# Tag Info

## Hot answers tagged applications

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 ...

20

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 should prefer to use some form of heuristic guided search if you can come up with a useful heuristic. Coming up with a useful heuristic usually requires a lot of ...

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,...

15

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 that compete for survival and reproduction. The ability of each individual to achieve these goals determines their chance to have children, in other words, to ...

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 ...

13

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 plentiful, and if the marginal advantage over alternative algorithms is valuable, then DL is a win. But, as you suggest, if your problem is amenable to ...

12

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 (e.g: natural language processing) If you have highly non-linear problem If you want to simplify feature engineering If you don't have prior distribution (e.g: ...

10

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 recommending them the dishes which they might like to order/eat. This can be done through the apriori and FP-growth algorithms. We then, automated the ...

10

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 described here, you can read for details: https://machinelearning.apple.com/research/hey-siri

9

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, so perhaps someone who does can improve this answer. Perceptron: Linear or logistic-like regression (and thus, classification). Feed Forward: Usually non-...

8

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 these solutions solve the problem, using a given fitness function. The attempted solutions with the best fitness value are used to generate a new population. ...

8

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 minimax algorithm. Given this context, I would recommend starting out with Minimax. Of the three algorithms, Minimax is the easiest to understand. Alpha-Beta, ...

7

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 claims good results; following the trail of papers that cited it will likely give you more recent work.

7

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 sort of coding of explicitly cognitive functions, then many routine economic tasks can benefit from artificial intelligence. Many search engines, for example, can ...

7

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, information retrieval and natural language processing techniques) from some text or corpus. Ontology learning can be divided into different phases or tasks ...

7

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 addressed to productionize RL to real world problems, the current approaches/solutions to solve the challenges, and metrics to evaluate them. I will only list them ...

6

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 proximity and use that information to predict the next word in a message you're typing. (Some are smarter than others. Relevant XKCD.) It can be used to personalize ...

6

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 you should NOT use them. If my tone seems harsh, it is because using GAs in any of the cases in the inappropriate section below will lead to your paper being ...

6

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 algorithm is. So, this will give an example. Let's say you have a neural network (although they are not the only application of it), which, from some given inputs, ...

6

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 that may be easily tackled without deep learning could be predicting the frequency and claim amounts of insurance vehicle claims, given historical claim data ...

6

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 is a large complex subject. Although it might take you on a detour from game-playing bots, you may want to study RL basics. A good place to start is Sutton &...

6

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 can't provide specific details. Transfer learning is useful when: You do not have the resources (time, processing power, etc.) to train a DL model from ...

5

Deep Learning these days mean a lot of things to a lot of people, its quickly becoming a buzz-word. But so far it still retains two very important conceptual properties: Does away with most feature engineering work. This was mentioned in the answer above, but this is very important. It really saves a lot of work. Allows you to make maximal use of ...

5

Bayes theorem states the probability of some event B occurring provided the prior knowledge of another event(s) A, given that B is dependent on event A (even partially). A real-world application example will be weather forecasting. Naive Bayes is a powerful algorithm for predictive modelling weather forecast. The temperature of a place is dependent on the ...

5

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 fitness function will provide some kind of smooth feedback about the quality of a solution, rather than simply being a 'Needle in a Haystack'. Here are some ...

5

Nim was actually one of the first games ever played by an electronic machine. It was called the Nimatron and was displayed at the 1940 New York World's Fair. It is also well known that neural networks can model the Xor-function, if they have enough layers. Despite that, Marvin Minsky is supposed to have killed neural networks in the sixties, by asserting ...

5

This is fairly boilerplate advice, but, since you're brand new to AI, I'd personally suggest writing a classical Tic-Tac-Toe AI, ideally using minimax. I suggest this because minimax is fundamental to AI, and there are many webpages devoted to this subject, such as How to make your Tic Tac Toe game unbeatable by using the minimax algorithm and Tic Tac Toe: ...

5

Take a robot that we want to be able to move from the bottom right corner to the top left corner of a 4x4 matrix full of random holes it should avoid. With holes represented by 1s, it could look something like: exit \/ [0,0,0,1] [0,1,1,0] [0,1,1,1] [0,0,0,0] /\ enter As we want it to get to an exit from a start, we have a natural fitness ...

5

I would guess no, because if the language is unknown (no data available on it), then we would not have training data with which the machine learning algorithm could learn from. If it is related to some known language, then some statistical analysis can lead to a guess at decipherment (assuming certain similarities among the two languages). If interested on ...

5

I say yes it definitely could be. But i agree with Skim you need some information as a starting point. Egyptian hieroglyphs were only (recently) understood following the discovery of the Rosetta Stone (https://en.m.wikipedia.org/wiki/Rosetta_Stone). With the same message in both known and unknown language the program could find the/a correlation. Without ...

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