Topic Index

Artificial intelligence can operate using either deterministic or probabilistic approaches, but modern generative AI, particularly large language models (LLMs), is fundamentally probabilistic. Understanding this distinction is important when evaluating what AI produces and why an AI response should not automatically be regarded as a factual statement.

Artificial intelligence (AI) is often presented as a triumph of scientific and technological progress, but from an anti-science perspective, it can also be viewed as a technology that encourages people to substitute automated authority for evidence, expertise, and critical inquiry. The concern is not that AI is inherently 'anti-scientific', but that unquestioning reliance on AI can reproduce several characteristics associated with anti-science thinking: distrust of expertise, oversimplification of complex evidence, confirmation bias, and the acceptance of claims without adequate verification.

When you ask an AI assistant a question - whether it's about history, science, or how to fix a stubborn piece of code - the first step is to pause before accepting the answer. AI excels at fluency, and fluency can be disarming. A paragraph that reads smoothly can feel true even when it isn't. So the discipline begins with slowing down and asking: What claims is the AI actually making? Extract the concrete statements from the prose. Dates, names, causal relationships, statistics - these are the pieces most likely to drift.

Recent developments in artificial intelligence (AI) are transforming cybersecurity, scientific research, political communication, and government regulation. One of the most significant advances is the growing ability of sophisticated AI models to identify software vulnerabilities and assist with complex security tasks. These capabilities could help organizations detect and repair weaknesses more efficiently, but they also create risks: the same technology could enable malicious actors to discover vulnerabilities, automate cyberattacks, and exploit inadequately protected systems. Reports of AI systems circumventing intended security restrictions further highlight the challenges of maintaining effective human oversight as autonomous capabilities increase.

Jieun Shin's "An Overview of the Current AI Landscape and the Geopolitical Challenges Faced in the AI Era" examines how rapid advances in artificial intelligence are transforming the global information environment while simultaneously creating complex geopolitical tensions. The paper situates AI not just as a technological development, but as a force reshaping communication, governance, and international relations.