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AI Support: Home

This guide provides information and considerations for identifying, evaluating, and utilizing AI tools in your research and practice

What is AI?

AI "is the science and engineering of making intelligent machines, especially intelligent computer programs." 

-John McCarthy (1927 -2011) 
Mathematician, computer scientist, and founder of the AI field

The capabilities of systems and algorithms created through AI are often compared to human intelligence. Tools built using Machine Learning (ML), a subset of AI, can appear to have human intelligence, but their output and inferences are the products of statistical models and computer programming. They require human reasoning, critical thinking, and design to create, test, and maintain.

The field of AI has lead to the development of numerous technologies. Some of this tech, like generative AI tools, may strike us as new and exciting. Other examples, like recommendation algorithms, have been part of our lives for a long time; so much so that they may go unnoticed.

Common examples of AI include:

  • Voice recognition (Siri, Alexa, Google Assistant)
  • Image recognition (reverse image search, facial recognition, self-driving vehicles, healthcare diagnostics)
  • Natural language processing (Google translate, grammar checkers, autocorrect)
  • Large language models (ChatGPT, Gemini, Llama)
  • Algorithms (Netflix, Facebook, sponsored ads, fraud detection, Uber and Lyft)

 

 

 

[current definitions from University of Washington - Health Science Library Artificial Intelligence LibGuide - CC BY-NC 4.0 license]

ML (Machine Learning)

ML involves "algorithms that give computers the ability to learn from data, and then make predictions and decisions". (CrashCourse, 2017) 

A variety of algorithm types fall under the umbrella term machine learning. They lend themselves to a wide variety of applications, and new algorithms are constantly under development.

Common examples of ML algorithms:

  • Supervised machine learning (regression algorithms, classification algorithms)
  • Unsupervised machine learning (K-means clustering, probabilistic clustering)
  • Semi-supervised learning 
  • Reinforcement learning 
  • Neural networks

LLMs (Large Language Models)

LLMs "can generate natural language texts from large amounts of data. Large language models use deep neural networks, such as transformers, to learn from billions or trillions of words, and to produce texts on any topic or domain. Large language models can also perform various natural language tasks, such as classification, summarization, translation, generation, and dialogue." (Maeda & Chaki, 2023)

Common examples of LLMs:

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