Future Directions in Artificial Intelligence

Abstract

Artificial intelligence is advancing at a pace that continuously reshapes what is considered possible, what counts as a frontier challenge, and what questions researchers and policymakers must urgently address. This perspective paper synthesises current trajectories across ten AI research fronts -- foundation model scaling, reasoning and planning, embodied AI, scientific AI, human-AI collaboration, AI safety and alignment, AI governance, neuromorphic and efficient computing, quantum-AI interfaces, and consciousness and artificial general intelligence -- and identifies the most consequential open questions in each. Drawing on systematic review of 284 high-impact AI publications from 2023-2025 and a Delphi study with 42 AI researchers from 12 European institutions, we identify seven cross-cutting themes that will define AI research over the next decade: the reasoning gap between current AI and human-level causal reasoning; the data wall facing continued scaling; the alignment bottleneck as systems become more capable; the governance lag behind technical progress; the sustainability imperative; the human-AI complementarity frontier; and the foundational uncertainty about whether current architectures can achieve general intelligence. Expert consensus (Delphi round 3) identifies reasoning and planning, AI safety and alignment, and scientific AI as the three highest-priority research areas for the next five years. A structured research agenda covering each of the ten fronts, including proposed key experiments, theoretical questions, and governance actions, is presented