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SUMMARY:Algorithms\, Tasks and Data structures - Caterina Mosca
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DTSTAMP:20260726T091046Z
UID:pretalx-iacap-2026-MTLXY9@pretalx.iacapconf.org
DESCRIPTION:A possible definition of algorithm is to look at it as a conce
 pt composed on one side of a task\, the aim that needs to be computed\, an
 d on the other of a data structure. Both these two notions are of fundamen
 tal importance and still need a good specification in the perspective of a
  proper theory of algorithms. The neural network framework is proving to b
 e a good setting for challenging some corroborated notion about the relati
 ons between algorithms and programs\, we will be using it as a stepping st
 one for proposing a new definition of the concept of algorithm. Based on o
 ne of the main theorems of the Kolmogorov complexity\, our main argument i
 s that there are more neural networks that are not implementing an algorit
 hm\, than the opposite. Or in other words\, there are more programs that a
 lgorithms. The main consequences being foremost that the concept of algori
 thm is characterised by its nature of fundamental epistemological unit\, s
 eparating it from a rather successful interpretation of mechanical manipul
 ation of symbols. Secondly\, that a theory of task gains a crucial role in
  the definition of algorithm. Moving on from unanimously accepted mathemat
 ical operations to only loosely formalizable computations (i.e.\, Cats and
  dogs’ classification)\, it scales up the theoretical difficulty of form
 ally define these tasks\, forcing to look for a paradigm able to account f
 or both. Lastly\, from this setting more than ever emerges the deep relian
 ce of the computation on the data structures built on sets. The role of th
 ese representations is inextricable and contribute to the epistemological 
 nature of algorithms.
LOCATION:Executive Conference Room
URL:https://pretalx.iacapconf.org/iacap-2026/talk/MTLXY9/
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