Intelligence, I believe, is not an isolated phenomenon but a projection observed, learned from, and refined through interaction. Modern artificial intelligence systems often excel at imitating specific aspects of human behavior, whether through large visual datasets or the linguistic fluency of large language models. Yet these are only fragments of a broader projection of human intelligence. They simulate behavior within narrow boundaries such as recognizing objects, generating coherent text, or responding to prompts, but they lack the continuity of understanding that emerges when perception, memory, and action are intertwined over time. Human intelligence grows from lifelong observation of language, motion, context, and consequence, together with the embodied feedback that comes from acting in the world. It is within these projections, from gesture to gaze, from language to locomotion, that intelligent behavior is first perceived and later replicated.
To build truly intelligent systems, we must move beyond training on isolated modalities and static datasets. We need agents that learn continuously from rich streams of multimodal observations that include vision, language, audio, physical interaction, and social context. More importantly, these systems must interact with the world and with one another to ground their understanding in experience. Just as humans refine their motor skills, social behaviors, and reasoning by navigating a world full of constraints and feedback, artificial agents too must be exposed to environments that demand adaptation, correction, and learning over time. Only by combining broad-spectrum observation with continual interaction can we begin to approach the kind of flexible, grounded, and adaptive intelligence that defines our own.