How Artificial Intelligence Feeds Human Intelligence

Artificial intelligence (AI) has been a trending topic of discussion in recent years owing to its rapid advancements and increasing integration into various aspects of human life, exploring how AI can feed human intelligence, pointing out its limitations, and indicating areas for future research. By analyzing academic sources, this essay evaluates the ethical outcomes that result from the proposed solutions and discusses whether AI is a global societal problem.
Artificial intelligence (AI) has become an integral part of modern society, with applications ranging from healthcare to finance, entertainment, and transportation. Its rapid advancement has sparked debate about the potential consequences and ethical implications of increasingly integrating AI into our lives. One of the critical questions that arises from this discussion is whether AI can feed on human intelligence, and if so, what are the limitations and areas for future research? (Bostrom, 2014; Russell & Norvig, 2020).
This essay addresses this research question by drawing from academic sources and discussing the ethical outcomes of the proposed solutions. AI and human Intelligence: Understanding Artificial intelligence can be understood as a set of algorithms and computational models designed to perform tasks that typically require human intelligence, such as problem-solving, pattern recognition, and decision-making (Bostrom, 2014). AI can enhance human capabilities in various domains by supplementing human intelligence through automation and optimization.
Thesis Statement:
Artificial Intelligence, while already playing a pivotal role in augmenting human intelligence and offering considerable potential for societal advancement, necessitates a careful, ethical approach to development and implementation to address its inherent limitations, mitigate potential biases, and ensure fairness and transparency.
Annotated Bibliography:
Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
This book provides a comprehensive overview of the potential future of AI, discussing both its immense benefits and significant risks. The author emphasizes the need for proactive management of AI development to prevent disastrous outcomes, which contributes to the thesis by highlighting the importance of ethical considerations in AI advancement.
Russell, S. J. and Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson.
This textbook is a foundational resource in the field of AI and provides detailed explanations of AI techniques and their applications. It contributes to the thesis by offering a thorough understanding of how AI can augment human intelligence and emphasizing the ethical implications of AI integration into society.
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomy, opportunities, and challenges of Responsible AI. Information Fusion, 58, 82-115.
This study offers a deep dive into the field of Explainable AI (XAI).

AI feeds human intelligence by augmenting cognitive abilities such as memory and decision-making. AI-powered tools, such as personal assistants and recommendation systems, enable humans to process information more efficiently and make better informed decisions (Russell & Norvig, 2020). For instance, AI-driven medical diagnosis systems can analyze vast amounts of patient data, provide healthcare professionals with valuable insights, and help them make more accurate diagnoses (Esteva et al., 2019). Another avenue through which AI can feed human intelligence is facilitating learning and knowledge acquisition.
AI-powered educational tools such as adaptive learning platforms can provide personalized learning experiences tailored to individual students’ needs and abilities (Cukurova et al., 2019). Moreover, AI can help researchers in various fields by automating data analysis and pattern detection, thereby accelerating the pace of scientific discovery (Gil et al., 2019). Limitations of AI Despite its potential benefits, there are limitations to the current state of AI.
One significant concern is the lack of explain ability and interpretability of AI algorithms, particularly in deep learning models (Arrieta et al., 2020), which can lead to difficulty in understanding how AI systems arrive at their conclusions, limiting their ability to trust and effectively integrate AI into human decision-making processes. Another limitation is the potential for bias and discrimination in the AI systems.
AI algorithms often rely on large datasets to learn patterns and make decisions; if these datasets contain biases, the AI system can perpetuate and amplify these biases (Crawford & Calo, 2016). However, this can lead to unfair outcomes and exacerbate existing social inequalities. Future Research Directions: Future research on AI should address its limitations and explore new ways to integrate AI more effectively and ethically into human life. Some potential areas for research include the following.

  1. Developing more explainable and interpretable AI models to facilitate better human-AI collaboration (Arrieta et al., 2020).
  2. Investigating methods to mitigate biases and ensure fairness in AI systems (Crawford & Calo, 2016).
  3. Exploring new AI applications in mental health, environmental sustainability, and social justice to promote positive societal outcomes (Raghavan et al., 2020).
  4. Examining the long-term consequences of AI adoption on human skill development and labor market dynamics and designing policies to support workers during the transition to an AI driven economy (Arntz et al., 2016).

Ethical Outcomes:
Evaluating the ethical outcomes of AI-driven solutions requires a careful examination of their potential benefits and risks. Although AI can significantly enhance human intelligence and decision-making, it also poses challenges related to transparency, accountability, and fairness (Cath et al., 2018). Ethical AI adoption requires a multipronged approach, including responsible AI design, public policy interventions, and increased awareness among AI users and developers.

Is AI a Global Societal Problem?
Whether AI is a global societal problem depends on one’s perspective or not. AI offers numerous benefits such as improved efficiency, productivity, and access to information. However, the rapid adoption of AI technologies could exacerbate existing social inequalities, displace human workers, and raise concerns about privacy and security (Bostrom, 2014; Russell & Norvig, 2020).

Ultimately, the rise of AI presents both challenges and opportunities. By addressing its limitations and focusing on ethical adoption, AI can positively impact society and feed human intelligence.

The rise of artificial intelligence has sparked debate regarding its potential consequences and ethical implications. This essay explores how AI can feed on human intelligence, pointing out its limitations, and indicating areas for future research. By analyzing academic sources, we evaluate the ethical outcomes that result from the proposed solutions and discuss whether AI is a global societal problem. As AI advances, researchers, policymakers, and society must engage in an ongoing dialogue about the responsible use and development of AI technologies.


Arntz, M., Gregory, T., & Zierahn, U. (2016). The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis. OECD Social, Employment and Migration Working Papers, No. 189.

Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., … & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities, and Challenges toward Responsible AI. Information Fusion, 58, 82–115.

Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.

Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2018). Artificial Intelligence and the ‘Good Society’: The US, EU, and UK Approach. Science and Engineering Ethics, 24(2), 505-528.

Seven Trends in Artificial Intelligence in 2023.

Ordikhani, M., Prugger, C., Hassannejad, R., Mohammadifard, N., & Sarrafzadegan, N. (2022). An evolutionary machine learning algorithm for cardiovascular disease risk prediction. PLoS One, 17(7), e0271723.

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