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 answer ChatGPT
Alan Mattison Turing Turing laid the foundational concepts of early artificial intelligence. By proposing a “learning machine” modeled after a child’s mind that learns through experience, he pioneered early machine learning concepts. Turing thought intelligence is identified when a machine’s behavioral outputs are completely indistinguishable from those of a human (Turing, 1950). A British mathematician and computer scientist, Turing helped establish the foundations of modern computing. In Computing Machinery and Intelligence (1950), he proposed the Turing Test, suggesting intelligence should be identified through behavior: if a machine can converse indistinguishably from a human, it may be considered intelligent.
John McCarthy McCarthy is a founding pioneer of artificial intelligence and co-authored the proposal that introduced the term “AI.” He defined intelligence as the computational ability to achieve goals and solve problems in the world. Intelligence is identified through problem-solving efficacy, aiming to develop human-level intelligence within computer programs. An American computer scientist who coined the term “artificial intelligence” and organized the 1956 Dartmouth conference that launched AI as a field. McCarthy viewed intelligence as the computational ability to achieve goals and solve problems. He believed machines could exhibit intelligence through symbolic reasoning and knowledge representation.
Herbert Simon Herbert Simon was an AI pioneer who co-developed the Logic Theory Machine, considered the first artificial intelligence program. According to this work, intelligence is identified not through raw computation, but through the capacity to solve complex problems efficiently by employing human-like heuristic strategies (Newell et al., 1957) An economist, psychologist, and cognitive scientist, Simon pioneered AI with early problem-solving programs such as the Logic Theorist. He argued that intelligence involves information processing under real-world constraints, introducing the concept of bounded rationality. For Simon, intelligent behavior could be identified by effective problem solving and decision making.
Marvin Minsky Marvin Minsky built one of the earliest neural network learning machines, shaping modern AI’s vision. He argued that intelligence should not be measured by raw computational power. Instead, intelligence is identified by the flexibility to solve common, everyday problems, much like the adaptive learning observed in young children. A leading AI researcher and co-founder of the MIT Artificial Intelligence Laboratory. Minsky developed theories about how minds work, especially in his “Society of Mind” framework. He saw intelligence as emerging from many interacting mental processes rather than a single mechanism, emphasizing flexible reasoning and learning.

 
Timnit Gebru Timnit Gebru, former co-lead of Google’s ethical AI team, was forced out for co-authoring a paper criticizing the risks of LLM. Based on this literature, “intelligence” is identified as the capacity to comprehend subtle linguistic nuances and produce or interpret language that aligns with evolving cultural norms. An Ethiopian-American computer scientist known for her work on AI ethics, algorithmic bias, and the social impacts of large AI models. Gebru argues that intelligence cannot be evaluated solely through performance on technical tasks; responsible AI must also consider fairness, accountability, transparency, and the effects systems have on different communities.

References

Alan Turing. (2026). In Wikipedia. https://en.wikipedia.org/wiki/Alan_Turing

AI pioneer Marvin Minsky dies aged 88. (2016, January 26). BBC News. https://www.bbc.com/news/technology-35409119

Hao, K. (2020, December 4). We read the paper that forced Timnit Gebru out of Google. Here’s what it says. MIT Technology Review. https://www.technologyreview.com/2020/12/04/1013294/google-ai-ethics-timnit-gebru-paper/

Herbert A. Simon. (2026). In Wikipedia. https://en.wikipedia.org/wiki/Herbert_A._Simon

John McCarthy (computer scientist). (2026). In Wikipedia. https://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist)

Legacy of Alan Turing. (2026). In Wikipedia. https://en.wikipedia.org/wiki/Legacy_of_Alan_Turing

Logic Theorist. (2026). In Wikipedia. https://en.wikipedia.org/wiki/Logic_Theorist

Marvin Minsky. (2026). In Wikipedia. https://en.wikipedia.org/wiki/Marvin_Minsky

McCarthy, J. (2007). What is artificial intelligence? Stanford University. https://www-formal.stanford.edu/jmc/whatisai/

Newell, A., Shaw, J. C., & Simon, H. A. (1957). Empirical explorations of the logic theory machine: A case study in heuristic. Proceedings of the Western Joint Computer Conference, 218-230. https://doi.org/10.1145/1455567.1455605

Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433-460. https://doi.org/10.1093/mind/LIX.236.433

2. How do “machine (programming) languages” differ from human (natural) ones?
My Answer ChatGPT’s
While human languages rely on unconscious, fluid, and latent cognitive processes, machine programming languages are strictly engineered with rigid rules and definitions. In natural language communication, identical expressions can convey different meanings based on non-verbal cues. For instance, the phrase “Stop it” signals a playful interaction when delivered with a smile, but it conveys an entirely different meaning when accompanied by a serious expression and a firm tone. Conversely, programming languages leave no room for ambiguity or spontaneity, ensuring their essence remains completely unaltered by context. Machine (programming) languages and human (natural) languages differ in purpose, structure, and interpretation. Programming languages are designed for precise communication with computers and follow strict rules of syntax and logic. Every instruction must be unambiguous, and even small errors can prevent a program from running correctly. In contrast, natural languages evolved for human communication and are often flexible, context-dependent, and open to multiple interpretations. Humans can usually infer meaning despite mistakes, incomplete sentences, or ambiguity. While programming languages prioritize accuracy and predictability, natural languages prioritize rich expression, creativity, social interaction, and adaptation to different contexts.

References

Ana, (2018, October 31). Languages vs. programming languages. Medium. https://medium.com/@anaharris/human-languages-vs-programming-languages-c89410f13252

Languages vs. Programming languagesLinks to an external site. (Harris, 2018). 

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

Crawford, K. (2021). Atlas of AI. Yale University Press. (Introduction: pp. 1-21)

 

3. How does “machine (artificial) intelligence” differ from the human version? (~100 words).
My Answer ChatGPT’s
Human intelligence adapts and evolves flexibly under uncertainty, demonstrating a unique capacity to solve new problems by synthesizing prior knowledge with new information and emerging relationships. Conversely, machine(artificial) intelligence operates strictly within predefined rules and statistical probabilities derived from data. Consequently, when faced with completely new scenarios absent from its training sets or algorithms, AI is inherently less efficient than humans. While humans can intuitively navigate unfamiliar contexts through conceptual leaps, machine intelligence remains structurally limited by its parameters, lacking the organic, holistic adaptability that defines human intelligence. Machine (artificial) intelligence differs from human intelligence in its scope, adaptability, and learning processes. According to François Chollet (2019), many AI systems excel at specific tasks but rely heavily on large amounts of data and narrowly defined environments. Human intelligence, by contrast, is characterized by the ability to generalize knowledge, adapt to unfamiliar situations, and learn efficiently from limited experience. Humans can transfer skills across diverse domains and use reasoning, common sense, and creativity in novel contexts. Chollet argues that true intelligence is not simply task performance but the capacity to acquire and apply new skills effectively under varying conditions and constraints.

References

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 (pp. 17-37). De Gruyter Mouton.

Crawford, K. (2021). Atlas of AI. Yale University Press. (Introduction: pp. 1-21)

Chollet, F. (2019, November 5). On the measure of intelligence.Links to an external site.

 

4. How does “machine learning” differ from human learning? (~100 words) 
My Answer ChatGPT’s
The fundamental difference between machine and human learning lies in agency. Machine learning depends entirely on pre-allocated datasets, calculating statistical patterns without the agency to select its input, thereby unmitigatedly absorbing and replicating data biases.In contrast, human learning extends beyond data consumption. Humans possess the agency to critically evaluate, filter, and interrogate information. While biased data can influence human cognition, correcting it does not strictly require an equal volume of unbiased data. Instead, humans dynamically recalibrate their understanding by evaluating situational importance and contextual cues, an organic flexibility machines lack. Machine learning differs from human learning in how knowledge is acquired and applied. Machine-learning systems learn patterns from large datasets and optimize their performance based on statistical relationships. Humans, however, can learn from relatively few examples, draw on context and common sense, and reflect on ethical consequences. As Heilweil (2020) and Joy Buolamwini (2019) note, machine-learning systems can inherit and amplify biases present in their training data, leading to unfair outcomes. Unlike humans, machines do not inherently understand fairness, social norms, or the reasons behind their decisions. As a result, machine learning often requires careful oversight to ensure accuracy, accountability, and equity.

Reference

Heilweil, R. (2020, February 18). Algorithms and bias, explained. Vox. https://www.vox.com/recode/2020/2/18/21121286/algorithms-bias-discrimination-facial-recognition-transparency

 

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. (~200 words)

To be honest, much of my answers heavily relied on the provided materials. When I compared my answers to ChatGPT’s, I was surprised to see many similarities. I guess it is because I had pasted the exact questions and provided references into the AI.

However, the difference is that I tried to think about my own experiences and gave a real-world example. In Q2, I integrated an example (Stop it) based on my observation, while ChatGPT provided purely abstract definitions, probably because I didn’t ask it to. Furthermore, in Q4, I focused on the idea of “Agency,” not mentioned in the reading material while ChatGPT remained strictly bounded by the provided data. This contrast mirrors the exact limitations of machine intelligence I argued earlier.

Does this mean my answer looks clearly human? Maybe not. Honestly, I’m confused. The AI’s answer was more organized and explained better than mine. If I had been instructed to look completely different from the AI, I would include more personal experiences. Yet, I chose not to retroactively rewrite my responses. I decided to keep my raw, original answers to record my reflective journey, which is perhaps the most defining feature of my human intelligence.