REPORT | Arm AI Readiness Index | CHAPTER 2
The Technical Foundation: Technology Requirements for AI Success
2.1 Overview of AI Technology Requirements
2.1.1 The Need for a Robust Technology Infrastructure
Artificial Intelligence (AI) systems are advancing rapidly, becoming more flexible, scalable, and powerful. But to unlock their full potential, they need a solid technological foundation. Robust infrastructure is critical to managing the increasing complexity, scale, and sophistication of AI applications. It must support vast data processing, storage, and analysis while ensuring models run efficiently. As AI workloads grow, infrastructure must evolve to handle diverse tasks, reduce latency, and process massive data volumes—all while maintaining the speed and reliability modern applications demand.
2.1.2 Technology Requirements: Main Challenges in Key Technological Areas
The development and deployment of robust AI technology infrastructures leverage various state-of-the-art developments, including high-performance computing architectures, power-efficient systems, scalable infrastructures, and a proper balance between edge AI and cloud AI deployments. Recent advances in the above-listed areas help overcome considerable performance, scaling, and energy efficiency challenges. Specifically:
Evolution of computing architectures: Over the past three decades we have witnessed a transition from simple, single-tier systems to complex multi-tier architectures that segment various aspects of an application (e.g., user interface, data access, business logic) into different functional layers. This transition has been driven by the need for more flexible, adaptable, and scalable systems. In line with this evolution, early AI systems were limited in their inability to efficiently process large datasets or perform complex computations. To address this limitation, there has been a shift towards multi-tier systems. This shift introduces multiple learning layers and service-oriented designs, enhancing
CHAPTER 1
The Global State of AI Readiness: A Data-Driven Analysis
Momentum, Without a MapIn boardrooms and strategy sessions across the globe, artificial intelligence has moved from a distant aspiration to an immediate priority. Our extensive survey of 655 business leaders from countries including the U.S., U ...
CHAPTER 2
The Technical Foundation: Technology Requirements for AI Success
2.1 Overview of AI Technology Requirements2.1.1 The Need for a Robust Technology InfrastructureArtificial Intelligence (AI) systems are advancing rapidly, becoming more flexible, scalable, and powerful. But to unlock their fu ...
CHAPTER 3
Policy and Governance: Shaping the AI Regulatory Landscape
The policy and governance landscape for artificial intelligence (AI) is multifaceted and complex due to several factors. First, a variety of actors at local, national, regional, and global levels contribute to the governance of AI. Second, the umbrella term “AI” ...
CHAPTER 4
AI Safety and Risk: Navigating the Path to Responsible Innovation
AI is evolving at a pace that promises unprecedented gains—improving healthcare diagnostics, streamlining logistics, and unlocking new avenues of creativity. Yet, this rapid growth raises questions about safety, reliability, and accountability. ...
CHAPTER 5
Trust and Security in the AI Era
5.1 Understanding AI’s Unique Security and Trust Challenges ...
CHAPTER 6