Artificial intelligence has advanced at a breathtaking pace in recent years, with breakthroughs in areas like large language models, diffusion-based image generation, and multimodal systems transforming what we thought possible. As we look to the future of AI research, several promising directions are emerging. Beyond scaling neural networks to unprecedented sizes, researchers are exploring alternative approaches like modular and compositional architectures, including Mixture of Experts (MoE) models, neural symbolic integration, and modular training approaches. Moving beyond disembodied models, researchers are increasingly focusing on embodied AI systems that can interact with the physical world through sensorimotor learning, multimodal integration, and affordance learning. AI is also being applied to accelerate scientific discovery across disciplines, and researchers are exploring how AI can enhance human capabilities through cognitive augmentation, interpretable AI, and adaptive interfaces.