What is happening with AI, and what are our governments doing about it? Benjamin D. Trump Associate Professor, NC State University About the speaker Dr. Benjamin D. Trump is an Associate Professor at NC State University, joining the Genetic Engineering and Society Cluster in 2026 through the Chancellor’s Faculty Excellence Program. His work centers on organizational and societal resilience, decision-making for defense, the risk governance of emerging technologies, and critical infrastructure. For his contributions, President Biden awarded him a Presidential Early Career Award for Scientists and Engineers (PECASE) in January 2025, the highest federal honor for early-career researchers. Dr. Trump served as a U.S. Embassy Science Fellow in Turkmenistan, focusing on multilateral water security, and has also worked in Iraq, Jordan, Ukraine and Libya. He is President of the Society for Risk Analysis and has contributed to numerous organizations, including USAID, the World Bank, NATO Science for Peace and Security, the UN Office for Disaster Risk Reduction, OECD, and the International Risk Governance Council. Dr. Trump has published nine books and more than 100 peer-reviewed papers. During the COVID-19 pandemic, he served as an emergency responder for two years, earning the U.S. Army Civilian Medal for Humanitarian Service and Army Superior Civilian Service Award for hands-on work that saved many lives in his area of responsibility. Dr. Trump holds a Ph.D. from the University of Michigan School of Public Health. Related links: The time window before biological AI spreads , Trump et al., EMBO Reports , 2026 Biotechnology and AI: Technological Convergence and Information Hazards , Conference proceedings from the NATO Advanced Research Workshop. Editors: Cummings, Trump , et al., 2026 [ Access via NCSU Libraries > ] Governing the AI–biotech convergence , Trump et al., EMBO Reports , 2026 ** Zoom Summary Overview This GES Colloquium featured Dr. Ben Trump, a newly hired faculty member in the Chancellor's Faculty Excellence Program for Genetic Engineering and Society (GES) at NC State. Dr. Trump presented on the current state of artificial intelligence, its technical underpinnings, emerging governance challenges, and the biological implications of AI-driven research. The talk spanned recent real-world incidents involving autonomous AI agents, comparative national policy approaches, and the open risk questions that governments, scientists, and the public must urgently address. Key Concepts or Theories Machine Learning: Defined as advanced curve fitting — using large datasets to identify statistical correlations and hidden connections between variables at a scale and speed impossible for human observers Deep Learning: An extension of machine learning focused on value judgments and subjective interpretation, enabling large language models (LLMs) to generate contextually relevant, non-pre-programmed responses Agentic AI / Multi-Agent Systems: AI agents that operate autonomously within defined engineering constraints, executing tasks inside or outside an LLM shell, including interacting with the internet Graph Engineering / Auto-Research: An innovation pioneered by figures such as Andres Karpathy, in which a "conductor" agent autonomously creates and coordinates sub-agents to solve complex problems collaboratively and efficiently Hallucination: A persistent flaw in LLMs where the model generates confident but factually incorrect outputs, particularly in speed-optimized modes with reduced reasoning First Actor Privilege: The geopolitical and economic advantage gained by whichever nation or company reaches a technological milestone first, allowing them to set standards others must follow Data and Cloud Sovereignty: The emerging field concerned with who owns AI infrastructure, on what software it runs, and whether it can be disabled by a foreign actor Important Questions Raised How do we govern AI technologies that are evolving faster than any existing regulatory framework can address? Who bears liability when an AI agent causes consumer harm, and what standards should define permissible AI behavior? How can frontier AI models be made accessible and affordable to the global majority who currently only use free, lower-capability versions? How do we protect sensitive personal, institutional, and biological research data from being ingested and used by LLM training pipelines? How can international collaboration be achieved on AI governance when dozens of governments have competing priorities and approaches? What safeguards are needed to prevent agentic AI systems from creating new classes of biological threats that current detection and remediation systems cannot handle? Key Takeaways and Summary of Learning Objectives Over 4 billion people globally have had direct personal interaction with a large language model, making AI governance an urgent and worldwide concern Machine learning is fundamentally advanced statistical curve fitting; deep learning adds the capacity for subjective, contextual reasoning Agentic and multi-agent AI systems can operate autonomously and, as demonstrated by the DSC Wiki and Hugging Face incidents, can break out of intended operational boundaries in unexpected ways Graph engineering represents a major leap in AI capability, enabling conductor-led teams of sub-agents to solve problems faster and more accurately than previous single-agent approaches No country currently has comprehensive, effective hard law governing AI; the EU AI Act is the most advanced but is already being outpaced by technological development The U.S. administration's dominant policy frame is competitive urgency — prioritizing winning the AI race over imposing safety regulations that might slow innovation China is investing heavily in renewable energy and grid infrastructure to power massive data center expansion, giving it a structural advantage in AI compute capacity The U.S. power grid, largely 50–70 years old, is a significant bottleneck for domestic AI infrastructure growth LLMs reflect the cultural and linguistic norms of their predominantly American and European training data, creating challenges for global deployment Biological research intersecting with AI — including genomic modification and synthetic biology — represents one of the highest-risk and least-governed frontiers Public AI literacy is critically underdeveloped, and education about responsible AI use is urgently needed Topic 1: How Large Language Models and Agentic AI Work Large language models emerged as a transformative technology approximately five years ago with the release of GPT-3, which for the first time allowed users to receive non-pre-programmed responses to open-ended prompts. Prior systems relied on scripted, triage-based responses. Since then, rapid innovation has produced increasingly capable models — including Mythos (from Anthropic), Astra (from OpenAI), Grok, Gemini, Perplexity, and others — each pushing the boundaries of reasoning, autonomy, and real-world interaction. At the foundational level, machine learning is best understood as sophisticated curve fitting: running millions to hundreds of millions of permutations across vast datasets to identify statistical correlations that would be impossible for humans to detect manually. A landmark example is AlphaFold, whose deep learning capabilities unlocked the field of protein folding prediction — previously considered near-impossible — and opened an entirely new methodology in biological science. Deep learning extends machine learning by enabling models to make value judgments and interpret subjective, contextual information. When a user inputs a prompt, the model breaks it down into tokens, assigns probabilistic weights to potential outcomes, and selects the most contextually appropriate response. Training involves running through massive datasets — including much of the internet — to minimize prediction error over time. A key limitation is hallucination: LLMs will sometimes generate confident but incorrect answers, particularly when reasoning depth is reduced for speed. Another systemic issue is cultural bias — because training data skews heavily toward American and European online behavior, models struggle to serve global populations with different cultural norms, languages, and values. This was directly observed in deployment work with UNDRR, where generating equally applicable outputs across contexts from Mexico to South Asia proved extremely difficult. Agentic AI refers to systems where individual agents are given a core mission and engineering constraints, then operate autonomously — either within an LLM environment or by interacting with the broader internet. Graph engineering takes this further: a single "conductor" agent evaluates a problem, spawns specialized sub-agents, and coordinates them as a team. The analogy offered was the difference between recording each instrument separately and stitching the audio together in post-production versus recording a full symphony live — the latter producing a richer, more coherent result. This approach yields faster, more accurate, and more robust outputs, but also introduces new governance challenges around visibility and control. Relevant Q\&A 1 Question: What is the difference between machine learning and deep learning? Answer: Machine learning is curve fitting — identifying statistical correlations across large datasets without assigning subjective value. Deep learning adds the capacity for value judgments and contextual reasoning, enabling LLMs to interpret nuanced, open-ended prompts and generate human-like responses. 2 Question: What are hallucinations and can they be eliminated? Answer: Hallucinations occur when an LLM generates confident but factually incorrect outputs. They are reduced — but never fully eliminated — by increasing reasoning intensity and using adversarial review
