John McCarthy (1927-2011): He is one of the “founding fathers” of artificial intelligence. He developed machine languages such as Lisp and ALGOL. He invented garbage collection ( automatic memory management)  and time-sharing technology (servers). He commented that “human material progress is desirable and sustainable.” In 2001, he released his predict on robot’s emotions and social networking in the future. (“John McCarthy – computer scientist,”)

Herb Simon (1916-2001): He is a pioneering in AI. In 1950s, he worked on AI and cognitive science. Simon’s models of decision-making influenced early AI and lead him to win Nobel prize. In the Sciences of the artificial he framed intelligence as problem solving. Simon concentrated on digging deeper to understand data.Simon was interested in how humans learn and, with Edward Feigenbaum, he developed the EPAM (Elementary Perceiver and Memorizer) theory, one of the first theories of learning to be implemented as a computer program.(“Herbert A. Simon,” ; “Herbert Simon,”)

Marvin Minsky (1927-2016): As one of the pioneers of robotics and telepresence, Minsky used computational ideas to understand human thinking processes and simulated it in machines. His studies have led to practical advances in the fields of artificial intelligence, cognitive psychology, and neural networks. Minsky proposed the “community of mind” theory, which is influenced by the developmental psychology of children and insights gained from the field of artificial intelligence. He mentioned that intelligence is the result of the interaction of diverse specialists. In 2006, he introduced The Emotion Machine, which includes a framework to explain human emotions and conscious thoughts in various (“AI pioneer Marvin Minsky dies aged 88,” 2016).

 Timnit Gebru (1982): A computer scientist born in Ethiopia, as one of the founders of Black in AI, has made extensive efforts to promote the active participation of Black and people of color in the field of artificial intelligence. As an expert in the ethics of artificial intelligence, she tries to enhance the capabilities of AI for the improvement of society. Her studies on large language models have highlighted the risks associated with using them in understanding fundamental concepts and the potential for misuse. She mentioned that AI models could generate misinformation or mistranslation, leading to laziness in people due to the convenient use of these models (Hao, 2020).

– John McCarthy, a key figure in AI history, coining the term “artificial intelligence” in 1955, propelled the field. His influence extends to developing the Lisp programming language and founding the Stanford AI Lab. McCarthy advocated for the idea that intelligence is computational and introduced the concept of “computer as a universal machine for all cognitive processes.”

– Herb Simon, a Nobel laureate and AI pioneer, emphasized cognitive psychology’s integration into artificial intelligence. His work on problem-solving algorithms and the “thinking machine” concept shaped AI’s early landscape. Simon proposed that intelligence is a product of rule-based symbol manipulation, influencing AI research and defining intelligence as a computational process.

– Marvin Minsky, a trailblazer in AI, co-founded MIT’s AI Lab and developed foundational ideas in perception and learning. His contributions include the concept of neural networks and co-authoring the influential book “Perceptrons.” Minsky viewed intelligence as a collection of interacting mechanisms, inspiring diverse AI research and shaping our understanding of machine intelligence.

– Timnit Gebru, a leading AI researcher, co-founded Black in AI to address diversity in the field. Her work at Google focused on ethical AI, shedding light on bias and accountability. Gebru advocates for inclusive AI development and redefines intelligence by emphasizing fairness, transparency, and social responsibility in machine learning systems.

I will categorize the differences between these two into three categories. Regarding language, both machine and human discussions encompass the diverse applications of their respective languages. However, human responses explore the origins of language creation, emphasizing that human language evolves from thousands of years of collective experience, with a focus on socialization. In contrast, machine languages, are made by computer experts, serves specific tasks, so human communication falls a secondary priority. In terms of intelligence, human responses highlight the significance of emotion, human concepts, creativity, and dreaming. In the context of learning, human responses focus attention on the role of bidirectional communication between learners and educators, integrating explanations that take into consideration structural variations such as age and gender.

Generally, human responses prioritize human behavior’s impact, while machine responses prioritize correctness, regardless of cultural diversity, emphasizing a fundamental distinction in their perspectives and considerations.