A Cognition Briefing
Contributed by: Matei Mancas, Faculté Polytechnique de Mons
Attentive Computers: What For?
Attention is sometimes conscious (for complex living forms only) but most of the time unconscious and it is the key to survival. Attention is also a sign of limited computation capabilities. Vision, audition, touch, smell or taste, they all provide the brain with a huge amount of information. Gigabits of rough sensorial data flow every second into the brain which cannot physically handle such an information rate. Attention provides the brain with the capacity of selecting the main information and building priority tasks.
Attention of live beings largely contributes to the brain computation optimization and its importance is crucial. However, this key process of attention is currently rarely used within computers. As with the brain, a computer is a processing unit. As with the brain it has limited computation capabilities and memory. As with the brain, computers should analyse more and more data. But unlike the brain they do not, or rarely, pay attention.
That is why a new transversal research field appeared for a few years gathering engineers and computer scientists, psychologists and neuroscientists: computational attention. Beyond the theory of attention, there is a wide application field and this new research domain may have an important impact on future. The aim of computational attention is not to replace classical signal processing techniques but to complement them in various situations.
Attention: a step towards intelligence
There is no widely accepted definition of attention because of the diversity of the disciplines which focused on it. Even if, at the beginning, only psychologists studied attention, its huge importance led other specialists like philosophical, clinical, anatomical, physiological and even computational scientists to provide their own definitions of attention. Nevertheless, John. K. Tsotsos suggested that the one core issue which justify attention regardless the discipline, methodology or intuition is “information reduction”.
Similarity may be used by the brain to obtain several steps of information reduction and comparison may represent the process of information understanding. A meaningful structure comes out from the rough initial information by comparisons at several scales:
Through these comparison steps, attention reduces information by transforming it into a meaningful structure. The concept of attention may be defined as the transformation of a huge acquired unstructured data set into a smaller structured one while preserving the main information: the attentional mechanism turns rough data into intelligence and, for sure, there is no intelligence without attention.
Bottom-up and Top-down competition
The influence of bottom-up or top-down approaches depends on how familiar the acquired signal is for a given observer.
Computational attention: a step towards artificial intelligence
Similarly to the fact that attention is the beginning of intelligence in biology, computational attention may be the starting point of artificial intelligence in engineering applications. Computational attention provides machines with human-like reactions and behaviours and let them free to make decisions even in unexpected situations:
Applications of computational attention are numerous and among them we may cite:
More generally, when a set of data is acquired and it should be processed, it may be interesting to reduce it by keeping only data which may attract attention.
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