Ancient Remedies, Modern Medicine: Bridging Traditional Wisdom and Contemporary Science for Holistic Health examines the evolving relationship between traditional medicine systems and modern biomedical science, positioning their integration not as an alternative pathway, but as a necessary progression in the pursuit of holistic healthcare. Rather than validating traditional practices solely through Western scientific frameworks, the paper argues for the development of shared methodologies that respect both empirical rigor and culturally embedded knowledge systems.
Through comparative analysis of Ayurveda, Traditional Chinese Medicine (TCM), and Indigenous healing practices alongside contemporary medical approaches, the paper identifies a persistent epistemological divide—and reframes it as a source of complementarity. It contends that the enduring global reliance on traditional medicine reflects not its inadequacy, but the limitations of reductionist models in addressing complex, multidimensional health needs.
Drawing on the WHO Global Traditional Medicine Strategy (2025–2034), emerging applications of artificial intelligence, and evolving ethical frameworks for Indigenous knowledge protection, the paper outlines pathways for integration through policy alignment, collaborative research, digital infrastructure, and governance mechanisms. It critically examines structural challenges—including regulatory asymmetries, cross-practice risks, biopiracy, and data bias—while assessing the role of technologies such as wearables, telemedicine, blockchain, and AI-assisted diagnostics as enabling infrastructure.
Ultimately, the paper advances a framework for integration that prioritizes patient safety, evidentiary integrity, and cultural respect, arguing that the future of healthcare will depend on the deliberate convergence of traditional wisdom and modern scientific innovation.

‍

AI Alignment with Human Preferences examines how the rapid advancement of generative AI and large language models has intensified the need to align AI systems with human values, intentions, and societal expectations. Written by Manas Talukdar, the paper surveys the evolving landscape of AI alignment research, tracing both the technical foundations of alignment methodologies and the ethical, governance, and operational challenges associated with integrating human preferences into AI systems. Drawing from academic literature and industry practice, it explores key approaches including supervised fine-tuning, reinforcement learning with human feedback (RLHF), direct preference optimization (DPO), constitutional AI, and human-in-the-loop systems, while analyzing their trade-offs in scalability, performance, safety, and implementation complexity.

The paper argues that AI alignment is not solely a technical optimization problem, but a broader socio-technical challenge involving questions of value representation, accountability, fairness, cultural relativism, privacy, and long-term societal impact. It highlights how the growing scarcity of high-quality training data has elevated the importance of human feedback and expert judgment in shaping next-generation AI systems. At the same time, it examines emerging risks such as reward hacking, distribution shift, adversarial manipulation, value lock-in, and scalable oversight limitations, positioning alignment as a critical frontier for the safe deployment of increasingly capable AI systems.

Intended for researchers, policymakers, technologists, and industry practitioners, the paper provides a comprehensive overview of the current state of AI alignment while identifying future research directions in mechanistic interpretability, adaptive alignment, multi-agent systems, governance frameworks, and aligned AGI development. Rather than presenting a single dominant solution, the paper concludes that effective alignment will likely depend on combining multiple methodologies within carefully designed institutional, technical, and ethical frameworks capable of evolving alongside increasingly advanced AI capabilities.