“Manifested AI” is
a term describing the physical embodiment of artificial intelligence in real-world hardware, like robots. Unlike traditional AI, which typically exists as software, manifested AI allows intelligent systems to move, interact, and learn within the physical world through robotics.
Core technologies
For AI to be “manifested,” it requires a combination of technologies that enable it to perceive and interact with the physical world. These include:
Neural networks: AI-powered robots use neural networks to make real-time decisions, adapt to new environments, and learn from experience.
Computer vision: Advanced vision systems and spatial awareness allow the robot to see and map its environment.
Sensors and actuators: Adaptive sensors provide inputs, and physical actuators control the robot's movement.
Edge computing: Processing information locally on the device (instead of in the cloud) is critical for real-time, physical reactions. Real-world examples
Manifested AI is already being used in several fields and has major implications for many more.
Autonomous vehicles: Self-driving cars perceive their surroundings and make driving decisions in real-time.
Humanoid robots: Projects like Tesla's Optimus and Boston Dynamics' Atlas are developing general-purpose robots that can perform complex, human-like tasks.
Warehouse automation: Robots are used to sort, retrieve, and transport items in fulfillment centers.
Surgical robotics: Robotic arms assist with delicate and precise operations. Impact and implications
The rise of manifested AI is expected to cause significant shifts in the economy and society.
Industrial impact: The convergence of AI and robotics could create a multi-trillion-dollar market by transforming industrial labor, supply chains, and manufacturing.
Evolving labor market: While some jobs involving manual and repetitive tasks may be replaced, new roles are expected to emerge, such as "robot-adjacent" careers in AI supervision, repair, and software.
New data collection: Mobile, autonomous robots will collect massive amounts of new, real-world data, which will, in turn, help train more advanced AI models.
Ethical considerations: As robots become more integrated into daily life, it raises important public and policy questions regarding safety, privacy, and ethics.