Comparing Human Thought Patterns to Those of AI during the Processing of Data

Wedero Quoek^*
*Correspondence: Wedero Quoek, Department of Economic and Regional Sciences, Hungarian University of Agriculture and Life Sciences, Pater Karoly Street 1, 2100 Godollo, Hungary, Email:

Introduction

The processing of data, a fundamental cognitive task, has witnessed a paradigm shift with the rise of Artificial Intelligence (AI). As AI systems become more sophisticated, drawing inspiration from human thought patterns, it prompts an intriguing examination of the parallels and distinctions between human cognition and AI data processing. This article delves into the realms of human and AI thought processes during data processing, exploring the strengths, limitations, and potential convergence of these two distinct modes of understanding. Human cognition is a complex interplay of perception, reasoning, memory, and creativity. When processing data, humans rely on a network of interconnected cognitive processes that allow them to make sense of information, identify patterns, and draw conclusions [1,2]. The process often begins with perception – the reception of sensory information – followed by the organization of data into meaningful categories, which aids in forming mental representations of the information. Reasoning is a cornerstone of human cognition during data processing. Humans utilize deductive, inductive, and abductive reasoning to draw conclusions based on existing knowledge and information [3].

Description

This process involves pattern recognition, critical thinking, and the application of logic. Moreover, memory plays a vital role as humans rely on their long-term and working memory to access relevant information, compare it with new data, and establish connections between past experiences and current observations. Creativity is another integral aspect of human thought patterns in data processing. Humans possess the ability to think beyond the presented data, generating novel insights and perspectives [4]. This imaginative capacity allows for innovative problem-solving and the generation of new hypotheses that might not be immediately evident from the data at hand. Artificial Intelligence, on the other hand, processes data through algorithms and computational models. AI systems, particularly those based on deep learning and neural networks, attempt to simulate certain aspects of human cognitive processes. However, there are fundamental differences in how AI and humans process data. AI systems, such as neural networks, process data in layers, each layer extracting different levels of abstraction from the input data. As AI continues to advance, ethical and societal considerations become paramount. The question of agency and responsibility arises when AI systems make decisions based on data. Ensuring transparency in AI decisionmaking processes, as well as addressing biases embedded in training data, is crucial. Additionally, the potential displacement of human jobs by AI systems demands discussions about reskilling and redefining the nature of work in a world increasingly reliant on automation [5,6].

Conclusion

In the grand tapestry of data processing, human thought patterns and AI algorithms are two distinct threads, each with its strengths and limitations. While AI systems excel in rapid data processing and pattern recognition, human cognition brings creativity, empathy, and ethical considerations to the table. The convergence and collaboration between these modes of understanding offer a glimpse into the future of data processing, where AI enhances human capabilities and augments decision-making processes. As technology continues to evolve, it is imperative to navigate the ethical, societal, and philosophical implications to ensure that the synergy between human thought and AI data processing leads to a more enlightened and equitable future.

Acknowledgement

None.

Conflict of Interest

None.

References

1. Ouyang, Fan, Luyi Zheng and Pengcheng Jiao. "Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020." Educ Inf Technol 27 (2022): 7893-7925.

Google Scholar, Crossref, Indexed at

2. Tang, Kai-Yu, Ching-Yi Chang and Gwo-Jen Hwang. "Trends in artificial intelligence-supported e-learning: A systematic review and co-citation network analysis (1998–2019)." Interact Learn Environ 31 (2023): 2134-2152.

Google Scholar, Crossref, Indexed at

3. Donthu, Naveen, Satish Kumar, Debmalya Mukherjee and Nitesh Pandey, et al. "How to conduct a bibliometric analysis: An overview and guidelines." J Bus Res 133 (2021): 285-296.

Google Scholar, Crossref, Indexed at

4. Thurmond, Veronica A. "The point of triangulation." J Nurs Scholarsh 33 (2001): 253-258.

Google Scholar, Crossref, Indexed at

5. Smith, Andrew E. and Michael S. Humphreys. "Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping." Behav Res Methods 38 (2006): 262-279.

Google Scholar, Crossref, Indexed at

6. Kotsiantis, Sotiris, Christos Pierrakeas and Panagiotis Pintelas. "Predicting students'performance in distance learning using machine learning techniques." Appl Artif Intell 18 (2004): 411-426.

Google Scholar, Crossref, Indexed at