Machine languages fundamentally differ from human languages in that they rely on strict predefined structures, do not evolve organically, and are not influenced by cultural considerations. They are systems of symbols and rules designed to enable humans to control a machine's actions and output. In contrast, human languages take many forms and convey subtle nuances including body language, spoken language, and tonal or emotional inflections, all of which can vary widely in interpretation. While both machine and human languages serve the purpose of communication, machine languages are deterministic and task-oriented, while human languages are expressive, flexible, and shaped by social and cultural contexts (Harris, 2018).
Unlike human intelligence, machine intelligence seems to be directly proportional to the confines of its programming and data. The output of machine intelligence depends entirely on pre-existing inputs, leaving little room for creativity and abstract reasoning. Comparatively speaking, machines cannot transfer knowledge or develop new and original ideas, thereby making AI inherently limited in depth and adaptability in foreign contexts (Crawford, 2021).
Human learning is multidimensional and shaped by sensory input, experiences, and social contexts that collectively influence how our knowledge and understanding of the world develop. In contrast, machine learning occurs in two main ways: by encoding prior knowledge directly into the system or by training it on large datasets to improve task-specific objectives (Chollet, 2019).
In working through the questions above, my approach differed from that of a machine as I was able to explore multiple sources and make my own judgments rather than rely on narrowly focused or surface-level materials and understandings. While AI depends on structured data and human-codified search parameters, it does not reflect on its own biases or adapt its approach when responding to questions. This refinement of self-awareness through writing is inherently a human based process which is something I believe generative AI will never be able to fully replicate.
References
BBC. (2016a, January 26). Ai pioneer Marvin Minsky dies aged 88. BBC News. https://www.bbc.com/news/technology-35409119
Chollet, F. (2019, November 25). On the measure of Intelligence. arXiv.org. https://arxiv.org/abs/1911.01547
Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
Turing, A. M. (1950). Computing, machinery and intelligence. Mind, 49(236), 433-460.