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How do the stunningly intricate patterns created by schools of fish emerge? For many scientists, this question presents an irresistible mathematical puzzle involving a substantial number of variables describing the relative speed and position of each individual fish and its many neighbors.
Various mathematical models were proposed to tackle this question, but according to Gonzalo de Polavieja, head of the Collective Behaviour lab at the Champalimaud Centre for the Unknown in Lisbon, Portugal, they would inevitably fall into one of two extremes: they would either be too simple, or too complex.
Rise - Field - Intelligence - Machine - Learning
"The rise of the field of artificial intelligence and machine learning has provided models that are very accurate in predicting the behavior of individuals in groups," says de Polavieja. "But these models are like black boxes: The way they process the data to generate their predictions could involve thousands of parameters, many of which may not even correspond to real-world variables. Humans are unable to make sense of such complex information."
"On the other extreme," he continues, "are the simpler models, with few parameters, that allow you to identify rules associated with one main component, such as the distance between the fish, or their relative velocity. But those models are too narrow and therefore are never accurate when it comes to predicting the overall behavior of the group."
Inspiration - Type - AI - Model - Attention
Drawing inspiration from a new type of an AI model called "attention networks," de Polavieja and his team were able to identify a solution that lies just between the two extremes: a model that is both insightful and predictive. They describe their results in an article published in the scientific journal Plos Computational Biology.
To solve the problem, the team decided to use AI techniques with a twist: instead of constructing the standard intact "black box," they organized the model into numerous interconnected modules, each of...
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