Machine Learning

The progress in machine learning is amazing today. And machines are getting more amazing every day. They clearly beat humans in games like chess and Go. They translate texts of ever better quality from one language to another. They can recognize and describe contents of audio, picture or video documents. Even in art, a domain that until recently was reserved for humans, they are impressive. Machine Learning has taken giant steps lately.

But how far do we understand this progress? And how do we deal with the ever-improving machine learning based computer programs in our everyday life? These questions arise both from the perspective of the user and the provider of such ‘intelligent’ services. Learning machine learning will become a central task of the individual and of society in the future.

Human Reaction

People react differently to this progress and often disruptive changes. Part with euphoria, zest for action and spirit of departure. Partly with skepticism, rejection or fear. But it is clear to us, soberly, that we live in everyday life with this progress. We will use the new services of machines, adapt to them and shape our lives and coexistence with them. Google, Facebook or Netflix have been using machine learning for some time now. And many of us are already using these or similar services every day. And we usually have no idea what will happen in the near future and how it affects our lives.

Necessary Discussion

We want to discuss machine learning soberly and practically:

  • What is possible?
  • Where do we have to be careful?
  • Where do machine learning models make mistakes?
  • How do complex models overwhelm people?
  • How do new applications of machine learning affect our lives? 

Often we lack the necessary foundations for this discussion. We therefore fall into either boundless euphoria or deep skepticism. A sober and practical discussion as well as a basic knowledge of the applied machine learning is needed.

Learning Machine Learning

How and with what effort can we realize machine learning projects? It’s amazing how easy it is today to create an application. With a current laptop computer and a few tools you can achieve a lot in little time. And the tools to provide such applications to a larger audience or clientele are available.

In our future portfolio we will show you how to realize real-world machine learning applications. The emphasis will be on language processing (but not exclusively). Here in the blog we will discuss the application-specific, technical and social boundary conditions. Where necessary, we will also focus on scientific aspects. The goal is to give you the foundations: A comprehensive, application-oriented understanding of the world of machine learning. In a way that you can start your own machine learning project.

Stay tuned!

About the author: Thomas studied computer linguistics and philosophy and graduated with a PhD in computer science. He has worked as a consultant for natural language processing and application development for major Swiss banks. Thomas is founder of ipublia. He lives with his family in Zürich.

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