Home AI News Honey Bees: Fast Decision-Makers with Lessons for AI and Robotics

Honey Bees: Fast Decision-Makers with Lessons for AI and Robotics

Honey Bees: Fast Decision-Makers with Lessons for AI and Robotics

The Amazing Decision-Making Abilities of Honey Bees and Their Implications for AI

Honey bees, despite their size, possess exceptional decision-making skills that have evolved over millions of years. A recent study published in the journal eLife sheds light on the mechanisms behind these rapid and accurate decisions, offering insights into insect brains, human brain evolution, and the potential for designing better robots.

Understanding the Bee Brain: A Path to Fast Decision-Making

The study, led by Professor Andrew Barron from Macquarie University and Dr. HaDi MaBouDi, Neville Dearden, and Professor James Marshall from the University of Sheffield, presents a model of how bees make decisions and explores the neural pathways involved in their rapid decision-making process.

Professor Barron highlights the importance of decision-making in animal cognition, stating that honey bees with brains smaller than a sesame seed can outperform humans in terms of speed and accuracy. In fact, replicating a bee’s decision-making abilities would require the computational power of a supercomputer.

On the other hand, current autonomous robots heavily rely on remote computing, limiting their ability to explore autonomously. Professor Barron compares these drones to the incredible rovers on Mars, emphasizing the vast difference in exploration capabilities.

The Challenges Faced by Bees: Efficient Decision-Making is Vital

Bees face numerous challenges in their quest to gather nectar efficiently while avoiding predators. For bees, decision-making is vital. Each flower poses the question: Will it provide nectar? While in flight, bees face aerial attacks, and once they land to feed, they become vulnerable to predators, including those that camouflage themselves as flowers.

In order to understand the bees’ decision-making process, Dr. MaBouDi and the team trained 20 bees to recognize flower disks of different colors. Blue flowers always contained sugar syrup, green flowers contained quinine with a bitter taste, and other colors sometimes contained glucose.

The bees were then introduced to a simulated garden where the flowers only contained distilled water. The researchers recorded video footage of the bees and analyzed their decision-making time.

Dr. MaBouDi shares that when the bees were confident in finding food, they decided to land on a flower within an average of 0.6 seconds. Conversely, if they were confident that a flower did not contain food, they also made a quick decision. However, when uncertain, the bees took longer (approximately 1.4 seconds), with the decision time reflecting the probability of finding food.

From Bee Brains to AI: Insights for the Future

The team then developed a computer model based on the principles observed in the bees’ decision-making process. Surprisingly, the structure of the model closely resembled the physical layout of a bee brain.

Professor Marshall emphasizes the significance of the study, stating that it demonstrates complex autonomous decision-making with minimal neural circuitry. The researchers now aim to investigate how bees gather and sample information with such astonishing speed, theorizing that their flight movements enhance their visual system, helping them detect the best flowers.

Researchers in the field of AI can learn valuable lessons from insects and other seemingly “simple” animals. The efficient and low-power brains that have evolved over millions of years offer inspiration for the future of AI in various industries.

Professor Marshall, a co-founder of Opteran, an organization that reverse-engineers insect brain algorithms for autonomous machine movement, believes that the future of AI will be greatly influenced by biology and nature’s ingenious strategies.

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