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Perception is a process by which individuals organize and interpret their sensory impressions in order to give meaning to their environment. It is important to the study of OB because peoples’ behaviors are based on their perception of what reality is, not on reality itself.Factors that shape and can distort perception include the perceiver, the target, or the situation.When an individual looks at a target and attempts to interpret what he or she sees, that interpretation is heavily influenced by personal characteristics of the individual perceive. The more relevant personal characteristics affecting perception of the perceive are attitudes, motives, interests, past experiences, and expectations.Attribution theory suggests that when we observe an individual’s behavior, we attempt to determine whether it was internally or externally caused. That determination depends largely on three factors: These are Distinctiveness, Consensus,Consistency. We’ll talk more about each these in a minute. There are a couple of confounding concepts that impinge on Attribute Theory. First, a Fundamental Attribution Error is that we have a tendency to underestimate the influence of external factors and overestimate the influence of internal or personal factors. In addition, Self-serving Bias is a tendency for individuals to attribute their own successes to internal factors such as ability or effort while putting the blame for failure on external factors such as luck.
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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.
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Powerful Google developer tools for immediate impact! (2023-24 C)
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Perception
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Which of the
two lines is bigger?
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Which of the
two lines is bigger?
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M-A-C-M-I-L-A-N M-A-C-T-A-V-I-S-H M-A-C-H-I-N-A-R-Y
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PERCEPTION
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Definition Perception is a
process by which individuals organize and interpret their sensory impressions in order to give meaning to their environment.
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Factors Influencing Perception: 1.)
Perceiver 2.) Target 3.) Situation
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Factors in perceiver • • • • Attitudes Motives Interests Past
experiences
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Factors in Target •
Background • Grouping
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Factors in Target •
Novelty • Sounds • Motion • Size
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Factors in Situation •
Time • Work setting • Social setting
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Person Perception: Making Judgments About
Others
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ATTRIBUTION THEORY When individuals
observe behaviour, they attempt to determine whether it is internally or externally caused.
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Attribution Theory Two Types
of Causes: 1) Internal 2) External
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Three Factors of
the Attribution Theory: 1. Distinctiveness 2. Consensus 3. Consistency
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Self Serving Bias
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Fundamental Attribution Error
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Shortcuts in Judging
Others: 1.) Selective Perception 2.) Halo Effect 3.) Contrast Effects 4.) Projection 5.) Stereotyping
28.
Specific Applications in Organizations: 1.
Employment Interview 2. Performance Expectations (Self Fulfilling Prophecy) 3. Performance Evaluation
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