Suddenly, a leopard print sofa appears - <AUTHOR> http://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.html <comment><USER2> This article would not come as a surprise to anyone who works with ConvNets. Sadly, that might not the case for those outside of the field, largely due to media's inadequate coverage of our advances (but this is common outside our field too). No one in the field really believes ConvNets see better than humans. They are very good single glance texture recognizers. It's as if you flashed an image and looked at it for a split second without giving yourself a chance to look around and take some time to gain any higher-level scene understanding. If you tried this with this image you might also think you had seen a leopard. Another point to make is not from modeling side but from data side. If in the training data the leopard texture is highly indicative of leopard, then the ConvNet will learn to strongly associate it as such. As the article mentions, a quick hack would be to make sure that your training data contains many leopard-textured items of different classes. You might then expect the ConvNet to seek other features to latch on to and become less reliant on the texture itself.

Also, we carried out an experiment on ImageNet and the outcome was that "One human labeler (me, incidentally) with a fixed amount of training and a slightly-above average determination reached ~5% top-5 error on a subset of ImageNet test set". The media sees this and it immediately gets spun to "AI now Super-Human. And we're all going to die." It makes a lot of us cringe every time.

Many people in Computer Vision now consider ImageNet "squeezed" out of juice - we're good at texture recognition and when an object is in plain view, and we're now searching for harder tasks and more dynamic range with respect to human performance, in areas such as harder 3D/Spatial tasks, Image Captioning, Visual Q&A, etc. The hope is that these harder datasets might in turn guide us in developing models with more nuanced understanding.

<comment><USER3> Might you by chance be familiar with Rodney Brooks' work on subsumption architectures [1]? If not, I would summarize the underlying idea (my words not his) as "don't try to jump too many layers of abstraction in one go" [2].

So I wonder to what extent you would consider this a predictable outcome from the classifier in question not being part of a subsumptive architecture --- which at a guess would look like

      - glance/texture responses fed into
      - boundary-recognition layers fed into
      - object persistence/tracking layers
      - fed into abstract scene reasoning

It seems to me, as a non-vision researcher (I mainly worked in planning and control), that the most obvious counterargument to the image being a spotted cat is based on boundary/object/scene reasoning, and that it's "reasonable" for the texture/glance layer to say "looks a lot like a cat texture".

[1] [https://en.wikipedia.org/?title=Subsumption_architecture](https://en.wikipedia.org/?title=Subsumption_architecture)

[2] I realize this may seem, superficially, anathema to deep network research, which advocates letting the network find its own intermediate levels of abstraction. But it's actually compatible in my view because Brooks advocates (again, paraphrasing quite a bit) that the separate layers should have different objective functions, and that in fact the need for different objective functions (in a prioritized order) is the cause of emergent layering in nature. "First, don't die. Second, find shelter. Third, find food etc." So one can imagine deep networks each finding their own _locally useful_ abstractions for each objective function in the "Maslow" chain, while still having some macro architecture that tracks human-imposed design principles.

<comment><USER4> the limit is that training cannot force abstraction. you can only reach abstraction if you have enough neuron space and the data set is big enough to avoid over-fitting textures.

the problem is.. human vision doesn't work just by feeding a bitmap. we have structure to decode space relationships, shapes and maybe even shadow/light relations. no way we gonna see classificator working on color arrays matching our vision capabilities

<comment><USER5> Seems simple enough to feed a NN with that abstracted data.

However, the advantage to the texture approach is it's abstracted from a lot of other information. You don't want a classifier to say sofa, when it's a picture of a person on a sofa.

<comment><USER4> but then you're biasing it toward your perception:

[http://www.bespokesofalondon.co.uk/assets/Uploads/bespoke- so...](http://www.bespokesofalondon.co.uk/assets/Uploads/bespoke-sofa-2.jpg)

anyway it does work perfectly if that's what you need, but most proponent are trying to use deep nn to classify 'as good as humans do'

</comment></comment></comment></comment></comment><comment><USER6> Somehow tangent but this made me think about this quote found on HN last year:

Context: Evolutionary algorithms and analog electronic circuits

> One thing stands out when you try playing with evolutionary systems. > Evolution is _really_ good at gaming the system. Unless you are very careful > at specifying all of the constraints that you care about you can end up with > a solution that is very clever but not quite what you had in mind. Here > power consumption is the issue. If you tried to evolve a sturdy chair you > might end up with something that is 1mm tall. or maybe a fuel efficient car > that exploits continental drift.

I think it's the same here: The net is never gonna better than what it needs to be, and it is probably always gonna take the easy route.

<comment><USER7> There's an alife program called DarwinBots where small bots powered by mutating code compete against each other to survive and reproduce.

Given enough time, you'd expect the to develop clever behaviors, but instead they just fuzz-tested the sim and locked in on exploits of bugs or environment settings. They only got a bit more clever when connecting different sims running on different conditions.

Eyes already use different kinds and densities of sensors optimized for either detail and color or movement/edges. I wouldn't expect a single learning method, even after optimizing it to its limits, to be above what two or more layers of different methods could do, especially when trying to avoid exploits like the tank story.

<comment><USER8> _Given enough time, you 'd expect the to develop clever behaviors, but instead they just fuzz-tested the sim and locked in on exploits of bugs or environment settings._

Classic A-life! Also, not so different from the spirit of actual biology.

 _They only got a bit more clever when connecting different sims running on different conditions._

Diversity is very important for evolution on many levels. What many don't realize (especially, I note, evolution deniers) is that the ecosystem as a whole provides a very complex and continually varying epiphenomenal fitness function to any given organism.

<comment><USER9> If you don't have a sufficiently complex genotype phenotype mapping and the system is not evolvabke (See Gunter Wager's work) the you shouldn't expect more complex phenotype. Understanding a genetic representation is going to be an important step toward open ended evolutionary systems.

</comment></comment></comment></comment><comment><USER10> > So I guess, there's still a lot of work to be done.

And I think this is the most interesting part.

One of the most depressing things about all of the "this image recognition algorithm performs better than humans on this task" is the idea that we've pretty much solved the problem, and it's just a matter of some more optimization and tweaking to handle a few edge cases.

This kind of problem, where the dominant solution simply gets it so wrong, and the problem cases are uncommon enough that any statistical solution is generally going to treat them as noise, reveals that in fact that there is likely plenty of room for entirely new, novel ways of approaching the problem to handle these kinds of cases better.

It's actually more exciting that there's so much more to be done, than to say "well, it's basically a solved problem, we just need to do some tweaking and optimization."

<comment><USER11> Definitely agree here, want to add a link to this paper which shows how far we still have to go (and questions whether the current models will ever replicate human vision): [http://arxiv.org/abs/1412.1897](http://arxiv.org/abs/1412.1897)

</comment></comment><comment><USER12> [https://neil.fraser.name/writing/tank/](https://neil.fraser.name/writing/tank/)

This is a classic story of a neural net failure.

The net was able to find tanks hiding in the trees with amazing accuracy. Too amazing. It turned out the photos of the hidden tanks were all photographed on a cloudy day. The images without tanks in a clear day.

</comment><comment><USER13> Thank you for this article; very thought provoking.

My nitpick:

> When each student was given a heavy book of MNIST database, hundreds of > pages filled with endless hand-written digit series, 60000 total, written in > different styles, bold or italic, distinctly or sketchy. > ... > So, are you > going to say that was not the case?

I understand the point the author is making. Human brains are really good at taking limited examples and correctly extrapolating them to new cases. That is, of course, the goal of intelligence. Machine Learning has gotten better at this generalization, but has a long way to go. And ConvNets as they exist today will not achieve that, no matter how much training you perform on them.

This specific example is inaccurate though. Let us aggressively simplify and low-ball by saying that humans see at 24fps. Humans of course don't see in discrete frames, but this simplification doesn't detract from my argument and makes quantifying easier. So, if you give a human a single page of numbers, and they look at it for an hour, they have now seen >86k examples. That's 86k examples with twitching saccades, and from both eyes. That's in just an hour of looking at numbers.

Prior to being given that page of numbers, most children will have been alive for 4-5 years. That's 3 billion examples from a wide variety of subjects (we ignore sleeping cycles, because we're already low-balling this fps figure, and because the brain is still learning and visualizing during sleep).

And humans are born with a pre-built visual cortex. Edge detection, gradient detection, etc. are all already built for us. CNNs learn that from scratch.

The author's real point is still valid, though, don't get me wrong. I'm just nitpicking.

<comment><USER14> Forget seeing a symbol once, you can recognise and represent a symbol without _ever_ having seen it.

Test your humanness; draw these symbols:

 _" Like an E but rotated so the prongs point upwards"_

 _" Like a snake but with two heads. Snakes down, up, down, up, down."_

 _" Like a walking stick with the handle pointing left and looping back around."_

(answer for A: Russian letter Sha) (answer for B: Kannada letter Uu) (answer for C: Tamil vowel sign I)

<comment><USER15> Convnets can do this. Geoffrey Hinton has a wonderful lecture, where he trained a digit recognizer on everything but 7's and 8's (IIRC.) He then let another convnet tell it which numbers looked more or less like 7's and 8's. E.g. "that 9 looks kind of like an 8. That 1 looks kind of like a 7", etc.

And then it was able to correctly recognize 7's and 8's, despite never having actually seen one. I'm simplifying somewhat, but it was super cool.

I don't know why people are so focused on one-shot learning, or think that NNs can't do it. Neural networks learn features from lots of (possibly unlabelled) data. That's the whole point. Once you have those features, you can use them for all sorts of things. You can show it an image, and then measure how close other images are too it. Thereby learning from a single example.

</comment></comment></comment><comment><USER16> On the other hand, nobody has been upset that humans are constantly misidentifying that jaguar print sofa as a leopard print.

<comment><USER17> Well-played!

I myself have researched leopard spots since I painted our toilet floor in them. It's a lead sheet, and the paint had worn off, which probably wasn't the healthiest thing. My housemates had filled the toilet with memorabilia from an African trip, so leopard-print paintjob it was.

Which entailed looking up leopardprint online. Very little of which actually looks like leopard rosettes, and now I have a problem with almost anything trying to pass itself off as leopardprint. Anyway, I can't say that my paintjob is a particularly good reproduction, but at least it's 'spiritually correct'... :)

</comment></comment><comment><USER18> _Leopards (or jaguars) are complex 3-dimensional shapes with quite a lot of degrees of freedom (considering all the body parts that can move independently). These shapes can produce a lot of different 2d contours_

My son keeps telling me that infants are fine with, say, a truck transforming into a clown (when it emerges from the other side of a visual barrier) but not with it transforming into TWO of something. Apparently, babies subjectively experience this (visual transformation) all the time -- mom moves a plate and what seemed like a big circle is now a flat line or whatever.

So humans apparently get tons and tons of experience with visually mapping 3d reality to mere 2d imagery. I have been thinking somewhat about this of late, in terms of physical attractiveness or "image" \-- that pictures of a woman posted on a blog capture a 2d version of her but people interacting with her are interacting with a 3d living, moving creature who also has smell and a voice and her movements may be elegant or may be not elegant. Which is a thought process relevant to a project of mine, something people here surely will have no interest in. But where it is relevant to this article is that we are doing this wrong: Humans have thousands of hours of practice of looking at 3d reality and figuring out how it to interpret 2d images as representative of that 3d reality. Image recognition software is just dealing with 2d images. I don't see how it can hope to compete. Humans don't come preinstalled with the software to make that distinction. We acquire it with enormous repetition.

When do we make a robot and give it some baseline parameters and a learning algorithm (and set it loose in 3d reality and have to learn)? That is when we can get scared about human like AI that can compete on image recognition.

<comment><USER19> Indeed, arguments like "My mother didn't have to purchase 30,000 mugs to teach me what one looks like" miss the fact that we humans spent so much time (almost all of our waking time in fact) interpreting 3D reality, an endless stream of repetitive tasks.

</comment></comment><comment><USER20> [https://www.imageidentify.com/](https://www.imageidentify.com/) correctly identified the images as "a small sofa". I think rotating the image is questionable, since there could be an algorithm for first orienting the image correctly based on light and shadow and then the image recognition could be run.

[https://www.imageidentify.com/result/1ixb9603m9ix1](https://www.imageidentify.com/result/1ixb9603m9ix1)

<comment><USER21> But those algorithms would be very limited in usefulness. Systems such as imageidentify.com should be at least trying an ensemble of algorithms, many of which I suppose should be invariant under translation and rotation.

Edit: There's a comment about invariance in this thread [1] and apparently CNNs are not invariant under rotation.

[1] [https://news.ycombinator.com/item?id=9750133](https://news.ycombinator.com/item?id=9750133)

</comment></comment><comment><USER22> Suddenly, a shaded rock appears.

[https://upload.wikimedia.org/wikipedia/commons/7/77/Martian_...](https://upload.wikimedia.org/wikipedia/commons/7/77/Martian_face_viking_cropped.jpg)

We're doing humans wrong. Maybe not all wrong, and of course, humans are extremely useful things, but think about it: sometimes it almost looks like we're already there. There always going to be an anomaly; lots of them, actually, considering all the things shaded in different patterns. Something have to change.

I agree that we aren't there, but we'll never be there, every system can be fooled, its just a question of 95%, 99% or 99.99%

<comment><USER23> > We're doing humans wrong.

I don't follow. If you asked a human what the linked image looked like, they'd likely say a face, but if you then asked them what it actually was, they're all going to change their answer to a rock, even specifically a rock on Mars (if given a colour version of this image).

It's true that humans see patterns that aren't there, but does that detract from our ability to recognise objects?

</comment></comment><comment><USER24> This is fascinating and well written.

I tried the unrotated sofa image on Wolfram's ImageIdentify and it correctly identified a settee [1]. So it presumably gathered that from the shape of the image rather than the pattern. It is peculiar though that it can't see the shape under a simple rotation. Or perhaps the margin of confidence levels between sofa and leopard were so narrow that a rotation was enough to tip it in favour of the leopard? I'd be interested to see the inner workings of this.

[1] [http://i.imgur.com/6f6Co5O.png](http://i.imgur.com/6f6Co5O.png)

<comment><USER25> I tried Wolrfram ImageIdentify with a bunch of bicycle photos and it insisted on identifying them as "Bicycle Saddle".

I kept trying different ones and it kept identifying as "Bicycle Saddle"...

<comment><USER26> To be fair that is one of the few things on a bike that isn't a triangle.

<comment><USER25> hmmm, good point!

</comment></comment></comment></comment><comment><USER27> Really wasn't expecting a Terry Davis comment at the bottom.

<comment><USER28> Who is Terry Davis.

<comment><USER29> I didn't know either, but found this: [http://motherboard.vice.com/read/gods- lonely-programmer](http://motherboard.vice.com/read/gods-lonely-programmer)

Scary and fascinating.

<comment><USER30> I felt like Alice down the rabbit hole after getting sucked into reading about TempleOS. Quite an unexpected side-effect of reading an article on neural networks!

</comment></comment></comment></comment><comment><USER31> The MNIST analogy reminds me of the "Teaching Me Softly" article that was posted here last year:

> _When Vladimir Vapnik teaches his computers to recognize handwriting, he > does something similar. While there's no whispering involved, Vapnik does > harness the power of "privileged information." Passed from student to > teacher, parent to child, or colleague to colleague, privileged information > encodes knowledge derived from experience. That is what Vapnik was after > when he asked Natalia Pavlovich, a professor of Russian poetry, to write > poems describing the numbers 5 and 8, for consumption by his learning > algorithms. The result sounded like nothing any programmer would write. One > of her poems on the number 5 read,_

> _He is running. He is flying. He is looking ahead. He is swift. He is > throwing a spear ahead. He is dangerous. It is slanted to the right. Good > snaked-ness. The snake is attacking. It is going to jump and bite. It is > free and absolutely open to anything. It shows itself, no kidding. > Brown_Cornerart_

> _All told, Pavlovich wrote 100 such poems, each on a different example of a > handwritten 5 or 8, as shown in the figure to the right. Some had excellent > penmanship, others were squiggles. One 5 was, "a regular nice creature. > Strong, optimistic and good," while another seemed "ready to rush forward > and attack somebody." Pavlovich then graded each of the 5s and 8s on 21 > different attributes derived from her poems. For example, one handwritten > example could have an ''aggressiveness" rating of 2 out of 2, while another > could show "stability" to a strength of 2 out of 3._

> _So instructed, Vapnik's computer was able to recognize handwritten numbers > with far less training than is conventionally required. A learning process > that might have required 100,000 samples might now require only 300. The > speedup was also independent of the style of the poetry used. When Pavlovich > wrote a second set of poems based on Ying-Yang opposites, it worked about > equally well. Vapnik is not even certain the teacher has to be right—though > consistency seems to count._

[http://nautil.us/issue/6/secret-codes/teaching-me- softly](http://nautil.us/issue/6/secret-codes/teaching-me-softly)

That article in turn reminded me strongly of "Metaphors We Live By" by Lakoff & Johnson, and the works they have written since, where they claim that humans make sense of the world using systems of rich, conceptual metaphors. As I understand, the work is well-known to machine learning researchers.

</comment><comment><USER32> Obviously these classifiers do often focus on patterns, rather than shapes, and that's probably something that could be worked on, but I don't think an image classifier can possibly be expected to, at the level it is operating, identify the leopard-print sofa all on its own. Clearly there's a higher order process at work than image recognition here - after all, when a human is faced with a sofa-shaped object with a leopardskin pattern on it, there are two hypotheses that need to be evaluated: 1) this is a sofa patterned to look like a leopard; or 2) this is a leopard, shaped like a sofa. Rejecting the less plausible of those two scenarios is obviously a higher-order activity. If the image classifier is at least firing off the concepts 'leopard' and 'sofa' with some level of probability, it's doing its job pretty well.

<comment><USER33> Then we need to integrate higher order knowledge about the world collected from text (Wikipedia and the like).

</comment></comment><comment><AUTHOR> OP here, and thank you kind sirs and ladies for you feedback.

I'd just like to answer the recurring objection: yes, our visual experience contains a lot of frames and that seemingly refutes my MNIST example; however, you do forget about the other part of a supervised dataset, namely labels. Do we have a label provided to each thing we see in our life? Obviously not. How much time do you need to familiarize yourself with a new entity, like an unknown glyph or symbol? Can't provide a concrete example, but I guess a single math class was enough for all of you to recognize all the digits the next day. You can test it right now by looking into some unknown alphabet and then looking into it again upside down - you'll recognize it perfectly, except for mental rotation issues (which occuur even for well-known letters and symbols).

<comment><USER34> > I guess a single math class was enough for all of you to recognize all the > digits the next day.

I'm curious what makes you think that. My experience with what's going on at my sons school is telling me that the children spends a massive amount of time on getting recognition of digits and letters right.

<comment><USER35> When I was living in China I had difficulty recognizing handwritten 9's and 1's. Their 9's are less half-circular than Westerner's are, and the 1's have a long stroke the top that looks like a sloppily written 7 to me. I would frequently look at a handwritten number and have to analyze what it was.

</comment></comment></comment><comment><USER36> That's right, CNNs tend to be more concerned with textures rather than with overall shapes. One problem is that CNNs discard a lot of pose information of the detected features during pooling. Another problem is that there is no top- down verification such as "hmm, leopards always have {heads, legs, tails ...}, I should scan the input for these... nope, it doesn't fit at all, I should exclude everything that has {heads, legs, tails ...} from my interpretation." In a human brain that likely happens in some distributed fashion without considering individual classes, but by just inhibiting everything that can't be verified upon looking twice (or more times).

</comment><comment><USER15> Awhile ago I tried an interesting experiment. I fed a famous psychology image into a bunch of different image recognition systems.

This image: [https://i.imgur.com/2aCqMx2.png](https://i.imgur.com/2aCqMx2.png)

And here are the results: [https://imgur.com/a/8ndyq](https://imgur.com/a/8ndyq)

This doesn't really prove anything, but I thought it was interesting. It is of course, unreasonable to expect ML algorithms to perform decently, so far outside of the space they were trained on.

But I suspect that part of the reason they don't do well is that they are purely feed forward. Humans also don't see the image at first. It takes time to find the pattern, and then everything clicks into place and you can't unsee it.

This might have something to do with recurrency. But more importantly, information feeds down the hierarchy as well as up. Features above, give information back down to features below. So once you see the dog, that tells the lower level features that they are seeing legs and heads, which says they are seeing outlines of more basic 3 dimensional shapes, and so on.

I think it also requires a descent understanding of 3d space, to fit the observed pattern to 3d models which could have produced it. I'm not certain if regular NNs observing static images, are optimal for learning that.

More here: [https://www.reddit.com/r/MachineLearning/comments/399ooe/tes...](https://www.reddit.com/r/MachineLearning/comments/399ooe/testing_image_recognition_platforms_on_a_famous/)

</comment><comment><USER37> For context, ImageNet _does_ have a sofa category for labels :/

[http://image-net.org/search?q=sofa](http://image-net.org/search?q=sofa)

<comment><USER38> The Caffe models are only trained on the 1000 category subset of ImageNet used for the competition: [http://image-net.org/challenges/LSVRC/2014/browse- synsets](http://image-net.org/challenges/LSVRC/2014/browse-synsets)

There are no sofa's in this list, the closest thing I can find is a "studio couch, day bed": [http://imagenet.stanford.edu/synset?wnid=n04344873](http://imagenet.stanford.edu/synset?wnid=n04344873)

<comment><USER37> Right. The "studio couches" are couches.

</comment></comment></comment><comment><USER39> Just to be sure, Wolfram Alpha Image Identify was correct, down to it was a particular type of sofa.

[https://www.imageidentify.com/result/1ixb9603m9ix1](https://www.imageidentify.com/result/1ixb9603m9ix1)

</comment><comment><USER40> Google image search says

      Best guess for this image: cat bed furniture

[https://goo.gl/vXwSaj](https://goo.gl/vXwSaj)

<comment><USER41> That's cheating though; you can see the first few results are for the exact same image with a dog pasted on it, and contain those terms.

</comment></comment><comment><USER42> ConvNets have gotten popular because of their strong empirical results. All the recent work on visualizing CNNs suggests that the community working on Deep Learning still has a lot to learn about their own algorithms.

But high-level notions like a Jaguar is a cat-like animal aren't necessary to perform well on an N-way classification task like ImageNet.

What's more important to note is everybody knows there's plenty wrong with a pure appearance-based approach like CNNs. Every few years a new approach pops up that is based on ontologies, an approach inspired by Plato, etc, but these systems require a lot of time and effort. More importantly, they don't perform as well on large-scale benchmarks. In the publish-or-perish world, you can jump on the CNN bandwagon or start reading Aristotle's metaphysics and never earn your PhD.

<comment><USER43> I doubt that reviewers for NIPS would reject a paper with a novel approach because it didn't perform at best in class level, provided it offered a way forward.

If it doesn't work at all, or isn't a new idea, that's different.

</comment></comment><comment><USER44> I have seen some kaggle competitions do image transformations and put the data back into the training set to increase the robustness of the classifier. For instance, rotating images, slightly skewing them, etc.

I would propose that for this leopard problem, instead of just skewing the images, you also performed transformations on the COLOR and put the images back into the training set.

Maybe applying certain filters, such asdimming the saturation or contrast of images, so that the contrast between the leopoard spots were less visible (i.e. "A Leopoard in low lighting") - maybe this would force the neural net to learn more than just its print.

Knowing the right set of color filters to apply to all images could be tricky though.

</comment><comment><USER45> Let's say I didn't want to use the ImageNet or CaffeNet pre-trained models but wanted to train my own model (say, of thousands of images of sofas, leopards, jaguars, and cheetahs); are there any tutorials that walk through the process of building a CNN on your own data?

(I've seen the comments like [https://news.ycombinator.com/item?id=9584325](https://news.ycombinator.com/item?id=9584325) and watched the lectures and youtube walkthroughs, but they're all theoretical and I'm looking for documented code to go along with that theory)

<comment><USER46> For doing image training, Caffe is pretty good. Here's a starting point: [http://caffe.berkeleyvision.org/tutorial/](http://caffe.berkeleyvision.org/tutorial/)

</comment></comment><comment><USER47> Yes, ConvNets are limited and results are pretty arbitrary sometimes.

The net correctly identified "leopard". Was it taught about sofas? Who knows, maybe Sofa had a high score as well on the output.

Or, look at the Dalmatian/Cherry picture. The net identified "Dalmatian" _which is a 100% valid response!_ But whoever labeled it wanted "cherry". The picture is 50% cherry 50% dalmatian.

Pictures often have more than one element and a pure ConvNet is "one picture to one label"

<comment><USER48> Ha! I was wondering this exact thing. How could it identify a sofa if it never learned about one? It seems very contrived. At the same time, the example is kind of clear, but we are making neural nets that are emulating Pollock and Dali, not Monet. If you can dig it. The whole beauty is the overlapping interstitial matrix of weighted values that leads to these beautiful discoveries. To rank such a fractal-like algorithm on whether or not it predicts a label satisfactory to the average human is to mis-apply the elegance and potential of these mathematical wonders, in my humble opinion.

</comment></comment><comment><USER49> This is the best HN submission I've seen in a very long time. Really thought- provoking.

<comment><USER50> I agree. I don't work with machine learning or neural nets, and thus don't have more than the most cursory laymans understanding of them. This article read really well and was quite informative of the problems this technology is facing.

</comment></comment><comment><USER51> Is there any work into building self-verification into these type of networks? For example based on hierarchical categories of concepts?

If part of the network is trained on the concept of a cat, and whether or not an image is a cat is fed into training of the leopard, it seems like the problems would be avoided. Or is the notion that with enough training data and deep enough networks the concept of "leopard is cat" will be learned?

</comment><comment><USER9> We spend so much effort trying to engineer intelligence, when we would get a lot farther reverse engineering intelligence. Whenever AI makes a big advance the analog was already known my neuroscientists. There is also clearly no comprehension of the importance of the topological (circuitry) defining a neural network. We always assume a fully connected network, and draw the out as such, but we don't stop to consider that many of those Wijk interactions are completely spurious, meaning they have no information bearing role. If you strip them away you'll start to reveal the underlying circuit at work. I've published theoretical results using artificial gene networks, but the results should be similar for ANNs. [http://m.msb.embopress.org/content/4/1/213.abstract](http://m.msb.embopress.org/content/4/1/213.abstract)

<comment><USER52> _We spend so much effort trying to engineer intelligence, when we would get a lot farther reverse engineering intelligence. Whenever AI makes a big advance the analog was already known my neuroscientists._

The problem with attempting to understand intelligence by reverse engineering the human brain is that we cannot know a priori which aspects of the human brain are necessary for intelligence to arise, and which are merely consequences/side effects of biology and chemistry. Once we discover some technique that works in a practical setting (e.g. on ImageNet), then it is fairly straightforward to find the biological analogy in the brain.

In fact, Geoff Hinton explicitly advocates an approach of "try things, keep what works, and figure out how it relates to the brain". The inverse is like finding a needle in a haystack.

 _There is also clearly no comprehension of the importance of the topological (circuitry) defining a neural network. We always assume a fully connected network, and draw the out as such, but we don 't stop to consider that many of those Wijk interactions are completely spurious, meaning they have no information bearing role._

The purpose of training a deep neural network from data is to automatically discover what the topological circuitry of the network should be, rather than engineering it by hand. In the brain, some prior knowledge is encoded via genetics, while the rest is learned. The effect of sparsity of the weights in deep neural networks is an active area of research [1].

 _If you strip them away you 'll start to reveal the underlying circuit at work. I've published theoretical results using artificial gene networks, but the results should be similar for ANNs._

Very interesting. If I understand correctly, the cost you are attempting to minimize is phenotypic variation, which you measure as the gross cost of perturbation (GCP). Would this cost be analogous to sensitivity to adversarial examples in the case of convolutional neural networks [2]?

[1] [http://www.jmlr.org/papers/volume14/thom13a/thom13a.pdf](http://www.jmlr.org/papers/volume14/thom13a/thom13a.pdf)

[2] [http://arxiv.org/abs/1412.6572](http://arxiv.org/abs/1412.6572)

</comment></comment><comment><USER53> Isn't the solution to simply have two NNs? One trained to identify leopards, another trained to identify sofas.

Regardless of how computationally expensive NNs may be now, wait a few years, and then train millions of them on different classes of objects and run them concurrently to identify new pictures.

</comment><comment><USER54> I don't work with NN at all, but it kind of seems like the author set up their CNN with a large enough filter to see a whole spot at once, but not large enough to see a whole cat at once. Then he complains that it doesn't know what a cat looks like. Would it be possible to make a larger filter with a lower resolution such that overhead is the same as the smaller filter but it can get a higher-level view of the image?

Also, the author spends the first section of the article determining that it is in fact a jaguar-print sofa (which the model also confirms) but continues to throw around the word "leopard". They're not making it any easier for the future machine learning algorithms that try to identify an image by the text surrounding it. ;)

</comment><comment><USER55> TinEye tells me that the leopard print "sofa" is actually a dog bed ;) [0]

[0] [http://www.tineye.com/search/4c4ce7b6558e8d3c4dd443439e80556...](http://www.tineye.com/search/4c4ce7b6558e8d3c4dd443439e805561ee7d4f26)

</comment><comment><USER8> Really, humans are not so different. Online and in person, we pattern-match on a few scant signals, sometimes jumping to ridiculous conclusions as a result. (1) Granted, we have much better machinery for recognizing the kinematics of fellow animals. There's compelling evolutionary reasons for animals to become really, really good at this.

If one paired the present classifiers with Amazon Mechanical Turk, just providing one bit of information -- "is-it-an-animal?" \-- I wonder how well the current classifiers would fare in relation to human beings?

(1) - Ironically, the more "cosmopolitan" people become, the _quicker_ they are to jump to such conclusions!

</comment><comment><USER56> Well, you can try this one, it is open source and based on Caffe : [http://imgdetect.alexgirard.com/#](http://imgdetect.alexgirard.com/#) The image link: [http://rocknrollnerd.github.io/assets/article_images/2015-05...](http://rocknrollnerd.github.io/assets/article_images/2015-05-27-leopard- sofa/sofa.jpg) It says 'studio couch, day bed' with 97% accuracy. Very likely because it is part of Imagenet.

</comment><comment><USER57> [http://cloudsightapi.com/](http://cloudsightapi.com/)

This, given the leopard couch, returns "brown leopard print couch".

</comment><comment><USER58> > the problem won't be solved by collecting even larger datasets and using > more GPUs, because leopard print sofas are inevitable.

The models have room for improvement, but it's not clear to me that larger datasets won't solve the problem. Larger datasets and more processing power is exactly why neural nets have surged in effectiveness recently. Who knows how much further current models can go with more data and processing power?

<comment><USER59> read the Hinton paper cited in the OP -- no amount of processing power will make CNNs represent structured latent variables

</comment></comment><comment><USER60> That is neat. Just for fun I tried to figure out how to set up Caffe on EC2. I got it to work, but I didn't get CUDA up and it probably would have been faster with Anaconda. But for what it is worth: here are my current notes [https://github.com/JohnMount/CaffeECSExample](https://github.com/JohnMount/CaffeECSExample)

</comment><comment><USER61> Sorry to go off-topic, but the way that font combined 'st' (with a loop back from the 't' to the top-center of the 's') was very visually distracting to me. Did that bother anyone else?

</comment><comment><USER62> How about detecting vector edge shapes and unifying that result with the existing classifier? Surely a leopard sofa cannot have the same edge vector shape as a real big cat.

<comment><USER63> That's an idea from 1970-1980s AI, called the "primal sketch" model. The concept was to take an image and try to turn it into a line drawing, then extract the topology and geometry.[1] Further processing might yield a 3D model.

This sort of works on simple situations without too much edge noise. It's been used for industrial robot vision, where what matters are the outside edges of the part. It's not too useful when there's clutter, occlusion, or noisy textures.

More recent thinking is to find surfaces, rather than edges. This works well if you have a 3D imager, such as a Kinect. You can get a 3D model of the scene. Occlusion remains a problem, but texture noise doesn't hurt.

[1] [http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/GOME...](http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/GOMES1/marr.html#Marr82)

</comment></comment><comment><USER64> I'm happy somebody tries to put some sense into the whole absurdly overblown machine learning field.

<comment><USER65> I'm not sure it's absurdly overblown but individual advances/findings can be way overhyped or at least over-generalized. ML/AI has been incrementally delivering pretty impressive results within certain constraints. That's great but there's then a widespread tendency to extrapolate those results to the broader case--and then absurd/stupid-looking results happen.

We certainly see the same thing with autonomous vehicles. Given very accurate mapping and a particular set of environmental and type-of-road conditions, cars can do so well that it's tempting to say they're 95% of the way to fully- autonomous. But dump them in a Boston snowstorm and you see they're really not even close. (Which isn't to say that bounded use cases can't be very useful.)

<comment><USER66> Overhype has always been the enemy of AI/ML, leading to unrealistic expectations, then disappointment, then distrust.

But it might be different this time...

</comment></comment></comment><comment><USER67> Maybe the algorithm did detect a face in the couch, as we see them in trees (wood sprites!).

</comment><comment><USER68> Suddenly Terrence Davis appears!

</comment><comment><USER69> After rotating the image 90 degrees, the predicted result changes substantially. The author should not be surprised. A convolutional neural network is translationally invariant, not rotationally invariant.

<comment><USER38> Standard convnets do not contain explicit rotational invariance (unless you include a layer such as this: arXiv:1506.02025v1). They can however learn rotational invariance if you feed them rotated images.

</comment></comment><comment><USER70> Fascinating read.

</comment><comment><USER71> gi/go

</comment><comment><USER72> I want that sofa.</comment>


David Bonderman resigns from Uber board after sexist remark - <AUTHOR> https://www.nytimes.com/2017/06/13/business/david-bonderman-resigns-from-uber-board-after-sexist-remark.html <comment><USER2> It takes a certain kind of idiot to make a sexist remark (even as a joke) in a meeting about how your company is going to tackle its terrible problem with sexism.

</comment><comment><USER3> I can't find it now, but there's a video of one of Ben Horowitz's lectures where he says one benefit of hiring women is that they communicate more openly and effectively, at least with each other. If a woman feels marginalized by another woman during a meeting, she's more likely to communicate that and solve the problem, rather than holding onto it.

I don't know if this is true or not, but since there was no backlash over this, it goes to show that context and tone influences what's seen as disparaging.

<comment><USER4> It's quite a stretch from that sentiment to what Bonderman said.

</comment></comment><comment><USER5> >"In a statement, Mr. Bonderman said his comment "came across in a way that was the opposite of what I intended, but I understand the destructive effect it had, and I take full responsibility for that."

Is this just a very selective part of the statement then? Because saying something wildly inappropriate and "something failing to have its intended affect" are very different things.

</comment><comment><USER6> When I saw this I swear I though it was a news story from the Onion

</comment><comment><USER7> The remark was discussed earlier in this (flagged to death) thread: [https://news.ycombinator.com/item?id=14548761](https://news.ycombinator.com/item?id=14548761)

<comment><USER8> I really wonder why that got flagged that much. It's like this post, it's being moderated down super fast. Weird

<comment><USER9> As much as HN has grown in the last decade there's still a sizable undercurrent of reactionary thinking. Look at the large number of comments that see nothing wrong with a sexist comment at the big board meeting about fixing Uber's sexist culture.

</comment></comment></comment><comment><USER10> Fun that his comment likely will result in more women on that board, and probably more (substantive) talking.

<comment><USER4> I hate meetings. I hate people who just love to hear themselves talk.

But, in my experience, it's not women who predominantly do that ;)

<comment><USER10> Not a meeting fan either, but what else would a board do other than talk?

<comment><USER4> Yes, talk. But some sorts of talk are more useful than others.

<comment><USER10> Right...maybe you missed the "substantive" bit?

<comment><USER4> Sure. But "substantive" can be a value-laden concept.

<comment><USER10> I think you may have misinterpreted something I wrote. We seem to be saying the same thing in different ways.

</comment></comment></comment></comment></comment></comment></comment><comment><USER5> This seems like a real missed opportunity by the Board of Directors to have had him replaced(fired?) and put out a press release emphasising that toxic culture would not be tolerated at any level.

I would be curious to know if he is still entitled to his full Board member salary.

</comment><comment><USER11> I wonder where the pressure came from that actually made him resign. His firm?

<comment><USER10> Maybe just the awkward air in the room that he just can't shake. Perhaps the comment in a different setting would have merited just an apology to the room...but tossing that out there, in that particular time and place. Just wow.

</comment></comment><comment><USER12> I'm offended that people are offended over a stupid hack joke (It's ok to be offended).

</comment><comment><USER13> Wait... isn't the entire purpose of a board to talk in board meetings? They certainly aren't driving the cars.

</comment><comment><USER14> The remark:

 _At an Uber staff meeting earlier in the day, Arianna Huffington, another Uber board member, said one woman on a board often leads to more women joining a board. Mr. Bonderman responded "that it's much more likely to be more talking."_

Seems ironic that Arianna's own site posted this:

Biological Evidence May Support Idea That Women Talk More Than Men, Study Says [http://www.huffingtonpost.com/2013/02/21/women-talk-more- tha...](http://www.huffingtonpost.com/2013/02/21/women-talk-more-than-men- study_n_2734215.html)

Is it not possible to say anything anymore without someone being offended?

<comment><USER15> There's two things I'd like to say:

1) The media rarely characterizes the actual underlying science correctly.

In this case, the journal entry [0] and an author of the paper herself[1] characterize it as simply a "difference" and are careful to not concretely link it to human-observable behavioral differences (such as "Women talk more", which the media is more than happy to expand upon to clickbaity levels in order to get more ad revenue).

2) History has shown that science, which strives for impartial and unbiased results, often has its results applied by biased and partial people who have agendas. Said agendas may include justification of the current status quo.

In my opinion, keeping these two points in mind is just an exercise of healthy critical thinking. When applied here, I reject your idea that this Huffington article justifies Bonderman's behavior as being unoffensive.

[0] [http://www.alphagalileo.org/ViewItem.aspx?ItemId=128545&Cult...](http://www.alphagalileo.org/ViewItem.aspx?ItemId=128545&CultureCode=en) [1] [https://www.sfn.org/Press-Room/News-Release- Archives/2013/La...](https://www.sfn.org/Press-Room/News-Release- Archives/2013/Language-Protein)

<comment><USER14> In my experience, women generally do talk more. Is that an offensive statement, or is it the context of David Bonderman making that statement at an Uber staff meeting?</comment></comment></comment>


Show HN: textlaundry.com - <AUTHOR> http://textlaundry.com/

<comment><USER2>
      > As a student, I couldn't stand my required english courses.
      > Textlaundry has helped me to improve my writing immensely
      > and now I'm getting fantastic grades. The best part was that
      > it was so cheap!  -- Lisa Paul

This quote led me to think that students who use this service are anti- intellectual lazy cheaters with tight fists. Ironically, the quote has at least one basic editing problem ("English")! This quote doesn't capture what professional editing is about. Surely there is a more positive way to express the value proposition.

</comment><comment><USER3>
      - Empty TITLE tag.
      - Favicon is a "P" icon?
      - Put 5-10px padding on the textarea.
      - 100% satisfaction guaranteed icon needs transparency.

The design is fine but very, very generic. Nothing relates to a laundry. Why not a clothes line with text documents cleaned, pegged and sparkling in the sun?

If you get some resistance in the HN feedback, IMO it is because the service seems slapped together. I like the automatic pricing calculator but I don't know that anything else but the domain name is going to be memorable. If the design shows a bit more personality, I think that would help.

It might've been funny if you had replied to each HN comment with a re-write of what they'd written to show the benefit of the service...

<comment><USER4> We just launched recently, and we rushed this out to validate our idea. So far, it's going pretty well and love all the feedback we've gotten. We've added those things to our list of things to fix. Thanks for the advice all the advice.

</comment></comment><comment><USER5> I was just wondering who were the "professional" editors. Some credentials might increase the trust in your service.

<comment><USER6> I agree completely. Is this service farmed out to Mechanical Turk with the supposition that more eye is equal to higher quality? Or do you legitimately have qualified individuals reviewing these submissions?

<comment><USER7> I agree. A turk-based proofreading system might not be a bad idea- a few layers of suggestion verification and you could get a pretty good first-pass proofreading out of it. However, the lack of transparency makes this more than a little shady.

</comment></comment></comment><comment><USER8> Only taking input by text box is pretty bad. You need to be able to accept common word processor file formats.

Targeting students is also ethically questionable. Planning to open a sideline where you help "proofread" math homework next?

<comment><USER9> Colleges provide "writing centers" where people (presumably upperclassmen in a relevant degree program) offer proofreading. I don't see how this is much different.

<comment><USER10> That service is free, this is not. Often times the student proofreading your paper has already taken the class and off much more guidance to what that particular teacher/professor is looking for. An anonymous web service, while convenient, doesn't have that added benefit.

<comment><USER11> How is a college writing center free?

</comment></comment></comment></comment><comment><USER12> Hey, congrats on launching. The service looks interesting. I'll have to keep it in mind next time I need proofreading help.

One small piece of feedback: Your #headline-wrap and #stage-wrap styles have overflow: hidden defined while the other top level divs do not. This makes it so that when the browser is smaller than your content, scroll bars appear but your main content is hidden beyond the window width and appears cut off when scrolling.

</comment><comment><USER13> Without seeing any examples of improved text, my first thought was that an API/Wordpress/Gmail plugin would be awesome.

Though before I pay, it would be nice to see some examples of improved text.

<comment><USER14> Thanks bigethan, we keep hearing this and are going to put up some examples up.

</comment></comment><comment><USER15> Dividing by quality "Good, Great, and Excellent" is weird to me. What metric distinguishes the different options? If I only check "Good," will you edit my text while watching television instead of devoting your full attention to it?

I would get rid of the quality gradient altogether, instead ensuring your users that all of your work is "excellent." If you still want an extra tier to your pricing, I guess you could do what translation services do and charge per revision or by number of people the text passes through, but the idea that you simply aren't trying as hard if I check "good" doesn't sit well.

<comment><USER14> The quality gradients are actually tied to number of revisions/number of people who proofread the text. We'll put up more information about the way we proofread and how they relate to the different options for quality.

<comment><USER16> I would word it differently then. Using the term quality in a sliding scale manner leaves doubt in my mind. I want to know all your work is excellent.

<comment><USER17> I am replying so that textlaundry know that not everyone is like parent: i.e., I am fine with "Pick a quality: good, better, best" (or "Pick a quality: good, great, excellent"). In fact, I do not like it when vendors pretend that everything they offer, even the cheap stuff, is the best that it can possibly be. I tend to conclude that the vendors\ is either deluded or insincere.

But yeah, if textlaundry can convey in that row of radio buttons (terminology?) that "great" service means that the text is worked on by more editors than work on text using the "good" service, that would be an improvement.

</comment></comment></comment></comment><comment><USER18> There is a glaring typo on the front page. The section titled "Fully anonymous and secure editing" contains the following sentence:

"Your confidential documents will be kept in our secure servers and can only accessed by you and our team."

I'm no professional proofreader but I think that "can only accessed" is non- English.

Fail.

<comment><USER19> You made your point in the first two lines; I think your comment would be much better without the last two.

</comment></comment><comment><USER20> Here's the first association I make with text laundry: money laundry.

Then there's a testimonial about a student who's too lazy to do his assignment and uses external help.

I don't know what's your target, but if you're looking to appeal to companies I think you're off.

</comment><comment><USER21> Cool service!

To echo a few other comments, I'd love to see examples. Specifically, I hope examples could explain the following:

1) What kind of changes are suggested? Spelling corrections? Grammar corrections? Improved sentence structure? Improved paragraph structure? Some combination of these? Something else?

2) What's the difference between good vs. great vs. excellent? The quality of the proofreader? The amount of time spent proofreading? The number of times something is proofread? A concrete example would be very helpful.

Also, I think showing a list of the types of documents you can proofread would be helpful. It might just be me, but when I saw "document" I started thinking about college essays or work-related documents like letters of reference. Then I saw a testimonial that mentioned blog posts and thought "oh yeah, I could use this service for those as well!" You might miss some customers who don't realize your service is very open-ended.

<comment><USER4> Hey lpolovets, I agree that's not clear yet. We actually provide all of those depending on the document we get. Some of them need more thorough editing, while other documents just need basic proofreading. The good/great/excellent corresponds to how many editors your text will be read by. Someone else suggested that we change it to that to make it clearer. Thanks for the comment.

</comment></comment><comment><USER22> Is the Terms & Conditions page just copy/pasted from elsewhere?

If not, things like the following are very concerning:

      > Advertisements. textLaundry reserves the right to display advertisements on your blog unless you have purchased an Ad-free Upgrade or a VIP Services account.

<comment><USER14> Yeah sorry, that's from Automattic's terms and conditions, we can't access your blog :P We'll change the wording soon.

<comment><USER23> Wait... Sorry, but you ... didn't proofread your ToS?

Really?

</comment></comment></comment><comment><USER24> I didn't quite know what to expect when visiting the site with a name textlaundry.com As tacky as it sounds proofreadmypaper.com would be much "cleaner" (excuse the irony).

Props on launching. I think your provide a useful service. I love the idea of submitting my paper immediately and getting a quote.

<comment><USER4> Thanks! The name is tentative, and just something we went with for now to test the idea. The instant quote is something we noticed no one else had, so it was something we could stand out with.

</comment></comment><comment><USER25> Congrats on launch. The instant price calculator is slick.

I notice it's on Rails. Shoot for an API and a Wordpress plugin. Both could be jets for this type of business.

</comment><comment><USER26> Would be interested for such service but like others mentioned you need to add some examples of what you guys can do.

<comment><USER14> This is a great idea, we'll put up some examples of our work up soon!

</comment></comment><comment><USER27> how do you guys handle the editors? like I was thinking about a different idea where I need multiple people to handle the projects. it seems quite a lot of work to either code a complete backend or distribute the stuff manually.

</comment><comment><USER28> I'm interested in knowing if there are any similar services.

<comment><USER14> There are a few but I'm confident we have one of the lowest price points per word (if not the lowest). A google search for "proofreading service" turns up a few.

</comment></comment><comment><USER29> nice job! nice design to streamline proofreading.

<comment><USER14> Thanks!

</comment></comment><comment><USER30> I could pay for this. When did you launch it?

<comment><USER14> We launched a couple of weeks ago - still testing the waters and seeing what kind of demand there is for a product like this, especially in academia.

</comment></comment><comment><USER31> IP scanned after visiting this site.

</comment><comment><USER32> Be careful using the verisign logo if you're not actually a client (which it appears since you're using an image, and not the verifying script). Also, paypal name shows Edison Labs, which is confusing given it doesn't match your domain name

</comment><comment><USER31> Correct me if I'm wrong, but this is just a cobbled together CMS front-end with clip art icons and generic photos for what appears to be a proof-reading service, am I correct? Or am I missing some sort of technological breakthrough?

<comment><USER33> yes, it seems to be a service for proof reading. what's your point?

<comment><USER31> It's seems to be a [very] poor attempt at creating a webpage with a form for an offline service. (Not much in the way of hacking, per se)</comment></comment></comment>


Floating island of garbage 3x size of Britain floating towards California - <AUTHOR> http://www.mirror.co.uk/news/world-news/floating-island-rubbish-three-times-2678207

<comment><USER2> Scary headline; mundane facts. According to the article below (based on NOAA trying to counteract the rumors):

* 70% of the debris is at the bottom of the pacific * the area with the most debris is just that; it's still scattered even in that area, and not a coalesced island * we're not about to have a 51st state

[http://www.salon.com/2013/11/05/no_a_massive_island_of_debri...](http://www.salon.com/2013/11/05/no_a_massive_island_of_debris_isnt_heading_toward_the_u_s/)

</comment><comment><USER3> Ocean cleaning seems to be an even greater concern than every other green issues. The scale of the pollution is so big, if you take pollution from micro plastic bubbles and heavy metals into account that i'm not even sure we'll get back to clean oceans in my lifetime.

As for now i'm starting to avoid sushis and eat much less fish in general.

<comment><USER4> While clean water is a serious concern I think that ocean acidification and the possibility of it radically altering the biological make-up of the ocean presents us with an even greater problem.

You may not have to give up on sushi, you just might have to acquire a taste for jellyfish.</comment></comment>


ObamaCare is No Starship Enterprise - <AUTHOR> http://www.bloomberg.com/news/2013-11-25/obamacare-is-no-starship-enterprise.html

<comment><USER2> Very insightful points about non-technical and technical personnel's interactions in business. In my experience the "illusion of omnicompetence" is pretty pervasive within technical professions.

</comment><comment><AUTHOR> closing paragraph contains a key piece of advice for entrepreneurs:

"We like to think that being "smart and competent" makes you less likely to make mistakes. But when you're out of your element, it may merely enable you to make more -- and larger -- mistakes."</comment>


Intel to Universities: No Patents, Please, Just Open Source - <AUTHOR> http://www.innovationexcellence.com/blog/2011/09/12/intel-to-universities-no-patents-please-just-open-source/ <comment><USER2> "So how did they finally fix the patent system, Grandpa?"

"They didn't. Nobody could. Over the years, people just got tired of the bullshit and quit using it."

<comment><USER3> The problem is they still go after open-source software with patent infringement claims (look at Microsoft).

</comment></comment><comment><USER4> The catch here is of course that intel is a hardware company.

In 2010 alone intel received 1600+ patents:

[http://en.wikipedia.org/wiki/List_of_top_United_States_paten...](http://en.wikipedia.org/wiki/List_of_top_United_States_patent_recipients)

If intel wants to stop the patent madness they could start with removing themselves from that top 10.

<comment><USER5> Companies almost have to own patents these day; for defensive reasons if nothing else.

<comment><USER6> The problem is patent trolls are immune to this kind of defense.

</comment></comment></comment><comment><USER7> I have difficulty reading that page, with light grey text on a grey, "hand- drawn", graph-paper background.

<comment><USER8> Something has gone wrong with your rendering. The text is supposed to be on a white background. #555555 text looks just fine like that.

Edit: It may have been a server error but only to some people? It's unclear from the comments on the site.

</comment></comment><comment><USER9> The money quote: _In search of better margins, Intel hopes to beef up its software practice to combat increasingly thin profit margins from chip sales._

Intel has plenty of large competitors in the software area. Still, they expect to make more money thanks to the existence of free software. Contrast this to those who say there's no way to make money if others can copy you.

</comment><comment><USER10> The rest of the world to Intel: No Patents, Just Open Source...

</comment><comment><USER11> How would universities get paid for the research they facilitate if this were the standard practice? My university would have to close or curtail its activities in nearly unimaginable ways without the billions of dollars that the research foundation has generated honestly (via things brought into existence by UW researchers: without their minds, their work/results/products would not exist). Incidentally, my university also sued Intel recently over a patent dispute and won.

<comment><USER12> The amount of money generated for universities by patents on CS/engineering research is quite small, though. The total for all fields in 2009 (according to an unfortunately paywalled report: [http://chronicle.com/article/Table- Licensing-Revenue-and/125...](http://chronicle.com/article/Table-Licensing- Revenue-and/125729/)) was $1.8 billion, of which the vast majority went to a handful of schools for blockbuster biotech patents (e.g., NYU has been pulling in $100m+ annually from the Remicade patents). I would be surprised if UW non- bio patents are bringing in more than low-single-digit millions, i.e. not much compared to what they get in corporate donations, not to mention NSF and DARPA grants, and even the state subsidy and tuition.

The former Dean of Georgia Tech's CS dept quotes John Hurt from the NSF as claiming: "Of 3,200 universities, perhaps six have made significant amounts of money from their intellectual property rights." I don't think there have been _any_ blockbuster university (non-bio) tech patents since the Akamai (MIT) / PageRank (Stanford) pair ten years ago.

<comment><USER13> This is really more of an issue with how universities value the research, I think. Most of the time, there isn't a very clear immediate business benefit, so they're willing to part with the IP for very little money.

Prime case is the Mosaic browser IP and the University of Illinois Urbana- Champaign. All the research was done by the National Center for Supercomputing Applications for the university, and when it was completed and released, didn't really bother to try to get anything from it. The university basically sold the rights to an offshoot company for a very small fee-I believe it was under $1 million, and didn't see any money from it after that.

<comment><USER12> Part of that is a deliberate strategy on the part of some universities, based on a guess that they'll get more in donations from corporations, spinoffs, and successful alumni voluntarily, than they would get by being tight-fisted with IP. Plus, it increases the university's prestige to have their stuff involved in more products, and their alumni involved in more successful ventures, so there's a cost to doing anything that would complicate stuff up-front. The goal is to get it on the back-end instead: don't hassle the not-yet-successful startup, but if they IPO, nag to see if they're willing to kick in a $50m building or write you into their will.

I'm not sure which version of the calculus is right, but it's at least plausible to me that universities stand to lose more in donations/prestige than they'd gain in royalties if they took stricter approaches to patent licensing.</comment></comment></comment></comment>


Show HN: Give gifts with a twist - <AUTHOR> http://www.ifting.com/

<comment><USER2> This isn't quite about gifting because a gift is given only expecting goodwill in return. This is more like an "on-demand personal assistant", which is a cool idea but would be even better with a name that conveys its true purpose.

</comment><comment><USER3> Neat, but those drawings scare me.

<comment><USER4> I love the playful style of the whole thing.

</comment></comment><comment><USER5> Looks very cool and i like the idea, up until i saw the log in with Facebook button.

No thanks.

</comment><comment><USER6> So this is sort of like Zaarly? Except targeted instead of broadcasted?

</comment><comment><USER7> Intriguing idea--can you tell us a bit more about the story behind it?

</comment><comment><USER8> I don't have a Facebook and I won't create one. No go.

</comment><comment><USER9> Wasn't Facebook recently granted a 'gifting' patent?</comment>


The non-technical hacker - <AUTHOR> http://www.wwtid.com/2012/10/31/a-non-technical-resource-to-hacking-the-web/

<comment><USER2> "The term "hacker" has morphed in meaning over the years. It started off as a way to describe folks trying to circumvent computer security (and it still is), then it became more of a pop-culture reference to web developers in general (still is), and today a term that's frequently appending other types of specialties and industies."

Stopped reading here. But then read it anyway, so I could see where it was going.

I think that; if you go ahead and read a book like _Hackers: Heroes of the computer revolution_ you'll find that the only definition in the article that _wasn't_ there at the start (Or at least implied, replacing the 'web developer' Jab with 'good computer programmer'.) is the security one.

A lot of the coolest hacks done by MIT hackers on their surface had nothing to do with computers, besides a computer being tangentially involved.

[http://en.wikipedia.org/wiki/Hacker_%28programmer_subculture...](http://en.wikipedia.org/wiki/Hacker_%28programmer_subculture%29)

<comment><USER3> Lets not be so quick to forget that the term hacker originally meant... someone messing around with other people's computer systems without their permission before it took on the benign connotation among computer enthusiasts[1]. The antedating of the term hacker is really quite something.

[1] [http://listserv.linguistlist.org/cgi- bin/wa?A2=ind0306B&...](http://listserv.linguistlist.org/cgi- bin/wa?A2=ind0306B&L=ads-l&P=R5831&m=24290)

<comment><USER2> It originally meant a lot of things.

<http://en.wikipedia.org/wiki/Phreaker>

[http://en.wikipedia.org/wiki/Hacker_%28term%29#Overlaps_and_...](http://en.wikipedia.org/wiki/Hacker_%28term%29#Overlaps_and_differences\[1\])

[1]: "The main basic difference between programmer subculture and computer security hackers is their mostly separate historical origin and development. However, the Jargon File reports that considerable overlap existed for the early phreaking at the beginning of the 1970s. An article from MIT's student paper The Tech used the term hacker in this context already in 1963 in its pejorative meaning for someone messing with the phone system.[10] The overlap quickly started to break when people joined in the activity who did it in a less responsible way.[28] This was the case after the publication of an article exposing the activities of Draper and Engressias." - Wikipedia; Tue Nov 6 16:27:58 PST 2012

</comment></comment></comment><comment><USER4> As someone who works in the security industry, I can tell you that we really get a kick out of every coffeeshop Rails or JavaScript developer who throws together a quick demo over a weekend and calls it "hacking."

I'm well aware of the history of the subculture and that the term precedes infiltration of computer systems. But that doesn't make every night that you stay up late programming a "hacking" session.

<comment><USER5> But what if you have a cool hair style and listen to electronic music at the same time!? I thought that was the definition of a h4x0r. :)

</comment></comment><comment><USER6> Clicking on the link to an article titled "The Non-technical Hacker" results in "Error establishing database connection." An example of truth in advertising?

<comment><AUTHOR> After this started getting upvoted my blog went down...odd timing.

<comment><USER7> Try this: <https://www.cloudflare.com/features-cdn>

Edit: More specifically, try it from the Bluehost cPanel: <https://my.bluehost.com/cgi/help/cloudflare>

<comment><AUTHOR> I installed cloudflare and de-activated the twitter widget that was being attacked. It fixed the issue. Thanks so much for your help!

</comment></comment></comment></comment><comment><USER8> My children are hackers then apparently

<comment><USER9> That's an interesting thought actually. Maybe hackers are those who 'kept their child inside'?

<comment><USER7> " _In the beginner's mind there are many possibilities, in the expert's mind there are few._ "

[1] <http://en.wikipedia.org/wiki/Shoshin>

</comment></comment></comment><comment><USER10> I like the old-school definition. When I hear somebody calling himself a hacker, it looks to me like when a teenager powder its face and call himself a vampire. Kid, you are not a vampire. Vampires are horrible creatures that sucks blood. Developers are not hackers, hackers enter your computer to steal stuff from you, and love it. (that said, I'm posting in a site calling "hacker news" heh)

<comment><USER11> _hackers enter your computer to steal stuff from you, and love it_

Those are crackers, not hackers.

<http://www.catb.org/esr/faqs/hacker-howto.html#what_is>

 _(that said, I'm posting in a site calling "hacker news" heh)_

That's because pg understands the correct use of this terminology.

<comment><USER10> >Those are crackers, not hackers.

No, crackers are people that bypass software copy protections.

Arguing about non-formal language definitions is absurd anyway.

</comment></comment></comment><comment><AUTHOR> My blog went down after this started to spread... hmmm.

"Error establishing a database connection"

</comment><comment><USER7> Google cache: [http://webcache.googleusercontent.com/search?q=cache:LwOvI6a...](http://webcache.googleusercontent.com/search?q=cache:LwOvI6aCLKkJ:www.wwtid.com/2012/10/31/a-non- technical-resource-to-hacking-the-web/)

</comment><comment><AUTHOR> Confirmed with bluehost:

"It looks like the reason you are running into these problems is due to the CPU throttling that your account is intermittently undergoing."

</comment><comment><USER12> Thanks for the laughs :>

<http://wavesum.net/lol.png></comment>


Facebook's Profit Soars Past Expectations, Fueled by Mobile Ads - <AUTHOR> http://www.nytimes.com/2014/07/24/technology/facebook-profit-soars-past-expectations-fueled-by-mobile-ads.html

<comment><USER2> Correct link:

[http://www.nytimes.com/2014/07/24/technology/facebooks- profi...](http://www.nytimes.com/2014/07/24/technology/facebooks-profit-soars- past-expectations-fueled-by-mobile-ads.html)</comment>


Show HN: My first site made purely to sell stuff. Would love feedback. - <AUTHOR> http://www.constantawesome.com

<comment><USER2> I like it a lot. I don't read comics, but your simple presentation, use of the cover images, and descriptive text made me interested in the comics.

Minor suggestion: If it's possible it might be better to extract the price and list price from Amazon, rather than hard-coding them; they could change.

The title shown in hover text is unnecessary since you have the title below the comic.

<comment><AUTHOR> Thanks - glad to hear it made a good impression. Right now I have a script to generate all the prices. I just run it manually and rsync to my dreamhost server, but soon I'm going to create a cron job so that the prices will stay up to date.

Thanks for the feedback!

</comment></comment><comment><AUTHOR> This is the first side project I've worked on solely for the purpose of making money. I don't expect to make a lot - rather, I hope to learn about selling. I think the very simple format will allow me to quickly experiment and run a/b tests.

Any feedback would be greatly appreciated!

</comment><comment><USER3> I'm no graphic designer, but the blank space above the third column in the grid of comics is really distracting until I scroll it off the top of the screen. That aside, I really like the strong grid.

<comment><AUTHOR> Thanks! I hadn't thought of that at all. I'll play around with it.</comment></comment>


Ask HN: Has Dropbox been compromised recently? - <AUTHOR> I just received an alert from a (paid) Internet surveillance service, stating:
Site WWW.DROPBOX.COM
"Your email address has been found compromised online. We monitor online properties to identify the illegal trading and selling of your personal information, and unfortunately we have found a match that may indicate possible identity theft."
I closed Dropbox a while ago, and the pwd was a randomly generated one. False positive or a hack surfacing just now? Anyone noticed suspicious activity related to their Dropbox account ? <comment><USER2> No. There have been a bunch of "leaks"/"dumps" claiming to be from Dropbox posted on pastebin for the last two years or so. Most are just rehashed from other pw dumps.

Troy hunt: [https://twitter.com/troyhunt/status/599150465335431169](https://twitter.com/troyhunt/status/599150465335431169)

And this latest FUD came from leakedsource.com, who published a list that was claiming to be Dropbox usernames and hashes, but if they did their due diligence they would have notice that the list was in fact Tumblr. They issued a correction later:

[https://twitter.com/LeakedSource/status/732225976210472961](https://twitter.com/LeakedSource/status/732225976210472961)</comment>


FlatGA – A Simplified Interface for Google Analytics - <AUTHOR> https://flatga.io <comment> King-Aaron This site doesn't instill a great deal of trust in me handing over google credentials. Some more information about who you are, where you're located etc would probably be beneficial.

<comment><USER2> I'm not affiliated with either product, but FlatGA uses OAuth so you're not "handing over Google credentials." The app requires explicit permissions to: (1) view your configuration information, (2) view your reports, (3) view your Google email address. You could revoke access at any time.

</comment></comment><comment><AUTHOR> Happy to answer any questions you have.

<comment><USER3> Idea: You could make this an embeddable widget (for a 12-24 USD yearly fee), ideal for CMSs like Joomla, WordPress etc. Basically an iframe with some settings page in the CMS (dead simple on both WordPress and Joomla) to store the login.

<comment><USER4> If I click on the small download (?) symbol, I get a js error. Its in the 'Top pages & Referrals (Top 15)' panel.

</comment></comment></comment><comment><USER5> Do GA users find it worrying that Firefox and edge beta are blocking GA by default now?

<comment><USER6> Firefox is only blocking the 3rd party cookie GA tries to set. That doesn't break the GA functionality. It still records the visit and all the data. [https://www.upbuild.io/blog/firefox-google-analytics- data/](https://www.upbuild.io/blog/firefox-google-analytics-data/)</comment></comment>