Lerner’s research has explored the remarkable ways that microorganisms steer themselves through fluids intentionally despite their lack of complex nervous systems. Benedikt Hartl, the lead author from the Institute of Theoretical Physics at TU Wien and the Allen Discovery Center at Tufts University, just published a remarkably interesting study. In Communications Physics, it treats this incredible phenomenon in detail. The study, titled “Neuroevolution of decentralized decision-making in N-bead swimmers leads to scalable and robust collective locomotion,” examines how simple artificial intelligence can enable individual components to contribute to a coherent movement strategy.
The full minute reveals the delicate and strange lives of microscopic organisms like these bacteria and this amoeba. These small organisms and blood cells all display purposeful, directed movement through fluid mediums. Hartl emphasizes the significance of understanding these movements, particularly given their implications for developing future technologies, such as autonomous nanobots.
Exploring Decentralized Decision-Making
In this study, Hartl explores how decentralized decision-making allows organisms to coordinate movement in an intricate environment. By using models comprising individual beads—each equipped with a rudimentary form of artificial intelligence—the study investigates the collective behavior resulting from simple programming. Each bead contains a small but powerful neural network with 20–50 parameters. This design allows them to be responsive and agile in how they activate and engage, maximizing the power of the collective.
The knowledge learned during this research lays the groundwork for understanding the ways that collective movements emerge within biological systems. An important question for the research team was how to have each individual piece work well despite each having a different role. Their focus was on creating accurate directional control.
“The crucial question now is: Is there a control system, a set of simple rules, a behavioral strategy that each bead can follow individually so that a collective swimming motion emerges—without any central control unit?”
The possible uses of this research reach far beyond academia, especially in law, technology and medicine. Hartl paints an electric vision of building nanobots. These microscopic robots might one day be able to independently find oil spills in the ocean or carry drugs precisely to their target within the human body. He explains:
Implications for Technology and Medicine
This ideal harmonizes well with the larger structural implications of decentralized control systems in engineered form. Andreas Zöttl from the University of Vienna adds that:
“It would be conceivable, for example, to build nanobots that actively search for oil pollution in water and help to remove it. Or even medical nanobots that move autonomously to specific locations in the body to release a drug in a targeted manner.”
These breakthroughs would fundamentally change the way work is done in nearly every industry, improving their efficiency and accuracy.
“This means that it would also be possible to create artificial structures that could perform complex tasks with very simple programming.”
These results demonstrate that even very complicated tasks can be accomplished with relatively simple code. Hartl explains that while microorganisms lack traditional neural networks, their movements can still be modeled effectively through basic physical-chemical circuits. He states:
The Role of Simple Programming
The study proves that even simple methods can result in complex, stable swimming patterns of microorganisms. Hartl concludes:
“The term neural network is perhaps somewhat misleading in this context; of course, a single-celled organism has no neurons. But such simple control systems can be implemented within a cell, for example, by means of very simple physical-chemical circuits that cause a specific area of the microorganism to perform a specific movement.”
This discovery in the study expands our understanding of how microbes travel. They promise to unlock novel and truly transformative applications in AI and robotics.
“We were able to show that this extremely simple approach is sufficient to produce highly robust swimming behavior.”
The study’s findings not only advance the understanding of microbial locomotion but also pave the way for innovative applications in artificial intelligence and robotics.
