Science & Technology, UK (Commonwealth Union) – A new study conducted by the University of Sheffield has shown the way honeybees are able to make prompt and precise decisions, which can assist in designing robots with greater efficiency together with autonomous machines was recently published.
Dr. HaDi MaBouDi from the University’s Department of Computer Science, in collaboration with Professor Andrew Barron from Macquarie University in Sydney, spearheaded a study delving into the intricate strategies employed by honeybees when determining which flowers are worth pursuing.
In spite of the complexity involved, the study unveiled the rapid decision-making abilities of these insects when it comes to foraging for nectar. Surprisingly, their accuracy surpasses that of humans, despite their brain being the size of a sesame seed.
The findings of this study, appeared in the journal eLife, have deepened the comprehension of the honeybee brain’s functionality and evolution. Furthermore, scientists from the University of Sheffield assert that these discoveries are inspiring the development of a new generation of robots and autonomous machines capable of mimicking the cognitive processes of bees. These artificial systems exhibit the capacity to make quick, precise, and efficient decisions independently.
During the study, the team trained a group of 20 bees to recognize artificial flowers of five distinct colors. Blue flowers consistently contained sugar syrup, green flowers always contained tonic water with a bitter taste that bees find unappealing, while the remaining colors occasionally contained glucose.
Following this the researchers introduced the bees to a custom-designed garden where the flowers just had distilled water to test their abilities in various scenarios. The scientists filmed every bee then followed their track and timed the length of the period it took them to take a decision in regards to which flower to visit.
The findings demonstrated that if the bees had confidence that a flower had the food, they promptly made a decision to land on it, on average time of 0.6 seconds. If they were certain that a flower was absent of food, they came to a decision just as fast.
The researchers then formed a computer model with the hope of replicating the bees’ decision-making technique. Once reviewed, they found that the structure of their computer model appeared extremely similar to the physical layout for a honeybee brain.
Dr HaDi MaBouDi, from the University of Sheffield, Department of Computer Science, indicated that every time a bee embarks on a quest for nectar, it faces the challenge of utilizing subtle differences in color or scent to determine the most suitable flower to land on and investigate. The consequences of making a wrong choice are significant, as it squanders precious energy and exposes the bee to potential hazards. Remarkably, bees accomplish this task with a brain that is no larger than a pinhead, housing fewer than a million neurons. Despite these limitations, they exhibit exceptional proficiency in this endeavor, demonstrating both speed and precision as they refine their choices through trial and error.
Dr MaBouDi further indicated that this study has brought to light the fundamental mechanisms that underlie the extraordinary decision-making abilities observed in honeybees. By understanding these mechanisms, they now have the opportunity to design improved robots and autonomous machines that exhibit bee-like cognition. Bees are renowned for being highly efficient navigators in the natural world, and by incorporating their decision-making strategies, technology can be made that is more resilient, reliable, and capable of avoiding risks.
Professor Andrew Barron, of Macquarie University says “A honeybee has a brain smaller than a sesame seed and yet it can make decisions faster and more accurately than we can. A robot programmed to do a bee’s job would need the backup of a supercomputer.”
With the rapid advancement of AI in just about every field, its applications in medicine are likely to be more and part of everyday life.
