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#6 A creative AI would correspond to a painting elephant

Interview Sequences with Leon Tsvasman powered by "Intelligent World"

Intelligent world: Dr. Tsvasman, can AI be creative at all? Or is it just a simulation of creativity?

Dr. Tsvasman: When I think of that question, I immediately think of a chef friend of mine. He really enjoys his work - which of it would he most like to automate? Does he leave the creative finesse to the learning infrastructure? Or maybe you prefer routine tasks? And what would a gourmet from his customer base particularly appreciate? The creation of a machine - or rather the particularly sophisticated creation of the chef made possible with the help of a machine?

I don't want to rule out the possibility that PR professionals may generate hype about the first experiments of a creative celebrity chef machine from the data cloud. But would such a hype last longer? What I'm getting at is the assumption implied in the question that AI should be creative at all.

A simulation may make sense to study the limits of creativity or when it comes to entertainment - as in the example of the celebrity chef machine just mentioned. But that would have more to do with the training of animals than with the questionable automation of creativity. Ultimately, a painting elephant and a robot creating works of art would have the same entertaining value. But human culture is more than the art of turning attention into money. But let's look at what creativity - the creative way of solving problems - really is.

Innovation means problem solving without causing follow-up problems

The human world is not just about solving problems, but above all about avoiding them. In other words, solutions to problems that do not create new problems. So far, mankind has not done this very well. Because all epoch-making solutions resulted in problems - for example due to negative influences on the environment. Why actually? Because people use their main tool to solve problems: thinking. A way of thinking that enables coordinated problem solving and action in topicality.

In other words, a conceptual, calculating, weighing, well-founded and thus rational thinking. But with this we inevitably reduce complexity in favor of “tangible” and thus current priorities.

With the means of rational thinking, we simplify reality - with the aim of achieving short-term relief for a current problem. We use models, simulate processes, focus on what is supposedly essential. What we inevitably ignore creates subsequent problems for us. Regardless of how cultures counteract this weak point of rational thinking - only the descendants are allowed to pay for the consequences. Because those who are rationally responsible prefer short-term problem solutions.

We can hardly design our living environment any other way. Innovative solutions to problems encounter resistance. And humanity often lacks the intelligence that could bring systems thinking and creativity under one roof over the long term. Until now, at least.

How does an AI know what is good for people?

To solve problems that do not create new problems, we not only need imagination, but also an intact sense of what is good for us. In the case of AI, instead of imagination, we have data-driven forecasting. But before that, an AI should know what is good for people? This is only possible based on data - that comes from the people themselves. With this, however, the AI ​​only ever looks at the digitized past. Because even if data is continuously evaluated in real time, it remains staggered.

What is not accessible to AI as an enabling tool is human potentiality - our nature, which we owe to our biological evolution. We don't really know what we are and what defines us. But we feel our way in all directions with the help of our feelings by trying different levers - patterns, models, theories. Artists and philosophers sometimes lean too much out of the window. Inventor’s work based on relatively manageable consequences. And designers build the bridges that people currently walk in a purposeful and functional manner.

Machine creativity only works in conjunction with human creativity

So data-driven prognoses only make sense if they are guided by human creativity. This not only has to be appropriately emancipated and cultivated, but also must be ethically founded.

All of this can be best summed up with the term “innovation”. Innovation is about adding value, which consists of questioning established problem-solving patterns to avoid creating any subsequent problems. So, the AI ​​interweaves intelligent technologies and human creativity in interaction with analytics and deep industry expertise. It already enables measurable, but above all sustainable and resilient results.

Thus, the strength of AI lies less in its own, autonomous creativity. But rather in a comprehensive, omnipresent, generally accessible, and safe auxiliary creativity - coupled to data quality - that assists human creativity.

I am convinced that sustainable innovation, which becomes a prerequisite for a sustainable living environment in harmony with natural balance, can only be achieved in this way. From a purely technical point of view, nothing stands in the way of this controlled, assisting swarm creativity, as I would call it - even if it can only be partially realized today. But the most important thing is, as already said: it can only work properly together with emancipated and cultivated human creativity.

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