As the plethora of applications for Artificial Intelligence keeps growing, so does the collection of tools and algorithms used to address their respective problems. Based on observations in nature, a class of novel algorithmic approaches has emerged in recent years, proving to be extremely powerful. For example, deep neural networks revolutionized machine learning by modeling how the brain passes information between neurons. Likewise, genetic algorithms inspired by Darwin's theory of natural evolution have solved problems in fields like engineering, social sciences, and medicine. Additionally, the designs of robots specialized for navigating specific terrains have been based on the anatomy of real-life organisms, including dogs, spiders, snakes, octopuses, and humans.

As the plethora of applications for Artificial Intelligence keeps growing, so does the collection of tools and algorithms used to address their respective problems. In recent years, a class of novel algorithmic approaches has emerged and proven extremely powerful that have been inspired from processes observed in nature. For example, deep neural networks revolutionized machine learning by modeling how the brain passes information between neurons; genetic algorithms inspired by Darwin's theory of natural evolution have solved problems in fields like engineering, social sciences, and medicine; and robots for navigating specific terrains are designed based on the body plans of real-life organisms such as dogs, spiders, snakes, octopuses, or humans.