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Artificial Intelligence Literacy

What is artificial intelligence?

According to contributors at IBM, artificial intelligence (AI) is a technology that allows computers and machines to mimic human learning, understanding, problem-solving, decision-making, creativity, and autonomy. Generative AI, a subset of artificial intelligence, takes things further by creating original text, images, videos, and other content.

How does AI differ from Algorithms? 

  • An algorithm is a set of instructions that a computer can follow.  If a trigger is met, the algorithm will execute a set of instructions.
    • An example is if you are shopping online and filter sweaters by color.  
  • AI algorithms are sets of instructions that allow machines to analyze data and make decisions (Morris, 2024). 

 

IBM Technology. (2024). AI, Machine Learning, Deep Learning and Generative AI Explained [Video]. YouTube. https://youtu.be/qYNweeDHiyU?si=meIJwJozjvK5CaL_ 

Morris, S. (2024). Unpacking AI [PowerPoint slides]. Exploring AI with Critical Information Literacy, ALA Learning.

Stryker, C. & Kavlakoglu, E. (2024, August 16). What is artificial intelligence?  IBM. https://www.ibm.com/topics/artificial-intelligence 

Glossary of Terms

  • Algorithm: a sequence of rules given to an AI machine to perform a task or solve a problem. Common algorithms include classification, regression, and clustering.
  • Chatbot: a software application designed to imitate human conversation through text or voice commands.
  • Data mining: the process of sorting through large data sets to identify patterns that can improve models or solve problems.
  • Deep Learning:  Layered neural networks that "weight" certain categories; you can see these at work in facial recognition systems, which use different analysis categories. 

  • Hallucination:  When generative AI produces wrong responses to a prompt or query.  This could involve mixing true and false statements; misstating a detail; making entirely false claims and fabricating information.

  • Large language model (LLM): an AI model that has been trained on large amounts of text so that it can understand language and generate human-like text. They function by guessing the next word in a sequence of words (somewhat like an auto-complete tool). 

  • Natural Language Processing:  A branch of AI that involves computers recognizing, analyzing, and understanding human language and generating responses
  • Neural networks:  Computing systems inspired by the human brain; algorithms that train on large data sets (such as Large Language Models or LLMs) over and over to detect patterns and relationships and continually improve
  • Pattern recognition: a method of using computer algorithms to analyze, detect, and label regularities in data. This informs how the data gets classified into different categories.
  • Predictive analytics: a type of analytics that uses technology to predict what will happen in a specific time frame based on historical data and patterns.
  • Prescriptive analytics: a type of analytics that uses technology to analyze data for factors such as possible situations and scenarios, past and present performance, and other resources to help organizations make better strategic decisions. 
  • Prompt engineering:  involves designing and refining inputs (questions or instructions) to elicit a specific response from an AI tool
  • Reinforcement learning:  training something to respond to an environment; uses a mix of unsupervised and supervised learning
  • Supervised Learning:  the data the AI is trained on has been tagged with correct answers
  • Semi-supervised Learning:  this is where a supervised learning data set is applied to a larger untagged data set; the model can then start making predictions and calculating the probability of correct answers
  • Training data: the information or examples given to an AI system to enable it to learn, find patterns, and create new content.
  • Turing test: created by computer scientist Alan Turing to evaluate a machine’s ability to exhibit intelligence equal to humans, especially in language and behavior. When facilitating the test, a human evaluator judges conversations between a human and a machine. If the evaluator cannot distinguish between responses, then the machine passes the Turing test.
  • Unsupervised learning:  the AI is trained on untagged data; this often has problems

Coursera. (2024, March 19). Artificial intelligence (AI) terms: A to Z glossary. https://www.coursera.org/articles/ai-terms

Morris, S. (2024). Exploring AI with critical information literacy. ALA Learning.