François Chollet Advocates for Hands-On Approach to Drive Intelligent Automation

San Francisco, CA – Leading AI researcher and Keras creator, François Chollet, recently emphasized the critical role of direct human engagement in automated processes, asserting that it is essential for fostering deeper understanding and enabling more effective, intelligent automation. His statement, shared on social media, highlighted a philosophy crucial for true innovation in an increasingly automated world.

Chollet underscored that to maintain the capability to perform a job oneself, individuals must "actually do it yourself at least from time to time." He posits that this hands-on experience is vital for identifying inefficiencies and developing superior automated systems. This perspective challenges the notion that full automation alone leads to optimal outcomes.

As an illustrative example, Chollet pointed to the renowned Toyota Production System. > "See also: Toyota deliberately retaining manual labor in certain processes as a calculated method to deeply understand the work, identify inefficiencies, and ultimately create more effective and intelligent automation," he stated. This practice, central to Toyota's lean manufacturing principles, allows for continuous improvement by ensuring human operators maintain a profound understanding of the work.

Chollet, known for distinguishing between "cognitive automation" and genuine "intelligence," views much of current artificial intelligence, including large language models (LLMs), as sophisticated memorization and interpolation machines. While highly skilled at tasks within their training data, he argues they lack the true intelligence required to adapt to novel situations or synthesize solutions from first principles, a capability he measures with his ARC benchmark.

His argument suggests that relying solely on scaling data and computational power for AI development will not yield true general intelligence. Instead, a foundational, human-like understanding of problems, often acquired through direct practical experience, remains indispensable for significant breakthroughs and for designing automation that is not just efficient but genuinely intelligent and adaptable.