Machine Learning Intelligence Other

Length: 2 pages Sources: 2 Subject: Psychology Type: Other Paper: #97462383 Related Topics: Learning Disabilities, Intelligence, Genetic Testing, Ibm
Excerpt from Other :

¶ … Machine Learning

It should be noted that there is a marked paucity of research material and scholarly articles existent, from the past three years, regarding the usage of intelligence tests that are deemed mainstream (i.e., not the Turing Test and more frequently utilized assessments such as any variety of the Wechsler tests or the Stanford-Binet assessment) to assess machine intelligence or that of expert systems. Machine intelligence such as IBM's Watson and even certain expert systems are still relatively novel, which is why the vast majority of research material pertaining to these purported types of intelligence utilize the Turing Test, which was explicitly designed to determine the intelligence of computers or machines.

Nonetheless, the first document examined within this assignment is van der Maas et al.'s "Intelligence is what the intelligence test measures. Seriously." This article sheds a good deal of insight on the g factor and its importance within the framework of mainstream intelligence tests. Specifically, it utilizes an alternative for the g...


The principle difference between the latter and the former is that in the mutalism model the g factor is not causal and is an index variable (van der Maas et al., 2014, p. 12). By examining the nature of the mutalism model and its effects on intelligence testing, the authors have revealed that any sort of searching for a genetic component associated with the g factor will inherently yield no results (van der Maas et al., 2014, p. 12). Thus, the authors believe that the results of intelligence tests are not actually revealing intelligence, and are instead merely issuing summary scores that are weighted and adhering to statistical and mathematical principles.

The results of these findings by the author are somewhat dubious. Firstly, they did not conduct any original research to come to their findings, and instead merely analyzed a host of factors related to the research that others had done and which applied to intelligence testing in general. Moreover, they claim a great deal of ambiguity in their findings, stating that both the mutualism model or g factor can apply, depending on the values for predictions (van der Maas et al., 2014, p. 14) of the researcher. What is truly intriguing, however, is the applicability of this concept to machine intelligence, and the fact that such an intelligence may not be possible depending on which index is used. Again,…

Sources Used in Documents:


Brostoff, M. (2015). A Turing Test for disability studies. Situations: Project of the Radical Imagination. 6(1), 33-51.

Van der Maas, H.L.J., Kan, K-J., Borsboom, D. (2014). Intelligence is what the intelligence test measures. Seriously. Journal of Intelligence. 2(1), 12-15.

Cite this Document:

"Machine Learning Intelligence" (2015, March 29) Retrieved January 27, 2022, from

"Machine Learning Intelligence" 29 March 2015. Web.27 January. 2022. <>

"Machine Learning Intelligence", 29 March 2015, Accessed.27 January. 2022,

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