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  • Essay / Theme Artificial Intelligence Methodology

    The gatekeeper between brands and consumersAll four articles share the same main theme, artificial intelligence: on people, government and the workforce. A Forbes Magazine article discusses the importance of trust between AI and the consumer, and how trust will play a major role in enabling AI to increase efficiency and effectiveness in the business and marketing sector (DeGobbi 2018). He claims that because trust in brands has declined for consumers, AI can be used as a way to save time and money, create more trusting relationships between brand and consumer, and enable marketers to make better-informed, less subjective decisions (DeGobbi 2018).Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get an original essay This article was chosen because of its relevance to IBA as a course and to the theme: Artificial Intelligence. It provides insight into how AI will and is still affecting the marketing sector of the workforce. Forbes was a suitable source because it dedicates an entire up-to-date section to artificial intelligence where multiple perspectives can be explored. Found while researching the effect of AI on business. Article 2: Artificial intelligence is on the move. But is the government ready? A second article from Forbes highlights the need for new, more effective regulation in terms of AI. With the dramatic increase in the use of AI, unemployment has increased and competition between popular technology platforms has decreased (Seamans 2018). The article concludes that this could be due to inexperienced and therefore uncomprehending policy makers. Seamans (2018) discusses two solutions: the creation of an AI-specific regulatory agency, or current agencies hiring their own staff after defining their perceived needs. Seamans (2018) concludes that the second solution is the best. This article was chosen due to its relevance in today's society, as it discusses the need for new regulations and more experienced policymakers in the AI ​​sector. Additionally, there is talk of another group of people who will be affected: the government. Forbes has been used again because it provides a platform that only discusses AI from different angles. Article 3: Do the benefits of artificial intelligence outweigh the risks? Like the second Forbes article, an article by the economist discusses the need to implement rigorous control in the AI ​​sector in order to avoid risks. However, this article focuses its arguments on the reliability and stability of different types of AI: narrow AI and artificial general intelligence. The main concern of the article is AGI, because scientists "must ensure that an Internet-enabled AGI is indefinitely stable and has beneficial properties such as value learning and corrigibility before being deployed" (Ruta 2018) and the need to align AI with human values. and morality. This article has provided an overview of the pros and cons of different types of AI and highlighted that much more regulation will be needed. For this reason, it was chosen because it allowed another perspective to be opened and a new angle to be addressed: the need for AI to have human values ​​and morals in order to avoid risks. The Economist is a different, but reliable, source that allowed for a different perspective to be analyzed. Found using the economist's search engine. Article 4: TheRobots in the workplace “could create double the jobs they destroy.” A fourth Guardian article also centers its argument on the effect of AI on the workforce; however, he explains that an increase in AI will increase the number of jobs available rather than decrease them, illustrating a different position on this argument. Furthermore, the article explains that for AI to have a positive impact on the workforce, greater investments in professional training and reskilling are necessary, because all work tasks in companies 'today can be achieved by machines by 2025 (Partington 2018). This article was chosen for its different spin on the AI ​​argument, as it argues that more jobs will be created rather than the general worry that jobs will decline due to AI. The Guardian provided a different perspective to other popular media outlets, it was chosen because its style was different from other websites and thus provided an insightful source. The fundamental difference between scientific and everyday knowledge is how the information is collected. Everyday knowledge can be based on individual beliefs and shared or personal (daily) experiences, and is therefore primarily collected using our own senses, intuitions or emotions. Therefore, everyday knowledge can also be defined as conventional knowledge. Unlike scientific knowledge, everyday knowledge does not require universal acceptance or be supported by measurable evidence. Therefore, everyday knowledge can never be universally true. Unlike everyday knowledge, scientific knowledge encompasses data that is both measurable and reproducible through the scientific method. When collected data is contextualized to form conclusions and relationships, it is separated from personal opinions or emotions, ensuring that scientific knowledge is universally acceptable and accurate. According to BusinessDictionary. com (nd), four factors are crucial when defining information as scientific knowledge; independent and rigorous testing, peer review and publication, measurement of actual and potential error rates and degree of acceptance within the scientific community. For a theory or knowledge to be considered scientific knowledge, it must conform to these four factors. “Information processing errors, which remain cognitive errors, occur when investors irrationally process the information they receive. » (6bc2 Behavioral biases: Cognitive errors). There are various problems related to information processing, namely framing bias, anchoring and adjustment as well as availability bias. The first common error in information processing is called framing bias. This is when the information is poorly analyzed, because the context or wording of the question suggests only one answer. Framing bias leads to false information, which can result in false research or a “failure to understand the risk of short-term market movements” (6bc2 Behavioral Bias: Cognitive Errors). This is a problem when drawing conclusions based on surveys or other types of questionnaires. Second, anchoring and adjusting is also a common processing error. This is usually during negotiations, when investment decisions are based on initial forecasts (6bc2 Behavioral biases: errors.