ELI-ESL 1650 (English for Academic Purposes): General Overview of AI and Robotics

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are designed to think and act like humans. This field of study merges computer science with extensive datasets to empower machines with problem-solving and decision-making abilities. AI includes several subfields, such as Machine Learning, Deep Learning, and Generative AI.

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

Part of the excitement around AI is that, in recent years, these technologies have outperformed even their creators' expectations. AI works by finding patterns in troves of data and then matching the found patterns to some desired output. Most AI systems are also the result of human intervention to help further guide the output towards desired goals. 

Though the underlying systems may be complicated, it’s important to understand that AI is dependent on rich and varied troves of data on a relevant topic for the technology to work. The quality of the data that is used is crucial in the ability of the technology to deliver useful results. Weaknesses in the data, such as biased or inexpert sources, will give weak results. Other issues of appropriateness of data usage that will be familiar to librarians are also relevant here (copyright, recency of publication, etc.).

Here are four overarching principles to help researchers and educators handle the intricacies of Generative AI.

1. Understand the Opaque Nature of AI: Generative AI models are often seen as "black boxes" due to their lack of transparency in decision-making processes. This can create challenges for researchers trying to comprehend how conclusions are derived. When working with generative AI. Recognize the lack of clarity in these models. Aim for models that provide some degree of interpretability or explainability.  
2. Be Wary of Data Biases: Generative AI models rely on extensive datasets that may contain inherent biases. These biases can influence outputs, resulting in skewed or prejudiced results. To address this issue. Investigate the data sources for possible biases. Assess how these biases might impact research outcomes.  
3. Recognize the Ethical Considerations: Using generative AI brings important ethical issues to light, such as privacy concerns, the potential for misuse, and the broader societal effects of AI-driven decisions. Researchers should undertake ethical assessments of AI applications. Promote transparency and accountability in research that relies on AI. Keep in mind that AI cannot replicate complex human emotions or moral reasoning, which limits its effectiveness in situations that demand empathy or ethical judgment.  
4. Stay Updated on AI Advancements: AI technology is progressing swiftly, with ongoing improvements in capabilities, methodologies, and ethical standards. To effectively utilize generative AI, stay current with the latest research and advances in AI. Collaborate with experts from different fields to gain insight into best practices and ethical frameworks.