Computing is bottlenecked by data. Large amounts of application data overwhelm the storage capability, communication capability, and computation capability of the modern machines we design today. As a result, many key applications' performance, efficiency, and scalability are bottlenecked by data movement. In this talk, we describe three major shortcomings of modern architectures in terms of 1) dealing with data, 2) taking advantage of vast amounts of data, and 3) exploiting different semantic properties of application data. We argue that an intelligent architecture should be designed to handle data well. We show that handling data well requires designing architectures based on three key principles: 1) data-centric, 2) data-driven, and 3) data-aware. We give several examples of how to exploit each of these principles to design a much more efficient and high-performance computing system. We especially discuss recent research that aims to fundamentally reduce memory latency and energy and practically enable computation close to data with storage-centric computing. There are at least two promising novel directions in storage-centric computing: 1) in-flash processing, which exploits analog operational properties in flash memory chips to perform massively parallel operations in memory with low-cost changes, and 2) processing near flash memory chips, which processing takes place inside the storage controller. We show both types of architectures can enable orders of magnitude improvements in performance and energy consumption of many important workloads, such as graph analytics, database systems, machine learning, video processing, and genomics. We discuss how to enable the adoption of such fundamentally more intelligent architectures, which we believe are key to efficiency, performance, and sustainability. We conclude with some guiding principles for future computing architecture and system designs.