Multimodality in AI allows for the integration of audio, video, images, text, and code, enhancing the utility of AI systems.

Current AI systems lack the ability to acquire common sense and learn from natural sensors like video, which presents a significant challenge.

AI systems are trained on a vast database of human knowledge, which includes both positive and negative visions, making it essential to understand the implications of this information.

Human data might provide a head start for AI, but sustainable progress will depend on reinforcement learning as a continuous source of experience.

The success of multimodal models has narrowed the diversity of ideas in AI, leading to concerns about reaching the limits of usable human data.

To achieve superhuman intelligence, we may need to step away from relying solely on human data in AI development.

Building a successful team for spatial intelligence requires multidisciplinary expertise, including system engineering, machine learning, and computer graphics.

Pre-training and unsupervised learning enhance the model's broad-based intelligence and improve its generalization capabilities, complementing reasoning abilities that can be more variable.

AI technology enables change but requires human involvement to implement and teach new skills effectively.