Blog of the EP-KI Team

Who hasn't experienced this: AI-supported systems suggest a wide variety of products, even though we have only looked at them once. This may be annoying or even irritating, but it becomes much more important when systems are supposed to detect potential fraud or errors. Then we need to be able to rely on them to behave as we intend. Unfortunately, the world of data is rarely uniform, but very diverse. In our new blog post, we share with you what we need to watch out for when developing algorithms that are underrepresented.

After explaining in our previous blog post how the two terms »Artificial Intelligence« (AI) and »Machine Learning« (ML) differ from each other, this text is specifically about ML and different categories. We introduce the four main types of ML below and show real-world examples. As an overview, they are shown in the graphic. The first three categories discussed differ in terms of what data is used. The fourth ML category is about learning through interaction with the environment.

The terms Artificial Intelligence (AI) and Machine Learning (ML) are popping up in many different fields and are being heavily promoted as potential technologies. When people are asked about their idea of AI or ML, we get a variety of definitions and images, such as robots behaving like humans. So far, we too have used both terms as a matter of course in our blog posts, too, without giving a distinction between the terms. In some cases, they have also been used as synonyms. With this blog post, we want to give a better understanding of the terms by answering the following questions: What is AI? What is ML? And how can the two terms be differentiated from each other?

Driving a car with a navigation device, cooking a dish according to a recipe or even filtering the mail folder for spam mails – these are all examples of daily processes in which algorithms are used. But what actually is an algorithm? And why do some claim that Artificial Intelligence (AI) is also just an algorithm? This is what the coming blog posts will be about.

In the last blog post, we opened up the topic of »Explainability of Artificial Intelligence« (AI), which we want to continue in this blog post. »AI systems should not only be the best possible. Sometimes they should say ‘I have no idea what I’m doing here, don’t trust me.’ That’s going to be really important«, says Stanford University professor Herb Lin.

Are predictions from AI systems misleading or can we rely on them? As a real-world example, in this post we look at sales around Christmas using time series prediction models.

In any application that uses Artificial Intelligence (AI), the first step is the data. The second step is to apply an algorithm and thus develop an AI application. We want to take a closer look at this process in today's blog entry.

While searching for current algorithms for special solutions, I keep coming across the most diverse designations and, upon closer inspection, realize that I already know this algorithm, but in a different setting. Therefore, the first step here is to clarify which possible questions we can/would like to answer with AI algorithms.

We at Fraunhofer ITWM are mostly mathematicians and do research in different fields. Two major fields and methods are »Artificial Intelligence« (AI) and »Machine Learning« (ML). Our goal is to improve and understand these methods and to open up new fields of application. This also includes the application in business processes.