What is Generative AI?
Generative AI is technology that can create new content such as text, images, or videos. It functions as an advanced tool for content creation across multiple media formats.
Large Language Models (LLMs) are one type of generative AI that specializes in creating text. These systems learn by analyzing millions of examples of human writing, then apply that knowledge to generate new text.
LLMs work similar to an advanced autocomplete feature. They predict what word should come next based on patterns learned during training. This allows them to produce confident-sounding responses, but they do not actually understand topics the way humans do—they excel at predicting what sounds appropriate based on their training data.
Sample Applications of Generative AI:
- Content Creation: Writes articles, generates code, creates music or images
- Accessibility: Makes content more accessible by simplifying complex language, generating alternative text, or presenting material in alternative formats (e.g., creating a podcast from an article).
- Personalization: Tailors content and experiences to individual contexts, preferences, and needs
- Writing Enhancement: Assists in editing and improving existing text for clarity and readability.
- Information Summarization: Processes large amounts of information quickly and extracts key points.
- Concept Explanation: Breaks down complex topics and supports exploration of new ideas.
- Idea Generation: Produces ideas on specified topics, presents different perspectives, and helps organize thoughts into structured formats.
- Scenario Simulation: Can adopt different roles or personas for training, practice, or educational purposes.
- Research Support: Provides topic overviews for research purposes, suggests relevant search terms, may suggest relevant sources.
Limitations and Concerns of Generative AI
- Limited Knowledge Base: Training data does not include the most recent information or specialized content that is not publicly available online.
- Accuracy Concerns: AI systems do not truly understand topics—they recognize patterns. This can lead them to generate incorrect information, fabricate plausible-sounding sources, and provide oversimplified explanations of complex subjects.
- Loss of Human Voice and Perspective: When you use AI to communicate, you lose your own unique voice. Additionally, AI-generated content often strips away attribution, obscuring the original human perspectives and creators from which it learned.
- Inherent Bias: AI models are trained on vast amounts of data from the internet, and they inevitably absorb and may even amplify the societal biases contained within that data.
- Copyright and Privacy Issues: AI development involves using extensive content without explicit permission from original creators. Further, users should exercise caution when sharing personal or confidential information with AI, as it could be used in future training or become exposed.
- Environmental and Ethical Concerns: Training and operating large-scale AI models require immense computational power, leading to significant energy consumption. Furthermore, their development can involve problematic labor practices for data annotation and content moderation.
- Potential Learning Impact: Excessive reliance on AI for writing, summarizing, or problem-solving can hinder the development of crucial skills and the critical thinking that emerges from tackling these challenges independently.
Sources:
Poorvu Center for Teaching and Learning. (n.d.) AI Guidelines. Yale University. https://poorvucenter.yale.edu/ai-guidelines
Claude AI and Gemini were used to edit this content. The prompts used included:
- "Revise the following content to make sure it is concise, accurate, consistent, and professional."
- "Revise the text to make it easier to understand"
- "Make the tone more professional and less conversational, but keep the simpler language and clear structure"
The final output was lightly edited to ensure that the original ideas behind the content were maintained.
Additional Resources:
