</comment></comment><comment><USER32> The problem with natural language processing is that we are trying to learn it (read: construct models of it) from utterances, things that are being said or transcribed. And that is a big, huge problem because there is a lot more to language than utterances. Hell, there is a lot more to language than language itself.

There are things you cannot put into words, and yet you think them. There are things that you can't put into words and yet you can make people around you understand them. There are things you understand without even knowing you understand them. But even before we go there- there are so many things that people can make utterances about that are not possible to collect into example sets and train models on.

How do you collect examples of whatever it is that makes people lie on the beach to get a sun tan? How do you collect examples of imagination, dreams, abstract thinking, all those things that your brain does that may be a side- effect of self-aware intelligence or the whole point of self-aware intelligence in the first place?

How do you collect a data set that's as big as the whole world you've experienced in your however many years of life? And even if you could, what machine has the processing power to train on that stuff, again and again, until it gets it right?

Machine learning meaning is hopeless, folks. Fuggeddabout it. There's not enough data in the whole world, there's no machine big enough to process it if it existed. We 'll make some advances in text processing, sure, we'll automate some useful stuff like translation (for languages close to each other) and captioning (for photographs) and then we'll stall until the next big thing comes about in a few generations from now.

That's what the current state of the art suggests.

<comment><USER33> > There are things you cannot put into words

 _I_ am a very bad example though, for one because English is just my second language. Sure there is thinking before words are learned. Language is a complicated problem to talk about, just like self awareness. Consciousness is a very nebulous term to me. Still, you'd have to prove that language is theoretically unfit. Any such logic might be incomplete if you suppose you cannot put it into words. A complete first order logic is expressible however, following Goedels completeness theorem.

</comment></comment><comment><USER34> Is there any hope that if chat bots do actually become widespread in usage that eventually we'll be able to aggregate their collective knowledge similar to reinforcement learning for a single system? That seems like the only likely way we'll ever be able to train AI in something as complex as language.

<comment><USER16> The primary issue is that language doesn't have as well of a defined "success" metric. So more data doesn't necessarily make better language.

Without a human analyzing the transcripts it's very difficult for the chat bots to know which inputs it's receiving are "good" or "better."

Even the idea of good language is subjective. We all know there is such a thing, but nearly everyone has different ideas of what this is.

</comment></comment><comment><USER35> AI as it's defined today is fundamentally reactive. If we applied the AlphaGo methodology to language, it would come up with what a good response would be to words it heard, but the purpose of such a conversation would be the conversation itself.

A real conversation is about conveying understanding, not about the words spoken.

AlphaGo was trained on however many zillions of games and playing against itself, but does it actually understand anything about the game? Or can it simply react to the current state of the game and suggest what the next move should be. It will never have a leap of intuition causing it to say "the only winning move is not to play."

Intelligence is not purely reactive.

<comment><USER36> How would one prove the opposite? That a human actually understands anything about the game and isn't reacting to the state of the game to suggest the next move? I'm not saying AI as it exists today understands, I'm just saying this "understanding" metric isn't a good metric unless it works in reverse.

<comment><USER37> I don't think we can with a game. Games are a progressive sequence of states with permitted transitions defined by the rules; they are inherently reactive. The only way to prove understanding is to ask things like, "Why did you make that move?", or maybe more specifically, "Why was that move the one that best maximizes your chances of winning?" I'm not sure AlphaGo could answer that question.

Basically, you need to ask questions that require meta-cognition, like, "What does Mary think about you?" That requires:

* Understanding of yourself as an entity.

* Understanding of Mary as another entity, with its own state.

* The capability to use previous interactions to approximate that other entity state.

</comment></comment></comment><comment><USER38> The focus on vector space as mentioned in the article "words can be represented as mathematical vectors, allowing similarities between related words to be calculated. For example, "boat" and "water" are close in vector space even though they look very different. Researchers at the University of Montreal, led by Yoshua Bengio, and another group at Google, have used this insight to build networks in which each word in a sentence can be used to construct a more complex representation—something that Geoffrey Hinton, a professor at the University of Toronto and a prominent deep-learning researcher who works part-time at Google, calls a "thought vector.""

Is to me, the most significant way in which we can mimic the way the human cognitive process develops associations between things. Auto-association is key here.

In addition, understanding how to calculate similarities between vectors is also important.

</comment><comment><USER39> I wonder if in the future we'll have matrix-style "blobs" of knowledge that we can plug into compatible AI systems. That way it has to just be trained once on a strong system, and then other systems can take advantage of the learning by forking the state and importing it. It would definitely speed up training AI, and possibly even enable it on lower powered hardware.

Imagine downloading "english teenager slang 2016 v2.0" to your home AI, so it can understand what the hell your kids are saying :)

<comment><USER40> If the blob of knowledge consists of the weights of some neural network and if this blob is public... Then an attacker could easily perform imperceptible perturbation to the input in order to make the network believe that the yogurt is an Eiffel tower or vice versa. (Can't find the related publications right now but it appeared several times on hn before).

So if you don't want the system to be gameable, such public blobs of weights may need to be avoided.

<comment><USER41> Likely by that time we will have overcome this problem.

</comment></comment></comment><comment><USER42> This is the "time flies like an arrow" problem again, isn't it? Fruit flies like a banana. Context, sense, meaning. All of which require facts about the real world.

</comment><comment><USER43> Language is just a protocol for synchronizing slices of two world models, the one in the head of the one who talks and the one in the head of who listens. If the recipient doesn't have the model similar to the one sender has, language is meaningless.

You can't "understand" language without having model of the world that humans construct during their life and education.

So pretty much next step for language recognition is indistinguishable from sentience.

</comment><comment><USER44> > his team was just as surprised as everyone else ... It was only several days > later, after careful analysis, that the Google team made a discovery

Since success has a higher priority for researchers than explicable success, and if the "singularity" is just progress that is not understood, it may be almost here - and not require true AI.

Though to be fair, by that definition, the singularity has always been with us, since we don't understand how we think.

</comment><comment><USER45> The problem with deep learning and language understanding is that the task is ill-defined end-to-end. For speech, image understanding, and translation, you can come up with large datasets of x->y and have deep learning learn a complex function to approximate the mapping. We don't have that luxury in language understanding, at least not yet.

</comment><comment><USER46> Consciousness is the secret sauce. Consciousness as in "subjective experience", what separates us from philosophical zombies, the sensation of the color blue, of middle-c. Our bodies evolved and kept this extremely rich phenomenon for a reason; it is extraordinarily unlikely for it to have arisen and remained through genetic drift alone.

My theory, and I'd love to find someone offering a similar and more fleshed- out hypothesis, is that conscious experience serves as a universal data type. It can encode and play-back any type of knowledge and memory, and relationships among them, from the color of the teacher's shirt that time you broke your bone in 3rd grade, to the formula for electron energy in quantum mechanics.

Unfortunately, the word consciousness is almost forbidden in most scientific circles. The dominant view is that there is no Hard Problem of Consciousness and that any discussion of it is quackery, or at least "not science". This taboo is holding us back.

<comment><USER47> I've had somewhat similar thoughts, and I am entirely unqualified (and highly likely not the first) to put forth the idea that consciousness's killer app is the ability to rapidly assembly abstract models of experiences (present from current sensory input, past from short/long-term memory or future from mental simulation) and be able to query/manipulate those models, and my (admittedly potentially naive) suspicion is that whatever does "that" is the machinery behind consciousness.

This is also why I think that deep learning / neural networks are only going to take us _so far_. I think there is more to the story of how the brain works than only neural networks that make predictions, and frankly I do not think that any system that does not at least attempt to do "that" (simulating consciousness's model building/manipulation feature) will have much better luck at language processing/understanding.

<comment><USER46> One possibility about the machinery of consciousness that would help address the Hard Problem is that the brain is not creating consciousness from scratch, but tapping into some currently misunderstood or unknown physical phenomenon. It's hard to see how information processing alone (which can be done by monks with paper and pencil, if incredibly slowly) can give rise to subjective experience.

It seems that this phenomenon, whatever it is, plays a central role in sensory perception, and there's reason to think that it's present even in animals with simple brains. So I suspect that we're looking for some kind of simple operation that can happen on the scale of a small number of neurons, maybe even a single neuron.

This is all speculation of course, informed by some knowledge and intuition, but speculation nonetheless. But it's the only way to push the frontier, and the unwillingness to engage with consciousness as a matter of serious study seems to be a major failing of brain science and AI.

</comment></comment></comment><comment><USER48> "If a lion could speak, we could not understand him." \-- Wittgenstein

</comment><comment><USER49> The web of research always spins around money. So far I am not seeing a monetary interest group with a declared imperative of creating a self- sustained artificial mind.

</comment><comment><USER50> Id rather see an AI that is smarter than humans. People are trying to make human-like entities but lets think outside the box a little more.

</comment><comment><USER51> > today's machine-learning and AI tools won't be enough to bring about real AI

This is important to understand in the midst of today's AI hype.

</comment><comment><USER52> Has anyone used something like an MMO to get people to help train their AI in an artificial environment?

</comment><comment><USER53> The problem is : The industry set upon making a skyscraper from the top floor (neocortex) down and assumes they can hack together a foundation and throw up ad-hoc scaffolding on their way down that will magically reflect the brain's capabilities. There are even ridiculous ideas held by industry 'experts' that the foundation will just magically arise from nothing more than a sheer amount of spaghetti wiring complexity. Nothing in the known universe has been proven to work this way. Yet, no one questions this outlandish belief system because the industry experts and notable names are stating it and.. hey look, their top-floor systems actually do something interesting..So, they must know what they're doing and saying.

So, the foundational problems remain...

They remain because there is no foundation to these cortical systems. Anyone who states this is railed and laughed at. So, you get what you get.....

The article states : "Machines that truly understand language would be incredibly useful–but we don't know how to build them."

There are people and groups who know how to build them. They are focusing on the 'foundation' first. That is not where the spotlight or money are directed. So, they remain in the dark.

We gained head-winds with a very trivial model of neurons and cortex like hierarchical neural network designs and the money sent people off to the races. People began writing wrappers, stuccoing the top floor, hacking up scaffolding, applying any C.S concept they could find in the parts bin to fancify the top floor.

That's where all of the attention and money is.. What does your system do? What benchmark can it beat? What data can it classify? What cool trick can it do to impress us? So, you get impressive trick systems that require massive amounts of data, training, and answer maps to obscure the lack of intelligence. As there is none explicitly designed into these systems, the system cannot convey its understanding.

It's nothing more than an answer map w/ annealing routines and memory... Very similar to cortical regions.

The foundation and supporting layers up to the top have been ignored, aren't getting any spot-light or money, nor are the individuals who continue to toil on it.

They're considered to be 'philosophers' and jokers and not real scientist/engineers/industry leaders. The A.I space shuts out a huge pool of varying opinions via its : If you don't have a PHD, one need not apply. If you're approaching it from any other methods than the ones subscribed to and you're not a name, face, or have a laundry list of papers you get the : Good luck (thumbs up).

And people stand around and wonder why the fundamental problems remain? Come on...

In any event, it wont remain for long and that will be due to someone/groups actually investing the time and energy to build a sound foundation. This begins first and foremost by deep philosophical questions about the nature of the universe and intelligence. The answers derived serve as a guiding light for further along scientific and engineering pursuits.

This article should be : AI's lack of a foundation. Whose going to build it? Whose going to invest the time to understand what exactly it is as opposed to hacking away at it?

It's the truth but would be considered a 'hit piece'. Until someone constructs a proper foundation, no one is going to give credence to the idea that current A.I lacks it. Hindsight is 20-20 as is a force-fed neo-cortex.

</comment><comment><USER54> Small child have to learn language from nothing. They just figure it out through exposure and practice. Even pets learn some language. This is the model to emulate.

Ultimately language use requires a few skills:

* a good parser * motor cognition/coordination * a good memory * semantics/context * vocabulary * situational awareness

The first two in the list are what small children struggle with the most. Fortunately, we can eliminate motor coordination as a need for AI. Although extremely powerful parsers demand a specialized expertise to produce this part of the problem is straight forward. I write open source multi-language/multi- dialect parsers as an open source hobby.

I discount vocabulary and situational awareness, because most children still haven't figure this out until they enter high school long after they have learned the basics of speech. That pattern of human behavior dictates that while it might be hard to teach these skills to a computer you can put this off a long ways down the road until after basic speech is achieved.

If somebody paid me the money to do this research my personal plan of attack would be:

1\. Focus on the parser first. Start with a text parser and do audio to text later. Don't worry about defining anything at this stage. When humans first learn to talk and listen they are focusing upon the words and absolutely not what those words mean.

The parser should not be parsing words. Parsing words from text is easy. The parser should be parsing sentences into grammars, which is harder but still generally straight forward with many edge cases.

2\. Vocabulary. Attempt to define words comprising the parsed grammar. Keep it simple. Don't worry about precision at first. Humans don't start with precision and humans get speech wrong all the time. This especially true for pronouns. Just provide a definition.

3\. Put the vocabulary together with the parsed grammar. It doesn't even have to make sense. It just has to have meaning for words and the words together in a way that informs an opinion or decision to the computer. Consider this sentence as an example: I work for a company high up in the building with a new hire that just got high and gets paid higher than my high school sweetheart.

4\. If the sentence is part of a paragraph or a response to a conversation you can now focus on precision. You have additional references from which to draw upon. You are going to redefine some terms, particularly pronouns. Using the added sentences make a decision as to whether new definitions apply more directly than the original definitions. This is how humans do it. These repeated processing steps means wasted CPU cycles and its tiring for humans too.

5\. Formulate a response. This could be a resolution to close the conversation, or it could be a question asking for additional information or clarity. Humans do this too.

6\. Only based upon the final resolution determine what you have learned. Use this knowledge to make decisions to modify parsing rules and amend vocabulary definitions. The logic involved is called heuristics.

This only way all this works is to start small, like a toddler, and expand it until the responses become more precise, faster, and more fluid. At least.... this is how I would do it.

<comment><USER55> It depends a bit on what you are trying to achieve but I think hooking neural type networks together to simulate human mental faculties might be a better way forward. For instance much of human thinking seems to work around visualizing things in 3d space so you can say to someone imagine a dog on a skateboard on top of a hill and you give it a push, what happens? Once you've got that kind of stuff working with spatial awareness, cause and effect and so on using neural type processing I think the language understanding would come fairly naturally.

</comment></comment><comment><USER56> since it was using movies maybe the 8 legs is for a human centipede?

</comment><comment><USER57> I don't think the issue has anything to do with cognition and more to do with something that we do so subconsciously we don't always notice it as we do it: error correction and context setting. A big part of language are our error correction channels. In text it's a lot less obvious because we twist the language to clear things up, but speech is full of a lot of "I'm sorry, what?" and "uh, you know" and hand gestures and furrowed brows and a million other side channels to get someone to repeat something or elucidate it or set a deeper context.

But that happens in text too: we group things into paragraphs and add a lot of punctuation and as we read we sometimes skim a bit, return as needed, reread what we missed the first time. (Or in texts/IMs our cultures are in the process of building whole new sub-dialects of error correction codes like emoji and "k?".)

A lot of people would think a machine is broken if it hemmed and hawed as much as people do in a normal conversation; if it needed full paragraphs of text to context set and/or explain itself.

The biggest thing lacking in voice recognition right now is not the lack in word understanding or any of the other NLP areas of research: it's in a lot of the little nuance of conversation flow. For now, most of the systems aren't very good at interruptions, for instance. From the easy like "let me respond to your question as soon as I understand what you are asking to save us both time" to the harder but perhaps more important things like "No [that's not what I mean]" and "Wait [let me add something or let me change my mind]" and "Uh [you really just don't get it]" and presumably really hard ones like _clears throat_ [listen carefully this time].

The point should not be that we hit 100% accuracy: real people in real conversations don't have 100% accuracy. The issue is how do you correct from the failures _in real time_ and keep that "conversational" without feeling strained or overly verbose (such as the currently common "I heard _x_ , is that correct?" versus " _x_?" and head nod or very quick "yup").

We don't consciously think about the error correction systems in play in a conversation so that makes them hard to judge/replicate and it's easy to imagine there's an uncanny valley waiting there for us to get from no "natural error correction" ability across to supporting error correction in a way that it works with our natural background mechanisms.

At least in my mind, that's probably the next big area to study in language recognition is deeper looks into things like error correction sub-channels and conversational timing (esp. interruption) and elocution ("uh", "um", "you know", "that thing", "right, the blue one"). I'd even argue that what we have today is probably already getting to "good enough" for the long run if it didn't require us to feel like we have to be "so exact" because you only get one sentence at a time and you don't have good error correcting channels with what we have today.

</comment><comment><USER58> Reminds me a lot of that classic Stephen Bond essay: [https://web.archive.org/web/20131204031113/http://plover.net...](https://web.archive.org/web/20131204031113/http://plover.net/~bonds/positivism.html)

<comment><USER59> Is there a story behind the pictures accompanying that article?

</comment></comment><comment><USER60> These guys might disagree. Sometimes they make sense which is interesting [http://sumve.com/ai-chatbots/relationships/relationship- bots...](http://sumve.com/ai-chatbots/relationships/relationship-bots.html)

<comment><USER16> They seem to be just talking over each other with vague references to what the other bots are saying.

If you pay attention for about 5 minutes it's just nonsense. I mean, they are clearly repeating things from somewhere else that were sensible in their original context, but now they seem to be saying things nearly randomly.</comment></comment>


I run a Silicon Valley startup – but I refuse to own a cellphone - <AUTHOR> http://www.theguardian.com/technology/2016/jan/11/steve-hilton-silicon-valley-no-cellphone-technology-apps-uber <comment><USER2> I too don't own a cell phone since 2001 and everything is just fine. I feel relaxed, but not carefree, and not happier. I don't run a SV startup...., that's a big difference!!</comment>


Harvard's Reinhart and Rogoff Say This Time Is Different - <AUTHOR> https://www.bloomberg.com/news/features/2020-05-18/harvard-s-financial-crisis-experts-this-time-really-is-different <comment><USER2> Their conclusion:

"We're going to see a lot of risk aversion. We'll be more inward-looking, self-sufficient in medical supplies, self-sufficient in food. If you look at some of the legacies of the big crises, those have all seen fixed investment ratchet down and often stay down."

So, more localization, less globalization?

<comment><USER3> That sounds an awful lot like the attitude of someone who lived through the Great Depression. So much for this time being different.

</comment></comment><comment><USER4> I don't like reading transcripts of interviews. I would much rather the author took the time to digest the material and present a summary. That said, this statement caught my eye:

"It's been a very intense period partly because I was teaching a lot. And there was the shift to Zoom, which created more work because you're trying to prepare differently and do your lectures differently. It's obviously a surreal experience overall."

So why didn't Rogoff refuse to teach that way? Probably because cancelling his classes would have obligated Harvard to refund the tuition. So he went along with the school and provided a degraded learning experience to his students, all so Harvard didn't have to dip into its massive endowment and do the right thing.

<comment><USER5> As a student at Harvard, cancelling classes means you lose progress that you would have to repeat. Maybe Zoom is not ideal for certain classes, but it's better than no class. Not sure what the endowment has to do with anything other than to give you something to whine about?

<comment><USER6> You're probably answering in bad faith but here goes: parent's point about the endowment is that Harvard is filthy rich and could easily afford refunding tuition for that semester without putting its business in jeopardy. Parent suggests that Harvard chose not to do it out of greed rather than necessity to avoid financial collapse.

<comment><USER4> Good summary, thanks. It's tragic that this pandemic has forced schools to close in the middle of the academic year. My point is that university students are paying a premium for an on-campus experience and that's not possible right now. I think it would be best for schools to simply cancel classes, partially refund tuition, and make provisions for students to finish their work later when campuses re-open.</comment></comment></comment></comment>


C# Quirks, Gotchas and Pitfalls – Predict the Output Challlenge - <AUTHOR> http://volatileread.com/Wiki?id=1073

<comment><USER2> I don't think any one at Hacker News uses/loves c#</comment>


Don't Do What Your Users Say - <AUTHOR> http://blog.hanfordlemoore.com/2007/04/16/dont-do-what-your-users-say

<comment><USER2> This is a great article, and it re-iterates something I've seen time and time again in website usability tests: People aren't stupid, they're just bad at expressing their desires. Generally a feature request has a good basis, but the proposed feature may not be the best solution.

<comment><USER3> Exactly. Listen to user's problems, not their solutions.

<comment><USER4> It's not just that users aren't good at expressing their desires. They're not good at _knowing_ their desires.

Look at what users do, not what they say. Good metrics and a little cleverness is usually better than any sort of feature request form.

edit: Hackers are not exceptions to this rule.

</comment></comment></comment><comment><USER5> I've seen this principle presented before (thanks paul), but I appreciate how a concrete example like the one in this article really makes it clear.

</comment><comment><USER6> I think one key takeaway of this article is also that a comments form is not the same as doing user testing.

</comment><comment><USER7> ...or as an old-school Mac developer once put it to me: _Get in your time machine and go back to 1982. Now take a survey: How many people ask for bit- mapped graphics and a mouse?_

</comment><comment><USER8> This is a great observation. Part of the reason for this behavior is almost all the people I've met who tout usability tests come from the old school market research/focus group approaches. Put people in a room and listen to what they talk. I've experienced this first hand. I've even put up mock print screens of the click flow and inevitably there is a disjoint. That does not work - the only reliable way is to put the actual working app out there and observe them use it. OBSERVE the users (with the UI guy in room) and correlate/validate what you see with objective page abandonment/scenario data on Google Analytics. Its a little bit of work but I think it totally completes the picture. Excellent observations.

</comment><comment><USER9> For some reason this article reminded me of this old TV segment that was hosted by the Genie from Aladdin. The title was "Great minds think for themselves." The title always stuck with me (along with apples "think differently" slogan) in that no one thinks the same. A group of people may approach a problem the same way, but there will never be one true way of doing something. This is why I think it's important to get external feedback from people totally unrelated to you. Sure it can be painful, but feedback almost always helps, I mean heck it's the entire philosophy behind web2.0.

Anyway great article and great responses here.

</comment><comment><USER10> Of course Jakob Nielson said this first quite some time ago.

<http://www.useit.com/alertbox/20010805.html></comment>


The Controversial Crisis of Caring - <AUTHOR> https://medium.com/drift-engineering/drift-engineering-and-the-controversial-crisis-of-caring-665a60a804cf <comment><AUTHOR> I'm posting there here because of the famously vigorous commenters. We asked a new engineer to write a blog post to expand on his appreciation for engineering culture at Drift.

He inadvertently met up with a group of ex-coworkers who were upset by the post.

I'm here asking, did we do a bad thing? I think what he experienced at his last company is a common complaint among devs.</comment>


Ask HN: Need of a much better GitHub/Gitlab? - <AUTHOR>

Github [1] has become slow and the hosted solution (enterprise version) is too costly, so we have been using Gitlab which is a Github like repository hosting platform that also has issue tracking and merge/pull request based workflow and integration with its CI (gitlab-ci project).
In the past couple of months, we've been facing a lot of problems for example -- Gitlab has broken our development workflow (due to bugs in it), the web hooks would stop working, the merge request diffs would be too slow to load, sometimes the ruby based backend would blow up and give us 500 etc. So, we've started working on a single page webapp based on Go and AngularJS that just does minimalistic things that we need. We wanted a tool that is easy to manage and install so we chose Go (single binary) and we wanted to keep the frontend easy to implement and use so we chose AngularJS (single page app).
What are the pain points you've faced while using these tools, and what features etc. you want in it and will you fund development of such an opensource project that solves these problems. What other opensource or cheaper hosted git etc. solutions do you use? Our team of three is planning to work on this project full-time but want to survey if crowd funded money would be enough to support us for at least a year till we figure out other ways to sustain while giving all our time and energy to the product.
[1] http://github.com [2] http://gitlab.org <comment><USER2> Personally I never had a problem with Github, aside from a few times it was down but that's rare and part of any internet service.

But then again, I've only ever used it as a code repository. For issue tracking I use a dedicated app (Lighthouse).</comment>


Actix – an actor framework for the Rust programming language - <AUTHOR> https://simplabs.com/blog/2018/06/11/actix.html <comment><USER2> This couldn't have been posted at a better time for me. I just started learning Rust last week and was interested in finding an actor library.</comment>


Show HN: Nextube – The missing next button for Chrome - <AUTHOR> https://chrome.google.com/webstore/detail/nextube-the-missing-next/kefiabkknppcjbbimoijcjbcehlcmcco <comment><AUTHOR> Ever had to find the current playing YouTube tab among thousand of different tabs/window just to move to the next song? Nextube to the rescue!

(PS: Please refresh YouTube tabs after installation)</comment>


People pay 50% more if physically present - <AUTHOR> http://www.americanscientist.org/issues/pub/2010/6/brains-like-to-keep-it-real <comment><USER2> They may be getting additional information about the product if it's in front of them, e.g. how comfy a shoe is.

This certainty in knowing that you won't have to return the item may well be worth a premium. Also, they're paying to get the item now, rather than in a few days.

I'm surprised the difference is 50% though.

<comment><USER3> They may also be less worried about defects in the product. Allowing someone to actually touch the product before purchase shows more confidence in the product than locking it in a glass cage.

</comment></comment><comment><USER4> Two words: Apple Store

</comment><comment><USER5> This is said a bit in humor, and might be seen as a bit of a jump in context, but is the effect the article discusses a reason we have to have so many friggin' meetings?</comment>


SupermealX, India's Soylent - <AUTHOR> http://www.nytimes.com/2015/10/29/world/asia/india-soylent-supermealx.html <comment><USER2> All this talk about liquid food as a holy grail to pursue is intruiging but I wonder, what harm does it do to the teeth, oral flora and digestive system if they have nothing to 'chew' on? Just like our body slowly atrophies when we sit to much and move to little, perhaps our body needs the traditional 'holistic' form of food (not necessarily including raw food)?

<comment><USER3> Probably best for Soylent drinkers to regularly chew gum. It'd be pretty awkward to discover after a few months that your jaw muscles have atrophied so severely that you have trouble chewing even the softest dry foods.

<comment><USER4> Perhaps its counter acted by them telling me all the time how much better it is than solid food :-).

But more seriously you do have a valid (if taken to an extreme) point. But similar arguments are made by people on segways losing walking endurance (or cars even). Actual atrophy seems to require complete non-use.

<comment><USER5> People using motor vehicles all the time absolutely causes atrophy. Lack of mobility among the sedentary (especially sedentary and obese) part of the population is a serious problem. There are a substantial number of people who have difficulty walking even a couple of blocks, not through any prior injury or disability, but just through disuse of their legs.

My wife and I were at the Biltmore estate in North Carolina a couple years ago. We wandered through the gardens, and we were chatting with a couple of ladies about what we had seen. They were shocked that we had walked a half mile from the parking lot instead of taking the shuttle. "You _walked_ all the way from the house to here?!"

</comment></comment></comment></comment><comment><USER6> _> Also, society is conditioned to believe there is something objectionable about the poor being made to drink a tasteless subsidized potion because they cannot afford the other kind of good food. There are movies in which people consuming tasteless but utilitarian nutrition is depicted as evidence of dystopia._

It seems conspicuous that it wasn't mentioned that Soylent is the namesake of one of those movies. Is it assumed that most NYT readers will make / have already made the association?

</comment><comment><USER7> > _Also, society is conditioned to believe there is something objectionable > about the poor being made to drink a tasteless subsidized potion because > they cannot afford the other kind of good food._

Others might object to a mandated government subsidy for a product without relevant prior analysis of the existing market for minimum-cost diets (e.g., determining the cost of the Stigler Diet for the region).

</comment><comment><USER8> That idea that a selection of vitamins and minerals, et al. can replace traditional food has largely been debunked, for the current state of the art at least. Perhaps in the future, but based on our current knowledge base we can not yet replace traditional food with synthesized pills and powder for the majority of one's natural life. We still do not know all of the nutritional elements of some of the most basic fruits we pick up every day in our grocery store. If you don't believe me (I wouldn't, who am I?) check out the book below. The author has been in the field for decades, well published, studied both reductionist and wholistic approaches to nutrition.

[http://www.huffingtonpost.com/t-colin-campbell/whole-book- ex...](http://www.huffingtonpost.com/t-colin-campbell/whole-book-excerpt- _b_3308560.html)

<comment><USER9> I think the debate around Soylent really suffers from disagreement of what users of Soylent use it for, what others think it is being used for out should be used for and what it is advertised as. I think it's foolish to believe that Soylent will provide a very healthy diet just I its own. As you point out we know way to little about what the body needs. That's why no one I know who uses Soylent eats it exclusively. Unfortunately that's not what is advertised as. If you see it as an alternative to a rushed meal that comes from a fast food chain and our prematr from the freezer in the grocery store, Soylent is the better choice. I eat/drink Soylent for breakfast add weekday lunches. For dinner I usually have streamed vegetables and done lean meat with done fresh fruit later in the evening. I think together that probably makes for a much better diet than most people have. Yes, it could be improved by replacing the Soylent with a more traditional, but healthy meal, but the effort I'm willing to invest I breakfast and weekday lunches prevents that as far as I can see. Especially at the price point that Soylent comes at. I'm certain that most Soylent users see Soylent as a replacement for a crappy sandwich or burger with a side of potato chips and not as a replacement for a wholesome meal.

<comment><USER8> Unfortunately Soylent does not market it as such. If they did, I'd be inclined to agree with you. However, on Soylent's website you can find the following:

"Soylent is a food product (classified as a food, not a supplement, by the FDA) designed for use as a staple meal by all adults." -[https://www.soylent.com/about/](https://www.soylent.com/about/)

"Soylent is designed as a simple staple food, and people incorporate it into their lives to varying degrees. Some people use it almost exclusively, while others use it 2-3 times per week. There is no right or wrong amount of Soylent to eat - the whole idea is to find a balance that works for you." -[https://faq.soylent.com/hc/en-us/articles/201273045-How- do-I...](https://faq.soylent.com/hc/en-us/articles/201273045-How-do-I-use- Soylent-)

"Our goal at Soylent is to engineer nutritionally-complete food products that are optimized for modern consumers' lifestyles and budgets. Above all, we want to make healthy nutrition easily attainable." -[https://faq.soylent.com/hc/en- us/articles/203709619-Soylent-...](https://faq.soylent.com/hc/en- us/articles/203709619-Soylent-Approach-to-Nutrition)

So by using terms or phrases such as 'complete', 'almost exclusively', and 'staple', and going so far as to remark that it is classified by the FDA as a 'food' and not a 'supplement', Soylent is explicitly marketing their product as a primary or complete food and nutrition source.

Their words. Not mine.

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

<comment><USER10> It sounds like the FDA classified it for them.

Given:

\- The FDA considers it a food

\- Soylent considers it a food

\- Soylent users are on record as consuming mostly or all the product for their primary complete food source..

\- ..over long periods of time, including the founder, who supposedly lives on the stuff.

\- Of those users, most are reporting positive results...

\- ..and the negative results are generally of the classes "don't like the taste" / "gave me bad gas"

..then I'm left to conclude that it is a food, and a pretty good one at that.

</comment></comment></comment></comment><comment><USER4> I got some Soylent for my earthquake supplies, its shelf stable and nicer than the 3kCal US Coast Guard biscuit rations. Are their disaster kits in India? Something to tide you over until the systems reboot sufficiently ?

<comment><USER11> (This sounds like an aggressive quibble, but it isn't. I am just interested.)

May I ask: how do you know soylent is shelf stable? Is it packed in a protective atmosphere? What testing have they done?

<comment><USER4> The current Soylent packaging comes with a one year shelf expiration date. I don't know the testing they have done to back that up. The USCG rations have a 10 year expiration date but other stuff in my earthquake kit is best rotated/replaced on an annual basis so it works for me.

</comment></comment></comment><comment><USER11> > Ever since an American electrical engineer invented a food that abolishes > the inconveniences of foraging and cooking, and contains all the nutrition a > human body is known to require but is devoid of the substances that harm, > there has been talk that it can end not only the problems of the overfed but > also the underfed. After all, it is in the tradition of Silicon Valley- > blessed projects to invent a solution for the rich that eventually "makes > the world a better place," to borrow an expression used by tech billionaires > and comedians.

Jesus fucking christ.

1) Soylent didn't invent anything. Complete liquid meals have been around for many years.

2) No one credible suggest soylent is useful to end world hunger. It's too expensive; it has the wrong nutrient balance; it's made in the wrong place; it requires too much clean water; it's worse than the existing emergency food products in many different ways.

<comment><USER12> I think any Soylent thread would benefit from a summary of typical comments, to make way for new ideas. Pick yours.

1\. Liquid diets aren't new

2\. Cooking is easy and enjoyable, I don't see why people avoid it

3\. Soylent is healthier than fast food even if it's inferior to well-made natural meals

4\. It hasn't undergone rigorous, clinical testing

5\. It's dystopic, dehumanizing

6\. It's a dietary choice of others that makes me inexplicably angry

7\. I've been trying it for 50-100% of my calories for X weeks and it's been great/terrible

8\. It's based on outdated FDA recommended allowances

9\. It ignores the social, cultural aspects of food

<comment><USER13> I pick #1. My biggest problem with Soylent is not what they are doing, but that they have an attitude that they invented the idea.

<comment><USER14> There are a many meal replacements out there that are meant to supplement a diet. Ensure for example is easy to find, but it's not designed to be the only thing you eat.

There are some medical products that fit the goal, but I don't know of any that are marketed to the general public. Though, if you actually have a better alternative in mind I think a lot of people here would be interested.

<comment><USER11> > Ensure for example is easy to find, but it's not designed to be the only > thing you eat.

Untrue. Ensure has many different products, and many of them are complete meal replacements.

<comment><USER14> Link?

From there site: [https://ensure.com/nutrition-facts-questions- answers](https://ensure.com/nutrition-facts-questions-answers)

"Can Ensure products be used as a meal replacement or a snack? Yes, Ensure ready-to-drink shakes and drinks can be used as a snack and as _an occasional meal replacement._ "

<comment><USER11> The fact they don't market themselves to the general public as a complete meal replacement just means they're a) aware that they're operating in a litigious market segment and b) not fucking arseholes who recognise that they have a duty of care to people who use their products.

It's telling that Soylent kickstarted with a bunch of bullshit medical claims, and that while they've dropped those claims they continue to make borderline dishonest claims.

Ensure is used as a total meal replacement for people with severe illness and for people being fed through a naso-gastric tube (sometimes against their will).

But if you're a doctor you probably one of the other Abbott brands, which have licencing and testing to support them.

[http://abbottnutrition.com/brands/abbott- brands](http://abbottnutrition.com/brands/abbott-brands)

EG: [http://abbottnutrition.com/brands/products/ensure-plus- thera...](http://abbottnutrition.com/brands/products/ensure-plus-therapeutic- nutrition)

> For interim sole-source nutrition.

<comment><USER14> Jevity one of the abbot brands you mention is clearly designed for long term use as 'sole-source nutrition'. And, I am sure someone has lived off of Ensure, but just because they also sell total nutrition products does not mean Ensure was designed for that role.

Design goals matter, Jevity/Oxepa/etc being designed for feeding tube use has little focus on taste.

Another example, Osmolite is sole-source nutrition but it's low-residue which is not good for healthy people who need more fiber.

PS: You can look through there brands, but there is a reason they sell so many different kinds as they each have different trade offs.

ED: I have had significant digestive issues in the past so I have done some research into this. And, talk to a nutritionist before going on any unusual diet, it's easy to mess things up long term.

</comment></comment></comment></comment></comment></comment></comment></comment><comment><USER15> For the starving poor - it is too expensive.

For the tasteful rich - it is too bland.

For the average house - it is too unlike a home-cooked meal.

For programmers being used as disposable slaves - it's perfect! Less time eating, more time programming.

</comment><comment><USER16> Designated food.</comment>


ISPs' costs, revenues don't support data cap argument - <AUTHOR> http://arstechnica.com/tech-policy/news/2009/05/isps-costs-revenues-dont-support-data-cap-argument.ars <comment><USER2> US ISPs are video providers. THere is no doubt in the next five to ten years online video will cannibalize their cable biz models. Economically I only see capping bandwidth as a way to sustain and increase their profits!

Online video currently provides me with freedom of choice I have not had with the cable biz model; no a la carte options! It's great now (not's sport fan) as it's free and I watch what i want to when I want to. Though I know this free ride won't last too long; hopefully it does for awhile though as I only pay the cost of Internet!

</comment><comment><USER3> Like any business, they are trying to grab as much money as they can, hoping no one will notice.

People noticed, in Time Warner's case, and they got called on it.

Not ethical or nice, but it is the way our system works.

</comment><comment><USER4> Why does this article not compare the gross margins of various industries?

It seems worthless (and disingenuous) to quote absolute figures in the billions.

For example, Exxon made 40.6 billion in profits in 2008: [http://www.usnews.com/articles/business/economy/2008/02/01/e...](http://www.usnews.com/articles/business/economy/2008/02/01/exxons- profits-measuring-a-record-windfall.html)

That's more than the entire ISP industry, but does it matter even a little bit?</comment>


Google blocks Microsoft's new YouTube Windows Phone app - <AUTHOR> http://www.theverge.com/2013/8/15/4624706/google-blocks-window-phone-youtube-app

<comment><USER2> Google's comment: "We're committed to providing users and creators with a great and consistent YouTube experience across devices, and we've been working with Microsoft to build a fully featured YouTube for Windows Phone app, based on HTML5. Unfortunately, Microsoft has not made the browser upgrades necessary to enable a fully-featured YouTube experience, and has instead re-released a YouTube app that violates our Terms of Service. It has been disabled. We value our broad developer community and therefore ask everyone to adhere to the same guidelines."[1]

Microsoft's responds by saying cooperation with Google has stalled because Google insists the app be built using HTML5, a technical restriction other mobile platforms are not held to.[2]

\--

[1][http://www.zdnet.com/why-is-google-blocking-the-new- youtube-...](http://www.zdnet.com/why-is-google-blocking-the-new-youtube-app- for-windows-phone-7000019460/)

[2] [http://blogs.technet.com/b/microsoft_on_the_issues/archive/2...](http://blogs.technet.com/b/microsoft_on_the_issues/archive/2013/08/15/the- limits-of-google-s-openness.aspx)

</comment><comment><USER3> This is what anti-trust laws are for.

Microsoft did stuff half as bad as this and had the Department of Justice regulating their business practices for more than a decade.

</comment><comment><USER4> I wonder what are the needed "browser upgrades" they are referring to?</comment>


How to keep your info private, even from the NSA - <AUTHOR> http://money.msn.com/leadership/article.aspx?post=527be5e4-1f34-4b92-8c05-11dddb6f5ba1

<comment><USER2> msn.com? really?</comment>


Mozilla launches an open-source voice assistant for Firefox - <AUTHOR> https://voice.mozilla.org/en <comment><USER2> I don't see anything about the launch of a voice assistant on that page, only info about their common voice project?

<comment><USER3> Last month they announced that they were starting the Scout project. [https://sanfranciscoallhandsjune2018.sched.com/mobile/#sessi...](https://sanfranciscoallhandsjune2018.sched.com/mobile/#session:4d773fd0bd1d892223a3c502befd44db) Looks like it's still in brainstorming stages.</comment></comment>


Rentable Textbooks Finally Here for College Students? - <AUTHOR> http://www.nytimes.com/2009/08/14/education/14textbook.html?em

<comment><AUTHOR> Hopefully this is a trend that continues in favor of students. Currently paying $100+ for a single textbook that has such a short lifespan (for the original owner, and for others due to the planned obsolence model by textbook publishers) puts quite dent in the wallet for "starving" students.

Renting for a semester makes much more sense.</comment>


Designing for Accessibility: UK Home Office Posters - <AUTHOR> https://github.com/UKHomeOffice/posters/tree/master/accessibility <comment><USER2> The "deaf / hard of hearing" poster is weird. Several of the points seem to have nothing to do with hearing.

[https://github.com/UKHomeOffice/posters/blob/master/accessib...](https://github.com/UKHomeOffice/posters/blob/master/accessibility/deaf.pdf)</comment>


History by lawsuit: After Gawkers demise, "inventor of email" targets Techdirt - <AUTHOR> https://arstechnica.com/tech-policy/2017/06/shivas-war-one-mans-quest-to-convince-the-world-that-he-invented-e-mail/ <comment><USER2> I'm with Ayyadurai. He did invent "email". BTW I invented "The Steam Engine" (c) 2017 [1]. James Watt? Pssh, never heard of him!

[1] [https://github.com/androm3da/the-steam- engine](https://github.com/androm3da/the-steam-engine)

</comment><comment><USER3> When I was a child, I sketched a pager with a keyboard and a front screen; at the time I'd seen nothing like it - I think I invented Text Messaging!

Thats my story and immm stickin' to it. Wish I kept that sketch though...

Great article, btw, quite detailed</comment>


Ask HN: How to become a CTO of a mid size company? - <AUTHOR> I am interested to become the CTO of a large/mid size company. I am 26. I have an undergrad in CS. I am looking for tips which can help me become a CTO. <comment><USER2> As an aspirational goal or as an actual opportunity at hand?

In the latter case I'd say you'd have quite a daunting task ahead of you. Without any previous management experience and existing leadership skills becoming the CTO of a company of roughly 250 people (which is the definition of a larger SME) might be biting off more than you can chew.

If it's more of an aspirational goal you should focus on people skills first and foremost. Being able to present and generally communicate complex technical matters in a way different target audiences (engineers, management, customers) will understand you is one of the most important traits of a successful CTO. Staying on top of current technological development and trends on a general level is crucial, too.

As a CTO you don't have to know how to exactly use a specific technology but you have to be able to decide which technology in a particular area will be most suitable for your organisation.

Finally, if you take your job as a CTO seriously you most certainly won't code or do any day-to-day engineering anymore. A CTO who still codes is an anti- pattern and almost always a sign that there's something seriously wrong with the company.

</comment><comment><USER3> High variance strategy: cofound a startup or be the first engineering hire at a startup. Work your ass off and hope that the startup becomes a mid-sized company.

You'll need to scale along with the company - in the early days you'll be writing a ton of code, but once the company is "mid-sized" your role is going to be managing people. As someone else mentioned, if at that point you're still coding either you're a terrible CTO or you're working for a terrible company (or both). :)

</comment><comment><USER4> Start as engineer, move up to senior, supervisor, lead, manager, VP roles.</comment>