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1

From my experience in industry, a lot of data science (operating on customer information, stored in a database) is still dominated by decision trees and even SVMs. Although neural networks have seen incredible performance on "unstructured" data, like images and text, there still do not appear to be great results extending to structured, tabular data (yet). ...


0

Speech recognition and Character recognition are not part of NLP. Everything else on your list can in principle be done with RNNs. But the field is quickly moving towards using transformers.


3

According to the Baidu Research's blog post How Baidu is harnessing the power of AI in the battle against coronavirus (12-03-2020), there are already some artificial intelligence tools or algorithms being used to fight the coronavirus. Given that I cannot confirm that these AI tools and algorithms I will mention are really being used in practice, I will ...


3

Neural networks seem to have a great deal of difficulty handling adversarial input, i.e., inputs with certain changes (often imperceptible or nearly imperceptible by humans) designed by an attacker to fool them. This is not the same thing as just being highly sensitive to certain changes in inputs. Robustness against wrong answers in that case can be ...


1

In the case of convolutional neural networks, the features may be extracted but without taking into account their relative positions (see the concept of translation invariance) For example, you could have two eyes, a nose and a mouth be in different locations in an image and still have the image be classified as a face. Operations like max-pooling may also ...


2

Large scale route optimization problems. The is progress made in using Deep Reinforcement learning to solve vehicle routing problems (VRP), for example in this paper: https://arxiv.org/abs/1802.04240v2. However, for large scale problems and overall heuristic methods, like the ones provided by Google OR tools are much easier to use.


15

In our deep learning lecture, we discussed the following example (from Unmasking Clever Hans predictors and assessing what machines really learn (2019) by Lapuschkin et al.). Here the neural network learned a wrong way to identify a picture, i.E by identifying the wrong "relevant components". In the sensitivity maps next to the pictures, we can see that the ...


3

I don't know if it might be of use, but many areas of NLP are still hard to tackle, and even if deep models achieve the state of the art results, they usually beat baseline shallow models by very few percentage points. One example that I've had the opportunity to work on is stance classification 1. In many datasets, the best F score achievable is around 70%....


3

A checkerboard with missing squares is impossible for a neural network to learn the missing color. The more it learns on training data, the worse it does on test data. See e.g. this article The Unlearnable Checkerboard Pattern (which, unfortunately, is not freely accessible). In any case, it should be easy to try out yourself that this task is difficult.


3

Yes, there are many, actually. A Google search turned this paper Artificial Neural Networks in Medical Diagnosis (2011) by Al-Shayea up. Not only are they used in disease diagnosis, but even with things like prescribing medicines. In fact, the top project for a hackathon at my school analysed thousands of research articles, and took a patient's medication ...


4

This is more in the direction of 'what kind of problems can be solved by neural networks'. In order to train a neural network you need a large set of training data which is labelled with correct/ incorrect for the question you are interested in. So for example 'identify all pictures that have a cat on them' is very suitable for neural networks. On the other ...


19

In theory, most neural networks can approximate any continuous function on compact subsets of $\mathbb{R}^n$, provided that the activation functions satisfy certain mild conditions. This is known as the universal approximation theorem (UAT), but that should not be called universal, given that there are a lot more discontinuous functions than continuous ones,...


28

Here's a snippet from an article by Gary Marcus In particular, they showed that standard deep learning nets often fall apart when confronted with common stimuli rotated in three dimensional space into unusual positions, like the top right corner of this figure, in which a schoolbus is mistaken for a snowplow: . . . Mistaking an ...


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