In our real-world example, we used a “feed-forward neural network” to recognise handwritten numbers. This is probably the most basic form of a NN. In reality, however, there are hundreds of types of mathematical formulas that are used – beyond addition and multiplication – to compute steps in a neural network, many different ways to arrange the layers, and many mathematical approaches to train the network.

AI and especially Neural Networks or Deep Learning have been the technological hype topic for some years now. However, since the subject is quite abstract – one could say it is uncharted territory for most people – we want to clear up some mistakes that we often encounter in our work.

In our past articles we mainly covered the basics of current AI research and tried to shed some light on them in a way that is understandable for non-IT scientists. We are now proceeding to the probably “hottest” current AI topic: Neural Networks (NN).

A (fairly) new kind of neural networks, so-called Generative Adversarial Networks or GANs, are nowadays capable of generating deceptively real images of people that do not actually exist. These fake images are indistinguishable from real photos at first glance. Fortunately, you might still uncover them if you look closely – if you know what to look for! 

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.

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.

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.

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.

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.