Emerging neural graphic pipelines represent 3D scenes using neural networks. While they can tackle ill-posed reconstruction problems by learning from data, they fall short in rendering speed compared to traditional graphic primitives like mesh, which are more compatible with graphic infrastructures.

Thus, promising advancements can be made in next-generation neural graphics by developing (1) hybrid scene representations that marry neural-based and mesh-based ones to hit a better sweet spot, and (2) tools and libraries to accelerate emerging neural/hybrid representations on commodity hardware.

AI invites exciting innovations, especially in multi-modal personal assistants. These systems will adapt to individual needs and contexts, paving the way for personalized technology that understands us deeply. Imagine a world where your devices continually learn from you, leading to unprecedented convenience and quality of life.

However, the intimacy of these AI systems with our lives necessitates robust safeguards for personal information and privacy. AI advancements must go hand-in-hand with stringent data protection measures, ensuring that our smart future is respectful of individual rights.

Humans empower AIs as intelligent tools to handle various tasks, such as traditional classification, segmentation, and detection. Nowadays, large models of trillion parameters revolutionize the way we interact, making some of the magical tools of science fiction a reality, e.g., voice changers, tracking glasses, personal AR assistants, etc.

Also, those powerful models show a trend to unify multiple tasks into one shared backbone, making it easier to be accelerated by a dedicated chip for mobile computing scenarios like autonomous driving, AR/VR, etc.

As foundational AI models become more prevalent due to larger model sizes and advanced pretraining techniques, they’re shaping the mainstream of AI-driven applications. With their exceptional ability to generalize and adapt, I foresee a revolution in these applications.

Notably, like humans, AI-driven applications will soon be adaptable to a wide range of tasks with just a few data prompts and minimal resource tuning. Thus, the erstwhile notion of data-intensive and exhaustive tuning in AI could soon be history.