What is this blog about (Introduction to AI)?

Nice that you came across my first blog article! I'd like to give you a little insight into what I'll be writing about on this blog. Of course, there's also an exciting bite of knowledge today to make your visit here worthwhile 🧠.

The focus of my blog is on artificial intelligence (AI) - a topic that particularly fascinates me due to my doctorate and my self-employment as a programmer for AI projects.

What can you look forward to?

I don't intend to give complex scientific lectures or explain the technology behind AI down to the smallest detail. Instead, I want to show you practical examples of AI applications from a wide range of industries that will provide you with inspiration and new ideas 💡. I will introduce you to specific use cases in which AI is already being used successfully 🚀.

Sometimes I'll also touch on technological concepts such as machine learning or deep learning and explain them in a simple way - don't worry, I'll keep it as understandable as possible.

However, to make sure we're on the same wavelength when I talk about AI, I've created a little graphic to show you that artificial intelligence is much more than just ChatGPT 🙈.

What exactly is artificial intelligence?

Artificial intelligence (AI) is an umbrella term for all systems that can imitate human intelligence. These include voice assistants such as Alexa, problem-solving software and image recognition systems.

Machine Learning – Lernen from Data

One area of AI is machine learning. This involves teaching machines to learn from existing data and recognize patterns. A simple example of this is decision trees that make better and better predictions based on data.

Deep learning - the next level

Then there is deep learning, which is based on neural networks inspired by the human brain. These networks are significantly more complex and powerful than conventional machine learning models. ChatGPT, which you are probably familiar with, is an example of a neural network. However, simpler machine learning models are often sufficient for many practical applications.