Canada: Focus sur l'IA — L'intelligence artificielle fondamentale et les jeux vidéo

Last Updated: July 23 2019
Article by Isi E. Caulder

Le nombre de demandes de brevets déposées pour des technologies fondamentales d'intelligence artificielle (IA) ne cesse d'augmenter. Un certain nombre d'entreprises technologiques de premier plan, dont IBM, Google, Amazon, Microsoft, Samsung et AT&T, déposent un nombre croissant de demandes de brevets visant des technologies fondamentales d'IA, comme l'apprentissage machine, les réseaux neuronaux, le traitement du langage naturel, le traitement de la parole, les systèmes experts, la robotique et la vision industrielle.1 Ces technologies fondamentales sont à leur tour appliquées pour traiter des problèmes dans les secteurs comme la santé, le secteur manufacturier et les transports. Le domaine des jeux vidéo s'est révélé être une source de développement technologique fondamental de l'IA quelque peu inattendue.

Les jeux de société traditionnels font depuis longtemps l'objet d'études pour la recherche sur l'IA. Dans les années 1990, IBM a créé une IA pour jouer aux échecs, Deep Blue, qui a été capable de vaincre des joueurs humains de haut calibre en utilisant des algorithmes de force brute.2 Plus récemment, des algorithmes d'apprentissage machine ont été développés pour des jeux de société plus complexes, qui incluent une plus grande variété de coups possibles. Par exemple, DeepMind (acquis depuis par Google) a récemment développé la première IA capable de battre les joueurs professionnels de Go, AlphaGo.3

Les jeux vidéo ont récemment suscité l'intérêt des chercheurs, en raison de leur ressemblance plus étroite avec l'aspect désordonné et non compartimenté du monde réel. Contrairement aux jeux de société, les jeux vidéo comportent généralement un plus grand nombre d'actions possibles qui doivent être déterminées en temps réel, en utilisant des informations imparfaites. Plus tôt cette année, DeepMind a annoncé le développement d'AlphaStar, une IA pour jouer à StarCraft II, un jeu de stratégie en temps réel.4 AlphaStar a réussi à battre de façon convaincante deux joueurs professionnels de haut niveau.5 Plus tôt l'année dernière, OpenAI, un organisme de recherche à but non lucratif, a développé OpenAI Five, une IA pour jouer à Dota 2, un jeu de combat d'arène multijoueurs en ligne.6 OpenAI Five a réussi à battre une équipe composée de joueurs du 99,95e centile, mais a finalement perdu contre une équipe de joueurs professionnels.7

OpenAI ne semble pas détenir de brevets et il ne semble pas y avoir de demandes de brevet publiées en lien avec cette IA au moment de la rédaction de cet article. (Il est à remarquer qu'OpenAI a déclaré que ses brevets, le cas échéant, seront partagés avec le monde entier.8) Toutefois, plusieurs autres sociétés technologiques de premier plan, dont DeepMind, ont déposé et obtenu des brevets portant sur des technologies fondamentales d'intelligence artificielle mises au point grâce aux jeux vidéo.

Un exemple de ce type de brevet est celui des États-Unis, portant le No. 9 679 258, nommé « Methods and Apparatus for Reinforcement Learning », originalement attribué à DeepMind (et maintenant à Google).9 Le brevet vise généralement à améliorer les méthodes d'apprentissage par le renforcement (c.-à-d. l'apprentissage machine fondé sur la maximisation de la récompense ou la minimisation des coûts). Le brevet décrit comment les jeux de console Atari 2600, y compris Pong, Breakout, Space Invaders, Seaquest et Beam Rider, ont été utilisés pour mettre à l'essai les méthodes divulguées.

Figure 1 du brevet US No. 9 679 258

Le brevet révèle comment un agent d'apprentissage par le renforcement est formé pour effectuer un ensemble d'actions (par exemple, des entrées de manette ou de bouton) sur un système sujet (par exemple, une console Atari 2600) pour faire passer le système sujet d'un état (par exemple, une première image) à un autre état (par exemple, une deuxième image). Les données de formation, y compris les données d'état et les données d'action, et un résultat cible, générés à partir d'un premier réseau neuronal, sont utilisés pour former un second réseau neuronal pour sélectionner les actions à effectuer par l'agent d'apprentissage par le renforcement. Périodiquement, pendant la formation, les paramètres du deuxième réseau neuronal sont utilisés pour mettre à jour les paramètres du premier réseau neuronal. Notamment, le brevet affirme que les méthodes divulguées ont permis d'atteindre un niveau de compétence dans certains des jeux pour consoles Atari 2600 qui surpassait celui d'un joueur humain expert.

Il ne fait aucun doute que de nombreux autres brevets continueront d'être déposés au cours des prochaines années, à mesure que le domaine de l'intelligence artificielle continuera à se développer. Reste à voir quel rôle les jeux vidéo, et les défis uniques qu'ils présentent, joueront dans ces développements.

Footnote

1 https://www.bereskinparr.com/doc/protecting-and-navigating-intellectual-property-for-artificial-intelligence-based-technologies

2 https://www.ibm.com/ibm/history/ibm100/us/en/icons/deepblue

3 https://deepmind.com/research/alphago/

4 https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/

5 Ibid.

6 https://openai.com/five

7 https://blog.openai.com/the-international-2018-results

8 https://blog.openai.com/introducing-openai

9 https://patents.google.com/patent/US9679258B2

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