The article, “What people ask me most. Also, some answers,” by Prof. Ethan Mollick, addresses common questions and misconceptions about Generative AI. Mollick, who has interacted with various professionals about Large Language Models (LLMs), notes that misinformation often spreads due to a lack of clear documentation from AI developers. He emphasizes that AI writing cannot be reliably detected, debunking the efficacy of AI writing detectors and highlighting the challenges in academic honesty. Mollick also discusses the difficulty in identifying AI-generated images, suggesting that distinguishing them from real ones will become nearly impossible. Prof. Ethan Mollick also shares insightful perspectives in his recent newsletter:
Detecting generated text, even for an AI-savvy individual like Mollick, can be challenging. When it happens, it suggests that students may not be putting forth much effort, relying on simple prompts.
Therefore, ensuring equal educational opportunities for all requires educators to teach students about AI tools, industrial basics, and more. Banning AI is a counterproductive approach. Instead, the focus should be on imparting AI skills, not just domain-specific knowledge.
Simultaneously, AI empowers educators to set higher standards and challenge students to achieve the seemingly impossible. If students struggle with memorization, they can turn to AI to assist them within a week. Students can benefit from Math and Code Interpreter for data analysis, while foreign language learners can generate essays in flawless English. The concept of exceptions becomes obsolete when you possess a “great equalizer” like AI.
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Additionally, Mollick provides valuable resources for incorporating AI into educational processes, including top-tier Large Language Models like GPT-4 and Claude 2 for handling extensive documents.
Regarding the practical use of AI, Mollick suggests that there is no one-size-fits-all guide. Success with AI requires hands-on experience, understanding its unpredictable nature, and recognizing its rapidly changing capabilities. He recommends using advanced LLMs like GPT-4, Google’s Bard, or Anthropic’s Claude 2 and experimenting with them across various tasks to understand their potential and limitations.
Mollick touches on policy issues, explaining that while data privacy concerns are valid, AI companies offer solutions to safeguard user data. He delves into the murky waters of copyright, where the legal status of AI-generated content remains uncertain, though some companies offer legal protections for users.
The article debunks the notion that AIs are deteriorating over time, clarifying that they are evolving, necessitating different interaction methods. Mollick acknowledges potential future hurdles in AI development, such as data scarcity or an internet saturated with AI-generated content, but he remains optimistic about continued progress in the field.
The convergence of AI and EdTech presents a wealth of opportunities.
Recently, OpenAI and Tools Competition have launched the “Learning Impact Prize” with a prize fund of over $5M to stimulate technology development to meet the growing needs of students worldwide. The competition has five directions, divided into three categories: Catalyst Awards ($50K prize), Growth Awards ($150k), and Transform Awards ($300K). The tracks include Accelerating & Assessing Learning, Preparing for the 21st Century World, Instructor Coaching for Early Childhold Education, Facilitating Learning Science Research, and Engaging Adult Learners in Higher Education.
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