60 votes
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How is it possible that deep neural networks are so easily fooled?

First up, those images (even the first few) aren't complete trash despite being junk to humans; they're actually finely tuned with various advanced techniques, including another neural network. The ...
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  • 2,559
51 votes
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What is fuzzy logic?

As complexity rises, precise statements lose meaning and meaningful statements lose precision. ( Lofti Zadeh ). Fuzzy logic deals with reasoning that is approximate rather than fixed and exact. This ...
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32 votes
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How is a deep neural network different from other neural networks?

The difference is mostly in the number of layers. For a long time, it was believed that "1-2 hidden layers are enough for most tasks" and it was impractical to use more than that, because training ...
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28 votes

How is it possible that deep neural networks are so easily fooled?

The images that you provided may be unrecognizable for us. They are actually the images that we recognize but evolved using the Sferes evolutionary framework. While these images are almost impossible ...
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  • 992
22 votes

What is fuzzy logic?

Fuzzy logic is based on regular boolean logic. Boolean logic means you are working with truth values of either true or false (or 1 or 0 if you prefer). Fuzzy logic is the same apart from you can have ...
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16 votes

How is it possible that deep neural networks are so easily fooled?

All answers here are great, but, for some reason, nothing has been said so far on why this effect should not surprise you. I'll fill the blank. Let me start with one requirement that is absolutely ...
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  • 1,847
13 votes

Is the pattern recognition capability of CNNs limited to image processing?

Convolutional Nets (CNN) rely on mathematical convolution (e.g. 2D or 3D convolutions), which is commonly used for signal processing. Images are a type of signal, and convolution can equally be used ...
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  • 1,480
12 votes
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What kind of problems require more than 2 hidden layers?

Formally, a single hidden layer is sufficient to approximate a continuous function to any desired degree of accuracy, so in that sense, you never need more than 1. This is called the Universal ...
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12 votes
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What is a deep neural network?

A deep neural network (DNN) is nothing but a neural network which has multiple layers, where multiple can be subjective. IMHO, any network which has 6 or 7 or more layers is considered deep. So, the ...
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  • 1,341
11 votes

How is it possible that deep neural networks are so easily fooled?

An important question that does not yet have a satisfactory answer in neural network research is how DNNs come up with the predictions they offer. DNNs effectively work (though not exactly) by ...
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  • 1,122
10 votes

How is a deep neural network different from other neural networks?

A deep neural network is just a (feed-forward) neural network with many layers. However, deep belief networks, Deep Boltzmann networks, etc., are not considered (debatable) deep neural networks, as ...
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  • 1,341
9 votes

What is fuzzy logic?

It's analogous to analogue versus digital, or the many shades of gray in between black and white: when evaluating the truthiness of a result, in binary boolean it's either true or false (0 or 1), but ...
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8 votes
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What is the "dropout" technique?

Dropout means that every individual data point is only used to fit a random subset of the neurons. This is done to make the neural network more like an ensemble model. That is, just as a random ...
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7 votes
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Are there any learning algorithms as powerful as "deep" architectures?

Have you read the book The Master Algorithm: by Pedro Domingos? He discusses the present day machine learning algorithms... Their strengths, weaknesses and applications... Deep Neural Network ...
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7 votes
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How many neurons would a network have after a training of 100k small images?

The neural network is typically a set size once it's created. You'd have to create a network big enough for your data-set.
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  • 358
7 votes

Why are deep neural networks and deep learning insufficient to achieve general intelligence?

Everyone dealing with neural networks misses an important point when comparing systems with human like intelligence. A human takes many months to do anything intelligible, let alone being able to ...
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7 votes
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How much of a problem is white noise for the real-world usage of a DNN?

The white noise that fools DNNs isn't really white noise. It has been altered in the same way as the synthetic misclassified pictures have been altered. You have to change many input pixels in exactly ...
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6 votes

What is the "dropout" technique?

The original paper1 that proposed neural network dropout is titled: Dropout: A simple way to prevent neural networks from overfitting. That tittle pretty much explains in one sentence what Dropout ...
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6 votes
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How can the generalization error be estimated?

Generalization error is the error obtained by applying a model to data it has not seen before. So, if you want to measure generalization error, you need to remove a subset from your data and don't ...
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6 votes
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Has any research been done on DNN Music?

The first thing is to define what is a «good» and a «bad» sound. This is an extremely tricky issue, since the networks need numeric inputs. And music is whole bunch of numbers. I know from people ...
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  • 528
6 votes
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Should deep residual networks be viewed as an ensemble of networks?

Imagine a genie grants you three wishes. Because you are an ambitious deep learning researcher your first wish is a perfect solution for a 1000-layer NN for Image Net, which promptly appears on your ...
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6 votes

Why are deep neural networks and deep learning insufficient to achieve general intelligence?

Deep Learning is mostly successful in supervised learning, whereas the brain builds categories mostly in an unsupervised way. We don't yet know how to do that. (Take a look at google brain: 16,000 ...
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5 votes

Is the pattern recognition capability of CNNs limited to image processing?

The simple answer is "no, they aren't limited to images": CNNs are also being used for natural language processing. (See here for an introduction.) I haven't seen them applied to graphical data yet, ...
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5 votes
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What is the purpose of the hidden layers?

"Hidden" layers really aren't all that special... a hidden layer is really no more than any layer that isn't input or output. So even a very simple 3 layer NN has 1 hidden layer. So I think the ...
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  • 3,679
5 votes

How is it possible that deep neural networks are so easily fooled?

How is it possible that deep neural networks are so easily fooled? Deep neural networks are easily fooled by giving high confidence predictions for unrecognizable images. How is this possible? Can you ...
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  • 388
5 votes

What is a deep neural network?

Deep networks have two main differences with 'normal' networks. The first is that computational power and training datasets have grown immensely, meaning that it's practical to run larger networks ...
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5 votes
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What's the difference between hyperbolic tangent and sigmoid neurons?

Sigmoid > Hyperbolic tangent: As you mentioned, the application of Sigmoid might be more convenient than hyperbolic tangent in the cases that we need a probability value at the output (as @matthew-...
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5 votes

Why is no activation function used at the final layer of super-resolution models?

I am not into the field of super-resolution, but I think this question applies to general neural network construction. Usually, you try to solve a classification problem or a regression problem with ...
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5 votes

Can prior knowledge be encoded in deep neural networks?

Neural nets incorporate prior knowledge. This can be done in two ways: the first (most frequent and more robust) is in data augmentation. For example in convolutional networks, if we know that the "...
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5 votes

What kinds of problems can AI solve without using a deep neural network?

I was hoping to see more answers here, but I'll get us started with some examples: Combinatorial Search Problems: If your problem can be phrased as movement through a combinatorial graph, you don't ...
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