Author: Joy Yu

ETEC511- IP8

ETEC511- IP3

ETEC511- IP5

1. Who were these people, and how did/does each contribute to the development of artificial intelligence? How did/does each think “intelligence” could be identified?
   

   My Response

   ChatGPT’s Response

   Alan Matheson Turing Alan Turing was a British cryptanalyst and mathematician who helped with decrypting Enigma machines during World War 2. He made foundational contributions to artificial intelligence and early computers. These contributions include the imitation game, universal machines, and teaching machines (Turing, 1950). He thought that intelligence could be identified by behaviour, a machine’s ability to respond indiscernibly from human responses. John McCarthy John McCarthy was an American cognitive and computer scientist who coined the term ‘artificial intelligence.’ His contributions to Artificial Intelligence include the organization of the Dartmouth Conference in 1956, the List Processing programming language, and pioneering Commonsense and Nonmonotonic Reasoning (McCarthy, 2007). He beliieved that intelligence could be identified by a system’s ability to use logic to reason, draw conclusions, and solve problems (McCarthy, n.d.). Herb Simon Herb Simon was an American computer scientist, cognitive psychologist, and economist known for his theory of  bounded rationality, which won  him the Nobel Prize (UBS, n.d.). His contributions to Artificial Intelligence include the Logic Theorist problem-solving program, the Physical Symbol Systems, and the General Problem Solver (Turner, 2023). He believed that intelligence could be identified by a system’s ability to reason and problem-solve rationally. Marvin Minsky Marvin Minsky was an American computer scientist and mathematician who co-founded the Artificial Intelligence Lab at the Massachusetts Institute of Technology (BBC News, 2016). His contributions to Artificial Intelligence include the first neural-network learning machine, The Society of Mind, and the confocal scanning microscope (BBC News, 2016). He believed that intelligence could be identified through the concept of frames, which involved articulating commonsense when programming directions for a computer. Timnit Gebru Timnit Gebru is an Eritrean Ethiopian-born computer scientist and ethics researcher known for cofounding the Black in AI  affinity group. Her contributions to Artificial Intelligence include co-authoring papers on the ethical risks of large language models and cofounding the Distributive AI Research Institute (DAIR, n.d.). She believed that intelligence could be identified as being equitable and ethical.

   Alan Matheson Turing Turing pioneered theoretical computer science and proposed the “Turing Test” to identify machine intelligence through conversational indistinguishability from humans. He laid foundational concepts for AI, suggesting that machines could simulate any cognitive process, challenging assumptions about human uniqueness in reasoning and logic. (OpenAI, 2025) John McCarthy Known as the “father of AI,” McCarthy coined the term “artificial intelligence” and developed LISP, a key AI programming language. He believed intelligence involved logical reasoning and abstract problem-solving, and that machines could simulate aspects of human thinking by manipulating symbols and using formal logic. (OpenAI, 2025) Herb Simon Simon, a cognitive psychologist and computer scientist, viewed intelligence as bounded rationality—problem-solving within constraints. He co-developed early AI programs like Logic Theorist and emphasized decision-making as central to intelligent behavior. He believed intelligence could be replicated by modeling human cognitive processes computationally. (OpenAI, 2025) Marvin Minsky Minsky co-founded the MIT AI Lab and advanced the idea of intelligence as a product of many simple processes. He viewed the mind as a “society” of agents, each performing different tasks. Intelligence, for him, was not unitary but emergent from interconnected mental functions. (OpenAI, 2025) Timnit Gebru Gebru is known for her work on AI ethics, algorithmic bias, and fairness. She critiques AI’s societal impacts, emphasizing that intelligence cannot be separated from context, data, and power dynamics. She challenges narrow technical definitions of intelligence, advocating for more inclusive, socially-informed perspectives in AI development. (OpenAI, 2025)

2. How do “machine (programming) languages” differ from human (natural) ones?

My Response

ChatGPT’s Response

Rooted in 1s and 0s, machine languages are precise, have one meaning and do not have room for mistakes or improvisation (Harris, 2018). While their written code can vary in style depending on its programmer, machine languages will either error or run the program as written. In contrast, human or natural languages are ambiguous and coloured by accents, culture, emotion, intonation, and body language. They are full of jargon, slang, annd errors in spelling, pronunciation, and punctuation, and make communication from one person to another unique (Harris, 2018).

Machine (programming) languages are structured, formal systems with precise syntax and semantics designed to communicate instructions to computers. They lack ambiguity and are strictly interpreted by machines. In contrast, human (natural) languages are rich, flexible, and often ambiguous, evolving over time and influenced by culture, emotion, and context. While programming languages prioritize logic and clarity for machine execution, natural languages are optimized for human expression, social interaction, and nuanced communication. Humans can infer meaning even with incomplete information; machines require exact instructions to function correctly. (OpenAI, 2025)

3. How does “machine (artificial) intelligence” differ from the human version?

My Response

ChatGPT’s Response

Machine intelligence is rooted in datasets and algorithms. It follows specific sets of parameters when problem-solving (Chollet, 2019). Human intelligence is shaped by prior knowledge and lived experiences and is flexible and adaptable  (Chollet, 2019). When compared, machine intelligence may seem to perform better on specific tasks, but its ability to do so depends on the data it contains, not how well it learned the information and what is achieved based on what it has learned.

Machine (artificial) intelligence operates through algorithms and data, excelling at narrow, specific tasks like pattern recognition or calculations. It lacks consciousness, self-awareness, and genuine understanding. Human intelligence, by contrast, includes emotions, intuition, ethical reasoning, and the ability to generalize from limited information. It is embodied, adaptive, and shaped by experience and social context. While AI can outperform humans in speed and scale for certain tasks, it does not possess creativity, empathy, or a sense of purpose—hallmarks of human cognition. AI mimics aspects of intelligence, but does not experience or understand the world as humans do. (OpenAI, 2025)

4. How does “machine learning” differ from human learning?

My Response

ChatGPT’s Response

Machine learning occurs through hard-coding existing knowledge or extensive training sets (Chollet, 2019), and its proficiency depends on the quality of the input data (Heilweil, 2020). Humans, on the other hand, learn through prior knowledge, experiences, perspectives, and biases and are able to adapt contextually and in accordannce with their surroundings. Humans are able to learn as they grow and with time, but machines require continuous updates in order to learn. Machines can also learn and amplify the bias found in their data, which can be difficult to detect as it is rooted in their algorithm (Buolamwini, 2019). In contrast. humans, although biased, are able to reflect on and address those same biases more readily than a machine can.

Machine learning involves training algorithms on large datasets to detect patterns and make predictions. It relies on statistical methods, optimization, and feedback loops but lacks comprehension. Human learning integrates sensory input, memory, emotion, and social interaction, often with minimal data. Humans generalize better from few examples and apply knowledge across contexts. Human learning is driven by curiosity, motivation, and personal meaning, while machine learning is task-specific and objective-driven. Moreover, humans reflect on what they learn, while machines adjust outputs based on data without understanding. Machine learning is powerful, but fundamentally mechanical and context-blind. (OpenAI, 2025)

5. How do YOUR answers to these questions differ from what a machine could generate? make sure you reference the specific differences between your answers and ChatGPT’s.

My answers differ from what ChatGPT generated because of how they were retrieved. I read the required readings, the suggested articles, and several other sites before I began to formulate my answer. My responses were composed after I compiled both relevant and irrelevant information to broaden my understanding of specific individuals and topics. This adaptive process allowed me to answer the questions above in a reflective, interpretive, and evolving manner, which ChatGPT struggles to do (Choolet, 2019). My response is written in my voice, which has been moulded and shaped by the many courses I have taken and who I am as a person. It is also based on my interpretation and understanding of the information I read in preparation for this assignment, my prior knowledge, and my point of view. Unlike ChatGPT’s answers, which were generated fairly quickly based on the task at hand and provided no sources or citations, my answers took time and  effort, where  my  words were carefully chosen, reflected  upon, revised, and reviewed before they were finalized.

References

BBC News. (2016, January 26). AI pioneer Marvin Minsky dies aged 88.

Buolamwini, J. (2019, February 7). Artificial intelligence has a problem with gender and racial bias. Here’s how to solve it. Time.

Chollet, F. (2019, November 5). On the measure of intelligence.

DAIR. (n.d.). Team. https://www.dair-institute.org/team/

Dennis, M. A. (2025, May 29). Artificial Intelligence. Encyclopædia Britannica. https://www.britannica.com/technology/artificial-intelligence

Harris, A. (2018, October 31). Languages vs. programming languages. Medium.

McCarthy, J. (n.d.). General information. Professor John McCarthy – General Information. http://jmc.stanford.edu/general/index.html

OpenAI. (2025). ChatGPT (Jun 1 version) [Large language model]. https://chatgpt.com

Turing, A. M. (1950). Computing, machinery and intelligence. Mind, 49(236), 433-460.

Turner, V. (2023, September 6). How Herbert Simon’s work on artificial intelligence continues to impact today’s AI Technologies. Medium. https://pub.aimind.so/how-herbert-simons-work-on-artificial-intelligence-continues-to-impact-today-s-ai-technologies-149dcaf98eb9

ETEC511- IP1