{"id":486,"date":"2019-01-14T08:00:25","date_gmt":"2019-01-14T08:00:25","guid":{"rendered":"https:\/\/campus.hesge.ch\/blog-master-is\/?p=486"},"modified":"2019-02-26T10:38:25","modified_gmt":"2019-02-26T10:38:25","slug":"biologie-la-muse-des-reseaux-de-neurones","status":"publish","type":"post","link":"https:\/\/campus.hesge.ch\/blog-master-is\/biologie-la-muse-des-reseaux-de-neurones\/","title":{"rendered":"Biologie : la muse des r\u00e9seaux de neurones"},"content":{"rendered":"

L’inspiration technologique<\/h2>\n

La biologie et la technologie sont \u00e9troitement li\u00e9es. L\u2019esp\u00e8ce humaine a depuis longtemps trouv\u00e9 l\u2019inspiration pour am\u00e9liorer son quotidien gr\u00e2ce \u00e0 l\u2019environnement et les autres esp\u00e8ces qui l\u2019entourent.<\/p>\n

En effet, l\u2019avion est tout simplement une version mat\u00e9rielle et adapt\u00e9e des oiseaux\u00a0:<\/p>\n

\"Comparaison<\/a>
Figure 1 : Comparaison d’un oiseau \u00e0 un avion (E\u2019M.C, 2016)<\/figcaption><\/figure>\n

 <\/p>\n

Les ruches des abeilles ont \u00e9galement inspir\u00e9 certaines de nos constructions\u00a0:<\/p>\n

\"Inspiration<\/a>
Figure 2 : Les ruches compar\u00e9es \u00e0 de l’architecture (Hennighausen, Roston, 2015)<\/figcaption><\/figure>\n

 <\/p>\n

Et, depuis que John McCarthy en a parl\u00e9 en 1956, les humains essaient de simuler quelque chose de tr\u00e8s int\u00e9ressant\u00a0: l\u2019intelligence<\/strong>. (Childs, 2011)<\/p>\n

Les r\u00e9seaux de neurones artificiels<\/h2>\n

Le pionnier dans cette mati\u00e8re est Frank Rosenblatt qui a publi\u00e9 un article en 1958 nomm\u00e9 \u00ab\u00a0THE PERCEPTRON: A PROBABILISTIC MODEL FOR INFORMATION STORAGE AND ORGANIZATION IN THE BRAIN<\/em>\u00a0\u00bb (Brain, Rosenblatt, 1958). Le lien entre le cerveau humain et les mod\u00e8les statistiques est \u00e9vident. Le perceptron<\/em> est la repr\u00e9sentation math\u00e9matique d\u2019un seul neurone biologique.<\/p>\n

\"Repr\u00e9sentation<\/a>
Figure 3 : Repr\u00e9sentation math\u00e9matique d’un neurone biologique (AppliedGo, 2016)<\/figcaption><\/figure>\n

 <\/p>\n

Depuis, d\u2019autres mod\u00e8les math\u00e9matiques ont fait apparition. Ces mod\u00e8les ont pour but de mod\u00e9liser le comportement des vraies connexions entre les neurones. Certains sont sp\u00e9cialis\u00e9s dans le traitement d\u2019image<\/a>, d\u2019autres sont sp\u00e9cialis\u00e9s dans le traitement du langage naturel<\/a> (Tchircoff, 2017). Les r\u00e9seaux de neurones artificiels sont en vogue et pr\u00e9voient de changer notre monde, o\u00f9 60% des emplois de la plan\u00e8te risquent de subir un changement dans les prochaines ann\u00e9es(Vincent, 2017).<\/p>\n

Les jeux<\/h3>\n

Les r\u00e9sultats \u00e9poustouflants des mod\u00e8les math\u00e9matiques les plus r\u00e9cents font peur et donnent lieu \u00e0 beaucoup de conf\u00e9rences et d\u00e9bats (voir les vid\u00e9os du symposium \u00ab\u00a0Mon coll\u00e8gue est une machine\u00a0\u00bb – HEG<\/a>). Cependant, il est n\u00e9cessaire d\u2019analyser le domaine o\u00f9 les r\u00e9seaux de neurones artificiels sont capables de battre un \u00eatre humain. En effet, celui o\u00f9 les r\u00e9sultats semblent \u00eatre les meilleurs, est dans les jeux.<\/p>\n

Il est tr\u00e8s impressionnant de savoir qu\u2019une machine est capable de battre le meilleur joueur au GO au monde(DeepMind, 2018). Ce jeu d\u2019origine chinoise poss\u00e8de beaucoup plus de possibilit\u00e9s<\/a> que les \u00e9checs<\/a>(Muoio, 2016). En revanche, il s\u2019agit \u2026 d\u2019un jeu. Le contexte est bien d\u00e9fini et les r\u00e8gles bien connues \u00e9galement. Un autre exemple de probl\u00e8me ayant un contexte bien d\u00e9fini est la reconnaissance d\u2019image. Un r\u00e9seau de neurones artificiels doit seulement utiliser des pixels pour classifier une image.<\/p>\n

L’\u00eatre humain<\/h3>\n

A l\u2019oppos\u00e9 de cela, nous avons l\u2019humain\u00a0: un \u00eatre tr\u00e8s complexe. Simuler son comportement actuellement est une t\u00e2che impossible. Chaque personnalit\u00e9 est diff\u00e9rente et l\u2019humeur d\u2019un individu peut \u00eatre d\u00e9fini par un caf\u00e9 br\u00fbl\u00e9, la m\u00e9t\u00e9o, un message re\u00e7u, et infiniment d\u2019autres param\u00e8tres.<\/p>\n

Pour les pessimistes qui pensent que l\u2019intelligence artificielle est tr\u00e8s puissante, il faut savoir que le que le cerveau humain poss\u00e8de 1000 trillions de connexions(Luske, 2018), tandis que l\u2019un des meilleurs r\u00e9seaux de neurones artificiels utilis\u00e9 pour de la reconnaissance d\u2019image, Inception V3<\/em> fait par Google, poss\u00e8de seulement 23 millions de param\u00e8tres(Jordan, 2018).<\/p>\n

Pour comparaison, 1 million de secondes correspond \u00e0 11.5 jours et 1 trillion est \u00e9gal \u00e0 31\u00a0710 ans(McCray, 2016).<\/p>\n

Nous pouvons donc constater que dans un r\u00e9f\u00e9rentiel temporel, le r\u00e9seau de neurones artificiel correspond \u00e0 264.5 jours alors que le cerveau humain correspond \u00e0 31\u00a0710\u00a0000 d\u2019ann\u00e9es. Et, si on rapporte ces chiffres \u00e0 notre histoire, il y a 264.5 jours<\/a>\u00a0nous \u00e9tions au printemps de l\u2019ann\u00e9e en cours et il y a 30 millions d\u2019ann\u00e9es est apparu le premier singe (l\u2019Homo Sapiens est apparu il y a 300\u00a0000 ans)(Hominid\u00e9s, 2018).<\/p>\n

La diff\u00e9rence de performance est donc \u00e9vidente. Cependant, le seul domaine o\u00f9 l\u2019intelligence artificielle peut actuellement gagner du terrain par rapport \u00e0 un humain se trouve \u00eatre la quantit\u00e9 de donn\u00e9es g\u00e9n\u00e9r\u00e9es et analys\u00e9es \u2013 Voir billet de blog sur le BigData<\/a>.<\/p>\n

Et nous ?<\/h3>\n

Pour finir, je trouve que les r\u00e9seaux de neurones artificiels ont un avenir prometteur. Ils sont capables de d\u00e9passer des humains dans des contextes tr\u00e8s pr\u00e9cis, ce qui est d\u00e9j\u00e0 tr\u00e8s impressionnant. En revanche, personne ne comprend exactement le fonctionnement de ces syst\u00e8mes<\/a>, ce qui les rend plus difficiles \u00e0 manipuler. Ce handicap rend son utilisation impossible dans certains domaines, comme la m\u00e9decine, o\u00f9 il faudrait pouvoir expliquer pourquoi la machine prend une telle d\u00e9cision\u00a0; chose que nous ne pouvons pas faire aujourd\u2019hui. M\u00eame si tout cela peut faire peur<\/a>, ceux qui doivent \u00eatre heureux ce sont les \u00e9l\u00e8ves en Sciences de l\u2019information<\/a>, qui seront l\u00e0 pour alimenter ces syst\u00e8mes, g\u00e9rer des donn\u00e9es et aider \u00e0 l\u2019\u00e9volution de cette technologie.<\/p>\n

 <\/p>\n

\u201cSome people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we’ll augment our intelligence.\u201d \u2014Ginni Rometty<\/p><\/blockquote>\n

 <\/p>\n

Bibliographie\u00a0:<\/h1>\n

APPLIEDGO, 2016. Perceptrons – the most basic form of a neural network. In\u00a0: Applied Go<\/em> [en\u00a0ligne]. 9 juin 2016. [Consult\u00e9\u00a0le\u00a09\u00a0d\u00e9cembre\u00a02018]. Disponible \u00e0 l\u2019adresse\u00a0: https:\/\/appliedgo.net\/perceptron\/.<\/p>\n

BRAIN, In The et ROSENBLATT, F., 1958. The Perceptron: A Probabilistic Model for Information Storage and Organization<\/em>. S.l.\u00a0: s.n.<\/p>\n

CHILDS, Martin, 2011. John McCarthy: Computer scientist known as the father of AI. In\u00a0: The Independent<\/em> [en\u00a0ligne]. 1 novembre 2011. [Consult\u00e9\u00a0le\u00a09\u00a0d\u00e9cembre\u00a02018]. Disponible \u00e0 l\u2019adresse\u00a0: http:\/\/www.independent.co.uk\/news\/obituaries\/john-mccarthy-computer-scientist-known-as-the-father-of-ai-6255307.html.<\/p>\n

DEEPMIND, 2018. AlphaGo. In\u00a0: DeepMind<\/em> [en\u00a0ligne]. 9 d\u00e9cembre 2018. [Consult\u00e9\u00a0le\u00a09\u00a0d\u00e9cembre\u00a02018]. Disponible \u00e0 l\u2019adresse\u00a0: https:\/\/deepmind.com\/research\/alphago\/.<\/p>\n

E\u2019M.C, 2016.\u00a0 QUI A DIT QUE L\u2019AVION EST LE F\u00c9MININ DE L\u2019OISEAU\u202f? . In\u00a0: Club de Mediapart<\/em> [en\u00a0ligne]. 23 f\u00e9vrier 2016. [Consult\u00e9\u00a0le\u00a09\u00a0d\u00e9cembre\u00a02018]. Disponible \u00e0 l\u2019adresse\u00a0: https:\/\/blogs.mediapart.fr\/edition\/palestine\/article\/230216\/qui-dit-que-lavion-est-le-feminin-de-loiseau.<\/p>\n

HENNIGHAUSEN, Amelia et ROSTON, Eric, 2015. 14 Smart Inventions Inspired by Nature: Biomimicry. In\u00a0: [en\u00a0ligne]. 23 f\u00e9vrier 2015. [Consult\u00e9\u00a0le\u00a09\u00a0d\u00e9cembre\u00a02018]. Disponible \u00e0 l\u2019adresse\u00a0: https:\/\/www.bloomberg.com\/news\/photo-essays\/2015-02-23\/14-smart-inventions-inspired-by-nature-biomimicry.<\/p>\n

HOMINID\u00c9S, 2018. Homo Sapiens – Hominid\u00e9s. In\u00a0: [en\u00a0ligne]. 12 juin 2018. [Consult\u00e9\u00a0le\u00a09\u00a0d\u00e9cembre\u00a02018]. Disponible \u00e0 l\u2019adresse\u00a0: https:\/\/www.hominides.com\/html\/ancetres\/ancetres-homo-sapiens.php.<\/p>\n

JORDAN, Jeremy, 2018. Common architectures in convolutional neural networks. In\u00a0: Jeremy Jordan<\/em> [en\u00a0ligne]. 20 avril 2018. [Consult\u00e9\u00a0le\u00a09\u00a0d\u00e9cembre\u00a02018]. Disponible \u00e0 l\u2019adresse\u00a0: https:\/\/www.jeremyjordan.me\/convnet-architectures\/.<\/p>\n

LUKE, Martin, 2018. Neurons & Synapses – Memory & the Brain – The Human Memory. In\u00a0: [en\u00a0ligne]. 2018. [Consult\u00e9\u00a0le\u00a09\u00a0d\u00e9cembre\u00a02018]. Disponible \u00e0 l\u2019adresse\u00a0: http:\/\/www.human-memory.net\/brain_neurons.html.<\/p>\n

MCCRAY, Becky, 2016. Million vs. Billion vs. Trillion. In\u00a0: Small Biz Survival<\/em> [en\u00a0ligne]. 12 mars 2016. [Consult\u00e9\u00a0le\u00a09\u00a0d\u00e9cembre\u00a02018]. Disponible \u00e0 l\u2019adresse\u00a0: https:\/\/smallbizsurvival.com\/2006\/03\/million-vs-billion-vs-trillion.html.<\/p>\n

MUOIO, Danielle, 2016. Why Google AI game Go is harder than chess – Business Insider. In\u00a0: [en\u00a0ligne]. 10 mai 2016. [Consult\u00e9\u00a0le\u00a09\u00a0d\u00e9cembre\u00a02018]. Disponible \u00e0 l\u2019adresse\u00a0: https:\/\/www.businessinsider.com\/why-google-ai-game-go-is-harder-than-chess-2016-3?IR=T.<\/p>\n

TCHIRCOFF, Andrew, 2017. The mostly complete chart of Neural Networks, explained. In\u00a0: Towards Data Science<\/em> [en\u00a0ligne]. 4 ao\u00fbt 2017. [Consult\u00e9\u00a0le\u00a09\u00a0d\u00e9cembre\u00a02018]. Disponible \u00e0 l\u2019adresse\u00a0: https:\/\/towardsdatascience.com\/the-mostly-complete-chart-of-neural-networks-explained-3fb6f2367464.<\/p>\n

VINCENT, James, 2017. Automation threatens 800 million jobs, but technology could still save us, says report. In\u00a0: The Verge<\/em> [en\u00a0ligne]. 30 novembre 2017. [Consult\u00e9\u00a0le\u00a09\u00a0d\u00e9cembre\u00a02018]. Disponible \u00e0 l\u2019adresse\u00a0: https:\/\/www.theverge.com\/2017\/11\/30\/16719092\/automation-robots-jobs-global-800-million-forecast.<\/p>\n

 <\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

L’inspiration technologique La biologie et la technologie sont \u00e9troitement li\u00e9es. L\u2019esp\u00e8ce humaine a depuis longtemps trouv\u00e9 l\u2019inspiration pour am\u00e9liorer son quotidien gr\u00e2ce \u00e0 l\u2019environnement et les autres esp\u00e8ces qui l\u2019entourent. En effet, l\u2019avion est tout simplement une version mat\u00e9rielle et … Lire la suite\u00ad\u00ad<\/a><\/p>\n","protected":false},"author":10,"featured_media":496,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[18,16,22,19,12],"tags":[58,87,82,88,86,83,28,52,50,84,54,89,85,90],"class_list":["post-486","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-advanced-neural-net","category-big-data","category-curation-des-donnees","category-machine-learning","category-reflexion-is","tag-advanced-neural-network","tag-alphago","tag-artificial-intelligence","tag-bigdata","tag-biologie","tag-convolutional-neural-network","tag-donnees","tag-intelligence-artificielle","tag-machine-learning","tag-natural-language-processing","tag-neural-networks","tag-neurones","tag-reseaux-de-neurones","tag-systemes-dinformation"],"yoast_head":"\nBiologie : la muse des r\u00e9seaux de neurones - Recherche d'Id\u00e9eS<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/campus.hesge.ch\/blog-master-is\/biologie-la-muse-des-reseaux-de-neurones\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Biologie : la muse des r\u00e9seaux de neurones - Recherche d'Id\u00e9eS\" \/>\n<meta property=\"og:description\" content=\"L’inspiration technologique La biologie et la technologie sont \u00e9troitement li\u00e9es. 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