Core Enabling Technologies for AI

[Lund University, Sweden - Mikael Riedal]

• Graphics Processing Units (GPUs): Originally designed for rendering computer graphics, GPUs are particularly efficient at the parallel processing of repetitive calculations required for deep learning and other AI applications. This makes them indispensable for both training large models and running them at scale.
• Neural Processing Units (NPUs): These specialized processors are designed specifically to accelerate machine learning workloads, especially for AI inference (running a trained model) on devices like smartphones and laptops. They offload AI tasks from the CPU and GPU, improving performance and energy efficiency.
• Heterogeneous compute: This involves using a mix of processors—including CPUs, GPUs, and NPUs—within a single system. By distributing different parts of an AI workload to the most optimized processor, this architecture boosts efficiency and scalability from the cloud down to edge devices.
• Chip manufacturing and design software: The creation of advanced semiconductors relies on specialized manufacturing equipment and electronic design automation (EDA) software. Advances in these areas allow for smaller, more powerful, and more energy-efficient chips that enable the AI boom.

These are the large-scale systems and platforms that support the development, deployment, and operation of AI applications.

• Cloud computing: AI models require enormous processing power and storage. Cloud infrastructure provides on-demand access to these resources, enabling both large-scale AI training and the easy deployment of AI services.
• Agile data centers: Modern data centers are being re-engineered to handle the specific demands of AI workloads, which require immense energy and high-speed data transfer.
• On-device AI: To improve privacy, reduce latency, and lower the infrastructure burden of data centers, an increasing amount of AI processing is moving directly onto user devices. This allows for more personalized, reliable, and responsive AI experiences.
• Hybrid AI: This approach distributes AI workloads between the cloud and edge devices, combining the massive power of cloud computing with the real-time context and privacy benefits of on-device processing.

- Key AI Software and Data Technologies

In addition to hardware and infrastructure, several software and data advancements have enabled the current AI era.

• Machine learning (ML): As the foundation of modern AI, ML allows systems to learn from vast datasets and improve performance without being explicitly programmed. It provides the core algorithms for AI applications like fraud detection, predictive maintenance, and personalized marketing.
• Deep learning and transformers: Deep learning is a subset of ML that uses neural networks with multiple layers to process data in complex ways. A major breakthrough was the development of the "transformer" architecture, which vastly improved the performance of large language models (LLMs) and other AI systems.
• Natural Language Processing (NLP): This technology enables AI to understand, interpret, and generate human language. It is foundational to the development of chatbots, virtual assistants, and real-time translation.
• Computer vision: Using AI systems like Convolutional Neural Networks (CNNs) to process and analyze images and videos has made technologies like facial recognition and autonomous vehicles possible.
• Big Data analytics: AI systems are only as good as the data they are trained on. Technologies for collecting, storing, and analyzing "big data" are crucial for fueling AI's ability to identify patterns and make decisions.

The human element is also a critical enabler in the AI era.

• AI enablers: These are specialized professionals with hybrid business and technical expertise who bridge the gap between an organization's strategic goals and the practical implementation of AI. They help identify high-value use cases and drive organizational adoption.
• Reskilling and training: A workforce with the skills to work alongside AI is necessary to realize its full potential. Training programs focusing on digital fluency and human-centric skills ensure that employees can leverage AI effectively.