Numérique, modèles et visions du monde

• modèle représentationnel
• modèle fonctionnel
• modèle physique

Les trois étapes ne sont pas étanches!

• donner une description en langage naturel de quelque chose

• Une voiture part un matin de Montréal et, en prenant l’autoroute se rend à Ottawa. L’autoroute part de…
• Andréa et Camille s’aiment. S’aimer signifie: …
• Proust est un grand écrivain.

• Transformer la représentation en unités atomiques discrètes et définir des fonctions pour les traiter

• Une voiture = 1 objet O
• Autouroute = ligne droite de 198 km
• Vitesse de O = 100 km/h
• Heure de départ = 10h
• Fonction pour calculer temps = t =e/v (198/100)
• …

• Andréa et Camille = A et C
• A et C ont une série de caractéristiques C à un certain niveau x:
  • C1 amour de la culture, valeurs de 0 à 10
  • C2 implication politique de 0 à 10
  • C3 …
• Fonction pour calculer le niveau d’amour qui lie A et C: calcul des différences de caractéristiques pondérées selon le critère z

• Implémenter le calcul fonctionnel dans une machine de Turing réelle.

calculable = computable

Ensemble d’instructions qui respectent deux conditions:

• à chaque instruction il est possible de connaître l’instruction suivante
• si on suit les étapes on arrive à une instruction qui demande l’arrêt.

Une machine de Turing

• modèles, algorithmes, formats, protocoles
• modèle basé sur des valeurs
• reproduction de ces valeurs
• prophétie autoréalisante

In this example, Harry is 10 years older than Sally, makes $10,000 more per year, and has a Master’s degree while Sally has a bachelor’s degree. Even with these disparities, matching server 20 will give Sally’s profile a high score which makes it more likely that Sally’s profile will appear in Harry’s result list. However, if it was Sally who submitted the search, and matching server 20 was evaluating Harry’s profile, a different score is possible. So, if it were Sally who was 10 years older, made $10,000 more per year, and had a Master’s degree while Harry had a Bachelor’s degree, matching server 20 would give a low score to Harry’s profile, making it less likely that his profile would appear in Sally’s result list. Matching server 20 may be configured this way because empirical data has shown that these demographic differences do not have an equivalent effect on the choices men and women make regarding matches.

In some embodiments, matching server 20 may analyze factors such as, but not limited to; average number of words per sentence, total number of words with greater than three syllables, and total number of words in the profile. Matching server 20 may also concatenate all of the collected responses with a single space between them. It may further break the text into sentences, words, and syllables. From these statistics, matching server 20 may also be configured to generate a readability score by, in one embodiment, taking the average of the Flesch Kincaid Reading Ease test, the Flesch Kincaid Grade Level test, and the Gunning Fox score. Other embodiments may utilize any other combination of these or other tests to determine a readability score. In some embodiments, analyses may be used to determine the IQ of an entity, the grade level of the writing, or how nervous the entity generally is. An advantage of this embodiment may be that the system provides user 14 with a metric for determining approximate intelligence of other users.

In some embodiments, matching server 20 may be configured to impute a level of physical attractiveness to an entity in pool 30. Matching server 20 may be configured to monitor how frequent an entity in pool 30 has been viewed as well as how many times that entity has been part of a result list in order to impute the level of physical attractiveness. Matching server 20 may further be configured to generate a score based on this data. Further, in some embodiments, matching server 20 may impute physical attractiveness to an entity based on the imputed physical attractiveness scores of other entities. Source: https://patents.google.com/patent/US9733811B2/en