Understanding AI – Part 5: Supervised & Unsupervised Learning in ML

In the previous article we introduced the basic concepts of Machine Learning and how the training of an ML model works, using a simple but practical algorithm. Next, we want to take a closer look at the different types of Machine Learning. ML can be further distinguished based on a variety of aspects. Let’s start […]

Understanding AI – Part 4: The basics of Machine Learning

After shedding some light onto Symbolic AI in the previous article, we’re now moving on to take a closer look at Machine Learning (ML). When it comes to Symbolic AI, breaking down a problem as minutely as possible is key for successfully solving it. Only this enables the computer to correctly access the “learned” answers […]

Understanding AI – Part 3: Methods of symbolic AI

Reading time approx. 10 minutes: In the previous article we added two distinctions to our initial definition of AI: On the one hand we distinguish between strong and weak AI (Terminator & Science Fiction vs. the scientific status quo). Also we pointed out the difference between symbolic AI and Machine Learning. Let’s remember: Symbolic AI attempts […]

Understanding AI – Part 2: Symbolic AI, Neural Networks and Deep Learning

Reading time approx. 10 minutes: Artificial Intelligence (AI) is as old as computer science itself. Calculations, logical deductions, complex assignments… all this was once restricted to humans, until computers came forth. Even the performance of the early and (from today’s perspective) primitive computing systems seemed to imitate the human mind and it’s accomplishments. No wonder Alan […]

Understanding AI – Part 1: What is AI?

Reading time approx. 10 minutes: From household help to doomsday scenario – there’s hardly a topic where public perception, state of research and reality seem so incongruent as with artificial intelligence. Reason enough to shed some light onto this subject with a series of articles. The aim is to explain in a comprehensible and approachable […]