Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Machine Learning Confronts the Elephant in the Room

quantamagazine.org

Post reply on HN
Print view View original

41–50 of 116 posts

Re: Machine Learning Confronts the Elephant in the Room

#41

Post by salty_biscuits »

sweezyjeezy wrote: post #22
weregiraffe wrote: post #13

Why can't you just train the network on artificially altered photos, with "elephants" randomly scattered around, until it is robust to them?

This study shows something more fundamental about the way that neural networks process images, other than just that random elephants are a problem. Rather than break the image down into constituent parts and reason about them individually, they use global context to help them classify. This lets them outperform humans on some tasks. The problem is if you show it something completely different to anything it has ever…

I just spent 15 minutes putting a picture of a bear with a top hat riding Abraham Lincoln with laser eyes into all the online demos I could find. They weren't unintelligible responses, just a bit crap. They said "weapon", "animal", "person", etc. I'm not sure how much global context they really use, in my experience it has been latching onto a weird bit of local texture. More work to do...

Re: Machine Learning Confronts the Elephant in the Room

#42

Post by useful »

hydrox24 wrote: post #40

I know very little about machine vision, so forgive the naïvete of this question: > an ability humans have that AI lacks: the ability to understand when a scene is confusing and thus go back for a second glance. Wouldn't the machine return a low confidence score when a scene is confusing? If not, why is this difficult to get around? If so, why can't we just call this a computation difficulty problem, simply requiring…

Each is harder:

* return a result

* return a list of results

* return a list of results with a confidence weightins

* return a list of results with multiple weightings (confidence and complexity)

how many if statements would you need to write for each if this was applied to a 3x3 grid like tic-tac-toe? how would you know you are correct?

Re: Machine Learning Confronts the Elephant in the Room

#43

Post by ivan_gammel »

ThePhysicist wrote: post #38
ivan_gammel wrote: post #30

Please, correct me if I'm wrong, but isn't the whole machine learning testing approach based on the recognition the whole image at once in full resolution in one attempt? If so, it's not how animals see and observe the world and this could be a key difference between AI and living beings. What if an algorithm actually had a focused sight? Look at the brightest feature, blur the rest, then try to find another distinct…

Yes but children can pass these tests when presented with a single photo as well (if I understand the setup correctly), so I'd say it's a fair comparison. The algorithm can "look" at the picture as long as it wants as well, e.g. using many different convolutional filters that can "see" features in different parts of the image and then combine these in later stages of the analysis.

How close are those convolutional filters to the physical sight of animals? Were they designed specifically to mimic nature or they are artificial concept with similar purpose?

Re: Machine Learning Confronts the Elephant in the Room

#44

Post by dpwm »

antpls wrote: post #39
dpwm wrote: post #32

Earlier quoted context omitted.

> It should be "Neural networks Confronts...". I disagree, for the following reasons: - The paper is called The Elephant in the Room. Technically the paper confronted the popular machine learning approaches, so the reversal would have been a more eye catching headline. "The Elephant in the Room confronts Machine Learning" is a rare opportunity to honestly play with word order in a way that would arguably be more atte…

> - The paper is called The Elephant in the Room. How is this argument related to the confusion of the terms Machine Learning and Neural Networks...? > - Machine learning is more general than Convolutional Neural Networks, but less general than AI. This seems appropriate for this type of popular science publication. The most appropriate term is the most precise one, in this case : neural networks > - There are approa…

> How is this argument related to the confusion of the terms Machine Learning and Neural Networks...?

We're talking about the title.

> The most appropriate term is the most precise one, in this case : neural networks

That is generalizing RNNs and CNNs into "neural networks". If I take a popular CNN architecture, chop off the final dense layer and activation and attach an SVM classifier, is it an SVM or a convnet I am using?

Somehow the title "The Elephant in the Room confronts faster_rcnn_inception_resnet_v2_atrous_coco, faster_rcnn_nas_coco, ssd_mobilenet_v1_coco, mask_rcnn_inception_resnet_v2_atrous_coco, mask_rcnn_resnet101_atrous_coco." (or the reverse) doesn't have the same ring to it and is probably inappropriate for the audience.

Re: Machine Learning Confronts the Elephant in the Room

#45

Post by ht85 »

hydrox24 wrote: post #40

I know very little about machine vision, so forgive the naïvete of this question: > an ability humans have that AI lacks: the ability to understand when a scene is confusing and thus go back for a second glance. Wouldn't the machine return a low confidence score when a scene is confusing? If not, why is this difficult to get around? If so, why can't we just call this a computation difficulty problem, simply requiring…

I think the article is referring to a scene that would be unlikely, not make sense given the context. Like identify a grey mass in the middle of the street as en elephant.

"Confusing" would be a poor choice of word here though.

Re: Machine Learning Confronts the Elephant in the Room

#46

Post by skierscott »

hydrox24 wrote: post #40

I know very little about machine vision, so forgive the naïvete of this question: > an ability humans have that AI lacks: the ability to understand when a scene is confusing and thus go back for a second glance. Wouldn't the machine return a low confidence score when a scene is confusing? If not, why is this difficult to get around? If so, why can't we just call this a computation difficulty problem, simply requiring…

> a low confidence score

Neural nets should return a low confidence score. But, the popular approach (described below) ignores that. Neural nets ignore confidence because of a technique called softmax [1].

This happens as the final operation of a neural net, and is required for training.

Softmax is a tool to make an array of positive numbers look like a probability distribution:

    out = x / x.sum()
x[i] is a class prediction, but x.sum() != 1. Say if the network was uncertain, x[cat, dog] = [0.03, 0.01]. These are small values that do not imply great confidence (the network was trained on vectors with out.sum() = 1. The network would predict “dog” using softmax because out[dog] = 0.75 > 0.25 = out[cat].

But then in inference/prediction, the confidence is ignored. What if x.sum() is small? That would imply that the network is uncertain.

[1]: https://en.m.wikipedia.org/wiki/Softmax_function

Re: Machine Learning Confronts the Elephant in the Room

#47

Post by sweezyjeezy »

salty_biscuits wrote: post #41
sweezyjeezy wrote: post #22

Earlier quoted context omitted.

This study shows something more fundamental about the way that neural networks process images, other than just that random elephants are a problem. Rather than break the image down into constituent parts and reason about them individually, they use global context to help them classify. This lets them outperform humans on some tasks. The problem is if you show it something completely different to anything it has ever…

I just spent 15 minutes putting a picture of a bear with a top hat riding Abraham Lincoln with laser eyes into all the online demos I could find. They weren't unintelligible responses, just a bit crap. They said "weapon", "animal", "person", etc. I'm not sure how much global context they really use, in my experience it has been latching onto a weird bit of local texture. More work to do...

Try this one : https://i.ytimg.com/vi/I11Vlcpz3jc/hqdefault.jpg

I tried it on a couple of online demos, neither spotted that there was a car in the picture.

Re: Machine Learning Confronts the Elephant in the Room

#48

Post by magicalhippo »

hnhg wrote: post #21
weregiraffe wrote: post #13

Why can't you just train the network on artificially altered photos, with "elephants" randomly scattered around, until it is robust to them?

Because there's always another type of 'elephant' to be thrown in. The networks are just not inherently robust against unexpected features (I'll call them outliers for want of a better term), and this is what needs to be fixed, and not increasing the variety and frequency of outliers.

Couldn't you train multiple nets with somewhat different training sets, and for each classification see how many agrees?

If they all agree you have a high confidence, if they mostly disagree you have a low confidence.

Re: Machine Learning Confronts the Elephant in the Room

#49

Post by kakarot »

I'm confused about what's so special here.

This is analogous to a person from the early days of photography having no knowledge of the possibility of image doctoring, and us feeling "smug" because a photo like this would elicit confusion.

It seems to me you just have to run your NN on pairs of doctored and undoctored images, and create a separation of concern between scene continuity and object recognition. It's likely that a lot of implementations currently rely too much on surrounding context for successful categorization.

It's also worth exploring certain image analysis techniques during pre-processing to draw out certain attenuated aspects. Edge detection is par for the course, but things like shading are also important to attenuate so that the NN has an easier time learning.

If we're dealing with multiple frames and not just a single image, this opens up a whole dimension of temporal analysis that should make it trivial to separate the background from juxtaposed images.

Re: Machine Learning Confronts the Elephant in the Room

#50

Post by gok »

ivan_gammel wrote: post #30

Please, correct me if I'm wrong, but isn't the whole machine learning testing approach based on the recognition the whole image at once in full resolution in one attempt? If so, it's not how animals see and observe the world and this could be a key difference between AI and living beings. What if an algorithm actually had a focused sight? Look at the brightest feature, blur the rest, then try to find another distinct…

This is what the article says but it's not really correct. Modern CV systems perform "segmenting" before identification.
Post reply on HN