AI FAQs

AI describes computer systems that perform tasks people usually do — including understanding language, recognizing images, making decisions, and learning from data.

Researchers began exploring AI in the 1950s. In 1956, scientists first used the term “Artificial Intelligence” at a conference at Dartmouth College. Learn more from Dartmouth’s overview of where artificial intelligence was coined (opens in a new tab).

Machine learning is a branch of AI that allows systems to learn from data and improve over time without direct programming.

Deep learning is a type of machine learning that uses layered neural networks to process data for tasks like image recognition and language translation.

Neural networks model how the human brain works. They use layers of connected nodes to process information and learn from data.

A large language model is an AI system trained on large text datasets. It understands and generates human-like language.

GPT stands for Generative Pre-trained Transformer. It was developed to generate text for writing, coding, and answering questions.

Generative AI creates new content such as text, images, or audio. Common examples include chatbots and image generators.

Agentic AI describes systems that act with limited human input. These systems plan tasks, make decisions, and adapt to change.

AI agents are software programs that act in an environment. They make decisions and take actions to achieve goals.

AI powers voice assistants, recommendation systems, fraud detection, and smart devices. Many industries use AI, including healthcare and education.

Learning AI builds valuable skills such as problem-solving, critical thinking, and technology awareness — all in high demand across Mississippi’s workforce.

Students use AI for research and writing support. Educators use it for lesson planning, grading assistance, and personalized learning.

No. Many AI courses focus on practical use and welcome beginners without technical experience. MAIN’s free courses are designed for all skill levels.

Ethical concerns include bias, privacy risks, misinformation, and misuse. Responsible use helps reduce these risks. For additional guidance, review the NIST AI Risk Management Framework (opens in a new tab).

AI can be safe when used responsibly. Users should verify information, follow their institution’s guidelines, and protect sensitive data.

Automation follows fixed rules. AI, by contrast, learns from data and adapts to new situations.

AI learns by identifying patterns in data and improving through training and feedback.

Manufacturing uses AI to improve production and efficiency. Healthcare uses AI for diagnostics and patient care. Education uses AI for personalized learning and instructional support.