{"id":207,"date":"2026-09-28T23:08:56","date_gmt":"2026-09-29T06:08:56","guid":{"rendered":"https:\/\/blogs.ubc.ca\/themusicallyinclined\/?p=207"},"modified":"2026-10-01T00:14:11","modified_gmt":"2026-10-01T07:14:11","slug":"ip-2-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/blogs.ubc.ca\/themusicallyinclined\/ip-2-artificial-intelligence\/","title":{"rendered":"IP 2 \u2013 Artificial Intelligence"},"content":{"rendered":"

IP 2 – Statement on Using AI Generated Material<\/a>
\n(Please “right click” and “Save link as” to view the statement! I’m not sure why you can’t directly download the file otherwise…)<\/p>\n

Prompt: Who was Alan Turing, and how did he contribute to the development of artificial intelligence? How would he define \u201cintelligence\u201d?<\/p>\n\n\n\n
My answer:<\/p>\n

Alan Turing was a mathematician and scientist who created the Turing Test to evaluate a machine\u2019s ability to imitate human-like behavior, which influenced how AI systems were designed for the next few decades. He would likely describe intelligence as acquiring new skills to fill the \u201cblank sheet\u201d of the mind (Chollet, 2019, p. 6).<\/td>\n

ChatGPT\u2019s answer:<\/p>\n

\u201cAlan Turing (1912\u20131954) is one of the foundational figures in modern computing. He proposed the Turing Test. Turing did not give a simple definition of intelligence. The important insight was we could investigate machine intelligence by looking at observable behavior, rather than agreeing on an abstract definition of \u2018thinking\u2019 (OpenAI, 2026a).\u201d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Prompt: Who was John McCarthy, and how did he contribute to the development of artificial intelligence? How would he define \u201cintelligence\u201d?<\/p>\n\n\n\n
My answer:
\nJohn McCarthy coined the term \u201cartificial intelligence\u201d at the Dartmouth Conference in 1956 and developed the Lisp programming language which became the standard for AI systems. He might define intelligence as the ability to adapt to and perform \u201cnever seen\u201d tasks that have \u201cnot been prepared for beforehand\u201d (Chollet, 2019, p. 6).<\/td>\n		ChatGPT\u2019s answer:<\/p>\n

\u201cJohn McCarthy (1927\u20132011) helped establish AI as an explicit academic discipline. McCarthy created Lisp, a programming language suited to AI research. He argued that intelligence is not tied to being human or having a brain. What matters is the capacity to accomplish goals in the world using computational processes (OpenAI, 2026d).\u201d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Prompt: Who was Herb Simon, and how did he contribute to the development of artificial intelligence? How would he define \u201cintelligence\u201d?<\/p>\n\n\n\n
My answer:
\nHerbert Simon created the Logic Theory Machine and General Problem Solver, some of the earliest automated reasoning and problem solving programs in the field of AI, while also predicting that machine capabilities would quickly surpass human ones. To him, intelligence might be shown by making \u201ccorrect, efficient, and practical\u201d decisions (\u201cHerbert A. Simon,\u201d 2026).<\/td>\n		ChatGPT\u2019s answer:<\/p>\n

\u201cHerbert A. Simon (1916\u20132001) was an American scholar. He and Allen Newell developed computer programs intended to simulate human problem solving. He viewed intelligence as the ability of a system to represent information and manipulate those representations in ways that enable it to solve problems and act effectively (OpenAI, 2026c).\u201d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Prompt: Who was Marvin Minsky, and how did he contribute to the development of artificial intelligence? How would he define \u201cintelligence\u201d?<\/p>\n\n\n\n
My answer:
\nMarvin Minsky co-founded the MIT Computer Science and Artificial Intelligence Laboratory and built the first artificial neural network named SNARC. He posited that intelligence is demonstrated by the mind\u2019s \u00a0\u201cwide collection of vertical, relatively static programs\u201d in which knowledge is stored like in a database (Chollet, 2019, p. 5).<\/td>\n		ChatGPT\u2019s answer:<\/p>\n

\u201cMarvin Minsky (1927\u20132016) co-founded the MIT Artificial Intelligence Laboratory with John McCarthy. Minsky built SNARC, an early neural-network machine. His most famous contribution was the \u201cSociety of Mind\u201d theory. His approach was: Intelligence emerges when many different processes\u2014reasoning, perception, memory, learning, planning, common sense, language\u2014work together (OpenAI, 2026g).\u201d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Prompt: Who is Timnit Gebru, and how does she contribute to the development of artificial intelligence? How would she define \u201cintelligence\u201d?<\/p>\n\n\n\n
My answer:
\nTimnit Gebru is an AI ethics researcher who published a study exposing algorithmic bias in facial recognition systems developed by some of the largest companies like Microsoft and IBM. She believes intelligence is used to \u201canthropomorphize\u201d AI technologies so companies relieve themselves from the responsibility of building ethical, \u201csafe systems\u201d (Angwin, 2022).<\/td>\n		ChatGPT\u2019s answer:<\/p>\n

\u201cTimnit Gebru has highlighted issues on bias, representation, accountability, and social consequences in AI. An interpretation of her research: Intelligence should not be equated with producing impressive outputs; we should ask what capabilities a system possesses, how those capabilities arise, and what happens when the system is deployed (OpenAI, 2026h).\u201d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Prompt: How do \u201cmachine (programming) languages\u201d differ from human (natural) ones?<\/p>\n\n\n\n
My answer:<\/p>\n

Human languages have evolved over time as our needs for expression have expanded, resulting in words constantly being redefined like \u201cviral\u201d or \u201cdab\u201d \u2013 or new terms being coined with the prevalence of Internet and meme culture. We are able to \u201cmaterially change\u201d the contexts of what is said depending on conditions like the content, audience, time, and location (Jones, 2020, p. 26). We also utilize a variety of modes \u2013 gestural (ASL), logographic (Chinese), phonetic (English) \u2013 while programming languages are \u201cpurely performative\u201d in which code literally \u201cdoes what it says\u201d (Jones, 2020, p. 26).<\/td>\n

ChatGPT\u2019s answer:<\/p>\n

\u201cThe key difference is who\u2014or what\u2014the language is designed for, and how precisely meaning must be specified. In English, \u201cCould you open the window?\u201d is a question about someone’s ability, but in conversation it is a request. A programming language doesn’t allow that kind of contextual interpretation. The computer doesn’t infer that you probably<\/em> meant something else. Both kinds of language have syntax and semantics. The major difference is that programming-language semantics are designed to be sufficiently formal and unambiguous for machines to execute, whereas natural-language meaning is heavily shaped by human cognition, context, convention, and social interaction (OpenAI, 2026e).\u201d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Prompt: How does \u201cmachine (artificial) intelligence\u201d differ from the human version?<\/p>\n\n\n\n
My answer:<\/p>\n

While intelligence has traditionally been measured in machines through benchmarks on \u201ctask-specific skill\u201d, more recent definitions focus on the goal of achieving \u201cgenerality\u201d by applying existing knowledge to adapt to new, previously unknown tasks (Chollet, 2019, p. 4). Importantly, a facet of human intelligence that cannot be replicated in machines comes in the form of \u201ccrystallized abilities\u201d that can only be acquired, developed, and mastered through direct experience (Chollet, 2019, p. 16). As programs are fully hard-coded by engineers, they only represent a \u201ccrystallized output\u201d of the \u201cthought process\u201d and do not showcase a machine\u2019s ability to solve problems or generate answers without guidance from its human creators (Chollet, 2019, p. 19).<\/td>\n

ChatGPT\u2019s answer:<\/p>\n

\u201cThe deepest difference is that machine intelligence is engineered, whereas human intelligence is a biological, embodied phenomenon. The comparison is complicated because \u2018intelligence\u2019 isn’t a single thing. Consider the word \u2018fire.\u2019 An AI can learn relationships involving the word. A human, however, can also have lived through an encounter with fire: seeing flames, feeling heat, smelling smoke, being frightened, remembering a campfire, learning from parents not to touch it, and so forth. Human concepts are embedded in a body, environment, history, and social world. Machine systems primarily acquire their concepts through computational representations derived from data and interaction (OpenAI, 2026b).\u201d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Prompt: How does \u201cmachine learning\u201d differ from human learning?<\/p>\n\n\n\n
My answer:<\/p>\n

Machines primarily learn by being trained on datasets, which can, in a way, become a limitation for answer generation depending on any biases present \u2013 they require human intervention to be taught what acceptable outputs are, while humans have logical reasoning and moral values, to deduce what is ethical. Machines can also \u201cartificially\u201d increase their performance by the incorporation of more priors or training data, which is not proof of generalization (Chollet, 2019, p. 19). While we also possess prior knowledge and experience, it can only be acquired and developed over a natural period of time alongside our ability to generalize and cannot be gamed.<\/td>\n

ChatGPT\u2019s answer:<\/p>\n

\u201cMachine learning changes a computational model by adjusting parameters from data; human learning changes a living brain through experience, perception, action, memory, and social interaction. Suppose a child learns what a dog is. The child gradually constructs a concept that is considerably richer than a visual category. It becomes connected to sound, movement, emotion, danger, affection, language, social knowledge, and personal memories. A machine-learning system might instead be trained on millions of labeled or otherwise structured examples and adjust its internal parameters so that it becomes increasingly good at recognizing or predicting things associated with \u2018dog\u2019 (OpenAI, 2026f).\u201d<\/p>\n

 <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

ChatGPT\u2019s answers are quite formulaic and can take a while to \u201cget to the point\u201d because it presents its information in bullet points, tables, and lists. Its conversational style and stochastic parrot nature has become easily distinguishable \u2013 I\u2019m not sure if another term already exists to describe it \u2013 and its cadence feels unnatural to me compared to how I or another human would answer. This language model\u2019s examples when comparing artificial intelligence and learning to human intelligence and learning, such as the case of the words \u201cfire\u201d and \u201cdog\u201d, but the sentences are structured using a hypothetical format, like when it says, \u201cConsider the word\u201d or \u201cSuppose [this]\u201d. In my answers, any examples I use (e.g. \u201cviral\u201d and \u201cdab\u201d) are more direct and relevant to the topic at hand instead of taking the reader out of the conversation to imagine another situation, which ChatGPT seems to frequently rely on.<\/p>\n

All of these aspects made it extremely difficult for me to parse and condense ChatGPT\u2019s answers to fit the word count. On the other hand, working within the given constraints allowed me to focus on synthesizing my original ideas with the course readings and other Internet sources in a manner where my answers had to be succinct and precise.<\/p>\n

References:<\/u><\/strong><\/p>\n

Angwin, J. (2022, May 28). Building ethical artificial intelligence.<\/em> The Markup. https:\/\/themarkup.org\/newsletter\/hello-world\/building-ethical-artificial-intelligence?utm_source=chatgpt.com<\/a><\/p>\n

Chollet, F. (2019, November 5). On the measure of intelligence. https:\/\/arxiv.org\/pdf\/1911.01547<\/a><\/p>\n

Crawford, K. (2021). Atlas of AI<\/em>. Yale University Press. (Introduction: pp. 1-21)<\/p>\n

Herbert A. Simon. (2026, August 29). In Wikipedia<\/em>. https:\/\/en.wikipedia.org\/wiki\/Herbert_A._Simon<\/a><\/p>\n

Jones, R. H. (2020). The rise of the Pragmatic Web: Implications for rethinking meaning and interaction. In C. Tagg & M. Evans (Eds.), Message and medium: English language practices across old and new media<\/em> (pp. 17-37). De Gruyter Mouton.<\/p>\n

OpenAI. (2026a, September 28). Alan Turing<\/em> [Generative AI chat]. ChatGPT. https:\/\/chatgpt.com\/s\/t_6abdfe794f4c8191a492703851667ea9<\/a><\/p>\n

OpenAI. (2026b, September 28). Artificial intelligence vs. human intelligence<\/em> [Generative AI chat]. ChatGPT. https:\/\/chatgpt.com\/s\/t_6abdffd20ba081919509463a11cc099b<\/a><\/p>\n

OpenAI. (2026c, September 28). Herbert Simon<\/em> [Generative AI chat]. ChatGPT. https:\/\/chatgpt.com\/s\/t_6abdfee7df9081918f39af1035c46c55<\/a><\/p>\n

OpenAI. (2026d, September 28). John McCarthy<\/em> [Generative AI chat]. ChatGPT. https:\/\/chatgpt.com\/s\/t_6abdfe98c3e08191b2fddaad293654bc<\/a><\/p>\n

OpenAI. (2026e, September 28). Machine languages vs. human languages<\/em> [Generative AI chat]. ChatGPT. https:\/\/chatgpt.com\/s\/t_6abdffa780e081918ad8172b4e8abe23<\/a><\/p>\n

OpenAI. (2026f, September 28). Machine learning vs. human learning<\/em> [Generative AI chat]. ChatGPT. https:\/\/chatgpt.com\/s\/t_6abdfff327e0819190f46b8cddf2d9e0<\/a><\/p>\n

OpenAI. (2026g, September 28). Marvin Minsky<\/em> [Generative AI chat]. ChatGPT. https:\/\/chatgpt.com\/s\/t_6abdff4c54d481918fd3f13ba5389af2<\/a><\/p>\n

OpenAI. (2026h, September 28). Timnit Gebru<\/em> [Generative AI chat]. ChatGPT. https:\/\/chatgpt.com\/s\/t_6abdff7921308191bd6772edd5426b9d<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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