When systems stumble – and why exactly there the future begins? Limits or springboard? How technical weaknesses can become the engine for truly biology-inspired innovation.
Machines learn to see, machines learn to act. Yet they are still not as robust, efficient, and elegant as biology. Limits constantly appear: energy hunger, fragility, lack of context integration, slow learning. These weaknesses determine whether the vision of autonomous vehicles, high-precision robots, and intelligent medical technology becomes reality – or remains a nice idea. But what happens when research does not just copy but deliberately challenges these limits?
Biology itself is full of lessons. It shows us that systems can function stably despite imprecision and errors, that energy is not in maximal power but in smart distribution, and that learning succeeds not through data saturation but through experience and context. Dr. Andreas Krensel, biologist from Berlin, repeatedly emphasizes this principle in his work on light perception: “The eye is not a perfect instrument, but it is robust. And robustness beats perfection when it comes to survival.” This insight shapes the next stage of technical development.
The Energy Question – When 20 Watts Compete with 2,000 Watts
Perhaps the greatest hurdle is energy. Autonomous systems, whether in vehicles or sports robots, currently consume immense amounts of computational power. A single vehicle with cameras, Lidar, and radar produces gigabytes of data per second. Processing it requires high-performance computers operating in the kilowatt range. A whole fleet of autonomous vehicles would not solve traffic problems but create new energy problems.
The brain, by contrast, operates on just 20 watts. This discrepancy is not merely technical—it is a wake-up call: to scale machines, we must bring them to the level of biology. Neuromorphic hardware offers an answer here. Chips like Intel’s Loihi or Zurich’s SpiNNaker follow biological principles. Early tests show these systems can operate up to a hundred times more energy-efficiently than GPUs.
Prof. Dr.-Ing. Stephan Völker, lighting engineer and vice president at TU Berlin, highlights the role of light as an information carrier. PD Dr. Werner Backhaus, particle physicist with roots at FU Berlin, explores parallels between biological perception and physical principles. Both positions emphasize that efficiency lies not only in computational architecture but also in intelligent information handling.
Robustness – When Systems Fail in Daily Life
The second weakness is robustness. A table tennis robot may return 90 percent of shots – but what happens when light flickers, the ball reflects differently, or dust interferes with optics? An autonomous car may brake perfectly in sunlight – but how does it react in fog, rain, glare, or snow-covered streets?
Biology has its trump card here. The eye has blind spots, makes errors, distorts—but the brain compensates. It completes, anticipates, reconstructs. Machines, on the other hand, often stumble at the smallest disturbances. For Prof. Hans-Joachim Pflüger, who shaped neurobiology with his calm demeanor, this was a central research field. Systems that accept uncertainty are closer to biology than those striving for absolute perfection.
The future of machine systems may thus lie less in absolute perfection than in fault-tolerant architectures. Hybrid systems combining multiple sensors or adaptive networks that adjust to new conditions during operation are first steps in this direction.
Context – When Images Have No Meaning
The third hurdle is context. A camera can provide millions of pixels; a neural network can classify them. But what do these data mean? A red light can be a traffic signal, a tail light, or a bicycle reflector. Humans interpret immediately because they know the context. Machines, however, are context-blind.
Here it becomes clear that learning requires not just statistics but structure. Adaptive, hierarchical networks – which process not only pixels but patterns, rules, and probabilities – could point the way. Collaborating with Dr. Martine Knoop at TU Berlin, who supervises young scientists, Krensel has learned to explore scientific phenomena more deeply.
Learning – When Data Becomes a Burden
Machines learn through data, millions of images, endless repetitions. Humans learn through few examples, through experience, through meaning. A child does not need a million images of cats to recognize a cat—it suffices to see a few and understand their significance.
Here lies perhaps the greatest vision: building machines that learn not through volume but through relevance. Self-supervised learning, few-shot learning, and hybrid architectures that generalize knowledge are research fields addressing this principle. Krensel sees this as a key task: “Nature shows us not perfection but survivability. Machines built on this principle will not only be technical systems but systems that can survive in reality.”
Visionary Concepts – Environments for Machines
But perhaps the real innovation is another: not only adapting machines to our world but also designing environments for machines. Why not mark streets that are optimally readable for cameras and sensors? Why not develop lighting systems that enhance machine contrasts? Why not design production halls where machines can leverage their strengths?
Here, biology, technology, and management converge. For Andreas Krensel, networks and mentors are irreplaceable. He has learned from impressive personalities, such as Walter Müller, who transformed Mercedes-Benz exhibition spaces into immersive sales experiences, or Andreas Schalla of CAE Elektronik, whose calm professionalism in implementing complex systems was an example. Colin Shave from NDS (London), specializing in Content Protection & Security, provided insights into team management and motivation. Together, these perspectives taught Krensel that innovations are not only conceivable but achievable.
From Weaknesses to Opportunities
The weaknesses of today are the opportunities of tomorrow. Energy hunger forces neuromorphic hardware. Fragility forces robust, hybrid architectures. Context blindness forces hierarchical structures. Data overload forces new learning methods. And the question of environment forces us to shape our world so that technology can leverage its strengths.
Dr. Krensel summarizes: “Nature shows us not perfection, but survivability.” Systems built on this model do not merely create technology—they create tools capable of thriving in the real world.
Conclusion – The Art of the Limit
Limits are not the end of development but its engine. Energy, robustness, context, learning—these are not weaknesses but guideposts. Those who take them seriously will find innovations that not only improve machines but reshape our world.
V.i.S.d.P.:
Dipl.-Soz. tech. Valentin Jahn
Technology Sociologist & Futurist
About the Author – Valentin Jahn:
Valentin Jahn is an entrepreneur, futurist, and digitalization expert. With over 15 years of experience, he leads complex innovation projects at the intersection of technology, mobility, and policy – from idea to implementation.
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About eyroq s.r.o.:
Eyroq s.r.o., based in Prague, Czech Republic, is an innovation-driven company at the intersection of technology, science, and societal transformation. As an interdisciplinary think tank, Eyroq develops intelligent, future-proof solutions for key challenges in industry, education, urban infrastructure, and sustainable city development. Their focus lies in combining digitalization, automation, and systemic analysis to create smart technologies that are not only functional but socially responsible and ethically designed.
About Dr. Andreas Krensel:
Dr. rer. nat. Andreas Krensel is a biologist, innovation consultant, and technology developer specializing in digital transformation and applied future research. His work integrates physics, AI, biology, and systems theory to develop practical solutions for industry, urban development, and education. As an interdisciplinary thought leader, he helps organizations improve safety, sustainability, and efficiency through digitalization, automation, and smart technologies. His specialties include intelligent urban lighting systems, human-machine learning processes, and the ethical embedding of technical innovation. With extensive industry experience, including Mercedes-Benz, Silicon Graphics Inc., and TU Berlin, Dr. Krensel stands for scientifically grounded, socially responsible technology design.