Définition de l’intelligence artificielle

L’intelligence artificielle est l’étude de la capacité d’une machine à faire preuve de réflexion, de prise de décision, de raisonnement et de coordination.

Exemples d’application d’intelligence artificielle

La restauration avec l’IA aidera sans aucun doute les restaurants à améliorer le service clients, mais au fur et à mesure de son développement, elle soulève des questions importantes. À son niveau le plus élémentaire, l’intelligence artificielle est une bonne chose. Elle peut par exemple aider les clients à mieux comprendre ce qu’ils commandent et à améliorer les livraisons pour un meilleur parcours d’achat. Par exemple, si quelque chose n’arrive pas au moment où la commande a été passée, ou  si ça n’a pas était livré…

Une importante transformation des entreprises grâce à l’intelligence artificielle…

L’IA transforme les entreprises plus que jamais auparavant. Considérez la façon dont les commerçants utilisent l’IA. Les moyens intelligents dont ils disposent pour détecter et éliminer la fraude, comme la reconnaissance faciale pour éviter les achats à risque pour des enjeux de connaissance clients.

Amélioration de la relation client

Amazon Echo, Google Home, Microsoft Echo et Echo Dot ont tous la même chance de fidéliser et de satisfaire davantage leurs clients, car ils disposent de technologies d’intelligence artificielle qui leur permettent de mieux comprendre leurs clients, d’y répondre et d’interagir avec eux de manière beaucoup plus efficace et précise. Amazon est probablement le plus connu des fournisseurs d’assistants vocaux internes, avec plus de deux douzaines de produits en vente. Il est également gratuit et open source.

Ceux qui préfèrent payer pour des services qui offrent des recommandations personnalisées aux consommateurs sans intervention humaine peuvent utiliser l’intégration Alexa d’Amazon Voice Service. Amazon a introduit cette fonctionnalité avec une mise à jour en juin 2015.

Intérêt grandissant pour les voitures autonomes

Il y a par exemple la voiture autonome que Google veut introduire sur le marché d’ici 2020, l’Uber autonome pour les déplacements à vélo en libre-service, et le McDonald’s autonome… Entre-temps, des géants technologiques japonais comme Panasonic et Toshiba ont révélé des plans de développement de leurs propres voitures, sans parler de Ford qui a déclaré, à son tour, vouloir développer sa propre voiture autonome d’ici 2020.

Mais ce sera plus difficile qu’il n’y paraît. Une étude de McKinsey publiée en 2015, suggère que le coût d’une voiture totalement autonome pourrait dépasser les 3 milliards de dollars.

Cela illustre parfaitement le fait que nous ne développerons jamais la capacité d’anticiper tous les résultats qui peuvent se produire au présent, et encore moins tous les résultats probables qui peuvent se produire à l’avenir. Tout comme la chance peut vous emmener loin, la chance du tirage au sort peut parfois vous emmener beaucoup plus loin que vous ne l’auriez jamais cru possible.

Les limites de l’IA, et non vous ne pourrez pas prédire le résultat des matches…

Si vous aimez vraiment le sport, souvenez-vous aussi qu’aucun ordinateur ne pourra jamais prédire le résultat des World Series. Comme les humains sont encore des meilleurs prédicteurs que les ordinateurs, ces derniers ne peuvent que calculer des probabilités, on peut dire sans trop se tromper qu’un ordinateur capable de prédire des événements de grande ampleur et de grande profondeur peut ne pas être physiquement possible et donc être loin de la réalité.

Toutefois, certains algorithmes de calcul (tels que minimax-minimiser, déficit positif, somme maximale ou recherche universelle) ne deviennent « utiles » qu’après un nombre suffisamment important de calculs. Un modèle correct de suivi dans le temps intégrerait de tels algorithmes pour un coût de calcul, le plus bas  possible.

De nombreux modèles de suivi du temps que nous connaissons, et qui sont efficaces lorsqu’ils sont appliqués à leurs domaines, reposent sur des contraintes de mise en attente et d’exclusion mutuelle. Par exemple, un modèle d’hypothèse initiale plausible » est basé sur des ensembles d’hypothèses scientifiques et leur adéquation les unes par rapport aux autres.

La définition d’un livre blanc marketing digital :

Exemple de livre blanc en Marketing digital :

  • Les livres blancs sur l’Inbound Marketing
  • Les livres blancs sur le mailing
  • Les livres blancs pdf

Ces 10 livres blancs sur le RGPD ont facilité la mise en conformité de plus de 1000 entreprises.

Notre sélection de livres blancs sur l’intelligence artificielle :

Intelligence économique

Intelligence Artificielle, les défis actuels et l’action d’Inria Livre blanc de Inria : le présent document expose ses points de vue sur les grandes tendances et les principaux défis de l’intelligence artificielle et décrit la manière dont ses équipes conduisent des recherches scientifiques, développent des logiciels et œuvrent pour le transfert technologique (…).

L’expérience client à l’heure de l’intelligence artificielle Livre blanc de Webhelp, qui vous propose une publication pour faire le point afin de situer l’IA et les chatbots en 2017, pour en préciser les opportunités et les enjeux.

La définition d’un livre blanc marketing digital :

Exemple de livre blanc en Marketing digital :

Les livres blancs sur l’Inbound Marketing
Les livres blancs sur le mailing
Les livres blancs pdf

Ces 10 livres blancs sur le RGPD ont facilité la mise en conformité de plus de 1000 entreprises.

Notre sélection de livres blancs sur l’intelligence artificielle :

Intelligence économique

Intelligence Artificielle, les défis actuels et l’action d’Inria Livre blanc de Inria : le présent document expose ses points de vue sur les grandes tendances et les principaux défis de l’intelligence artificielle et décrit la manière dont ses équipes conduisent des recherches scientifiques, développent des logiciels et œuvrent pour le transfert technologique (…).

L’expérience client à l’heure de l’intelligence artificielle Livre blanc de Webhelp, qui vous propose une publication pour faire le point afin de situer l’IA et les chatbots en 2017, pour en préciser les opportunités et les enjeux.

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