The AI Revolution
The rise of artificial intelligence (AI) represents a golden age of computing for the technology industry. The potential of enabling AI everywhere – across societies and the vast technology spectrum – is staggering. It promises to change the lives of almost every person on the planet, providing the foundation for the next era of technology innovation and ushering in a new era of possibilities for society.
Following the scrutinization of 63 AI-based use cases, from automating repetitive tasks to generating novel ideas and designs, McKinsey predicted that generative AI’s impact on productivity could contribute between $2.6tn and $4.4tn to the global economy annually.
Making this enormous potential a reality requires AI innovation across every single piece of technology, from the smallest sensor running IoT workloads to the largest supercomputer running vast large language models (LLMs). This means enhanced AI capabilities at every touchpoint, not just in the cloud and vast datacenters, but also at the edge – the actual devices and technologies we all use and interact with daily.
The arrival of more sophisticated generative AI models has sparked significant innovation, but also increasingly high levels of power and compute that are not sustainable in the long-term. As these AI models continue to grow, gathering more information and getting smarter, more energy will be consumed. The International Energy Agency's (IEA) Electricity 2024 report expects power consumption across the world's AI datacenters to be ten times the amount it was in 2022. This is leading to a heightened focus on power efficiency.
Currently, AI workloads are largely a combination of training and inference. AI training refers to the process of teaching an AI system to perform a specific task or learn from data, whereas AI inference refers to the process of using the trained AI model to make predictions, classifications or decisions based on new data.
At the start of the AI revolution, much of the focus was on training models. However, with AI models maturing and smaller models suitable for mobile and edge devices becoming available, there has been a massive increase in AI inference at the edge.
This will power AI applications for countless, specialized use cases and industries, bringing a number of benefits to users and businesses, including improved latency, bandwidth and data privacy. However, the challenge with AI at the edge is the increasing compute demands across many different devices.
With a heritage grounded in pushing the boundaries of power efficiency, Arm is central to the AI revolution. We enable AI capabilities across all technology touchpoints and performance points, from the big datacenters that use countless processors to train LLMs right through to AI at the edge across the world’s IoT and consumer devices. This is made possible by Arm's open, pervasive CPU architecture and leading compute platform that provide the technology foundation for AI everywhere.
This AI guide explores how a renewed focus on efficient computing is enabling AI innovation, the vital role of the AI ecosystem, and the AI-powered experiences that are being built on Arm technology.
These vital elements are accelerating the AI journey and helping to transform the tech industry and society as a whole.
