Merge branch 'kd_Quellen' into 'main'

Quellen auf englische geupdated und Wikipedia einheitlich

See merge request !119

book/src/AI-Models/Deep_Neural_Network/Deep_Neural_Network.md

@@ -13,7 +13,7 @@ The neurons of one layer are connected via **weights** to the neurons of the nex
 
 ## The learning
 The actual learning relates to the weights. These are determined by numerical optimization and adapt with each run. The weights are well chosen if the validation error is as small as possible.
-Theoretically, with a 'normal' neural network, i.e. with only one hidden layer, any continuous function can be approximated if enough neurons are used. The advantage of a deep neural network is that it is easier to approximate this continuous function by different optimization methods and with less computing power. The goal is to find the minimum of the **loss function**. This function can be for example the Mean Square Error.  
+Theoretically, with a 'normal' neural network, i.e. with only one hidden layer, any continuous function can be approximated if enough neurons are used *("Neuronale Netze mit Keras -- in a Nutshell -- (Teil 1 von 2)", 2022)*. The advantage of a deep neural network is that it is easier to approximate this continuous function by different optimization methods and with less computing power. The goal is to find the minimum of the **loss function**. This function can be for example the Mean Square Error.  
 
 <!-- This is necessary because for some reason mdbook doesn't render this formula correctly -->
 <!-- $$ {MSE} ={\frac {1}{n}}\sum _{i=1}^{n}(Y_{i}-{\hat {Y_{i}}})^{2} $$ -->
@@ -79,4 +79,7 @@ During learning only the weights and the bias are changed. The number of layers,
 - In the input layer you need to specify the number of features(X). For example, for image recognition this would be the number of pixels.
 - The number of neurons in the output layer must be equal to the target set(Y). For example, if we perform a simple digit recognition (digits 0-9), the number of neurons should be 10.
 
-*Written by Kevin de Riese-Meyer*
+
\ No newline at end of file
+{{#include ../../References.md:Deep_Neural_Network}}
+
+*Written by Kevin de Riese-Meyer*

book/src/AI-Models/K-Means/K-Means.md

 # K-Means
-The K-Means algorithm is a clustering method to divide data from an n-dimensional continuous space into groups. It belongs to the unsupervised learning algorithms. It tries to detect patterns in the input data that deviate from the featureless noise *("Unüberwachtes Lernen", 2022)*. In the case of the K-Means algorithm, it looks for a fixed number (k) of clusters in a data set. A cluster refers to a collection of data points that have been aggregated based on a certain similarity.
+The K-Means algorithm is a clustering method to divide data from an n-dimensional continuous space into groups. It belongs to the unsupervised learning algorithms. It tries to detect patterns in the input data that deviate from the featureless noise *("Wikipedia: Unüberwachtes Lernen", 2022)*. In the case of the K-Means algorithm, it looks for a fixed number (k) of clusters in a data set. A cluster refers to a collection of data points that have been aggregated based on a certain similarity.
 
 ## Procedure
 These are the 1-dimensional Points we want to cluster.
@@ -65,11 +65,15 @@ Now we put the values into a graph to determine **k**. The value for k will be f
 *("StatQuest: K-means clustering", 2022)*   
 
 ![13](./images/13.png)  
-*("Elbow_method", 2022)*
+*("Wikipedia: Elbow_method", 2022)*
 
 ## Application areas
 - Because of the low memory and computing requirements, it is well suited for data analysis in the Big Data environment.
 - In image processing for segmentation of image data such as the separation of foreground and background.
 - In marketing to form customer groups with similar buying behavior.
 
-*Written by Kevin de Riese-Meyer*
+
\ No newline at end of file
+### References
+{{#include ../../References.md:K-Means}}
+
+*Written by Kevin de Riese-Meyer*

book/src/Introduction.md

@@ -13,19 +13,20 @@ Before we get to the algorithms of artificial intelligence, we would like to cla
 ### Intelligence
 > "Intelligence is the best researched characteristic in psychology." (Rost, 2013)
 
-The term is also controversial in education, social science and brain research. This is the reason why there is no unified definition and the term is considered diluted. In general, intelligence comes from the latin word *intellegere (=to see / to understand)* and is equated in everyday use with "mental ability". The term refers primarily to the ability of living beings to use the totality of cognitive abilities to solve a problem. The word living being is deliberately chosen because it is not only applicable to humans, but also observed in the animal kingdom *(Rost, 2013)*. According to many intelligence researchers, there is no way to measure intelligence accurately. The IQ test, for example, is criticized as being classist. That means it disadvantages socially low classes and minorities. Others even speak of methodological errors in these tests. But the exact criticism of the intelligence concept would go beyond the scope here. There are numerous theories and approaches that try to describe the cause and effect of intelligence. All of them have their supporters, but also opponents.   
+Despite the fact that it is a subject on which so much research is being done, we are still far from understanding it.
+The term is also controversially debated in education, social science, and brain research. This is the reason why there is no unified definition and the term is considered diluted. In general, intelligence comes from the latin word *intellegere (=to see / to understand)* and is equated in everyday use with "mental ability". The term refers primarily to the ability of living beings to use the totality of cognitive abilities to solve a problem. The word living being is deliberately chosen because it is not only applicable to humans, but also observed in the animal kingdom *(Rost, 2013)*. According to many intelligence researchers, there is no way to measure intelligence accurately. The IQ test, for example, is criticized as being classist. That means it disadvantages socially low classes and minorities. Others even speak of methodological errors in these tests. But the exact criticism of the intelligence concept would go beyond the scope here. There are numerous theories and approaches that try to describe the cause and effect of intelligence. All of them have their supporters, but also opponents.   
 One approach is the theory of multiple intelligences according to Howard Gardner from 1983. This theory does not stand up to empirical testing and is therefore widely rejected. Within the academic-psychological intelligence research multiple intelligences are no longer seriously discussed. Nevertheless, it provides interesting approaches which play an important role in artificial intelligence. We will explain why this is so in a moment.
 According to Wikipedia it understands intelligence as a number of abilities which are necessary to solve problems. This also includes the recognition of these problems. For him, there are 8 intelligences:
 
-- The **linguistic intelligence** includes sensitivity to spoken and written language, the ability to learn languages and to use languages for specific purposes.
+- The **linguistic-verbal intelligence** includes sensitivity to spoken and written language, the ability to learn languages and to use languages for specific purposes.
 
 - **Logical-mathematical intelligence** is the ability to analyze problems logically, perform mathematical operations, and investigate scientific questions.
 
-- **Musical intelligence** represents the ability to make music, to compose and to have a sense of musical principles such as sound and rhythm.
+- **Musical-rhythmic and harmonic intelligence** represents the ability to make music, to compose and to have a sense of musical principles such as sound and rhythm.
 
-- **Spatial intelligence** includes the theoretical and practical sense of grasping structures and spaces themselves.
+- **Visual-spatial intelligence** includes the theoretical and practical sense of grasping structures and spaces themselves.
 
-- The **physical-kinesthetic intelligence** means to use and control the body and individual body parts precisely. Surgeons and sportsmen possess high physical-kinesthetic intelligence.
+- The **bodily-kinesthetic intelligence** means to use and control the body and individual body parts precisely. Surgeons and sportsmen possess high physical-kinesthetic intelligence.
 
 - The **naturalistic intelligence** includes the ability to observe natural phenomena, to distinguish between them, as well as to develop a sensitivity for them. It also includes the effects of actions on the environment.
 
@@ -33,7 +34,7 @@ According to Wikipedia it understands intelligence as a number of abilities whic
 
 - **Intrapersonal intelligence** is the ability to understand and influence one's own feelings, moods, weaknesses, drives and motives.
   
-*("Theory of Multiple Intelligences," 2022)*
+*("Wikipedia: Theory of multiple intelligences", 2022)*
 
 ### Weak AI
 A weak AI has no explicit capabilities to learn on its own or even to perform creative activities. Its learning ability is limited to recognizing patterns and searching large data sets. For this, the tasks must be clearly defined and it must follow a fixed methodology. A weak AI is not able to search or recognize a task independently. It is mainly used for text, image and speech recognition. In addition, translating texts is a classic task. Digital assistance systems such as Alexa, Siri and Google Assistant as well as the Deepl translator are weak AIs *(Weak AI, n.d.)* Now we see why the concept of multiple intelligences is so interesting. These AIs only operate inside a small part of the previously mentioned intelligences, and even then they are still far behind human capabilities. These voice assistants master a part of the linguistic and the logical-mathematical intelligence. However, they are still very limited in these areas. There are even programs which can independently create pieces in the same style of the composer on the basis of composed classical music. Only for interpersonal and intrapersonal intelligence there are no solutions yet. Some experts even assume that they will never exist.
@@ -44,7 +45,7 @@ A weak AI has no explicit capabilities to learn on its own or even to perform cr
  A strong AI can independently identify and define tasks and independently acquire knowledge in order to solve them. The devised solutions can be creative and novel. This AI must use all the previously mentioned intelligences to achieve a goal. It must also be able to make decisions in the face of uncertainty. It is unclear whether this intelligence also needs intrapersonal intelligence, i.e. consciousness and sentience, in order to act logically.
 
 ### Intelligence measurement for machines
-This is at least as difficult as for humans and involves the same complications and criticisms. Consequently, there is not yet a suitable test to accurately measure it. However, there are a number of good approaches. One of them is the **Turing Test**. Here, a questioner has a conversation with a machine and a human without sight or hearing contact. The test is passed if the questioner does not find out during the conversation which of the two partners is the machine. Now the machine can be assumed to have human-like thinking ability *("Turing test", 2022)*. This principle can be extended to painted pictures, composed music and other areas. These Turing-like tests have already been conducted and partially passed. In 2017, researchers at Rutgers University exhibited pictures of an AI at an art fair and the test subjects were asked to guess which ones were painted by humans and which ones were painted by the machines. Overall, the AI paintings were considered more human than the human paintings.  
+This is at least as difficult as for humans and involves the same complications and criticisms. Consequently, there is not yet a suitable test to accurately measure it. However, there are a number of good approaches. One of them is the **Turing Test**. Here, a questioner has a conversation with a machine and a human without sight or hearing contact. The test is passed if the questioner does not find out during the conversation which of the two partners is the machine. Now the machine can be assumed to have human-like thinking ability *("Wikipedia: Turing test", 2022)*. This principle can be extended to painted pictures, composed music and other areas. These Turing-like tests have already been conducted and partially passed. In 2017, researchers at Rutgers University exhibited pictures of an AI at an art fair and the test subjects were asked to guess which ones were painted by humans and which ones were painted by the machines. Overall, the AI paintings were considered more human than the human paintings.  
 Continuations of the Turing Test are the **Lovelace Test** and the **Metzinger Test**. The Lovelace Test asks for evidence of a creative activity. For example, writing an essay according to certain content specifications. The Metzinger Test states that an AI must enter the discussion about artificial consciousness with its own arguments and convincingly argue for its own theory of consciousness. There are still numerous interesting approaches with which one could fill whole books and which invite to philosophize. Therefore we will not go further into it and rather clear up about the misbelief **"Games as a Test for Intelligence of Machines"**.  
 These tests measure intelligence on the basis of false criteria. An AI may beat the world champion in GO, but the same AI can neither translate texts nor write an essay. So it has only an insular talent. This particular GO AI has been trained with 30 million moves by masters and has also played against itself thousands of times to develop new strategies. So this AI needed an enormous number of games to become so good. Whether this can be called intelligent is questionable. So we see that a variety of tasks is required to evaluate the intelligence of a machine and not just test its insular talent.
 

book/src/References.md

@@ -93,10 +93,10 @@ ANCHOR_END: Deep_Neural_Network
 ### Intelligence and Learning
 ANCHOR: Intelligence_and_Learning
 [1] Rost (2013). Handbuch Intelligenz S. 11\
-[2] [Wikipedia: Theorie der multiplen Intelligenzen (last accessed on 06.01.2022)](https://de.wikipedia.org/wiki/Theorie_der_multiplen_Intelligenzen)\
+[2] [Wikipedia: Theory of multiple intelligences (last accessed on 06.01.2022)](https://en.wikipedia.org/wiki/Theory_of_multiple_intelligences)\
 [3] [Schwache KI. (o. D.). Hochschule für angewandte Wissenschaften Würzburg-Schweinfurt (last accessed on 06.01.2022)](https://ki.fhws.de/thematik/starke-vs-schwache-ki-eine-definition/)\
 [4] Künstliche Intelligenz ein moderner Ansatz, 3 aktualisierte Auflage, Stuart Russell, Peter Norvig, 2012 (Russel & Norving, 2012)\
-[5] [Wikipedia: Turing-Test (last accessed on 06.01.2022)](https://de.wikipedia.org/wiki/Turing-Test)
+[5] [Wikipedia: Turing test (last accessed on 06.01.2022)](https://en.wikipedia.org/wiki/Turing_test)
 ANCHOR_END: Intelligence_and_Learning
 
 ### Multi-Layer Perceptron