Is the foundation of both machine learning and intelligence fundamentally linked to the art of prediction? As we navigate the intricate landscape of technological advancement, this question becomes increasingly pertinent. Yann LeCun's assertion that "Prediction is the essence of intelligence" invites us to explore this intriguing parallel. Historical perspectives, as suggested by Yuval Noah Harari, offer valuable insights into our understanding of intelligence. The evolution of dominant technologies, from the hydraulic models of ancient Greeks to the computational paradigms of today, has continuously reshaped our interpretation of intelligence. In each era, the prevailing technology has metaphorically mirrored our conception of the human mind – from hydraulic flows to electrical switchboards, and now to complex information processing systems. In this context, the current age of artificial intelligence (AI) perceives prediction and learning as core elements of intelligence. This perception aligns closely with the characteristics of machine learning, a prominent technology of our time. This correlation raises an interesting possibility: our understanding of intelligence might be more a reflection of our technological advancements rather than an objective truth. Examining the concept of intelligence through various lenses offers diverse viewpoints. William Calvin's exploration in "The Emergence of Intelligence" highlights that intelligence has not always been synonymous with prediction. It encompasses a spectrum of attributes like creativity, exploration, and problem-solving versatility. Yet, in the realm of machine intelligence, the community, as guided by thinkers like Shane Legg and Marcus Hutter, gravitates towards a more focused definition. They describe intelligence as "an agent’s ability to achieve goals in a wide range of environments," emphasizing goal-oriented, autonomous functionality. Marcus Hutter's work on Universal Artificial Intelligence further delves into the significance of prediction in AI. He integrates concepts of universal induction and reinforcement learning to propose a theory for rational agents operating in both known and unknown environments. Here, inductive inference, inherently predictive, emerges as a crucial element in enabling machines to navigate and adapt to diverse scenarios. Jeff Hawkins, in his book "On Intelligence," also underscores the role of prediction in natural intelligence. He views intelligence through the lens of a memory-prediction framework, where the human cortex functions as a series of prediction units. Hawkins' perspective, while aligning with the predictive essence of intelligence, also recognizes the complexities and multifaceted nature of the human brain. The debate on the essence of intelligence is not confined to academic circles. It has permeated into popular discourse, often oversimplified in media soundbites and tweets. Influential figures in the AI industry, like Yann LeCun, have further propelled the idea that prediction underpins intelligence. Their assertions, backed by the success of predictive models in deep learning, shape the research and development trajectories in AI. This consensus, however, is not without contention. The field of artificial intelligence is vast and varied, with numerous competing theories and technologies. While the current focus is on machine learning, particularly deep learning, it represents just one facet of a much broader landscape. As AI continues to evolve, so will our understanding of intelligence. The dominance of prediction as its essence is both a reflection of our current technological capabilities and a guiding principle for future explorations. It remains an open question whether a new technological paradigm will reshape this understanding once again. Prediction, as of now, stands as the cornerstone of intelligence – a testament to our current technological achievements and aspirations. It will continue to define our journey in AI, at least until a new technological revolution suggests otherwise. [[Essence of Mathematical Reasoning]] ----