The Role of Artificial Intelligence in Constructing Human Identity in Jordan Harrison's "Marjorie Prime"

 Abstract:

Artificial intelligence (AI) has become one of the most influential aspects of contemporary life, significantly impacting various sectors, including human memory and identity construction. Jordan Harrison's play Marjorie Prime explores the integration of AI into human lives through the concept of "Primes"—AI representations of deceased loved ones that help in reconstructing memories and identities. This article examines how these Primes serve as sources of learning and identity construction while highlighting their limitations and susceptibility to manipulation. Drawing on Jean-François Lyotard's critique of the postmodern era, the study underscores the necessity of reevaluating human agency and knowledge preservation in an increasingly digitalized world.

Introduction: In Marjorie Prime, Jordan Harrison presents a futuristic scenario where AI is used to create Primes—advanced AI entities designed to emulate deceased individuals. These Primes interact with the living, helping them recall and relive memories. The play raises crucial questions about the reliability of AI in preserving human knowledge and identity. By examining these interactions, the study aims to shed light on the potential and pitfalls of using AI for such purposes.

AI and Memory Construction: The Primes in Marjorie Prime serve as digital repositories of memories, offering a unique method of coping with loss and preserving personal histories. However, the accuracy of these memories is contingent on the input provided by human counterparts. As memories are inherently subjective and prone to distortion, the Primes' ability to construct a true representation of an individual's identity is questionable.

The Influence of Jean-François Lyotard: Jean-François Lyotard's critique of the postmodern condition emphasizes the fragmented nature of knowledge and the impact of technological advancements on human thought. According to Lyotard, the postmodern era is characterized by skepticism towards grand narratives and an increased reliance on technology. Marjorie Prime embodies these themes by showcasing the fragmented and potentially unreliable nature of AI-generated identities.

AI's Limitations and Manipulation: Despite the advanced capabilities of the Primes, the study argues that AI cannot fully replace human cognition and judgment. The Primes' dependence on pre-existing data and their potential for manipulation underscore the need for human oversight in AI applications. This aspect is critical as it highlights the ethical considerations and the necessity of maintaining human agency in the face of technological advancements.

Reevaluation of Human Agency: The emergence of AI in Marjorie Prime prompts a reevaluation of human agency and the role of technology in shaping our identities. The play suggests that while AI can significantly aid in memory preservation, it is not infallible and requires careful scrutiny. This reevaluation is essential for understanding the implications of integrating AI into intimate aspects of human life.

Conclusion: Jordan Harrison's Marjorie Prime provides a thought-provoking exploration of the role of AI in constructing human memory and identity. By examining the interplay between AI and human cognition, the study highlights both the potential and the limitations of using AI for such purposes. The findings emphasize the importance of maintaining human oversight and agency in the face of rapidly advancing technologies, ensuring that AI remains a tool for enhancement rather than a replacement for human thought.

References:

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The Unimaginable: What Happens When the World Speaks One Language

 Imagine a world where every person, regardless of their background or nationality, communicates in a single universal language. This hypothetical scenario raises intriguing questions about the dynamics of society, culture, and human interaction. While such a scenario may seem far-fetched, exploring its implications offers valuable insights into the potential for unity, diversity, and innovation on a global scale.

Language is a cornerstone of human civilization, shaping how we perceive the world, communicate with others, and transmit knowledge across generations. With over 7,000 languages spoken around the globe, linguistic diversity is a defining feature of our species. However, it also presents barriers to communication, understanding, and collaboration, hindering our ability to address global challenges and foster cross-cultural exchange.

In a world where everyone speaks the same language, one immediate consequence would be a newfound sense of unity and interconnectedness. Language barriers that once divided people would vanish, enabling seamless communication and collaboration on a global scale. Imagine travelers navigating foreign countries without the need for translation, scientists sharing research findings without linguistic constraints, and diplomats negotiating peace agreements without misunderstandings.

Moreover, a universal language would facilitate the exchange of ideas, cultures, and traditions, enriching human experience and fostering a deeper appreciation for diversity. People from different backgrounds would have greater opportunities to learn from one another, celebrate their shared humanity, and cultivate empathy and understanding across cultural divides. This cross-pollination of ideas could spark innovation and creativity, leading to breakthroughs in science, technology, and the arts.

However, the adoption of a universal language would also raise questions about cultural identity, heritage, and linguistic heritage. Language is more than just a means of communication; it embodies a people's history, values, and worldview. In a world where everyone speaks the same language, would we risk losing the richness and diversity of linguistic heritage that defines our collective identity? How would minority languages and dialects be preserved and celebrated in such a scenario?

Furthermore, the transition to a universal language would not be without challenges. Language is deeply intertwined with identity, and many people take pride in their native tongue. Resistance to adopting a new language could stem from cultural pride, fear of losing cultural identity, or concerns about power dynamics and linguistic imperialism. Additionally, the process of standardizing and promoting a universal language would require substantial investment in education, infrastructure, and linguistic resources.

Despite these challenges, the idea of a universal language offers a tantalizing glimpse into a future where communication barriers are a thing of the past, and humanity is united by a common tongue. While such a scenario may remain purely speculative for now, it prompts us to reflect on the power of language to shape our perceptions, bridge cultural divides, and forge connections that transcend borders.

In conclusion, the concept of all people in the world speaking one language is a thought-provoking exercise that invites us to imagine a world of unprecedented unity, diversity, and collaboration. While the practicalities and challenges of such a scenario are formidable, exploring its implications enriches our understanding of the profound role that language plays in shaping human society and culture. Whether or not such a future ever materializes, the idea of a universal language serves as a testament to the enduring quest for connection and understanding in an increasingly interconnected world.

Synopsis of "Investigating the Quality of AI-Generated Distractors for a Multiple-Choice Vocabulary Test"

 Title: Investigating the Quality of AI-Generated Distractors for a Multiple-Choice Vocabulary Test

Author: Wojciech Malec
Institute: Institute of Linguistics, John Paul II Catholic University of Lublin, Al. Racławickie, Lublin, Poland
Keywords: AI-Generated Items, ChatGPT, Vocabulary Assessment, Multiple-Choice Testing, Distractor Analysis

Abstract: This paper evaluates the effectiveness of AI-generated distractors for multiple-choice vocabulary tests. Using OpenAI’s ChatGPT (version 3.5), distractors were created for a test administered to 142 advanced English learners. The study found the test to have relatively low reliability, with some items having ineffective distractors. Qualitative analysis indicated mismatched options, and follow-up queries often failed to correct initial errors. The study concludes that while AI can enhance test practicality, ChatGPT-generated items require human moderation to be operationally viable.

Introduction: Advancements in AI offer significant benefits in educational technologies, including personalized learning, automated essay scoring, and intelligent tutoring systems. OpenAI's ChatGPT, capable of natural language processing, has been used for generating test items. Despite its potential, ensuring the quality and appropriateness of AI-generated items remains a challenge.

Method: Fifteen multiple-choice vocabulary items were created using ChatGPT and administered to 142 advanced English learners. The study used an AI-powered platform, Twee, for generating context sentences and ChatGPT for distractor suggestions. The items were then analyzed for reliability and effectiveness.

Results: The test showed moderate reliability, with some items having distractors that did not perform well. Point-biserial correlation and trace line analysis indicated that many distractors were not functioning as intended. Follow-up attempts to improve distractors using ChatGPT yielded inconsistent results.

Discussion: The study highlighted the need for human oversight in using AI-generated test items. While AI can streamline test development, the current capabilities of ChatGPT are insufficient for producing reliable distractors without human intervention.

Conclusion: AI tools like ChatGPT hold promise for practical test development but require human moderation to ensure item quality. The study suggests that AI-assisted item generation should be viewed as a supplementary tool rather than a standalone solution.

Acknowledgements: Thanks to the teachers and students of XXI Liceum Ogólnokształcące im. św. Stanisława Kostki in Lublin, Poland, for their participation in the study.

References:

• Arslan et al. (2021), Attali & Fraenkel (2000), Attali et al. (2022), Bachman (2004), Bezirhan & von Davier (2023), Bonner et al. (2023), Bruno & Dirkzwager (1995), Circi et al. (2023), Clare & Wilson (2012), Franco & de Francisco Carvalho (2023), Fulcher (2010), Gardner et al. (2021), Gierl et al. (2017), Haladyna (2016), Haladyna & Downing (1989), Holmes et al. (2019), Hoshino (2013), Khademi (2023), Kıyak et al. (2024), Kumar et al. (2023), Ludewig et al. (2023), Malec & Krzemińska-Adamek (2020), March et al. (2021), Nation & Beglar (2007), OpenAI (2024), Papenberg & Musch (2017), Parkes & Zimmaro (2016), Pokrivcakova (2019), Read (2000), Rodriguez (2005), Sayin & Gierl (2024), Segall (2023), Sullivan et al. (2023), Susanti et al. (2018), Twee (2024).

Demystifying Metrics and KPIs: Understanding the Key Differences

In the realm of performance measurement and management, metrics and Key Performance Indicators (KPIs) are two fundamental tools used to gauge organizational success, track progress, and drive decision-making. While often used interchangeably, metrics and KPIs serve distinct purposes and possess unique characteristics. In this article, we unravel the differences between metrics and KPIs, shedding light on their definitions, functionalities, and practical applications.

Understanding Metrics

Metrics, in their broadest sense, refer to quantifiable measures used to assess various aspects of organizational performance, processes, and activities. Metrics can encompass a wide range of quantitative and qualitative data points, including financial indicators, operational efficiency measures, customer satisfaction scores, and employee productivity metrics. Metrics provide insights into performance trends, identify areas for improvement, and facilitate data-driven decision-making across different functional areas within an organization.

Deciphering Key Performance Indicators (KPIs)

Key Performance Indicators (KPIs) are a subset of metrics that are specifically tailored to reflect critical success factors and strategic objectives within an organization. Unlike generic metrics, KPIs are carefully selected, targeted, and aligned with organizational goals and priorities. KPIs serve as actionable indicators of performance, providing a clear and focused view of progress towards key objectives. By monitoring KPIs, organizations can assess their performance against predefined targets, identify deviations from expected outcomes, and take corrective actions to drive performance improvement and goal attainment.

Key Differences Between Metrics and KPIs

1. Strategic Alignment: Metrics may cover a broad spectrum of performance measures across different functional areas, whereas KPIs are strategically aligned with specific organizational goals, objectives, and priorities. KPIs serve as strategic drivers of performance, guiding organizational efforts towards desired outcomes and results.

2. Relevance and Focus: Metrics may include both relevant and non-relevant measures, whereas KPIs are carefully selected to focus attention on critical success factors and areas of strategic importance. KPIs provide a clear and concise view of performance, enabling stakeholders to prioritize efforts and resources effectively.

3. Actionability and Accountability: Metrics provide insights into performance trends and patterns, whereas KPIs are actionable indicators that drive accountability and performance improvement. KPIs are tied to specific targets, thresholds, or benchmarks, enabling stakeholders to take proactive measures to address performance gaps and achieve desired outcomes.

4. Performance Measurement Hierarchy: Metrics can be aggregated and analyzed at different levels of granularity, whereas KPIs represent the highest level of performance measurement hierarchy within an organization. KPIs encapsulate critical success factors and overarching objectives, providing a holistic view of organizational performance and progress towards strategic goals.

Practical Applications in Business Management

In practice, organizations leverage both metrics and KPIs to assess performance, monitor progress, and drive continuous improvement. While metrics provide comprehensive insights into various aspects of operations and performance, KPIs serve as focused indicators of success and strategic alignment. By establishing a balanced framework of metrics and KPIs, organizations can effectively manage performance, optimize resources, and achieve sustainable growth and success in today's competitive business landscape.

Conclusion

In conclusion, metrics and KPIs are essential tools for performance measurement, management, and decision-making in organizations. While both serve critical functions in assessing performance and driving improvement, they differ in terms of strategic alignment, relevance, focus, and actionability. By understanding the distinctions between metrics and KPIs, organizations can develop robust performance management frameworks that enable them to achieve their strategic objectives, enhance stakeholder value, and maintain competitive advantage in today's dynamic business environment.

Relationship and Dependency in Business Analysis: Key Concepts and Practical Insights

In the realm of business analysis, understanding the dynamics of relationships and dependencies is paramount to successful project outcomes. These concepts form the backbone of effective stakeholder management, requirements elicitation, and solution design. In this article, we delve into the intricacies of relationship and dependency in business analysis, exploring their definitions, significance, and practical implications.

Defining Relationship and Dependency

• Relationship: In the context of business analysis, a relationship refers to the connection or association between two or more entities, such as stakeholders, requirements, processes, or components within a system. Relationships can be formal or informal, hierarchical or lateral, and may involve various types of interactions and dependencies.

• Dependency: A dependency denotes the reliance or interdependence between two or more entities, where changes or actions in one entity can impact or influence another. Dependencies can manifest in different forms, including sequential dependencies (where one task depends on the completion of another), resource dependencies (where tasks require specific resources), and logical dependencies (where tasks are logically linked).

Significance of Relationship and Dependency in Business Analysis

1. Stakeholder Management: Understanding the relationships between stakeholders is essential for effective stakeholder management. By identifying key stakeholders, mapping their relationships, and assessing their interests and influence, business analysts can tailor communication strategies, address conflicts, and foster collaboration among stakeholders.

2. Requirements Elicitation and Analysis: Relationships between requirements are crucial for understanding the scope, priorities, and dependencies of a project. Business analysts must identify and analyze relationships between requirements to ensure coherence, traceability, and alignment with business objectives. Dependencies between requirements help prioritize tasks, allocate resources, and mitigate risks during the project lifecycle.

3. Solution Design and Implementation: Dependencies play a critical role in solution design and implementation. Business analysts must identify dependencies between system components, processes, and external interfaces to ensure seamless integration, interoperability, and functionality of the solution. By mapping dependencies and managing risks proactively, business analysts can optimize resource allocation, streamline workflows, and enhance project outcomes.

Practical Insights into Managing Relationships and Dependencies

1. Stakeholder Mapping and Analysis: Utilize stakeholder mapping techniques, such as stakeholder matrices or influence diagrams, to identify key stakeholders, assess their relationships, and prioritize engagement strategies based on their level of influence and interest.

2. Requirements Traceability Matrix (RTM): Develop a Requirements Traceability Matrix (RTM) to trace relationships and dependencies between requirements, business objectives, and solution components throughout the project lifecycle. The RTM helps ensure alignment between business needs and technical solutions and facilitates change management and impact analysis.

3. Dependency Management Tools: Leverage dependency management tools and techniques, such as Gantt charts, network diagrams, or dependency tracking software, to visualize, track, and manage dependencies between tasks, resources, and deliverables. Regularly monitor and update dependencies to mitigate risks, optimize resource allocation, and maintain project timelines.

Conclusion

In conclusion, relationship and dependency are fundamental concepts in business analysis that underpin effective stakeholder management, requirements analysis, and solution design. By understanding the dynamics of relationships and dependencies, business analysts can enhance communication, foster collaboration, and mitigate risks throughout the project lifecycle. Embrace relationship and dependency management as strategic imperatives in business analysis, and unlock the full potential of your projects in today's dynamic business environment.