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If deep learning is a black box, then why are companies still investing in it?

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    $\begingroup$ Companies invest because deep learning has been very effective for many companies in achieving desired goals. The 'black box' neural network itself is the easy part. The hard part is deciding what inputs to use, what representation should those inputs be presented in, what should be the expected outputs and representation, what utility functions will achieve the best results and in many cases collecting a set of training data that will actually lead to good results. That's what they are investing in. I disagree there are only small numbers of systems doing something real. $\endgroup$ – Dunk Jan 2 at 18:39
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Easy answer: utility.

The strength and applicability of "black box" NNs has been regularly validated in the past few years, and business is concerned with results. (i.e. they don't care how the sausage is made, so long as it gets made.)

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  • $\begingroup$ :Very useful answer. One of my friend ask me the question about iPhone facial recognition .Can we define this process to lay person?How things are happening at backend to tag my photos?How iPhone facial recognition works.Any mathematical evidance?.How can we inspire someone who is machine learning enthusiast and needs some facts to enter into this research.I need to motivate my junior researchers with useful answers $\endgroup$ – Case Msee Jan 1 at 3:49
  • $\begingroup$ Yes, I think you're right, but I think it would be also nice to at least mention that, in certain areas, like healthcare, people are reluctant to use deep learning (or ANNs) because of its "black box" nature. See also explainable AI. $\endgroup$ – nbro Aug 20 at 23:20
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Deeplearning itself can be questioned if it's working. Most users are reporting, that they are not able to train a network under a certain error. That means, they are using the latest hardware and software and aren't able to figure out the right weights. Additional, the problem with the blackbox phenomena is there, which means that even after a network was trained successfully, nobody knows what the connections internally are doing, so the result of the learning process has to be questioning.

The reason, why the hype around deeplearning isn't over has to do with the process in which neural network datasets are created. Before a 12 layer convolutional neural network can be trained so called raw data are needed. This can be a collection of images, motion capture recordings of biped walking or annotated game-logs from Starcraft AI. The prestep before the training itself is the reason why the subjects remains interesting. Even if the dataset isn't used for training neural networks, it's an important step in engineering artificial intelligence. AI without datasets isn't working, and neural networks have a large demand for datasets.

Instead of simply calling it neural networks, the underlying technology should be stated as datadriven algorithms. The idea is to connect machine intelligence which is stored in computer programs with real life domains which are provided as images, trajectories and textual corpora.

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The human brain is a black box too (so far), but we keep investing in them too.

There is no back end to a network. There are inner layers, and one can analyze what they might represent conceptually sometimes, such as in the case of computer vision convolution layer sequences. That is also true of brain neural networks.

There are many proofs in AI literature, so it is not that there are baseless and arbitrary designs behind AI system design, but the need to explicitly understand what each neuron is doing is usually wasted investigation to find that the neuron does very little or it does so much that the characterization of its purpose is as large as the dataset that trains the entire network.

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I think that the universal approximation theorem plays a large role in why companies and governments are investing in deep learning, it states that theoretically an ann can approximate any continuous function with n-dimension input variables. Specifically it states that feed forward nets with a single hidden layer can do this lending credence to the implication that rnns and cnns are also capable of universal function approximation. So they are investing because they have continuous functions that need to be approximated and really the best tool for the job is neural networks.

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    $\begingroup$ RNNs have been proven to be Turing complete and CNNs have also been proven to be universal approximators, so the powerfulness of RNNs and CNNs is not just hypothetical. See ai.stackexchange.com/a/13319/2444. Anyway, I don't think companies are investing in neural networks because they are theoretically powerful. AIXI is also theoretically an optimal agent, but nobody uses it. $\endgroup$ – nbro Aug 20 at 23:22
  • $\begingroup$ well i think AIXI isnt used or invested in do to it being incomputable and the approximations of it being less effective that other than other comparable rl algorithms. $\endgroup$ – nickw Aug 21 at 13:59

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