In the last 120 years we observed how technologically driven innovations to construct increasingly complex and intelligent machines pushed the mass of workers through the various sectors of the economy – from agriculture to industry and from there to the service sector.

The currently ongoing digitalization is primarily revolutionizing the service sector and, on the one hand, statistically speaking, over 40% of all jobs will be destroyed in the next 20 years. At the same time, we see that social and economic phenomena become considerably more complex, which makes them deprive us of our understanding and thus our control. This is due to the fact that all these phenomena have already started to develop a complex dynamics. And complex dynamics means that there can be no long term control. In this situation, mathematics is the only known approach to gain access to complex phenomena in data-driven analyzes. One of the blessings of digitalization is to make these data available.

On the other hand, there are also completely new opportunities due to digitalization: By understanding how the various phenomena of an augmented cognition arise from the interaction of people with intelligent machines or AI inspired algorithms and how economic opportunities arise from that, we are facing the responsibility not to leave the process of digitalization for itself, but to reshape it for the benefit of all of us.


To our understanding this reshaping of digitalization and hence the future development of AI and its core components, machine learning and data analytics, can be characterized and predicted on the basis of a simple paradigm of technological development:

  • (A) For a technology with a low barrier to entry and centralized development there are little chances for a fast progress and will will observe only innovations restricted to the whims of the platform owner and developers have to work within the confines of the platform with limited ability to influence overall direction.
  • (B) A technology with high barrier to entry and centralized development means proprietary innovation and product development as we find it in the development of operating systems.
  • (C) A technology with a high barrier to entry and decentralized development is open only for innovations from experts. The result e.g. cloud computing can be significant innovation, but with solutions that are not easy to realize by developers.
  • (D) A technology with a low barrier to entry and decentralized development is optimal in terms of an environment that foster innovation. However fully distributed innovation is difficult to scale over time because without any central coordination, you often end up with a fragmented ecosystem with many incompatible solutions.

Typically AI is thought be of type (D), i.e. having low barrier to entry and decentralized development and of course there are some tasks in AI that can be solved by downloading and simply using some of the well known toolboxes.

However AI as a mathematical means to tackle complexity problems obviously has a very high intellectual barrier to entry. In fact algorithms from the fields of machine learning and artificial intelligence are high experimental in most cases. And this last fact for many companies causes unexpected and unwelcome obstacles.

Hence we as a mathematical think tank aim to make a difference as a business incubator.