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Artificial Intelligence

Guide to Artificial Intelligence (AI) for academic research

What is AI?

Artificial intelligence (AI) is also referred to as machine learning (ML) although they are different. Similarly, Large Language Models (LLMs) are often referred to as AI and fit under the umbrella of AI with ML but neither demonstrates actual intelligence.

AI (Artificial Intelligence) "is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable [emphasis added]." (McCarthy, n.d.)

Intelligence of created systems and algorithms is typically compared to human intelligence. Sometimes LLMs and ML products can appear to have human intelligence, but it is simply the product of coding, not actual intelligence.

ML (Machine Learning) is "algorithms that give computers the ability to learn from data, and then make predictions and decisions". Examples include automatically detecting spam emails, suggesting videos to watch after finishing one, etc. (CrashCourse, 2017) 

LLMs (Large Language Models) "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)

GPT (Generative Pre-trained Transformer) "models give applications the ability to create human-like text and content (images, music, and more), and answer questions in a conversational manner." (What Is GPT AI?, n.d.)

Many of us already live with artificial intelligence, but researchers say interactions with the technology will become increasingly personalized (Smith, 2021).

Uses and Limitations of Artificial Intelligence

Uses Limitations

It can be a starting place, but do not rely on it for factual information or research.

  • It is not a search engine, but uses vast amounts of data to generate responses that appear to make sense.
  • LLMs and AI are known for producing hallucinations, where the program presents and defends false information as if it were factual.
    • Most LLMs have created citations to defend its statements, but these citations can be entirely fabricated.
  • LLMs have access to knowledge up to a given date. You may ask the LLM when that date is, but it is best to go to the developer's notes, if available, to confirm whether its information is up to date.
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  • LLMs' privacy policies may allow the creators to sell and profit off of your personal information.
  • Submitting a manuscript to a LLM for writing assistance may violate requirements from journals you wish to publish in or your institution.
  • Anything you submit to an LLM may become a part of the LLM's learning corpus.

Discussion starter regarding privacy, intellectual property, research integrity, ethical consumption, and more!

Many LLMs are for-profit tools and by engaging with them you are adding to their learning corpus which is, at its core, unpaid labor while benefitting from the devastation of paid workers and the environment.

(University of Washington Health Sciences Library, 2023).