The human biological advantage over AI

• URL: http://arxiv.org/abs/2509.04130v1
• Date: Thu, 04 Sep 2025 11:54:27 GMT
• Title: The human biological advantage over AI
• Authors: William Stewart,
• Abstract summary: Recent advances in AI raise the possibility that AI systems will one day do anything humans can do, only better.<n>But a deeper consideration suggests the overlooked differentiator between human beings and AI is not the brain, but the central nervous system.<n>A CNS cannot be manufactured or simulated; it must be grown as a biological construct.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in AI raise the possibility that AI systems will one day be able to do anything humans can do, only better. If artificial general intelligence (AGI) is achieved, AI systems may be able to understand, reason, problem solve, create, and evolve at a level and speed that humans will increasingly be unable to match, or even understand. These possibilities raise a natural question as to whether AI will eventually become superior to humans, a successor "digital species", with a rightful claim to assume leadership of the universe. However, a deeper consideration suggests the overlooked differentiator between human beings and AI is not the brain, but the central nervous system (CNS), providing us with an immersive integration with physical reality. It is our CNS that enables us to experience emotion including pain, joy, suffering, and love, and therefore to fully appreciate the consequences of our actions on the world around us. And that emotional understanding of the consequences of our actions is what is required to be able to develop sustainable ethical systems, and so be fully qualified to be the leaders of the universe. A CNS cannot be manufactured or simulated; it must be grown as a biological construct. And so, even the development of consciousness will not be sufficient to make AI systems superior to humans. AI systems may become more capable than humans on almost every measure and transform our society. However, the best foundation for leadership of our universe will always be DNA, not silicon.

Related papers

• When Autonomy Breaks: The Hidden Existential Risk of AI [0.0]
  I argue that there is an underappreciated risk in the slow and irrevocable decline of human autonomy.<n>What may follow is a process of gradual de-skilling, where we lose skills that we currently take for granted.<n>The biggest threat to humanity is not that machines will become more like humans, but that humans will become more like machines.
  arXiv  Detail & Related papers  (2025-03-28T05:10:32Z)
• Evaluating Intelligence via Trial and Error [59.80426744891971]
  We introduce Survival Game as a framework to evaluate intelligence based on the number of failed attempts in a trial-and-error process.<n>When the expectation and variance of failure counts are both finite, it signals the ability to consistently find solutions to new challenges.<n>Our results show that while AI systems achieve the Autonomous Level in simple tasks, they are still far from it in more complex tasks.
  arXiv  Detail & Related papers  (2025-02-26T05:59:45Z)
• Aligning Generalisation Between Humans and Machines [74.120848518198]
  AI technology can support humans in scientific discovery and forming decisions, but may also disrupt democracies and target individuals.<n>The responsible use of AI and its participation in human-AI teams increasingly shows the need for AI alignment.<n>A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
  arXiv  Detail & Related papers  (2024-11-23T18:36:07Z)
• Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
  We examine what is known about human wisdom and sketch a vision of its AI counterpart.<n>We argue that AI systems particularly struggle with metacognition.<n>We discuss how wise AI might be benchmarked, trained, and implemented.
  arXiv  Detail & Related papers  (2024-11-04T18:10:10Z)
• Is artificial consciousness achievable? Lessons from the human brain [0.0]
  We analyse the question of developing artificial consciousness from an evolutionary perspective. We take the evolution of the human brain and its relation with consciousness as a reference model. We propose to clearly specify what is common and what differs in AI conscious processing from full human conscious experience.
  arXiv  Detail & Related papers  (2024-04-18T12:59:44Z)
• Keep the Future Human: Why and How We Should Close the Gates to AGI and Superintelligence, and What We Should Build Instead [0.20919309330073077]
  Advances in AI have transformed AI from a niche academic field to the core business strategy of many of the world's largest companies.<n>This essay argues that we should keep the future human by closing the "gates" to smarter-than-human, autonomous, general-purpose AI.<n>Instead, we should focus on powerful, trustworthy AI tools that can empower individuals and transformatively improve human societies' abilities to do what they do best.
  arXiv  Detail & Related papers  (2023-11-15T23:41:12Z)
• AI for Mathematics: A Cognitive Science Perspective [86.02346372284292]
  Mathematics is one of the most powerful conceptual systems developed and used by the human species. Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems.
  arXiv  Detail & Related papers  (2023-10-19T02:00:31Z)
• Natural Selection Favors AIs over Humans [18.750116414606698]
  We argue that the most successful AI agents will likely have undesirable traits. If such agents have intelligence that exceeds that of humans, this could lead to humanity losing control of its future. To counteract these risks and evolutionary forces, we consider interventions such as carefully designing AI agents' intrinsic motivations.
  arXiv  Detail & Related papers  (2023-03-28T17:59:12Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence [1.9143819780453073]
  The benefits of human-like artificial intelligence include soaring productivity, increased leisure, and perhaps most profoundly, a better understanding of our own minds. But not all types of AI are human-like. In fact, many of the most powerful systems are very different from humans. As machines become better substitutes for human labor, workers lose economic and political bargaining power. In contrast, when AI is focused on augmenting humans rather than mimicking them, then humans retain the power to insist on a share of the value created.
  arXiv  Detail & Related papers  (2022-01-11T21:07:17Z)
• Making AI 'Smart': Bridging AI and Cognitive Science [0.0]
  With the integration of cognitive science, the 'artificial' characteristic of Artificial Intelligence might soon be replaced with'smart' This will help develop more powerful AI systems and simultaneously gives us a better understanding of how the human brain works. We argue that the possibility of AI taking over human civilization is low as developing such an advanced system requires a better understanding of the human brain first.
  arXiv  Detail & Related papers  (2021-12-31T09:30:44Z)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)
• Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles [95.58955174499371]
  We describe various aspects of multiple human intelligences and learning styles, which may impact on a variety of AI problem domains. Future AI systems will be able not only to communicate with human users and each other, but also to efficiently exchange knowledge and wisdom.
  arXiv  Detail & Related papers  (2020-08-07T21:00:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.