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Artificial intelligence might have wide-ranging effects on the evolution of the English language and, in turn, on education, communication, and global knowledge sharing.
brain with superimposed circuitry
Credit: Ole.CNX / Shutterstock.com © 2024
In an increasingly interconnected world, English has long been the global lingua franca, bridging diverse cultures and disciplines. But as we stand on the cusp of the artificial intelligence (AI) era, we must consider how this universal language might evolve—or be revolutionized—in the coming years. This article explores the concept of English as a programming language for human ideas, its potential transformation into an AI-improved version, and the implications this could have for education, communication, and global knowledge sharing. Along the way, we'll draw parallels and contrasts with George Orwell's dystopian vision of language control in his novel 1984 to better understand the possibilities and pitfalls of this linguistic revolution.
Observatorio IA - artículo
EDUCAUSE
(17/10/2024)
This report presents a comprehensive framework for AI Literacy in Teaching and Learning (ALTL) in higher education, addressing the need for institutions to adapt to the rapidly evolving landscape of artificial intelligence (AI). The framework equips students, faculty, and staff to engage effectively and ethically with AI technologies in academic and professional contexts.
ALTL involves understanding AI fundamentals, critically evaluating AI applications, and maintaining vigilance against misuse and bias. The framework provides tailored definitions, competencies, and outcomes for students, faculty, and staff, focusing on four key areas: Technical Understanding, Evaluative Skills, Practical Application, and Ethical Considerations.
For students, ALTL emphasizes understanding and ethically applying AI in academic contexts. The focus for faculty is on integrating AI in teaching, research, and administrative responsibilities. Staff concentrate on supporting AI implementation in administrative and operational processes.
This guide was created by participants of the Critical AI Literacy for Reading, Writing, and Languages Workshop, an initiative of the MLA-CCCC Task Force on Writing and AI.
Ethical and effective use of GenAI technologies is emerging as an essential skill that students must develop in order to live, learn, and work. Yet GenAI comes with potential pitfalls for students–from the risk of being accused of academic misconduct to missing out on foundational skills in reading, writing, research, and learning.
Developing literacy with a tool means becoming a more skilled and thoughtful user of that tool. For example, developing literacy in reading means being able to reread, tackle increasingly difficult texts, and do research in order to further build your capability as a reader. Literacy also assumes you have enough knowledge to question and evaluate what you are studying.
Similarly, developing AI literacy requires that you learn certain basics about how GenAI works, how to use it, and how to evaluate its output. You should also learn when not to use it. Developing GenAI literacy should be your starting point for using this technology. When you build skills and habits for using GenAI ethically and effectively you will establish yourself as a thoughtful creator and consumer of GenAI content as technologies change over time.
From its emerging stages as a theoretical concept to its current status as a transformative force, artificial intelligence (AI) has seen a remarkable evolution. The trajectory of AI's advancement—from simple algorithms to sophisticated machine learning models capable of outperforming human expertise in specific tasks—heralds a future in which AI's role is central to every aspect of our lives. The implications for future generations are profound: a shift in job structures, the emergence of new industries, and the overhaul of existing societal norms.
Yoshua Bengio, Geoffrey Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Trevor Darrell, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, Gillian Hadfield, Jeff Clune, Tegan Maharaj, Frank Hutter, Atilim Günes Baydin, Sheila McIlraith, Qiqi Gao, Ashwin Acharya, David Krueger, Anca Dragan, Philip Torr, Stuart Russell, Daniel Kahneman, Jan Brauner, Sören Mindermann
Science
(20/05/2024)
Artificial intelligence (AI) is progressing rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. Increases in capabilities and autonomy may soon massively amplify AI’s impact, with risks that include large-scale social harms, malicious uses, and an irreversible loss of human control over autonomous AI systems. Although researchers have warned of extreme risks from AI (1), there is a lack of consensus about how to manage them. Society’s response, despite promising first steps, is incommensurate with the possibility of rapid, transformative progress that is expected by many experts. AI safety research is lagging. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness and barely address autonomous systems. Drawing on lessons learned from other safety-critical technologies, we outline a comprehensive plan that combines technical research and development (R&D) with proactive, adaptive governance mechanisms for a more commensurate preparation.
Este recurso educativo está centrado en la aplicación de la Inteligencia Artificial en los contextos educativos, especialmente de primaria y secundaria. En una era caracterizada por avances tecnológicos constantes, el sector educativo se enfrenta a la necesidad de adaptación e innovación. Aspira a servir como una guía que habilite a los docentes para integrar las tecnologías de Inteligencia Artificial en su práctica pedagógica.
Se abordarán una serie de temáticas esenciales, comenzando con una introducción a la naturaleza de la Inteligencia Artificial, seguida de un análisis sobre los aspectos éticos y limitaciones inherentes a su uso. Se proporcionarán también pautas y recursos para el desarrollo de material didáctico, haciendo uso de IA generativa.
En secciones subsiguientes, se tratará el arte de la interacción con sistemas de IA generativa, incluyendo técnicas para la formulación precisa de preguntas e instrucciones. Además, se tratarán métodos para la creación de contenido educativo asistido por IA, cubriendo aspectos como la evaluación y retroalimentación, el seguimiento del progreso académico y la gestión del aula.
También se presentará una compilación de herramientas y recursos tecnológicos diseñados para asistir a los docentes en la implementación práctica de la IA en los entornos educativos.
Comenzamos el artículo con la pregunta que le da título: ¿la Inteligencia Artificial sustituirá a los docentes?. Seguro que todos nos los hemos planteado alguna vez, especialmente en este último año y pico. Y es una pregunta que se hacen numerosos colectivos profesionales. ¿Hasta qué punto nuestros puestos de trabajo están en riesgo por la Inteligencia Artificial?
Que la balanza de pros y contras de la IA se decline del lado positivo va más allá de formar en su conocimiento técnico. Supone, sobre todo, enseñar cómo utilizar su potencial de manera ética y responsable.
A.I. tools like ChatGPT did not boost the frequency of cheating in high schools, Stanford researchers say.
According to new research from Stanford University, the popularization of A.I. chatbots has not boosted overall cheating rates in schools. In surveys this year of more than 40 U.S. high schools, some 60 to 70 percent of students said they had recently engaged in cheating — about the same percent as in previous years, Stanford education researchers said.
Artificial intelligence chatbots exhibits similar biases to humans, according to new research published in Proceedings of the National Academy of Sciences of the United States of America (PNAS). The study suggests that AI tends to favor certain types of information over others, reflecting patterns seen in human communication.
The motivation behind this research lies in the burgeoning influence of large language models like ChatGPT-3 in various fields. With the wide application of these AI systems, understanding how they might replicate human biases becomes crucial.
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