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Machine learning, deep learning et search : à quand ces innovations dans nos entreprises ?

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FORCE EST DE CONSTATER QUE DURANT CES 10 DERNIÈRES ANNÉES, IL N'Y A PAS EU D'ÉVOLUTION DANS LE DOMAINE DES MOTEURS DE RECHERCHE POUR LES ENTREPRISES. ET POURTANT LA TOILE BRUISSE DE LA RÉVOLUTION DU MACHINE LEARNING.
Ces nouvelles approches mathématiques révolutionnent le traitement de l'information. Les géants du web s'en sont saisis depuis quelques années déjà et les premiers résultats sont là. Votre recherche Web est plus personnalisée, elle prédit plus qu'elle ne trouve, elle anticipe.
Mais les travailleurs du savoir dans les entreprises classiques n'ont pas encore accès à ces innovations. Ont-ils été oubliés ?
La recherche d'information en entreprise est-elle condamnée à exploiter des technologies du 20ème siècle ?
William Lesguillier, responsable de l'offre Valorisation des Données chez Antidot, revient sur l'intérêt de ces approches de machine learning afin de comprendre à quoi elles servent. A travers divers retours d'expériences, nous illustrerons ce qu'elles apportent dans la recherche d'information.
Nous ouvrirons enfin les portes du laboratoire d'Antidot pour présenter les derniers travaux de recherche sur les algorithmes de pertinence. l

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Machine learning, deep learning et search : à quand ces innovations dans nos entreprises ?

  1. 1. Machine learning, deep learning : à quand ces innovations dans nos entreprises ? Search Day 2015 William Lesguillier – Product manager offre Valorisation des Données
  2. 2. 2 Antidot – Qui sommes-nous? ● Editeur de logiciels ● Moteurs de recherche| enrichissement des données ● Depuis 1999 | Paris, Lyon, Aix-en-Provence ● 3.5 M€, 45 collaborateurs, +150 clients ● Mission : délivrer à nos clients des solutions hautement configurables et innovantes qui créent de la valeur à partir de leurs données et augmentent leur performance opérationnelle
  3. 3. 3 Parmi nos clients ● Production de contenus ● Industrie ● E-Commerce ● Santé
  4. 4. 4 Qu’est-ce que le Machine Learning ● Définition : faculté donnée à un ordinateur d’apprendre un comportement à partir d’exemples ● Fondement scientifique : l’Intelligence Artificielle (IA)
  5. 5. 5 Re-re-renaissance de l’IA ● L’Intelligence Artificielle est de retour sur le devant de la scène ● Deux facteurs principaux : ● Le développement de la puissance de calcul ● La disponibilités accrues des données !
  6. 6. 6 L’événement qui change tout
  7. 7. 7 L’événement qui change tout ● Puissance de calcul : 100 servers ● Données disponibles : 200 millions de pages
  8. 8. 8 L’événement qui change tout
  9. 9. 9 ML : illustrations des principes Dire si un logement se trouve à New York ou San Francisco Altitude des logements Concept d’attribut Crédit : http://www.r2d3.us
  10. 10. 10 ML : illustrations des principes Dire si un logement se trouve à New York ou San Francisco Concept de modèle d’entrainement
  11. 11. 11 Données d’entrainement Données de test ML : illustrations des principes Dire si une maison se trouve à New York ou San Francisco Concept de surentrainement
  12. 12. 12 Qu’est-ce que le Deep Learning ? ● Se base sur les algorithmes de type réseaux de neurones ● Le système découvre lui-même les attributs et construit le modèle d’entrainement ● Il peut même aller jusqu’à s’affranchir d’une base d’entrainement (pas de documents étiquetés, ie apprentissage non supervisé) ● Avènement grâce à la puissance des GPU (Graphic Computing Units) très adaptés à ce type de calcul
  13. 13. 13 Qu’est-ce que le Deep Learning ?
  14. 14. 14 Machine Learning : pour quoi faire ? ● Reconnaissance du texte (y compris manuscrit) et de la voix ● Traduction automatique ● Text mining ● Aide à la recherche d’information ● Assistants personnels ● Assistance au diagnostic médical ● Smart cities ● Sécurité informatique ● Robotique ● …
  15. 15. 15 Le Machine Learning chez Antidot Enrich Access Search Semantic & ComplexMachine Learning Graph & Linked Data
  16. 16. 16 Classification automatique
  17. 17. 17 Classification automatique
  18. 18. 18 Extraction d’entités
  19. 19. 19 Les avantages du ML pour le text mining
  20. 20. 20 Et pour les moteurs de recherche ?
  21. 21. 21 Que s’est-il passé ces 10 dernières dans le monde du moteur de recherche d’entreprise ? Notre réponse : Rien de bien folichon
  22. 22. 22 3 types de moteurs de recherche
  23. 23. 23 Comment un moteur trouve-t-il ? ● Il recherche dans les documents le ou les mots-clés renseignés ● Il filtre les documents trouvés avec les critères fournis (format de document, fourchette de prix…) ● Et enfin, le plus important : il ordonne les document dans l’ordre jugé le plus pertinent
  24. 24. 24 Qu’est-ce que la pertinence ? Mot Mot Mot Mot Mot Mot Mot Mot 1 Mot
  25. 25. 25 Qu’est-ce que la pertinence ? ● Cette méthode de pondération s’appelle tf-idf (Term Frequency-Inverse Document Frequency) ● Elle a été inventée par Gerry Salton, appelé le père de la recherche d’information, en 1970
  26. 26. 26 Pertinence des moteurs spécialisés
  27. 27. 27 Pertinence des moteurs Web ● Etape 1 (90s) : algorithmes tf idf ● Etape 2 (2000) : Google invente le Page Rank ● Etape 3 (2005) : explosion du nombre de paramètres de pondération ● Etape 4 (2010) : utilisation du ML pour trouver la meilleure pertinence en fonction de tous et de chacun
  28. 28. 28 Pertinence des moteurs d’entreprise ● tf-idf est toujours implémenté dans tous les moteurs de recherche d’entreprise via les algorithmes appelés Best Match ou Vector Space Model ● L’état de l’art de ces moteurs à 45 ans !!
  29. 29. 29 ● Today with AFS : 0 results ● Tomorrow with Taruqa : Sneak peak sur nos travaux de recherche comment configurer le back office Antidot d’AFS Rechercher
  30. 30. 30 ● Today with AFS : filtering only is possible ● Tomorrow with Taruqa : Boost documents by facet values Sneak peak sur nos travaux de recherche configuration back office Rechercher
  31. 31. 31 Rendez-vous en 2016 !
  32. 32. 32 Sources et ressources ● Wikipedia : Histoire de l’intelligence artificielle ● TEDx: Ken Jennings, « Watson, Jeopardy and me, the obsolete know-it-all » ● TechRepublic : IBM Watson: The inside story of how the Jeopardy-winning supercomputer was born, and what it wants to do next ● A visual introduction to machine learning ● Wired : Google’s Artificial Brain Learns to Find Cat Videos ● IBM : machine learning applications ● Wikipedia : TF-IDF ● Wikipedia : BM25 ● Le Monde : série d’articles de Morgane Tual sur l’intelligence artificielle
  33. 33. Merci de votre attention Des questions ?

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