GEIT 113: AI in Decision-Making Lecture Notes

This set of vocabulary flashcards covers key concepts, ethical considerations, learning methods, and future trends of AI in decision-making based on the GEIT 113 module.

Alan Turing’s Question

The foundational question in AI evolution: “Can machines think?”

Natural Language Processing (NLP)

Technology used by chatbots and personal assistants to understand and respond to spoken or written queries.

Scalability

The ability of AI systems to simultaneously handle thousands of tasks, such as managing customer queries for a global audience.

Predictive Power

The use of historical data by AI to forecast trends, such as weather patterns or customer behavior.

Deep Learning

A learning method that uses neural networks to handle complex tasks like image recognition and language translation.

Bias in AI Systems

Prejudices inherited from training data or developers' unintentional assumptions that result in unfair or discriminatory recommendations.

Explainable AI

A concept aimed at making AI decision-making transparent and easier for users to understand by clarifying the processes behind decisions.

EU AI Act

An example of an AI governance framework designed to ensure the ethical and responsible deployment of AI systems.

Generative AI

AI that creates new content such as text, images, and media based on user prompts using foundation models (FMs).

Hallucinations

A risk in Generative AI where the system generates incorrect facts that appear accurate.

Foundation Models (FMs)

Very large deep learning models used by Generative AI to produce creative outputs and support decision-making.

Teachable Machine

A Google tool used to train simple AI models to recognize images, sounds, or poses.

Quantum Computing

An emerging technology expected to enable significantly faster and more accurate decision-making in the future.

AI Governance

Frameworks designed to promote safety, fairness, transparency, and inclusivity in AI use across industries.

Accountability

The ethical requirement for developers and organizations to take responsibility for an AI's actions and ensure alignment with societal values.

Supervised Learning

One of the primary ways AI learns, allowing it to find patterns and make predictions through feedback.