An introduction to the AI Technology Stack
Artificial Intelligence (AI) is integrated into our core business processes and daily workflows. You’ve heard the buzz, but how does it work? It’s actually a combination of different technologies. That’s what we’ll break down in this post.
I have divided AI into functional layers and created a tower diagram to guide our discussion.
The Foundation: Hardware and Chips
Computer hardware and chips are the foundation layer for AI. They are the components that perform the programmed calculations. We’re talking about powerful CPU-based servers and workstations integrated with specialized processors like GPUs.
AI is not only about algorithms and models, but also about the underlying physical infrastructure that makes higher-level abstractions possible.
The Fundamentals: Data
AI systems fundamentally require data for input. Imagine data as patterns that energize fluctuations in a complex network. Each data point—whether a customer record, a sales figure, a product image, or text—contributes to the overall flow and output of the system.
This diverse data landscape is used by AI to evaluate and generate results. The quality and quantity of data directly impact AI performance.
The Network Infrastructure: Cloud
How is AI data exchanged and saved? The cloud, or network, layer is the infrastructure that interacts with and backs up the devices you use. AI processes run on a shared cloud of computers that automatically scale up or down, and restart, as needed.
Commercial cloud is not the only option. You can also maintain a private network and keep everything on-premises, meaning that all the hardware and software is located at your place of business.
The Tools: Frameworks and Toolkits
AI developers don’t build from scratch; they use frameworks and toolkits. These are software resources that serve as building blocks for creating AI systems. They allow engineers to design the flow of data processing.
This level provides access to foundation models, and supports the creation of powerful AI applications.
Pre-Built Components: Foundation Models
Only a small group of large high-tech companies create what are known as foundation models. These are pre-trained AI models that have already learned from massive amounts of data, providing the base for a wide range of applications. They avoid the need to train models from scratch and accelerate innovation.
This level is a powerful shortcut, that can be adapted for various tasks, such as generating text or images, and recognizing patterns that are otherwise undetectable.
The Final Product: AI Applications
At the top of the AI tech stack are user-facing AI applications. These are designed to be intuitive, easy to use, and keep humans in the loop (HITL). Examples include a chatbot that answers your questions, a recommendation system that suggests your next query, or an image app that creates the pictures you describe.
The application layer we interact with every day is where AI becomes genuinely useful. It is the culmination of all the layers below, working together to put the power of AI beneath our fingertips.
Putting it all together
With the layers of the AI Tech Stack explored, we can see how it all comes together. For me, working with this framework helps make sense of the fast-paced and sometimes bewildering changes in the tech world. I hope this framework helps you frame your own thoughts about the evolution of artificial intelligence.