Artificial Intelligence

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Artificial Intelligence are “…algorithms which seek to create expert systems which make predictions or classifications based on input data. At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving.” (IBM 2023)

Artificial Narrow Intelligence, also known as "ANI, or weak AI, is the type of AI that has been achieved so far.” (UNESCO)​ ANI is "AI trained and focused to perform specific tasks." (IBM 2023)

Artificial General Intelligence, also known as Strong AI or AGI. “AGI, if ever reached, would be comparable to human intelligence.” (UNESCO) 

for a full glossary of AI-related terminologies, see the AIPRM's "Ultimate Generative AI Glossay" (and Thank you to the STEM Club folks of Fuller Library in London, UK, for suggesting this resource!)

Different kinds of Artificial Intelligence include:

• Machine Learning: "Rather than being programmed with rules to produce answers, computers receive data and the answers expected from the data and, as a result, produce rules by identifying patterns between the two” (UNESCO)"
  • artificial neural networks: a kind of AI machine learning that uses "...a method of mimicking the way the human brain learns, through its connection of neurons, using a computer model.3 This form of AI can evaluate complex relationships between inputs and outputs through a hidden layer (or layers) of calculations" (CADATH 2018)
  • deep learning: "...a form of artificial neural network with many hidden layers between inputs and outputs that allow the program to analyze complex data of various structures.8 In health care, a common form of deep learning is the convolutional neural network" (CADATH 2018)
  • generative AI: deep learning models that use data to “generate statistically probable outputs when prompted" (IBM 2023) ChatGPT is an example of generative AI.
  • large language models: “… trained on large text datasets to learn to predict the next word in a sentence and, from that, generate coherent and compelling human-like output in response to a question or statement." (UNESCO)
    • examples: ChatGPT, Bard
    • A July 2023 Ars Technica post provides this jargon-free "gentle primer" on how LLMs work.
  • Natural Language Processing​: "...(NLP) is a branch of AI concerned with understanding and interpreting human language.6,10,11 In health care, NLP could be used to analyze the content of electronic medical records or as an automated agent to respond to patient questions" (CADATH 2018)
    • example: Google search engine

ChatGPT is "...a variant of the GPT (Generative Pre-training Transformer) language model, which was developed by OpenAI... trained to generate human-like text by predicting the next word in a sequence based on the words that come before it" (response from ChatGPT on 1/3/2023) (Montclair State 2023)

ChatGPT is a type of Artificial Intelligence, one of many programs called "large language models" which are “… trained on large text datasets to learn to predict the next word in a sentence and, from that, generate coherent and compelling human-like output in response to a question or statement. In the case of ChatGPT, 570gb of data representing 300 billion words have been supplied to the system and it has around 175 billion parameters.” (UNESCO)

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Above is an image depicting a form of machine learning called "reinforcement learning with human feedback" from Sasha Luccioni's April 12th 2023 ArsTechnica article, "The mounting human and environmental costs of generative AI"

This process of training LLMs on large text datasets uses a process called "machine learning", where, “Rather than being programmed with rules to produce answers, computers receive data and the answers expected from the data and, as a result, produce rules by identifying patterns between the two” (UNESCO)

Stephen Wolfram also describes how ChatGPT ​​in this post, "What is ChatGPT Doing... and Why Does it Work?"

For a more technical in-depth description of ChatGPT and Machine Learning, watch this video titled "But what is a GPT?"

Our choice for understanding how ChatGPT works:

• Chiang, T. (2023, February 9). ChatGPT Is a Blurry JPEG of the Web. The New Yorker.

Measured responses to AI drama:

• John Warner, the author of the book Why They Can’t Write, has been railing against the five-paragraph essay for years and wrote a Twitter thread about how ChatGPT reflects this rules-based, standardized form of writing: “Students were essentially trained to produce imitations of writing,” he tweeted. The AI can generate credible writing, but only because writing, and our expectations for it, has become so unaspiring. (Bogost 2023)
• Bogost, I. (2022, December 7). ChatGPT Is Dumber Than You Think. The Atlantic.
• In New York Magazine, John Herrman analyzed the shifting nature of ChatGPT, which could be interpreted as a decline in the tool’s effectiveness, an impression perhaps confirmed by a Stanford research team’s finding that ChatGPT went from producing correct responses to math problems at a high of 90 percent to down to a rate of less than 3 percent in recent months.(Gannon 2023)

Ethical issues, environmental and human costs:

• Ciurria, M. (2023, March 30). Ableism and ChatGPT: Why People Fear It Versus Why They Should Fear It. Blog of the APA.
• Marks, A. (2023, January 18). Bestiality and Beyond: ChatGTP Works Because Underpaid Workers Read About Horrible Things. Rolling Stone.
• Lucciono, S. (2023, April 12). The mounting human and environmental costs of generative AI. Ars Technica.
• Perrigo, B. (2023, January 18). Exclusive: The $2 Per Hour Workers Who Made ChatGPT Safer. Time.
• Wong, M. (2023, June 2). AI Doomerism Is a Decoy. The Atlantic.

Baked-in Bias:

• Getahun, H. (n.d.). ChatGPT could be used for good, but like many other AI models, it’s rife with racist and discriminatory bias. Insider. Retrieved July 13, 2023
• Hamilton, I. A. (n.d.). Why it’s totally unsurprising that Amazon’s recruitment AI was biased against women. Business Insider. Retrieved July 13, 2023
• Mattu, J. L., Julia Angwin,Lauren Kirchner,Surya. (n.d.). How We Analyzed the COMPAS Recidivism Algorithm. ProPublica. Retrieved July 13, 2023
• Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.
• Palmer, K. (2022, April 6). ‘We need to be much more diverse’: More than half of data used in health care AI comes from the U.S. and China. STAT.
• Why Meta’s latest large language model survived only three days online. (n.d.). MIT Technology Review. Retrieved July 13, 2023

Why ChatGPT lies:

• Smith, C. (2023, March 13). Hallucinations Could Blunt ChatGPT’s Success - IEEE Spectrum. IEEE Spectrum.

Being human in the age of AI:

• Sætra, H. S. (2019). The Ghost in the Machine: Being Human in the Age of AI and Machine Learning. Human Arenas, 2(1), 60–78.

Comprehensive Briefs:

• United Nations Educational, Scientific and Cultural Organization, 7. (2023). ChatGPT, artificial intelligence and higher education: What do higher education institutions need to know? – UNESCO-IESALC.

Bibliographies:

• In The Chronicle’s pages, you can find a primer on AI and teaching, advice on how to prepare for ChatGPT in your classroom, and tips on how to devise “compelling” writing assignments in an AI era.
• Practical Responses to ChatGPT and Other Generative AI. (n.d.-a). Retrieved July 12, 2023. Montclair University.
• Higher-ed futurist Bryan Alexander has compiled the most comprehensive online list I’ve seen of resources (both text and multimedia) on ChatGPT and AI tools.