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Computer Science: Machine Learning & Artificial Intelligence

Machine Learning & Artificial Intelligence

What is AI?Terms under the Artificial Intelligence umbrella: Machine Learning, Natural Language Processing, Computer Vision, Neural Networks, Autonomous Vehicles, Recommender Systems

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.)

AI can be considered as an umbrella term with specific areas of study under it such as Machine Learning, Natural Language Processing, Computer Vision, Recommender Systems, etc.

ML (Machine Learning) consists of "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.)

AI Tools for Research

Disclaimer: Please refer to syllabus for course work or journal guidelines for use of AI/ML tools in research or writing process. This guide is purely for helping identify tools and limitations of those tools. 

Guidance concerning the usage and development of AI

Some of the most popular and well-known AI Tools:
Not an all-inclusive or exhaustive list

Limitations and Concerns of AI

Overall, these tools can be a good starting place and be useful for you when you are brainstorming topic ideas or trying to come up with keywords to search on a specific topic. However, it is important to remember that these tools are not search engines, but uses vast amounts of data to generate responses that appear to make sense. They are fluency-based text-language generators, which means that they literally guess what word comes next.

Chat tools are known for producing "hallucinations" - where the program presents and defends false information as if it were factual. When prompted a chatbox can produce realistic looking articles from legitimate journals from actual authors but they are fabricated. These tools have been able to generate abstracts for these non-existent articles.

In addition to these limitations of the tool there are several of concerns around the development AI.

  • 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. Additionally, many companies do not disclose their training data so an author's work could be included without their consent.
  • These AI systems are designed by humans and based off design and programming decisions can lead to biases and harmful results.

Learn More

Finding Datasets:

Journals and eBooks about AI/ML:


  • CrashCourse (Director). (2017, November 1). Machine Learning & Artificial Intelligence: Crash Course Computer Science #34.
  • Maeda, J., & Chaki, E. (2023, April 3). Concepts Overview for LLM AI. Microsoft Build. McCarthy, J. (n.d.).
  • What is AI?: Basic Questions. Professor John McCarthy. Retrieved May 3, 2023, from
  • What is GPT AI? - Generative Pre-Trained Transformers Explained - AWS. (n.d.). Amazon Web Services, Inc. Retrieved August 10, 2023, from