In today's rapidly evolving landscape of artificial intelligence technology, the latest natural language processing systems launched by international tech companies have once again shaken the industry. Behind this technological revolution lies a perplexing cognitive puzzle—despite the sophistication of the most advanced learning algorithms, their decision-making processes remain shrouded in a foggy maze, making it difficult for researchers to discern their true nature.

The academic community is embarking on a unique "cognitive decryption" initiative. Recent publications in authoritative academic journals indicate that research teams are employing interdisciplinary approaches to reverse-engineer intelligent systems. Just as neuroscientists analyze neuronal activity, computer experts are monitoring the feature activation maps during algorithmic decision-making, attempting to reconstruct their thought processes. A laboratory team has developed real-time tracking technology that effectively identifies whether there are logical biases in the system's information processing, achieving an accuracy that meets practical standards.

In this cognitive revolution, the research paradigms of psychology are proving particularly valuable. A European research team has innovatively introduced conversation analysis methods into algorithm studies, designing interactive experiments in specific contexts to observe how intelligent systems' representations change when confronted with complex problems. Interestingly, some systems exhibit reasoning characteristics akin to humans: when solving multi-step problems, they autonomously form logical chains. However, deeper investigations reveal that the computational mechanisms underlying this appearance differ fundamentally from biological thinking—the systems tend to reach conclusions through data associations rather than causal reasoning.

Technological breakthroughs are gradually dismantling the barriers of algorithmic opacity. A feature deconstruction technology developed by an innovative team has successfully broken down basic units into recognizable cognitive features, paving new pathways for understanding the decision logic of intelligent systems. By establishing visual models of decision pathways, researchers can more accurately assess the reliability of these systems, which holds significant value for technological safety certification.

The collaborative exploration between academia and industry is reshaping the ethical framework of technology. An increasing number of experts are calling for the establishment of interpretability standards for intelligent systems, asserting that research institutions have an obligation to provide verifiable explanations for their decisions. Regulatory agencies are also formulating corresponding norms for technological transparency, striving to find a balance between innovative breakthroughs and social responsibility.

This technological journey to unveil the intelligent black box not only concerns the evolution of the technology itself but also touches upon the expansion of human cognitive boundaries. As algorithms gradually exhibit human-like thinking characteristics, we must maintain a rational perspective while embracing a new era of cognitive science with an open mind. This bidirectional decoding process will ultimately drive artificial intelligence toward a more controllable and trustworthy future. The unexplainability of AI makes it difficult for humans to make greater progress in studying models. Therefore, accelerating research and analysis on AI thinking is crucial. At the same time, leveraging new interdisciplinary technologies makes AI development more in line with human thinking, making it more helpful for social development. In addition, the development of AI is not a one-time event, but rather a long-term investment and research.

（Writer：Hoock）