Advances & challenges in foundation agents: Section 1.2 – A parallel comparison between human brain and AI agents

This article is Chapter 1, Section 1.2 of a series of articles featuring the book Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems.

The rapid integration of LLMs into intelligent agent architectures has not only propelled artificial intelligence forward but also highlighted fundamental differences between AI systems and human cognition. As illustrated briefly in Table 1.1, LLM-powered agents differ significantly from human cognition across dimensions such as underlying “hardware”, consciousness, learning mechanisms, creativity, and energy efficiency. However, it is important to emphasize that this comparison provides only a high-level snapshot rather than an exhaustive depiction. Human intelligence possesses many nuanced characteristics not captured here, while AI agents also exhibit distinct features beyond this concise comparison.

Human intelligence operates on biological hardware, the brain, that demonstrates extraordinary energy efficiency, enabling lifelong learning, inference, and adaptive decision-making with minimal metabolic costs. In contrast, current AI systems require substantial computational power, resulting in significantly higher energy consumption for comparable cognitive tasks. Recognizing this performance gap emphasizes energy efficiency as a critical frontier for future AI research.

Human learning is continuous, interactive, and context-sensitive, deeply shaped by social, cultural, and experiential factors. Conversely, LLM agents primarily undergo static, offline batch training with limited ongoing adaptation capabilities. Despite research work using instruction tuning and reinforcement learning from human feedback (RLHF)¹, LLM agents still fall short of human-like flexibility. Bridging this gap through approaches such as lifelong learning, personalized adaptation, and interactive fine-tuning represents a promising research direction, enabling AI to better mirror human adaptability and responsiveness.

Creativity in humans emerges from a rich interplay of personal experiences, emotional insights, and spontaneous cross-domain associations. Emotional states not only motivate creative expression but also influence the originality, depth, and resonance of the outcomes, imbuing them with personal meaning and affective significance. In contrast, creativity in large language models (LLMs) stems primarily from the statistical recombination of training data (what might be described as “statistical creativity”). While often fluent and occasionally surprising, this form of creativity lacks emotional grounding, lived experience, and intentional originality. This contrast reveals opportunities for advancing AI agents by incorporating deeper contextual understanding, simulated emotional states, and experiential memory. Such developments could lead to more authentic and emotionally attuned creative processes.

In terms of consciousness and emotional experience, LLM agents lack genuine subjective states and self-awareness inherent to human cognition. Although fully replicating human-like consciousness in AI may not be necessary or even desirable, appreciating the profound role emotions and subjective experiences play in human reasoning, motivation, ethical judgments, and social interactions can guide research toward creating AI that is more aligned, trustworthy, and socially beneficial.

Considering the time scale, the human brain has evolved over millions of years, achieving remarkable efficiency, adaptability, and creativity through natural selection and environmental interactions. In stark contrast, AI agents have undergone rapid yet comparatively brief development over roughly 80 years since the advent of early computational machines. This parallel comparison between human cognition and AI systems is thus highly valuable, as it uncovers fundamental differences and provides meaningful insights that can guide advancements in AI agent technologies. Ultimately, drawing inspiration from human intelligence can enhance AI capabilities, benefiting humanity across diverse applications from healthcare and education to sustainability and beyond.

Next part: Section 1.2.1 – Brain functionalities and AI parallels.

Article source: Liu, B., Li, X., Zhang, J., Wang, J., He, T., Hong, S., … & Wu, C. (2025). Advances and challenges in foundation agents: From brain-inspired intelligence to evolutionary, collaborative, and safe systems. arXiv preprint arXiv:2504.01990. CC BY-NC-SA 4.0.

Header image: AI is Everywhere by Ariyana Ahmad & The Bigger Picture / Better Images of AI, CC BY 4.0.

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