¶ … 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 factor, the mutualism model. 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...
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, the lack of…
Machine Learning Method in Bioinformatics Bioinformatics involves an integrated approach involving the use of information technology, computer science to biology and medicine as professional and knowledge fields. It encompasses the knowledge associated with information systems, artificial intelligence, databases, and algorithms, soft computing, software engineering, image processing, modeling and simulation, data mining, signal processing, computation theory and information, system an d control theory, discrete mathematics, statistics and circuit theory. On the other
Netflix and Machine Learning Machine Learning (ML) represents a data analysis technique involving automation of analytical model development. This segment of AI (artificial intelligence) is grounded in the notion that a system is able to learn using information provided, discern patterns, and engage in decision-making without much human involvement required. Owing to technological advancements in computing, contemporary ML differs from ML of earlier times. The concept traces its roots to pattern
Abstract This paper discusses the issue of privacy in social networks with respect to advances in machine learning. It shows how machine learning protocols have been developed both to enhance and secure privacy as well as to invade privacy and collect, analyze, predict data based on users’ information and experience online. The conflict between these two directions in machine learning is likely to lead to a system wherein machine learning algorithms
Stevens Star Model and New Tech to Improve Patient OutcomesThe Stevens Star Model of Knowledge Transformation is a framework that guides the transformation of knowledge from research into practice. This model consists of five points: discovery, summary, translation, integration, and evaluation. Each point represents a step in the process of moving from scientific evidence to practical application in patient care (Song et al., 2021). This paper shows how in the
Apa.org). Critical thinking input: Good teachers that truly understand how distracted today's young people are (with technology, etc.) learn how to get the most out of students by combining proven strategies of engagement with scholarship challenges that are both entertaining and compelling to their active minds. B.F. Skinner Historical views of transfer. When something is said to you and it reminds you (without you having to conjure up memories) instantly of something from
The report mentions that almost 3-4% of the keys could not be resolved. Thereby, it can be argued here that great advantages were gained when Americans decoded Japanese conversation 2. Radio Traffic Unit There is a naval intelligence installed at the Pearl Harbor was using the radio traffic unit and it was working to find out and analyze the location of Japanese ships. In this case, the Japanese messages could not