A standard laboratory possesses components akin to a computer, with a human scientist typically acting as the operating system to direct experimental processes. A self-driving lab integrates AI with automated hardware, enabling the system to learn and adapt based on experimental outcomes. This represents a shift from simple automation to true autonomy, where the lab can modify its experimental path based on real-time results.

• Laboratories share similarities with computers, featuring sensors, actuators, memory, protocols, and data outputs.
• Traditionally, a human scientist serves as the ‘operating system,’ making decisions, manipulating samples, and interpreting results.
• Self-driving labs incorporate AI with automated hardware to create an autonomous experimental loop.
• This system learns and adapts, influencing subsequent experiments based on previous outcomes, distinguishing it from mere automation.
• The core loop of a self-driving lab involves design, make, test, learn, and redesign.