29 votes
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What are some well-known problems where neural networks don't do very well?

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 ...
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24 votes
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Is it possible to train the neural network to solve math equations?

Yes, it has been done! However, the applications aren't to replace calculators or anything like that. The lab I'm associated with develops neural network models of equational reasoning to better ...
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  • 356
21 votes
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How do I choose the best algorithm for a board game like checkers?

tl;dr: None of these algorithms are practical for modern work, but they are good places to start pedagogically. You should always prefer to use Alpha-Beta pruning over bare minimax search. You ...
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19 votes

What are some well-known problems where neural networks don't do very well?

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 ...
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  • 34.4k
16 votes
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What exactly are genetic algorithms and what sort of problems are they good for?

Evolutionary algorithms are a family of optimization algorithms based on the principle of Darwinian natural selection. As part of natural selection, a given environment has a population of individuals ...
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15 votes

What are some well-known problems where neural networks don't do very well?

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 ...
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  • 251
14 votes
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When is deep learning overkill?

It's all about Return On Investment. If DL is "worth doing", it's not overkill. If the cost of using DL (computer cycles, storage, training time) is acceptable, and the data available to train it is ...
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  • 671
12 votes

When is deep learning overkill?

Deep learning is powerful but it is not a superior method than bayesian. They work well in what they are designed to do: Use deep learning: Cost for computation is much cheaper than cost of sampling ...
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  • 1,390
10 votes
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What are some examples of Statistical AI applications?

There are several examples. For example, one instance of using Statistical AI from my workplace is: Analyzing the behavior of the customer and their food-ordering trends, and then trying to upsell by ...
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  • 1,351
10 votes
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How can fuzzy logic be used in creating AI?

A classical example of fuzzy logic in an AI is the expert system Mycin. Fuzzy logic can be used to deal with probabilities and uncertainties. If one looks at, for example, predicate logic, then ...
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  • 1,174
10 votes
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What are all the different kinds of neural networks used for?

I agree that this is too broad, but here's a 1 sentence answer for most of them. The ones I left out (from the bottom of the chart) are very modern, and very specialized. I don't know much about them, ...
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10 votes
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How do AIs like Siri and Alexa respond to their names being called?

Is it because their listening function reloads in milliseconds or even nanoseconds Yes, it expects the keyword to start every moment of time and it ignores the rest. Overall, the algorithm is ...
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9 votes

What exactly are genetic algorithms and what sort of problems are they good for?

A genetic algorithm is an algorithm that randomly generates a number of attempted solutions for a problem. This set of attempted solutions is called the "population". It then tries to see how well ...
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  • 1,174
9 votes

Is it possible to train the neural network to solve math equations?

Not really. Neural networks are good for determining non-linear relationships between inputs when there are hidden variables. In the examples above, the relationships are linear, and there are no ...
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  • 1,335
8 votes

What are some examples of Classical AI applications?

The term classical AI refers to the concept of intelligence that was broadly accepted after the Dartmouth Conference and basically refers to a kind of intelligence that is strongly symbolic and ...
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  • 538
8 votes
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Is there any research on the application of AI for drug design?

Yes, many people have worked on this sort of thing, due to its obvious industrial applications (most of the ones I'm familiar with are in the pharmaceutical industry). Here's a paper from 2013 that ...
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8 votes

How do I choose the best algorithm for a board game like checkers?

So far, I have considered only three algorithms, namely, minimax, alpha-beta pruning, and Monte Carlo tree search (MCTS). Apparently, both the alpha-beta pruning and MCTS are extensions of the basic ...
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  • 9,429
7 votes

Is it possible to train the neural network to solve math equations?

It is possible! In fact, it's an example of the popular deep learning framework Keras. Check out this link to see the source code. This particular example uses a recurrent neural network (RNN) to ...
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7 votes

What exactly are genetic algorithms and what sort of problems are they good for?

There are a number of good answers here explaining what genetic algorithms are, and giving example applications. I'm adding some general purpose advice on what they are good for, but also cases where ...
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  • 1,134
7 votes

What exactly are genetic algorithms and what sort of problems are they good for?

This answer requests a practical example of how one might be used, which I will attempt to provide in addition to the other answers. They seem to due a very good job of explaining what a genetic ...
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  • 171
7 votes
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Is AI programming useful in everyday programs?

Yes, but probably only to a limited degree in the near term. Where people draw the boundaries around 'artificial intelligence' is fuzzy, but if one takes the broad view, where it incorporates any ...
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7 votes
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How can artificial intelligence help software developers to develop software?

An umbrella term for the application of heuristic techniques to software development is Search-Based Software Engineering (SBSE). SBSE emerged as a distinct activity around the turn of the century, ...
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7 votes

What does deep learning offer with respect to standard machine learning?

Deep learning allows you to solve complex problems without necessarily being able to specify the important "features" or key input variables for the model in advance. To give an example, a problem ...
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  • 171
7 votes
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Examples of ontologies made with AI

Ontology learning is a relatively new field that aims to automatically (or semi-automatically) learn or create ontologies (using machine learning, text mining, knowledge representation and reasoning, ...
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  • 34.4k
7 votes
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What are the real-life applications of transfer learning?

One application I know of being used in industry is of image classification, by only training the last layer of one of the inception models released by Google, with the desired number of classes. I ...
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  • 699
7 votes
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What are the biggest barriers to get RL in production?

There is a relatively recent paper that tackles this issue: Challenges of real-world reinforcement learning (2019) by Gabriel Dulac-Arnold et al., which presents all the challenges that need to be ...
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  • 34.4k
6 votes

Is AI programming useful in everyday programs?

Adaptive/predictive features are useful in at least some everyday applications. Take text messaging, for instance. All smartphone SMS apps that I know of keep track of the words you use in close ...
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  • 2,559
6 votes

What exactly are genetic algorithms and what sort of problems are they good for?

As observed in another answer, all you need to apply Genetic Algorithms (GAs) is to represent a potential solution to your problem in a form that is subject to crossover and mutation. Ideally, the ...
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6 votes
Accepted

Can I use AI to interpret XML documents?

XML, HTML and less formal languages all respond quite nicely to being transformed or interrogated within a graph framework. XML and HTML are particularly useful in that they conform strictly to a tree-...
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6 votes
Accepted

How could I use reinforcement learning to solve a chess-like board game?

I would like to use reinforcement learning to make the engine improve by playing against itself. I have been reading about the topic but I am still quite confused. Be warned: Reinforcement learning ...
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