Systems Programming refers to the activity of programming system software which is designed to serve operating system and network systems. The primary aim of system programming is to create and maintain an operating system ecosystem that enhances efficient utilization of system resources like CPU, Memory, I/O devices.

Containerization on the other hand is the use of linux containers to deploy applications. It is becoming popular because containers are flexible, lightweight, interoperable, portable, and capable of abstracting the host infrastructure. This means they can be run anywhere irrespective of the infrastructural setup.

Using ChatGPT-4 to Understand Containerization

The recent advancements in AI technology have given rise to AI like ChatGPT-4 which helps users understand complex topics like containerization. By feeding the AI with relevant texts and examples, it can provide detailed explanations of what containerization is, why it is beneficial, and how you can utilize it within your system programming projects.

Key Concepts of Containerization

In the world of software engineering, containerization is an alternative or sidekick to virtualization. Here are some key concepts explained using ChatGPT-4:

Portability

Containers package your software along with all the dependencies that it needs to run. By doing so, they abstract away the underlying system infrastructure and offer high portability, allowing your software to run on any system that has the required container software installed.

Efficiency

Containers are lightweight. Because they share the host OS's kernel, they do not need an operating system of their own, unlike virtual machines. They are started and stopped in a matter of seconds, which makes them a lot more efficient compared to virtual machines.

Scalability

Containers make it easy to scale out your applications. You can quickly start more instances of your software in separate containers to handle more load. Because each instance is isolated from the rest, you don't have to worry about one instance's workload affecting the performance of the others.

Implementing Container-Based Development

Container technologies, such as Docker, have made it easier for developers to use containers. ChatGPT-4 can be used to learn about creating Dockerfiles, building Docker images, and running Docker containers. Apart from Docker, there are other technologies like LXC, rkt, and containerd which are also talked about in the systems programming and DevOps landscape.

Populating a Dockerfile

To create a Docker container for your application, you begin by writing a Dockerfile. A Dockerfile is a text file that contains instructions for how Docker should build your image. The idea is to include all the necessary installation, setup and runtime commands that you would manually execute if you were to run your application on a brand new system.

Building a Docker Image

To build a Docker image, you use the 'docker build' command followed by a build context and optional flags. The build context is usually the path to your Dockerfile, but it can also be a URL or a compressed tar archive file. During this process, Docker reads your Dockerfile, executes the instructions in it, and produces a Docker image as the result.

Running a Docker Container

A Docker container is a running instance of a Docker image. To run a Docker container, you use 'docker run' followed by the name or ID of the image that you want to run. Docker will start a new container and execute the command specified in the CMD instruction of your Dockerfile.

These are only fundamental touch points on containers and systems programming landscape. There are areas such as orchestrating containers with Kubernetes, integrating with DevOps pipelines etc., that further widen the usage of containerization. The usage of AI such as ChatGPT-4, can assist in understanding these complex concepts effectively.